acres | tbeptools | Tampa Bay intertidal and supratidal land use and cover | grouped_df | 90 | 3 |
benthicdata | tbeptools | Benthic data for the Tampa Bay Benthic Index current as of 20241212 | tbl_df | 3 | 2 |
bsmap | tbeptools | Terrain basemap | ggmap | 461 | |
catchpixels | tbeptools | Catchments and radar pixels (for precip) of selected Enterococcus stations | tbl_df | 3289 | 2 |
catchprecip | tbeptools | Daily precip by catchment for selected Enterococcus stations | tbl_df | 561376 | 3 |
enterodata | tbeptools | Enterococcus data from 53 key Enterococcus stations since 1995 | data.frame | 6266 | 16 |
epcdata | tbeptools | All bay data as of 20250210 | tbl_df | 28501 | 26 |
fibdata | tbeptools | All Fecal Indicator Bacteria (FIB) data as of 20250321 | tbl_df | 29175 | 18 |
fimdata | tbeptools | FIM data for Tampa Bay Nekton Index current as of 07092024 | tbl_df | 52042 | 19 |
fimstations | tbeptools | Spatial data object of FIM stations including Tampa Bay segments | sf | 7771 | 3 |
hcesdfibdata | tbeptools | Hillsborough County Environmental Services Division (ESD) FIB data as of 20250306 | tbl_df | 980 | 13 |
hmptrgs | tbeptools | Habitat Master Plan targets and goals | data.frame | 15 | 5 |
iwrraw | tbeptools | FDEP IWR run 66 | data.frame | 562974 | 11 |
mancofibdata | tbeptools | Manatee County FIB data as of 20250211 | tbl_df | 1616 | 13 |
pascofibdata | tbeptools | Pasco County FIB data as of 20250304 | tbl_df | 209 | 13 |
phytodata | tbeptools | Phytoplankton data current as of 10312024 | tbl_df | 40521 | 8 |
polcofibdata | tbeptools | Polk County FIB data as of 20250304 | tbl_df | 833 | 13 |
seagrass | tbeptools | Seagrass coverage by year | data.frame | 20 | 3 |
sedimentdata | tbeptools | Sediment data for the Tampa Bay current as of 20241212 | tbl_df | 226536 | 24 |
sgmanagement | tbeptools | Seagrass management areas for Tampa Bay | sf | 30 | 2 |
sgseg | tbeptools | Seagrass segment reporting boundaries for southwest Florida | sf | 22 | 2 |
stations | tbeptools | Bay stations by segment | data.frame | 45 | 4 |
subtacres | tbeptools | Tampa Bay subtidal cover | tbl_df | 65 | 3 |
swfwmdtbseg | tbeptools | Spatial data object of SWFWMD Tampa Bay segments | sf | 7 | 2 |
targets | tbeptools | Bay segment targets | data.frame | 9 | 8 |
tbniref | tbeptools | Reference conditions for Tampa Bay Nekton Index metrics | grouped_df | 16 | 12 |
tbnispp | tbeptools | Reference table for Tampa Bay Nekton Index species classifications | data.frame | 196 | 10 |
tbseg | tbeptools | Spatial data object of Tampa Bay segments | sf | 4 | 3 |
tbsegdetail | tbeptools | Spatial data object of detailed Tampa Bay segments | sf | 7 | 3 |
tbseglines | tbeptools | Spatial data object of lines defining major Tampa Bay segments | sf | 3 | 2 |
tbsegshed | tbeptools | Spatial data object of Tampa Bay segments plus watersheds | sf | 7 | 3 |
tbshed | tbeptools | Spatial data object of Tampa Bay watershed | sf | 1 | 2 |
tidalcreeks | tbeptools | Spatial data object of tidal creeks in Impaired Waters Rule, Run 66 | sf | 620 | 7 |
tidaltargets | tbeptools | Tidal creek nitrogen targets | data.frame | 2 | 4 |
transect | tbeptools | Seagrass transect data for Tampa Bay current as of 11032024 | tbl_df | 158352 | 11 |
trnlns | tbeptools | Seagrass transect locations | sf | 61 | 8 |
trnpts | tbeptools | Seagrass transect starting locations | sf | 66 | 12 |
NASIS_table_column_keys | soilDB | NASIS 7 Tables, Columns and Foreign Keys | data.frame | 346 | 5 |
SCAN_SNOTEL_metadata | soilDB | USDA-NRCS Station Metadata for SCAN, CSCAN, SNOTEL, SNOWLITE Networks | data.frame | 1186 | 19 |
gopheridge | soilDB | Example 'SoilProfilecollection' Objects Returned by 'fetchNASIS'. | SoilProfileCollection | | |
loafercreek | soilDB | Example 'SoilProfilecollection' Objects Returned by 'fetchNASIS'. | SoilProfileCollection | | |
metadata | soilDB | NASIS 7 Metadata | data.frame | 20459 | 12 |
mineralKing | soilDB | Example 'SoilProfilecollection' Objects Returned by 'fetchNASIS'. | SoilProfileCollection | | |
state_FIPS_codes | soilDB | USDA-NRCS Station Metadata for SCAN, CSCAN, SNOTEL, SNOWLITE Networks | data.frame | 51 | 3 |
us_ss_timeline | soilDB | Timeline of US Published Soil Surveys | tbl_df | 5208 | 4 |
cuts | coconots | Time Series of Monthly Counts of Claimants Collecting Wage Loss Benefit | ts | | |
downloads | coconots | Time Series of Daily Downloads of a TeX-Editor | ts | | |
goldparticle | coconots | Time Series of Gold Particle Counts in a Well-Defined Colloidal Solution | ts | | |
Nurses | BFI | Nurses' stress in different hospitals | tbl_df | 1000 | 8 |
trauma | BFI | Trauma patients from different hospitals | data.frame | 371 | 6 |
depression | RESI | Depression Treatment Data | data.frame | 1020 | 5 |
insurance | RESI | US Health Insurance Data | data.frame | 1338 | 7 |
Fujita2023 | parafac4microbiome | Fujita2023 longitudinal microbiome data | list | | |
Shao2019 | parafac4microbiome | Shao2019 longitudinal microbiome data | list | | |
vanderPloeg2024 | parafac4microbiome | vanderPloeg2024 longitudinal dataset | list | | |
SO_schema | crunch | Schema for the 2017 Stack Overflow developer survey | data.frame | 23 | 2 |
SO_survey | crunch | R users who responded to the 2017 Stack Overflow developer survey | data.frame | 1634 | 23 |
db.cont | cytosignal | Interaction database derived from CellphoneDB V2 | list | | |
db.diff | cytosignal | Interaction database derived from CellphoneDB V2 | list | | |
g_to_u | cytosignal | Interaction database derived from CellphoneDB V2 | data.frame | 977 | 3 |
inter.index | cytosignal | Interaction database derived from CellphoneDB V2 | data.frame | 1396 | 11 |
distruct_colours | tidypopgen | Distruct colours | character | | |
CNA | BoutrosLab.plotting.general | Copy number aberration (CNA) data from colon cancer patients | data.frame | 30 | 58 |
SNV | BoutrosLab.plotting.general | Single nucleotide variant (SNV) data from colon cancer patients | data.frame | 30 | 58 |
microarray | BoutrosLab.plotting.general | Microarray dataset of colon cancer patients | data.frame | 2402 | 62 |
patient | BoutrosLab.plotting.general | Dataset describing qualities of 58 colon cancer patients | data.frame | 58 | 9 |
Bechtoldt | psych | Seven data sets showing a bifactor solution. | matrix | 17 | 17 |
Bechtoldt.1 | psych | Seven data sets showing a bifactor solution. | matrix | 17 | 17 |
Bechtoldt.2 | psych | Seven data sets showing a bifactor solution. | matrix | 17 | 17 |
Chen | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 18 | 18 |
Dwyer | psych | 8 cognitive variables used by Dwyer for an example. | matrix | 8 | 8 |
Garcia | psych | Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010) | data.frame | 129 | 6 |
Gleser | psych | Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory. | data.frame | 12 | 12 |
Gorsuch | psych | Example data set from Gorsuch (1997) for an example factor extension. | matrix | 10 | 10 |
Harman.5 | psych | Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 12 | 5 |
Harman.8 | psych | Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 8 | 8 |
Harman.Burt | psych | Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 8 | 8 |
Harman.Holzinger | psych | Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 9 | 9 |
Harman.political | psych | Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 8 | 8 |
Holzinger | psych | Seven data sets showing a bifactor solution. | matrix | 14 | 14 |
Holzinger.9 | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
Reise | psych | Seven data sets showing a bifactor solution. | matrix | 16 | 16 |
Schmid | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 12 | 12 |
Tal.Or | psych | Data set testing causal direction in presumed media influence | data.frame | 123 | 6 |
Tal_Or | psych | Data set testing causal direction in presumed media influence | data.frame | 123 | 6 |
Thurstone | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
Thurstone.33 | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
Thurstone.33G | psych | Seven data sets showing a bifactor solution. | matrix | 9 | |
Thurstone.9 | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
Tucker | psych | 9 Cognitive variables discussed by Tucker and Lewis (1973) | data.frame | 9 | 9 |
West | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 16 | 16 |
bfi | psych | 25 Personality items representing 5 factors | data.frame | 2800 | 28 |
bfi.dictionary | psych | 25 Personality items representing 5 factors | data.frame | 28 | 7 |
bfi.keys | psych | 25 Personality items representing 5 factors | list | | |
bock.table | psych | Bock and Liberman (1970) data set of 1000 observations of the LSAT | data.frame | 32 | 8 |
cattell | psych | 12 cognitive variables from Cattell (1963) | matrix | 12 | 12 |
lsat6 | psych | Bock and Liberman (1970) data set of 1000 observations of the LSAT | matrix | 1000 | 5 |
lsat7 | psych | Bock and Liberman (1970) data set of 1000 observations of the LSAT | matrix | 1000 | 5 |
sat.act | psych | 3 Measures of ability: SATV, SATQ, ACT | data.frame | 700 | 6 |
schmid.leiman | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 12 | 12 |
small.msq | psych | A small example data set taken from a larger data set | data.frame | 200 | 14 |
withinBetween | psych | An example of the distinction between within group and between group correlations | data.frame | 16 | 10 |
obs_cat_data | tidyvpc | Example observed data with categorical DV | data.table | 4014 | 4 |
obs_data | tidyvpc | Example observed data with continuous DV | data.table | 600 | 9 |
sim_cat_data | tidyvpc | Example simulated data with categorical DV | data.table | 401400 | 4 |
sim_data | tidyvpc | Example simulated data with continuous DV | data.table | 60000 | 10 |
chagas2012 | serofoi | Chagas seroprevalence data in serofoi | data.frame | 72 | 5 |
chik2015 | serofoi | Chikungunya seroprevalence data in serofoi | data.frame | 4 | 5 |
veev2012 | serofoi | Venezuelan Equine Encephalitis Virus (VEEV) seroprevalence data in serofoi | data.frame | 6 | 5 |
bering_sea | tinyVAST | Survey domain for the eastern and northern Bering Sea surveys | sfc_POLYGON | | |
bering_sea_pollock_ages | tinyVAST | Survey catch-rates at age for Alaska pollock in the Eastern and Northern Bering Sea | data.frame | 247545 | 5 |
bering_sea_pollock_vast | tinyVAST | Estimated proportion-at-age for Alaska pollock using VAST | matrix | 15 | 41 |
condition_and_density | tinyVAST | Condition and density example | list | | |
red_snapper | tinyVAST | Presence/absence, count, and biomass data for red snapper | data.frame | 8687 | 6 |
red_snapper_shapefile | tinyVAST | Shapefile for red snapper analysis | sf | 1 | 2 |
salmon_returns | tinyVAST | North Pacific salmon returns | data.frame | 2688 | 4 |
sea_ice | tinyVAST | Arctic September sea ice concentrations | data.frame | 33151 | 4 |
contours | pliman | Contour outlines from five leaves | list | | |
WYcond | FIESTA | FIA data. Condition-level data from FIA public database. | data.frame | 3224 | 26 |
WYp2veg_subp_structure | FIESTA | FIA data. P2 vegetation structure data from FIA public database. | data.frame | 57200 | 6 |
WYp2veg_subplot_spp | FIESTA | FIA data. P2 vegetation species data from FIA public database. | data.frame | 8265 | 9 |
WYplt | FIESTA | FIA data. Plot-level data from FIA public database. | data.frame | 3047 | 20 |
WYpltassgn | FIESTA | FIA data. Plot assignment data from FIA public database. | data.frame | 3047 | 24 |
WYseed | FIESTA | FIA data. Seedling data from FIA public database. | data.frame | 1607 | 10 |
WYstratalut | FIESTA | FIA data. Post-stratification data from FIA public database. | data.frame | 35 | 7 |
WYsubp_cond | FIESTA | FIA data. Subplot condition data from FIA public database. | data.frame | 12214 | 6 |
WYsubplot | FIESTA | FIA data. Subplot data from FIA public database. | data.frame | 12188 | 6 |
WYtree | FIESTA | FIA data. Tree-level data from FIA public database. | data.frame | 18574 | 19 |
WYunitarea | FIESTA | FIA data. Acres data from FIA public database. | data.frame | 23 | 4 |
WYunitzonal | FIESTA | Zonal data. Zonal means for auxiliary data in counties in Wyoming. | data.frame | 23 | 9 |
ASVAB | mirt | Description of ASVAB data | data.frame | 16 | 8 |
Attitude | mirt | Description of Attitude data | data.frame | 22 | 9 |
Bock1997 | mirt | Description of Bock 1997 data | data.frame | 64 | 4 |
LSAT6 | mirt | Description of LSAT6 data | data.frame | 30 | 6 |
LSAT7 | mirt | Description of LSAT7 data | data.frame | 32 | 6 |
SAT12 | mirt | Description of SAT12 data | data.frame | 600 | 32 |
SLF | mirt | Social Life Feelings Data | data.frame | 32 | 6 |
Science | mirt | Description of Science data | data.frame | 392 | 4 |
deAyala | mirt | Description of deAyala data | matrix | 32 | 6 |
spdx_license_list | piecepackr | SPDX License List data | data.frame | 478 | 8 |
dataCI | AQuality | Data Sets~~ | data.frame | 6 | 9 |
dataICHS | AQuality | Data Sets | data.frame | 411 | 14 |
dataTSSS | AQuality | Data Sets~~ | data.frame | 411 | 16 |
dme_subset | scGOclust | Drosophila gut scRNA-seq data, 10X Chromium Subset to 45 cells per cell type as an example data | Seurat | | |
dme_tbl | scGOclust | Drosophila EMSEMBL gene and GO annotation, subset to genes present in 'dme_subset' | data.frame | 40764 | 6 |
mmu_subset | scGOclust | Mouse stomach and intestine scRNA-seq data, microwell-seq Subset to 50 cells per cell type as an example data | Seurat | | |
mmu_tbl | scGOclust | Mouse EMSEMBL gene and GO annotation, subset to genes present in 'mmu_subset' | data.frame | 115323 | 6 |
Baker2009 | netmeta | Network meta-analysis of pharmacologic treatments for chronic obstructive pulmonary disease | data.frame | 94 | 6 |
Dogliotti2014 | netmeta | Network meta-analysis of antithrombotic treatments in patients with non-valvular atrial fibrillation | data.frame | 44 | 5 |
Dong2013 | netmeta | Network meta-analysis for chronic obstructive pulmonary disease | data.frame | 99 | 4 |
Franchini2012 | netmeta | Network meta-analysis of treatments for Parkinson's disease | data.frame | 7 | 13 |
Gurusamy2011 | netmeta | Network meta-analysis on blood loss during liver transplantation | data.frame | 29 | 4 |
Linde2015 | netmeta | Network meta-analysis of treatments for depression | data.frame | 66 | 24 |
Linde2016 | netmeta | Network meta-analysis of primary care depression treatments | data.frame | 124 | 13 |
Senn2013 | netmeta | Network meta-analysis in diabetes | data.frame | 28 | 7 |
Stowe2010 | netmeta | Network meta-analysis of adjuvant treatments to levodopa therapy for Parkinson's disease | data.frame | 29 | 14 |
Woods2010 | netmeta | Count statistics of survival data | data.frame | 8 | 4 |
dietaryfat | netmeta | Network meta-analysis of dietary fat | data.frame | 10 | 10 |
parkinson | netmeta | Network meta-analysis of treatments for Parkinson's disease | data.frame | 7 | 13 |
smokingcessation | netmeta | Network meta-analysis of interventions for smoking cessation | data.frame | 24 | 9 |
bdv.connection | archeofrag | Dataset: Refitting relationships between lithic fragments from the Bout des Vergnes site | matrix | 3903 | 2 |
bdv.fragments | archeofrag | Dataset: Refitting relationships between lithic fragments from the Bout des Vergnes site | data.frame | 4458 | 5 |
chauzeys.connection | archeofrag | Dataset: Refitting relationships between lithic fragments from the Chauzeys site | matrix | 1879 | 2 |
chauzeys.fragments | archeofrag | Dataset: Refitting relationships between lithic fragments from the Chauzeys site | data.frame | 2166 | 9 |
cuzoul.cave.connection | archeofrag | Dataset: Refitting relationships between fauna fragments from the Cuzoul de Gramat site | matrix | 66 | 2 |
cuzoul.cave.fragments | archeofrag | Dataset: Refitting relationships between fauna fragments from the Cuzoul de Gramat site | data.frame | 125 | 9 |
cuzoul.south.connection | archeofrag | Dataset: Refitting relationships between fauna fragments from the Cuzoul de Gramat site | matrix | 135 | 2 |
cuzoul.south.fragments | archeofrag | Dataset: Refitting relationships between fauna fragments from the Cuzoul de Gramat site | data.frame | 233 | 9 |
fontjuvenal.connection | archeofrag | Dataset: Refitting relationships between pottery fragments from Font-Juvenal cave | matrix | 351 | 2 |
fontjuvenal.fragments | archeofrag | Dataset: Refitting relationships between pottery fragments from Font-Juvenal cave | data.frame | 354 | 4 |
fumane.connection | archeofrag | Dataset: Refitting relationships between lithic fragments from the Fumane cave | matrix | 262 | 2 |
fumane.fragments | archeofrag | Dataset: Refitting relationships between lithic fragments from the Fumane cave | data.frame | 508 | 8 |
grande.rivoire.connection | archeofrag | Dataset: Refitting relationships between lithic fragments from the Grande Rivoire site | matrix | 71 | 2 |
grande.rivoire.fragments | archeofrag | Dataset: Refitting relationships between lithic fragments from the Grande Rivoire site | data.frame | 91 | 3 |
liangabu.connection | archeofrag | Dataset: Archeological relationships between pottery fragments in Liang Abu | matrix | 56 | 2 |
liangabu.fragments | archeofrag | Dataset: Archeological relationships between pottery fragments in Liang Abu | data.frame | 177 | 11 |
liangabu.similarity | archeofrag | Dataset: Archeological relationships between pottery fragments in Liang Abu | matrix | 147 | 2 |
tai.cave.connection | archeofrag | Dataset: Refitting relationships between pottery fragments from the Tai site, Cave sector | matrix | 91 | 2 |
tai.cave.fragments | archeofrag | Dataset: Refitting relationships between pottery fragments from the Tai site, Cave sector | data.frame | 112 | 7 |
tai.south.connection | archeofrag | Dataset: Refitting relationships between pottery fragments from the Tai site, South entrance sector | matrix | 79 | 2 |
tai.south.fragments | archeofrag | Dataset: Refitting relationships between pottery fragments from the Tai site, South entrance sector | data.frame | 82 | 7 |
fmexample | fmesher | Example mesh data | list | | |
bills | ibawds | Summarised Data on Restaurant Bills | tbl_df | 8 | 4 |
breast_cancer | ibawds | Wisconsin Breast Cancer Database | tbl_df | 699 | 11 |
cran_history | ibawds | History of the Number of Available CRAN Packages | tbl_df | 70 | 4 |
dentition | ibawds | Dentition of Mammals | tbl_df | 66 | 9 |
dice_data | ibawds | Simulated Dice Throws | list | | |
galton_daughters | ibawds | Galton's data on the heights of fathers and their children | tbl_df | 176 | 2 |
galton_sons | ibawds | Galton's data on the heights of fathers and their children | tbl_df | 179 | 2 |
mtcars2 | ibawds | Dataset mtcars without row names | tbl_df | 32 | 12 |
mtcars2_na | ibawds | Dataset mtcars without row names | tbl_df | 32 | 12 |
noisy_data | ibawds | Noisy Data From a Tenth Order Polynomial | list | | |
protein | ibawds | Protein Consumption in European Countries | tbl_df | 25 | 10 |
seatbelts | ibawds | Road Casualties in Great Britain 1969-84 | tbl_df | 576 | 3 |
wine_quality | ibawds | Wine Quality | spec_tbl_df | 6497 | 13 |
TN2016 | LNPar | Number of employees in year 2016 in all the firms of the Trento district | integer | | |
BaseDataSet.ConversionFactors | Luminescence | Base data set of dose-rate conversion factors | list | | |
BaseDataSet.FractionalGammaDose | Luminescence | Base data set of fractional gamma-dose values | list | | |
BaseDataSet.GrainSizeAttenuation | Luminescence | Base dataset for grain size attenuation data by Guérin et al. (2012) | data.frame | 16 | 7 |
CWOSL.SAR.Data | Luminescence | Example data from a SAR OSL and SAR TL measurement for the package Luminescence | Risoe.BINfileData | | |
CW_Curve.BosWallinga2012 | Luminescence | Example CW-OSL curve data for the package Luminescence | data.frame | 2000 | 2 |
ExampleData.CW_OSL_Curve | Luminescence | Example CW-OSL curve data for the package Luminescence | data.frame | 1000 | 2 |
ExampleData.CobbleData | Luminescence | Example data for calc_CobbleDoseRate() | data.frame | 14 | 24 |
ExampleData.DeValues | Luminescence | Example De data sets for the package Luminescence | list | | |
ExampleData.Fading | Luminescence | Example data for feldspar fading measurements | list | | |
ExampleData.RLum.Data.Image | Luminescence | Example data as RLum.Data.Image objects | RLum.Data.Image | | |
ExampleData.ScaleGammaDose | Luminescence | Example data for scale_GammaDose() | data.frame | 9 | 12 |
ExampleData.SurfaceExposure | Luminescence | Example OSL surface exposure dating data | list | | |
ExampleData.TR_OSL | Luminescence | Example TR-OSL data | RLum.Data.Curve | | |
ExampleData.portableOSL | Luminescence | Example portable OSL curve data for the package Luminescence | list | | |
IRSAR.RF.Data | Luminescence | Example data as RLum.Analysis objects | RLum.Analysis | | |
Lx.data | Luminescence | Example Lx and Tx curve data from an artificial OSL measurement | data.frame | 100 | 2 |
LxTxData | Luminescence | Example Lx/Tx data from CW-OSL SAR measurement | data.frame | 7 | 4 |
MortarData | Luminescence | Example equivalent dose data from mortar samples | data.frame | 40 | 2 |
OSL.SARMeasurement | Luminescence | Example data for a SAR OSL measurement and a TL spectrum using a lexsyg reader | list | | |
TL.SAR.Data | Luminescence | Example data from a SAR OSL and SAR TL measurement for the package Luminescence | Risoe.BINfileData | | |
TL.Spectrum | Luminescence | Example data for a SAR OSL measurement and a TL spectrum using a lexsyg reader | RLum.Data.Spectrum | | |
Tx.data | Luminescence | Example Lx and Tx curve data from an artificial OSL measurement | data.frame | 100 | 2 |
data_CrossTalk | Luminescence | Example Al2O3:C Measurement Data | list | | |
data_ITC | Luminescence | Example Al2O3:C Measurement Data | RLum.Analysis | | |
values.cosmic.Softcomp | Luminescence | Base data set for cosmic dose rate calculation | data.frame | 33 | 2 |
values.curve | Luminescence | Example data for fit_LMCurve() in the package Luminescence | data.frame | 4000 | 2 |
values.curveBG | Luminescence | Example data for fit_LMCurve() in the package Luminescence | data.frame | 4000 | 2 |
values.factor.Altitude | Luminescence | Base data set for cosmic dose rate calculation | data.frame | 7 | 2 |
values.par.FJH | Luminescence | Base data set for cosmic dose rate calculation | data.frame | 12 | 4 |
disgust | bayestestR | Moral Disgust Judgment | data.frame | 150 | 2 |
baseline_1_iso | trps | Stable isotope data for amphipods (baseline 1) | tbl_df | 14 | 5 |
baseline_2_iso | trps | Stable isotope data for dreissenids (baseline 2) | tbl_df | 12 | 4 |
combined_iso | trps | Stable isotope data for lake trout, amphipods (benthic baseline; baseline 1) and dreissenids (pelagic baseline; baseline 2), | tbl_df | 117 | 13 |
consumer_iso | trps | Stable isotope data for lake trout (consumer) | tbl_df | 30 | 4 |
ex1_delayed_effect | simtrial | Time-to-event data example 1 for non-proportional hazards working group | tbl_df | 361 | 4 |
ex2_delayed_effect | simtrial | Time-to-event data example 2 for non-proportional hazards working group | tbl_df | 272 | 4 |
ex3_cure_with_ph | simtrial | Time-to-event data example 3 for non-proportional hazards working group | tbl_df | 280 | 4 |
ex4_belly | simtrial | Time-to-event data example 4 for non-proportional hazards working group | tbl_df | 774 | 4 |
ex5_widening | simtrial | Time-to-event data example 5 for non-proportional hazards working group | tbl_df | 165 | 4 |
ex6_crossing | simtrial | Time-to-event data example 6 for non-proportional hazards working group | tbl_df | 290 | 4 |
mb_delayed_effect | simtrial | Simulated survival dataset with delayed treatment effect | data.frame | 200 | 4 |
emperors | manydata | Emperors datacube documentation | list | | |
coffee_data | modelbased | Sample dataset from a course about analysis of factorial designs | data.frame | 120 | 5 |
efc | modelbased | Sample dataset from the EFC Survey | data.frame | 908 | 28 |
fish | modelbased | Sample data set | data.frame | 250 | 9 |
mbquartR_example | mbquartR | Manitoba Original Survey Legal Descriptions data | spec_tbl_df | 11 | 14 |
example_data | MariNET | Example Dataset: Psychological and Behavioral Responses | data.frame | 4372 | 22 |
linnet | ewp | Linnet clutch sizes | data.frame | 5414 | 3 |
Carto_SpainMUN | bigDM | Spanish colorectal cancer mortality data | sf | 7907 | 9 |
Data_LungCancer | bigDM | Spanish lung cancer mortality data | data.frame | 197675 | 6 |
Data_MultiCancer | bigDM | Spanish cancer mortality data for the joint analysis of multiple diseases | data.frame | 23721 | 5 |
POWOcodes | expowo | Complete list of vascular plant families and associated URI addresses | data.frame | 453 | 3 |
angioData | expowo | List of Angiosperm species | data.frame | 3122 | 13 |
angioGenera | expowo | List of Angiosperm genera | data.frame | 63 | 12 |
botregions | expowo | Countries and associated classification of botanical divisions | data.frame | 392 | 2 |
fish | parameters | Sample data set | data.frame | 250 | 9 |
qol_cancer | parameters | Sample data set | data.frame | 564 | 7 |
efc_insight | insight | Sample dataset from the EFC Survey | data.frame | 908 | 28 |
fish | insight | Sample data set for count models | data.frame | 250 | 9 |
F1_points_2017 | hyper2 | Formula 1 dataset | integer | | |
F1_table_2016 | hyper2 | Formula 1 dataset | data.frame | 24 | 21 |
F1_table_2017 | hyper2 | Formula 1 dataset | data.frame | 25 | 20 |
F1_table_2018 | hyper2 | Formula 1 dataset | data.frame | 20 | 21 |
F1_table_2019 | hyper2 | Formula 1 dataset | data.frame | 20 | 21 |
NBA | hyper2 | Basketball dataset | hyper2 | | |
NBA_maxp | hyper2 | Basketball dataset | numeric | | |
NBA_table | hyper2 | Basketball dataset | data.frame | 131 | 23 |
RCLF3 | hyper2 | Dataset from four Scottish football clubs | hyper3 | | |
RCLF3_lambda_max | hyper2 | Dataset from four Scottish football clubs | numeric | | |
RCLF3_maxp | hyper2 | Dataset from four Scottish football clubs | numeric | | |
RCLF3_table | hyper2 | Dataset from four Scottish football clubs | list | | |
T20 | hyper2 | Indian Premier League T20 cricket | hyper2 | | |
T20_maxp | hyper2 | Indian Premier League T20 cricket | numeric | | |
T20_table | hyper2 | Indian Premier League T20 cricket | data.frame | 633 | 5 |
T20_toss | hyper2 | Indian Premier League T20 cricket | hyper2 | | |
baseball | hyper2 | Baseball results, following Agresti | hyper2 | | |
baseball_maxp | hyper2 | Baseball results, following Agresti | numeric | | |
baseball_table | hyper2 | Baseball results, following Agresti | matrix | 7 | 7 |
carcinoma | hyper2 | Carcinoma dataset discussed by Agresti | lsl | | |
carcinoma_count | hyper2 | Carcinoma dataset discussed by Agresti | numeric | | |
carcinoma_maxp | hyper2 | Carcinoma dataset discussed by Agresti | numeric | | |
carcinoma_table | hyper2 | Carcinoma dataset discussed by Agresti | data.frame | 20 | 11 |
chess | hyper2 | Chess playing dataset | hyper2 | | |
chess3 | hyper2 | Karpov, Kasparov, Anand | hyper3 | | |
chess3_maxp | hyper2 | Karpov, Kasparov, Anand | numeric | | |
chess_maxp | hyper2 | Chess playing dataset | numeric | | |
chess_table | hyper2 | Chess playing dataset | matrix | 3 | 3 |
constructor_2020 | hyper2 | Formula 1 dataset: the constructors' championship | hyper3 | | |
constructor_2020_maxp | hyper2 | Formula 1 dataset: the constructors' championship | numeric | | |
constructor_2020_table | hyper2 | Formula 1 dataset: the constructors' championship | data.frame | 20 | 18 |
constructor_2021 | hyper2 | Formula 1 dataset: the constructors' championship | hyper3 | | |
constructor_2021_maxp | hyper2 | Formula 1 dataset: the constructors' championship | numeric | | |
constructor_2021_table | hyper2 | Formula 1 dataset: the constructors' championship | data.frame | 20 | 23 |
counterstrike | hyper2 | Counterstrike | hyper2 | | |
counterstrike_maxp | hyper2 | Counterstrike | numeric | | |
curling1 | hyper2 | Curling at the Winter Olympics, 1998-2018 | hyper2 | | |
curling1_maxp | hyper2 | Curling at the Winter Olympics, 1998-2018 | numeric | | |
curling2 | hyper2 | Curling at the Winter Olympics, 1998-2018 | hyper2 | | |
curling2_maxp | hyper2 | Curling at the Winter Olympics, 1998-2018 | numeric | | |
curling_table | hyper2 | Curling at the Winter Olympics, 1998-2018 | ordertable | 13 | 6 |
eurodance | hyper2 | Eurovision Dance contest dataset | hyper2 | | |
eurodance_maxp | hyper2 | Eurovision Dance contest dataset | numeric | | |
eurodance_table | hyper2 | Eurovision Dance contest dataset | matrix | 14 | 16 |
eurovision | hyper2 | Eurovision Song contest dataset | hyper2 | | |
eurovision_maxp | hyper2 | Eurovision Song contest dataset | numeric | | |
eurovision_table | hyper2 | Eurovision Song contest dataset | matrix | 18 | 20 |
formula1 | hyper2 | Formula 1 dataset | hyper2 | | |
handover | hyper2 | Dataset on communication breakdown in handover between physicians | hyper2 | | |
handover_maxp | hyper2 | Dataset on communication breakdown in handover between physicians | numeric | | |
handover_table | hyper2 | Dataset on communication breakdown in handover between physicians | matrix | 3 | 3 |
hepatitis | hyper2 | Hepatitis dataset discussed by Agresti | lsl | | |
hepatitis_count | hyper2 | Hepatitis dataset discussed by Agresti | numeric | | |
hepatitis_maxp | hyper2 | Hepatitis dataset discussed by Agresti | numeric | | |
hepatitis_table | hyper2 | Hepatitis dataset discussed by Agresti | data.frame | 6 | 5 |
icons | hyper2 | Dataset on climate change due to O'Neill | hyper2 | | |
icons_maxp | hyper2 | Dataset on climate change due to O'Neill | numeric | | |
icons_table | hyper2 | Dataset on climate change due to O'Neill | matrix | 9 | 6 |
interzonal | hyper2 | 1963 World Chess Championships | hyper2 | | |
interzonal_collusion | hyper2 | 1963 World Chess Championships | hyper2 | | |
interzonal_collusion_maxp | hyper2 | 1963 World Chess Championships | numeric | | |
interzonal_maxp | hyper2 | 1963 World Chess Championships | numeric | | |
interzonal_table | hyper2 | 1963 World Chess Championships | matrix | 23 | 23 |
javelin1 | hyper2 | Javelin dataset | hyper3 | | |
javelin1_maxp | hyper2 | Javelin dataset | numeric | | |
javelin2 | hyper2 | Javelin dataset | hyper3 | | |
javelin2_maxp | hyper2 | Javelin dataset | numeric | | |
javelin_table | hyper2 | Javelin dataset | data.frame | 8 | 6 |
javelin_vector | hyper2 | Javelin dataset | numeric | | |
jester | hyper2 | Jester dataset | hyper2 | | |
jester_maxp | hyper2 | Jester dataset | numeric | | |
jester_table | hyper2 | Jester dataset | matrix | 91 | 150 |
karate | hyper2 | Karate dataset | hyper2 | | |
karate_maxp | hyper2 | Karate dataset | numeric | | |
karate_table | hyper2 | Karate dataset | data.frame | 86 | 5 |
karate_zermelo | hyper2 | Karate dataset | numeric | | |
karpov_kasparov_anand | hyper2 | Karpov, Kasparov, Anand | hyper2 | | |
kka | hyper2 | Karpov, Kasparov, Anand | numeric | | |
kka_3draws | hyper2 | Karpov, Kasparov, Anand | hyper2 | | |
kka_3whites | hyper2 | Karpov, Kasparov, Anand | hyper2 | | |
kka_array | hyper2 | Karpov, Kasparov, Anand | array | | |
masterchef | hyper2 | Masterchef series 6 | list | | |
masterchef_constrained_maxp | hyper2 | Masterchef series 6 | numeric | | |
masterchef_maxp | hyper2 | Masterchef series 6 | numeric | | |
moto | hyper2 | MotoGP dataset | hyper2 | | |
moto_maxp | hyper2 | MotoGP dataset | numeric | | |
moto_table | hyper2 | MotoGP dataset | data.frame | 28 | 20 |
pentathlon | hyper2 | Pentathlon | hyper2 | | |
pentathlon_maxp | hyper2 | Pentathlon | numeric | | |
pentathlon_table | hyper2 | Pentathlon | ordertable | 7 | 5 |
powerboat | hyper2 | Powerboat dataset | hyper2 | | |
powerboat_maxp | hyper2 | Powerboat dataset | numeric | | |
powerboat_table | hyper2 | Powerboat dataset | ordertable | 21 | 7 |
rowing | hyper2 | Rowing dataset, sculling | hyper2 | | |
rowing_maxp | hyper2 | Rowing dataset, sculling | numeric | | |
rowing_minimal | hyper2 | Rowing dataset, sculling | hyper2 | | |
rowing_minimal_maxp | hyper2 | Rowing dataset, sculling | numeric | | |
rowing_minimal_table | hyper2 | Rowing dataset, sculling | list | | |
rowing_table | hyper2 | Rowing dataset, sculling | list | | |
skating | hyper2 | Figure skating at the 2002 Winter Olympics | hyper2 | | |
skating_maxp | hyper2 | Figure skating at the 2002 Winter Olympics | numeric | | |
skating_table | hyper2 | Figure skating at the 2002 Winter Olympics | ordertable | 23 | 9 |
soling | hyper2 | Sailing at the 2000 Summer Olympics - soling | hyper2 | | |
soling_after | hyper2 | Sailing at the 2000 Summer Olympics - soling | hyper2 | | |
soling_after_maxp | hyper2 | Sailing at the 2000 Summer Olympics - soling | numeric | | |
soling_maxp | hyper2 | Sailing at the 2000 Summer Olympics - soling | numeric | | |
soling_qf | hyper2 | Sailing at the 2000 Summer Olympics - soling | data.frame | 6 | 6 |
soling_rr1 | hyper2 | Sailing at the 2000 Summer Olympics - soling | data.frame | 6 | 6 |
soling_rr2 | hyper2 | Sailing at the 2000 Summer Olympics - soling | data.frame | 6 | 6 |
soling_table | hyper2 | Sailing at the 2000 Summer Olympics - soling | ordertable | 16 | 6 |
surfing | hyper2 | Surfing dataset | hyper2 | | |
surfing_maxp | hyper2 | Surfing dataset | numeric | | |
surfing_table | hyper2 | Surfing dataset | data.frame | 61 | 13 |
surfing_venuetypes | hyper2 | Surfing dataset | data.frame | 11 | 2 |
sushi | hyper2 | Sushi dataset | hyper3 | | |
sushi_eq_classes | hyper2 | Sushi dataset | numeric | | |
sushi_maxp | hyper2 | Sushi dataset | numeric | | |
sushi_table | hyper2 | Sushi dataset | preftable | 50 | 10 |
table_tennis | hyper2 | Match outcomes from repeated table tennis matches | hyper2 | | |
tennis | hyper2 | Match outcomes from repeated doubles tennis matches | hyper2 | | |
tennis_ghost | hyper2 | Match outcomes from repeated doubles tennis matches | hyper2 | | |
tennis_ghost_maxp | hyper2 | Match outcomes from repeated doubles tennis matches | numeric | | |
tennis_maxp | hyper2 | Match outcomes from repeated doubles tennis matches | numeric | | |
universities | hyper2 | New Zealand University ranking data | hyper2 | | |
universities_maxp | hyper2 | New Zealand University ranking data | numeric | | |
universities_table | hyper2 | New Zealand University ranking data | data.frame | 72 | 7 |
volleyball | hyper2 | Results from the NOCS volleyball league | hyper2 | | |
volleyball_maxp | hyper2 | Results from the NOCS volleyball league | numeric | | |
volleyball_table | hyper2 | Results from the NOCS volleyball league | matrix | 52 | 9 |
volvo | hyper2 | Race results from the 2014-2015 Volvo Ocean Race | hyper2 | | |
volvo_maxp | hyper2 | Race results from the 2014-2015 Volvo Ocean Race | numeric | | |
volvo_table | hyper2 | Race results from the 2014-2015 Volvo Ocean Race | ordertable | 7 | 9 |
zacslist | hyper2 | Counterstrike | list | | |
berkeley | bsitar | Berkeley Child Guidance Study Data | tbl_df | 2883 | 10 |
berkeley_exdata | bsitar | Berkeley Child Guidance Study Data for Females | data.frame | 770 | 3 |
berkeley_exfit | bsitar | Model Fit to the Berkeley Child Guidance Study Data for Females | brmsfit | | |
isotopes_ds | eprscope | Nuclear Isotope Data Frame (Dataset) with ENDOR Frequencies | data.table | 351 | 9 |
solvents_ds | eprscope | Solvent Properties Data Frame (Dataset) for EPR/ENDOR | tbl_df | 46 | 10 |
benchmarkData | CohortConstructor | Benchmarking results | list | | |
finches | rD3plot | Data: Finches' attributes in Galapagos islands. | data.frame | 13 | 4 |
galapagos | rD3plot | Data: Finches' presence in Galapagos Islands. | data.frame | 60 | 3 |
miserables | rD3plot | Coappearance network of characters in Les Miserables (undirected) | list | | |
sociologists | rD3plot | Data: Sociologists born in the 19th century. | data.frame | 33 | 5 |
anchor.case | cholera | Anchor or base case of each stack of fatalities. | data.frame | 578 | 2 |
border | cholera | Numeric IDs of line segments that create the map's border frame. | numeric | | |
fatalities | cholera | Amended Dodson and Tobler's cholera data. | data.frame | 578 | 5 |
fatalities.address | cholera | "Unstacked" amended cholera data with address as unit of observation. | data.frame | 321 | 6 |
fatalities.unstacked | cholera | "Unstacked" amended cholera fatalities data with fatality as unit of observation. | data.frame | 578 | 5 |
frame.data | cholera | Map frame data c("x", "y") and c("lon", "lat"). | data.frame | 106 | 8 |
frame.sample | cholera | Partitioned map frame points (segment endpoints). | list | | |
frame.segments | cholera | Dodson and Tobler's "Map Frame" street data transformed into road segments. | data.frame | 56 | 11 |
landmark.squares | cholera | Centers of city squares. | data.frame | 2 | 6 |
landmarks | cholera | Landmark coordinates. | data.frame | 20 | 11 |
latlong.ortho.addr | cholera | Orthogonal projection of observed address (latlong) cases onto road network. | data.frame | 321 | 7 |
latlong.ortho.pump | cholera | Orthogonal projection of 13 original pumps (latlong). | data.frame | 13 | 7 |
latlong.ortho.pump.vestry | cholera | Orthogonal projection of the 14 pumps from the Vestry Report (latlong). | data.frame | 14 | 7 |
latlong.regular.cases | cholera | "Expected" cases (latlong). | data.frame | 19982 | 4 |
latlong.sim.ortho.proj | cholera | Road "address" of simulated (i.e., "expected") cases (latlong). | data.frame | 19982 | 8 |
meter.to.yard | cholera | Meter to yard conversion factor. | numeric | | |
ortho.proj | cholera | Orthogonal projection of observed cases onto road network. | data.frame | 578 | 6 |
ortho.proj.pump | cholera | Orthogonal projection of 13 original pumps. | data.frame | 13 | 6 |
ortho.proj.pump.vestry | cholera | Orthogonal projection of the 14 pumps from the Vestry Report. | data.frame | 14 | 6 |
plague.pit | cholera | Plague pit coordinates. | data.frame | 13 | 2 |
pumps | cholera | Dodson and Tobler's pump data with street name. | data.frame | 13 | 6 |
pumps.vestry | cholera | Vestry report pump data. | data.frame | 14 | 6 |
rd.sample | cholera | Sample of road intersections (segment endpoints). | list | | |
rectangle.filter | cholera | Rectangular filter data. | data.frame | 4 | 2 |
regular.cases | cholera | "Expected" cases. | data.frame | 19993 | 2 |
road.segments | cholera | Dodson and Tobler's street data transformed into road segments. | data.frame | 658 | 11 |
roads | cholera | Dodson and Tobler's street data with appended road names. | data.frame | 1243 | 8 |
sim.ortho.proj | cholera | Road "address" of simulated (i.e., "expected") cases. | data.frame | 19993 | 6 |
sim.pump.case | cholera | List of "simulated" fatalities grouped by walking-distance pump neighborhood. | list | | |
sim.walking.distance | cholera | Walking distance to Broad Street Pump (#7). | data.frame | 19899 | 5 |
snow.neighborhood | cholera | Snow neighborhood fatalities. | numeric | | |
voronoi.polygons | cholera | Coordinates of Voronoi polygon vertices for original map. | list | | |
voronoi.polygons.vestry | cholera | Coordinates of Voronoi polygon vertices for Vestry Report map. | list | | |
BCMV | SetMethods | Berg-Schlosser and Cronqvist (2005) | data.frame | 18 | 5 |
EMMF | SetMethods | EMMFnegger (2011) | data.frame | 19 | 8 |
FSR | SetMethods | Freitag and Schlicht (2009) | data.frame | 16 | 8 |
FakeCS | SetMethods | Fake crisp-set data | data.frame | 30 | 5 |
FakeMV | SetMethods | Fake data for mvQCA | data.frame | 25 | 4 |
JOBF | SetMethods | Fake fuzzy-set job motivation data. | data.frame | 51 | 7 |
KAF | SetMethods | Koenig-Archibugi (2004) | data.frame | 13 | 5 |
LIPC | SetMethods | Lipset (1959), crisp-set | data.frame | 18 | 6 |
LIPF | SetMethods | Lipset (1959), fuzzy-set | data.frame | 18 | 6 |
LIPR | SetMethods | Lipset (1959), raw data | data.frame | 18 | 6 |
PAYF | SetMethods | Paykani et al. (2018) | data.frame | 131 | 9 |
PAYR | SetMethods | Paykani et al. (2018) | data.frame | 131 | 8 |
PENF | SetMethods | Pennings (2003) | data.frame | 45 | 5 |
SAMF | SetMethods | Samford (2010) | data.frame | 61 | 4 |
SC | SetMethods | Selbst, practicing the truth table algorithm data | data.frame | 130 | 4 |
SCHF | SetMethods | Schneider et. al. (2010) | data.frame | 76 | 7 |
SCHLF | SetMethods | Schneider et. al. (2010) | data.frame | 76 | 9 |
SDC | SetMethods | Selbst, disappearing necessary condition data | data.frame | 98 | 4 |
STUF | SetMethods | Fake fuzzy-set student data. | data.frame | 131 | 6 |
STUR | SetMethods | Fake raw, student data. | data.frame | 17 | 3 |
THOF | SetMethods | Thomann (2015) | data.frame | 76 | 7 |
VISC | SetMethods | Vis (2009), crisp set data | data.frame | 25 | 4 |
VISF | SetMethods | Vis (2009), fuzzy set data | data.frame | 25 | 4 |
dictionary_game_summary | nflseedR | Data Dictionary: Simulations | Game Summary | data.frame | 11 | 2 |
dictionary_games | nflseedR | Data Dictionary: Simulations | Games | data.frame | 9 | 2 |
dictionary_overall | nflseedR | Data Dictionary: Simulations | Overall | data.frame | 11 | 2 |
dictionary_standings | nflseedR | Data Dictionary: Simulations | Standings | data.frame | 21 | 2 |
dictionary_team_wins | nflseedR | Data Dictionary: Simulations | Team Wins | data.frame | 4 | 2 |
divisions | nflseedR | NFL team names and the conferences and divisions they belong to | tbl_df | 36 | 4 |
sims_games_example | nflseedR | Example Games Data used in NFL Simulations | tbl_df | 284 | 9 |
sims_teams_example | nflseedR | Example Teams Data used in NFL Simulations | tbl_df | 64 | 5 |
colon_s | finalfit | Chemotherapy for Stage B/C colon cancer | data.frame | 929 | 32 |
wcgs | finalfit | Western Collaborative Group Study | data.frame | 3154 | 15 |
classAbbreviations | birdscanR | Default class abbreviations table of the birdscanR package | data.frame | 9 | 2 |
manualBlindTimes | birdscanR | Example file on how to include manual blind times for your 'Birdscan MR1' database. | data.frame | 3 | 3 |
age_classification | pxmake | Age classification | tbl_df | 16 | 4 |
greenlanders | pxmake | Greenlanders | tbl_df | 100 | 4 |
population_gl | pxmake | Population Greenland | tbl_df | 30 | 4 |
px_keywords | pxmake | px keywords | tbl_df | 87 | 10 |
HRFsim | gimme | Hemodynamic Response Function (HRF) GIMME example. | list | | |
ms.fit | gimme | Fitted gimme object with multiple solutions | gimmemsp | | |
simData | gimme | Large example, heterogeneous data, group, subgroup, and individual level effects. | list | | |
simDataLV | gimme | Latent variable example, heterogeneous data, group, subgroup level effects. | list | | |
ts | gimme | Small example, heterogeneous data, group and individual level effects | list | | |
admiral_adlb | admiral | Lab Analysis Dataset | tbl_df | 3779 | 111 |
admiral_adsl | admiral | Subject Level Analysis Dataset | tbl_df | 306 | 54 |
atoxgr_criteria_ctcv4 | admiral | Metadata Holding Grading Criteria for NCI-CTCAEv4 | tbl_df | 40 | 13 |
atoxgr_criteria_ctcv5 | admiral | Metadata Holding Grading Criteria for NCI-CTCAEv5 | tbl_df | 37 | 13 |
atoxgr_criteria_daids | admiral | Metadata Holding Grading Criteria for DAIDs | tbl_df | 63 | 15 |
country_code_lookup | admiral | Country Code Lookup | tbl_df | 249 | 3 |
dose_freq_lookup | admiral | Pre-Defined Dose Frequencies | tbl_df | 86 | 5 |
ex_single | admiral | Single Dose Exposure Dataset | tbl_df | 22439 | 17 |
example_qs | admiral | Example 'QS' Dataset | tbl_df | 161 | 11 |
queries | admiral | Queries Dataset | tbl_df | 15 | 8 |
queries_mh | admiral | Queries MH Dataset | tbl_df | 14 | 8 |
pbc_orsf | aorsf | Mayo Clinic Primary Biliary Cholangitis Data | data.frame | 276 | 20 |
penguins_orsf | aorsf | Size measurements for adult foraging penguins near Palmer Station, Antarctica | tbl_df | 333 | 8 |
NouraguesCoords | BIOMASS | Nouragues plot coordinates | data.frame | 16 | 8 |
NouraguesHD | BIOMASS | Height-Diameter data | data.frame | 1051 | 7 |
NouraguesPlot201 | BIOMASS | Nouragues plot 201 coordinates | data.frame | 40 | 8 |
NouraguesTrees | BIOMASS | Nouragues forest dataset | data.frame | 2050 | 8 |
apgFamilies | BIOMASS | Angiosperm Phylogeny Group (APG III) dataset | data.frame | 502 | 2 |
feldCoef | BIOMASS | Feldpausch et al. 2012 coefficients for generalized height-diameter models | data.frame | 12 | 4 |
genusFamily | BIOMASS | Genus Family database | data.frame | 31355 | 2 |
param_4 | BIOMASS | Posterior distribution of Chave et al.'s 2014 equation 4 parameters | data.frame | 1001 | 3 |
param_7 | BIOMASS | Posterior distribution of parameters associated with the equation 7 by Chave et al. 2014. | data.frame | 1001 | 9 |
sd_10 | BIOMASS | Mean standard deviation of wood density estimates at different taxonomic levels | data.frame | 3 | 2 |
wdData | BIOMASS | The global wood density database | data.frame | 16467 | 7 |
cd4 | lqmix | CD4 Data | data.frame | 2376 | 10 |
pain | lqmix | Pain Data | data.frame | 357 | 4 |
Tengeler2020_pq | MiscMetabar | This tutorial explores the dataset from Tengeler et al. (2020) available in the 'mia' package. obtained using 'mia::makePhyloseqFromTreeSE(Tengeler2020)' | phyloseq | | |
data_fungi | MiscMetabar | Fungal OTU in phyloseq format | phyloseq | | |
data_fungi_mini | MiscMetabar | Fungal OTU in phyloseq format | phyloseq | | |
data_fungi_sp_known | MiscMetabar | Fungal OTU in phyloseq format | phyloseq | | |
Growth_Inflation | DPTM | Example Dataset Growth_Inflation | tbl_df | 814 | 15 |
d1 | DPTM | Example Dataset d1 | data.frame | 1000 | 7 |
example_ground_truth | inferCSN | Example ground truth data | data.frame | 19 | 4 |
example_matrix | inferCSN | Example matrix data | matrix | 5000 | 18 |
example_meta_data | inferCSN | Example meta data | data.frame | 5000 | 2 |
Owls | glmmTMB | Begging by Owl Nestlings | data.frame | 599 | 8 |
Salamanders | glmmTMB | Repeated counts of salamanders in streams | data.frame | 644 | 9 |
epil2 | glmmTMB | Seizure Counts for Epileptics - Extended | data.frame | 236 | 12 |
spider_long | glmmTMB | Spider data from CANOCO, long format | data.frame | 336 | 9 |
maestro_tags | maestro | Maestro Tags | character | | |
atrial_fibrillation | multinma | Stroke prevention in atrial fibrillation patients | data.frame | 63 | 11 |
bcg_vaccine | multinma | BCG vaccination | data.frame | 26 | 6 |
blocker | multinma | Beta blockers to prevent mortality after MI | data.frame | 44 | 5 |
diabetes | multinma | Incidence of diabetes in trials of antihypertensive drugs | data.frame | 48 | 7 |
dietary_fat | multinma | Reduced dietary fat to prevent mortality | data.frame | 21 | 7 |
hta_psoriasis | multinma | HTA Plaque Psoriasis | data.frame | 36 | 9 |
ndmm_agd | multinma | Newly diagnosed multiple myeloma | data.frame | 2819 | 6 |
ndmm_agd_covs | multinma | Newly diagnosed multiple myeloma | data.frame | 4 | 15 |
ndmm_ipd | multinma | Newly diagnosed multiple myeloma | data.frame | 1325 | 10 |
parkinsons | multinma | Mean off-time reduction in Parkison's disease | data.frame | 15 | 7 |
plaque_psoriasis_agd | multinma | Plaque psoriasis data | data.frame | 15 | 26 |
plaque_psoriasis_ipd | multinma | Plaque psoriasis data | data.frame | 4118 | 16 |
smoking | multinma | Smoking cessation data | data.frame | 50 | 5 |
social_anxiety | multinma | Social Anxiety | tbl_df | 248 | 8 |
statins | multinma | Statins for cholesterol lowering | data.frame | 38 | 7 |
thrombolytics | multinma | Thrombolytic treatments data | data.frame | 102 | 5 |
transfusion | multinma | Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction | data.frame | 12 | 4 |
paramsMWR | MassWateR | Master parameter list and units for Characteristic Name column in results data | tbl_df | 46 | 4 |
thresholdMWR | MassWateR | Master thresholds list for analysis of results data | tbl_df | 28 | 10 |
cdata | FastJM | Simulated competing risks data | data.frame | 1000 | 7 |
ydata | FastJM | Simulated longitudinal data | data.frame | 3067 | 6 |
eg_TS | CMIP6VisR | eg_TS | data.frame | 31411 | 2 |
zone_grid_df | CMIP6VisR | zone_grid_df | data.frame | 169584 | 4 |
ConfidenceOrientation | dynConfiR | Confidence and response time data | tbl_df | 25920 | 12 |
cas_demo_corpus | castarter | | spec_tbl_df | 451 | 7 |
cas_google_client | castarter | | gargle_oauth_client | | |
casdb_empty_contents_id | castarter | | tbl_df | | 5 |
casdb_empty_download | castarter | | tbl_df | | 5 |
casdb_empty_ia_check | castarter | | tbl_df | | 6 |
casdb_empty_index_id | castarter | Empty data frame with the same format as data stored in the 'index_id' table | tbl_df | | 3 |
cass_cicerone_help | castarter | | Cicerone | | |
common_na_strings | cleanepi | Common strings representing missing values | character | | |
dps567 | FLa4a | dps567 | FLStock | | |
dps567.idx | FLa4a | dps567.idx | FLIndex | | |
hakeGSA7 | FLa4a | hakeGSA7 | FLStock | | |
hakeGSA7.idx | FLa4a | hakeGSA7.idx | FLIndices | | |
hke1567 | FLa4a | hke1567 | FLStock | | |
hke1567.idx | FLa4a | hke1567.idx | FLIndex | | |
index_cd_len | FLa4a | index_cd_len | FLIndex | | |
index_pt_len | FLa4a | index_pt_len | FLIndex | | |
index_sp_len | FLa4a | index_sp_len | FLIndex | | |
mut09 | FLa4a | mut09 | FLStock | | |
mut09.idx | FLa4a | mut09.idx | FLIndices | | |
rfLen.stk | FLa4a | rfLen.stk | FLStockLen | | |
rfTrawl.idx | FLa4a | rfTrawl.idx | FLIndex | | |
rfTrawlJmp.idx | FLa4a | rfTrawlJmp.idx | FLIndex | | |
rfTrawlTrd.idx | FLa4a | rfTrawlTrd.idx | FLIndex | | |
shake_len | FLa4a | shake_len | FLStockLen | | |
ScotchWhiskey | adespatial | Scotch Whiskey Data Set | list | | |
Tiahura | adespatial | Tiahura Transect Fish Data Set | list | | |
bacProdxy | adespatial | Bacterial production data set | data.frame | 25 | 2 |
mastigouche | adespatial | Mastigouche Lake network data set | list | | |
trichoptera | adespatial | Trichoptera data set | data.frame | 220 | 58 |
barents | PLNmodels | Barents fish data set | data.frame | 89 | 6 |
mollusk | PLNmodels | Mollusk data set | list | | |
oaks | PLNmodels | Oaks amplicon data set | data.frame | 116 | 13 |
scRNA | PLNmodels | Single cell RNA-seq data | data.frame | 3918 | 3 |
trichoptera | PLNmodels | Trichoptera data set | list | | |
hampi | dodgr | Sample street network from Hampi, India. | sf | 236 | 15 |
os_roads_bristol | dodgr | Sample street network from Bristol, U.K. | sf | 29 | 20 |
weighting_profiles | dodgr | Weighting profiles used to route different modes of transport. | list | | |
DALSM_IncomeData | DALSM | Income data | data.frame | 756 | 5 |
ar1_data | mlts | Simple Time-Series Data | data.frame | 2500 | 3 |
ts_data | mlts | Simple Time-Series Data | data.frame | 1100 | 4 |
available_indicators | b3gbi | Indicators Available for Use in the Package | available_indicators | | |
example_cube_1 | b3gbi | Cube of GBIF Mammal Occurrences in Denmark | processed_cube | | |
example_indicator_ts1 | b3gbi | Time Series of Observed Species Richness for Mammals in Denmark | indicator_ts | | |
example_indicator_ts2 | b3gbi | Time Series of Cumulative Species Richness for Insects in Europe | indicator_ts | | |
demographic_data | NHSRwaitinglist | demographic data | data.frame | 5 | 9 |
opcs4 | NHSRwaitinglist | OPCS4 data | data.frame | 12279 | 9 |
daily | pammtools | Time-dependent covariates of the 'patient' data set. | tbl_df | 18797 | 4 |
patient | pammtools | Survival data of critically ill ICU patients | data.frame | 2000 | 12 |
simdf_elra | pammtools | Simulated data with cumulative effects | nested_fdf | 250 | 9 |
staph | pammtools | Time until staphylococcus aureaus infection in children, with possible recurrence | tbl_df | 374 | 6 |
tumor | pammtools | Stomach area tumor data | tbl_df | 776 | 9 |
map_df0 | WorldMapR | Initial dataset with geometries for each country | sf | 242 | 3 |
testdata1 | WorldMapR | Simulated data set 1 | data.frame | 90 | 3 |
testdata1b | WorldMapR | Simulated data set 1b | data.frame | 46 | 4 |
testdata1c | WorldMapR | Simulated data set 1c | data.frame | 237 | 4 |
aichi_districts | aichicities | Aichi prefecture administrative district data | sf | 69 | 5 |
birthDistribution | FDboost | Densities of live births in Germany | list | | |
emotion | FDboost | EEG and EMG recordings in a computerised gambling study | list | | |
fuelSubset | FDboost | Spectral data of fossil fuels | list | | |
viscosity | FDboost | Viscosity of resin over time | list | | |
children | questionr | A fertility survey - "children" table | tbl_df | 1584 | 6 |
enfants | questionr | A fertility survey - "enfants" table | tbl_df | 1584 | 6 |
femmes | questionr | A fertility survey - "femmes" table | tbl_df | 2000 | 17 |
happy | questionr | Data related to happiness from the General Social Survey, 1972-2006. | data.frame | 51020 | 10 |
hdv2003 | questionr | Histoire de vie 2003 | data.frame | 2000 | 20 |
households | questionr | A fertility survey - "households" table | tbl_df | 1814 | 5 |
menages | questionr | A fertility survey - "menages" table | tbl_df | 1814 | 5 |
rp2012 | questionr | 2012 French Census - French cities of more than 2000 inhabitants | tbl_df | 5170 | 60 |
rp2018 | questionr | 2018 French Census - French cities of more than 2000 inhabitants | tbl_df | 5417 | 62 |
women | questionr | A fertility survey - "women" table | tbl_df | 2000 | 17 |
ACTG175 | mets | ACTG175, block randmized study from speff2trial package | data.frame | 2139 | 28 |
TRACE | mets | The TRACE study group of myocardial infarction | data.frame | 1878 | 9 |
base1cumhaz | mets | rate of CRBSI for HPN patients of Copenhagen | data.frame | 1076 | 2 |
base44cumhaz | mets | rate of Occlusion/Thrombosis complication for catheter of HPN patients of Copenhagen | data.frame | 58 | 2 |
base4cumhaz | mets | rate of Mechanical (hole/defect) complication for catheter of HPN patients of Copenhagen | data.frame | 170 | 2 |
bmt | mets | The Bone Marrow Transplant Data | data.frame | 408 | 5 |
calgb8923 | mets | CALGB 8923, twostage randomization SMART design | data.frame | 593 | 30 |
dermalridges | mets | Dermal ridges data (families) | data.frame | 106 | 10 |
dermalridgesMZ | mets | Dermal ridges data (monozygotic twins) | data.frame | 36 | 5 |
diabetes | mets | The Diabetic Retinopathy Data | data.frame | 394 | 7 |
drcumhaz | mets | Rate for leaving HPN program for patients of Copenhagen | data.frame | 386 | 2 |
ghaplos | mets | ghaplos haplo-types for subjects of haploX data | data.frame | 555 | 4 |
hapfreqs | mets | hapfreqs data set | data.frame | 19 | 3 |
haploX | mets | haploX covariates and response for haplo survival discrete survival | data.frame | 2003 | 9 |
hfactioncpx12 | mets | hfaction, subset of block randmized study HF-ACtion from WA package | data.frame | 2132 | 7 |
melanoma | mets | The Melanoma Survival Data | data.frame | 205 | 6 |
mena | mets | Menarche data set | data.frame | 2000 | 7 |
migr | mets | Migraine data | data.frame | 4065 | 6 |
multcif | mets | Multivariate Cumulative Incidence Function example data set | data.frame | 400 | 8 |
np | mets | np data set | data.frame | 10000 | 7 |
prt | mets | Prostate data set | data.frame | 29222 | 6 |
sTRACE | mets | The TRACE study group of myocardial infarction | data.frame | 500 | 9 |
tTRACE | mets | The TRACE study group of myocardial infarction | data.frame | 1000 | 9 |
ttpd | mets | ttpd discrete survival data on interval form | data.frame | 1000 | 6 |
twinbmi | mets | BMI data set | data.frame | 11188 | 7 |
twinstut | mets | Stutter data set | data.frame | 32894 | 6 |
corn_data | maize | Synthetic Corn Dataset for Corny Example | tbl_df | 300 | 3 |
RHSA109582 | genomicper | Reactome Pathway examples | numeric | | |
RHSA1474244 | genomicper | Reactome Pathway examples | numeric | | |
RHSA164843 | genomicper | Reactome Pathway examples | numeric | | |
RHSA446343 | genomicper | Reactome Pathway examples | numeric | | |
RHSA8876384 | genomicper | Reactome Pathway examples | numeric | | |
RHSA8964572 | genomicper | Reactome Pathway examples | numeric | | |
SNPsAnnotation | genomicper | SNPs-Genes annotation to Distance 0 (SNPs within a gene) | data.frame | 1007 | 6 |
demo | genomicper | GWAS p_values demo data | data.frame | 1000 | 10 |
GO2ALLEGS_BP | BioM2 | An example about pathlistDB | list | | |
GO_Ancestor | BioM2 | Pathways in the GO database and their Ancestor | data.frame | 73849 | 4 |
GO_Ancestor_exact | BioM2 | Pathways in the GO database and their Ancestor | data.frame | 158450 | 4 |
MethylAnno | BioM2 | An example about FeatureAnno for methylation data | data.frame | 7055 | 3 |
MethylData_Test | BioM2 | An example about TrainData/TestData for methylation data | data.frame | 20 | 10001 |
TransAnno | BioM2 | An example about FeatureAnno for gene expression | data.table | 13210 | 3 |
TransData_Test | BioM2 | An example about TrainData/TestData for gene expression | data.frame | 20 | 10000 |
s_curve_noise | quollr | S-curve dataset with noise dimensions | tbl_df | 5000 | 8 |
s_curve_noise_test | quollr | S-curve dataset with noise dimensions for test | tbl_df | 1250 | 8 |
s_curve_noise_training | quollr | S-curve dataset with noise dimensions for training | tbl_df | 3750 | 8 |
s_curve_noise_umap | quollr | UMAP embedding for S-curve dataset which with noise dimensions | tbl_df | 3750 | 3 |
s_curve_noise_umap_predict | quollr | Predicted UMAP embedding for S-curve dataset which with noise dimensions | tbl_df | 1250 | 3 |
s_curve_noise_umap_scaled | quollr | Scaled UMAP embedding for S-curve dataset which with noise dimensions | tbl_df | 3750 | 3 |
s_curve_obj | quollr | Object for S-curve dataset | list | | |
bayes.sample | ensembleTax | Example output of dada2 assignTaxonomy function | list | | |
gg_13_8_train_set_97 | ensembleTax | All unique taxonomic assignments from the GreenGenes v13.8 clusted at 97% | data.frame | 4163 | 7 |
idtax.pr2.sample | ensembleTax | Example output of DECIPHER idtaxa function with pr2 taxonomy | Taxa | | |
idtax.silva.sample | ensembleTax | Example output of DECIPHER idtaxa function with silva taxonomy | Taxa | | |
pr2v4.12.0 | ensembleTax | All unique taxonomic assignments from the pr2 reference database v4.12.0 | data.frame | 45352 | 8 |
rdp_train_set_16 | ensembleTax | All unique taxonomic assignments from the RDP Train Set 16 | data.frame | 2472 | 6 |
rubric.sample | ensembleTax | Example rubric with ASV-identifying data | DNAStringSet | | |
silva.nr.v138 | ensembleTax | All unique taxonomic assignments from the Silva SSU nr database v138 | data.frame | 6011 | 6 |
synonyms_v2 | ensembleTax | Taxonomic synonyms searched by the taxmapper algorithm | data.frame | 174 | 11 |
FIGS | icardaFIGSr | FIGS subset for wheat sodicity resistance | data.frame | 201 | 15 |
durumDaily | icardaFIGSr | durumDaily | data.frame | 200 | 1461 |
durumWC | icardaFIGSr | durumWC | grouped_df | 200 | 58 |
septoriaDurumWC | icardaFIGSr | septoriaDurumWC | tbl_df | 200 | 56 |
PrimarySchool | gsbm | Network of interactions within a primary school in the course of a day | list | | |
blogosphere | gsbm | Political blogs network | list | | |
les_miserables | gsbm | Character network from "Les miserables" novel | list | | |
df_pisa18 | Rrepest | Program for International Student Assessment (PISA) 2018 noisy data subset | tbl_df | 1269 | 1109 |
df_talis18 | Rrepest | Teaching and Learning International Survey (TALIS) 2018 noisy data subset | tbl_df | 548 | 491 |
rrepest_pisa_age_gender | Rrepest | Rrepest table of results for PISA 2018 showing age and gender | tbl_df | 3 | 7 |
rrepest_pisa_age_isced | Rrepest | Rrepest table of results for PISA 2018 showing the age and completed schooling level of students' mothers | tbl_df | 3 | 13 |
talis18_tt3g23o_freq | Rrepest | Rrepest table of results for TALIS 2018 showing a frequency for other areas of professional development | tbl_df | 3 | 7 |
GSE6631 | NewmanOmics | Datasets to Illustrate the Newman Tests | matrix | 2000 | 44 |
LungPair | NewmanOmics | Datasets to Illustrate the Newman Tests | matrix | 20531 | 2 |
simeq | kequate | Simulated Test Data | list | | |
classes | mlergm | Polish school classes data set. | mlnet | | |
lonlat_prec | CSTools | Sample Of Experimental Precipitation Data In Function Of Longitudes And Latitudes | s2dv_cube | | |
lonlat_prec_st | CSTools | Sample Of Experimental Precipitation Data In Function Of Longitudes And Latitudes with Start | s2dv_cube | | |
lonlat_temp | CSTools | Sample Of Experimental And Observational Climate Data In Function Of Longitudes And Latitudes | list | | |
lonlat_temp_st | CSTools | Sample Of Experimental And Observational Climate Data In Function Of Longitudes And Latitudes with Start | list | | |
ToyData | l1spectral | Toy data for running the l1-spectral clustering algorithm | list | | |
simdata | gsynth | simdata | data.frame | 1500 | 15 |
turnout | gsynth | turnout | data.frame | 1128 | 6 |
GUM.H.1 | metRology | Example H.1 from the _Guide to the Expression of Uncertainty in Measurement_ | list | | |
Pb | metRology | Lead in wine | data.frame | 11 | 7 |
RMstudy | metRology | Collaborative study results for metals in a reference material certification study | data.frame | 145 | 9 |
apricot | metRology | Collaborative study results for fibre content in an apricot test material | data.frame | 18 | 2 |
chromium | metRology | Chromium data for two different materials included in an interlaboratory study | data.frame | 28 | 2 |
potassium | metRology | Potassium data for two different materials included in an interlaboratory study | data.frame | 25 | 2 |
neuronal.data | mixedsde | Trajectories Interspike Of A Single Neuron Of A Ginea Pig | list | | |
acs2016 | mimi | Excerpt of the 2016 Public Use American Census Survey (Alabama only) | list | | |
f_1D | glober | Output values of the evaluation of a function with one input variable and defined as a linear combination of B-splines | numeric | | |
f_2D | glober | Output values of the evaluation of a function with two input variables and defined as a linear combination of a tensor product of univariate B-splines | numeric | | |
x_1D | glober | Observation vector x of one variable | matrix | 70 | |
x_2D | glober | Observation matrix x of two variables | matrix | 100 | 2 |
xpred_1D | glober | Values of the single input variable for which a function has to be estimated | numeric | | |
xpred_2D | glober | Values of the two input variables for which a function has to be estimated | matrix | 10000 | 2 |
y_1D | glober | Values of the response variable of the noisy observation set of one input variable | matrix | 70 | |
y_2D | glober | Values of the response variable of the noisy observation set of two input variables | matrix | 100 | 1 |
G.aparine | drc | Herbicide applied to Galium aparine | data.frame | 240 | 3 |
H.virescens | drc | Mortality of tobacco budworms | data.frame | 12 | 4 |
M.bahia | drc | Effect of an effluent on the growth of mysid shrimp | data.frame | 40 | 2 |
O.mykiss | drc | Test data from a 21 day fish test | data.frame | 70 | 2 |
P.promelas | drc | Effect of sodium pentachlorophenate on growth of fathead minnow | data.frame | 24 | 2 |
RScompetition | drc | Competition between two biotypes | data.frame | 49 | 3 |
S.alba | drc | Potency of two herbicides | data.frame | 68 | 3 |
S.capricornutum | drc | Effect of cadmium on growth of green alga | data.frame | 18 | 2 |
acidiq | drc | Acifluorfen and diquat tested on Lemna minor. | data.frame | 180 | 3 |
algae | drc | Volume of algae as function of increasing concentrations of a herbicide | data.frame | 14 | 2 |
auxins | drc | Effect of technical grade and commercially formulated auxin herbicides | data.frame | 150 | 5 |
chickweed | drc | Germination of common chickweed (_Stellaria media_) | data.frame | 35 | 3 |
chickweed0 | drc | Germination of common chickweed (_Stellaria media_) | data.frame | 34 | 2 |
daphnids | drc | Daphnia test | data.frame | 16 | 4 |
decontaminants | drc | Performance of decontaminants used in the culturing of a micro-organism | data.frame | 128 | 3 |
deguelin | drc | Deguelin applied to chrysanthemum aphis | data.frame | 6 | 4 |
earthworms | drc | Earthworm toxicity test | data.frame | 35 | 3 |
etmotc | drc | Effect of erythromycin on mixed sewage microorganisms | data.frame | 57 | 4 |
finney71 | drc | Example from Finney (1971) | data.frame | 6 | 3 |
germination | drc | Germination of three crops | data.frame | 192 | 5 |
glymet | drc | Glyphosate and metsulfuron-methyl tested on algae. | data.frame | 113 | 3 |
heartrate | drc | Heart rate baroreflexes for rabbits | data.frame | 18 | 2 |
leaflength | drc | Leaf length of barley | data.frame | 42 | 2 |
lepidium | drc | Dose-response profile of degradation of agrochemical using lepidium | data.frame | 42 | 2 |
lettuce | drc | Hormesis in lettuce plants | data.frame | 14 | 2 |
mecter | drc | Mechlorprop and terbythylazine tested on Lemna minor | data.frame | 102 | 3 |
metals | drc | Data from heavy metal mixture experiments | data.frame | 543 | 3 |
methionine | drc | Weight gain for different methionine sources | data.frame | 9 | 3 |
nasturtium | drc | Dose-response profile of degradation of agrochemical using nasturtium | data.frame | 42 | 2 |
ryegrass | drc | Effect of ferulic acid on growth of ryegrass | data.frame | 24 | 2 |
secalonic | drc | Root length measurements | data.frame | 7 | 2 |
selenium | drc | Data from toxicology experiments with selenium | data.frame | 25 | 4 |
spinach | drc | Inhibition of photosynthesis | data.frame | 105 | 4 |
terbuthylazin | drc | The effect of terbuthylazin on growth rate | data.frame | 30 | 2 |
vinclozolin | drc | Vinclozolin from AR in vitro assay | data.frame | 53 | 3 |
history_example | LTASR | Example History File for Testing | spec_tbl_df | 4 | 5 |
person_example | LTASR | Example Person File for Testing | spec_tbl_df | 3 | 9 |
us_119ucod_19602021 | LTASR | 119 UCOD U.S. Death Rate, 1960-2021 | list | | |
us_119ucod_recent | LTASR | 119 UCOD U.S. Death Rate, 1960-2022 | list | | |
pm10 | freqdom.fda | PM10 dataset | fd | | |
covid_delay_dist | incidental | Delay distribution from COVID-19 pandemic. | data.frame | 61 | 4 |
covid_new_york_city | incidental | New York City data from the COVID-19 pandemic. | data.frame | 615 | 5 |
spanish_flu | incidental | Daily flu mortality from 1918 flu pandemic. | data.frame | 122 | 4 |
spanish_flu_delay_dist | incidental | Delay distribution from 1918 flu pandemic. | data.frame | 31 | 2 |
addl_expr | medExtractR | Additional expressions for 'drug_list' | data.frame | 162 | 2 |
dosechange_vals | medExtractR | Keywords Specifying Dose Change | data.frame | 24 | 1 |
doseschedule_vals | medExtractR | Keywords Specifying Dose Schedule | data.frame | 31 | 1 |
duration_vals | medExtractR | Keywords Specifying Duration | data.frame | 69 | 1 |
frequency_vals | medExtractR | Keywords Specifying Frequency | data.frame | 82 | 2 |
intaketime_vals | medExtractR | Keywords Specifying Intake Time | data.frame | 26 | 1 |
preposition_vals | medExtractR | Keywords Specifying Preposition | data.frame | 15 | 1 |
route_vals | medExtractR | Keywords Specifying Route | data.frame | 13 | 2 |
rxnorm_druglist | medExtractR | List of Medications | character | | |
time_regex | medExtractR | Keywords Specifying Time Expressions | character | | |
timekeyword_vals | medExtractR | Keywords Specifying Time Keyword | data.frame | 33 | 1 |
transition_vals | medExtractR | Keywords/Symbols Specifying Transition | data.frame | 4 | 1 |
K.add | mlmm.gwas | Dataset for examples with the mlmm.gwas package, additive and additive+dominance models | matrix | 110 | 110 |
K.dom | mlmm.gwas | Dataset for examples with the mlmm.gwas package, additive and additive+dominance models | matrix | 110 | 110 |
K.female | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | matrix | 36 | 36 |
K.hybrid | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | matrix | 303 | 303 |
K.male | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | matrix | 36 | 36 |
Xa | mlmm.gwas | Dataset for examples with the mlmm.gwas package, additive and additive+dominance models | matrix | 110 | 500 |
Xd | mlmm.gwas | Dataset for examples with the mlmm.gwas package, additive and additive+dominance models | matrix | 110 | 500 |
Xf | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | matrix | 303 | 500 |
Xfm | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | matrix | 303 | 500 |
Xm | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | matrix | 303 | 500 |
female | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | factor | | |
floweringDateAD | mlmm.gwas | Dataset for examples with the mlmm.gwas package, additive and additive+dominance models | numeric | | |
floweringDateFMI | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | numeric | | |
hybrid | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | factor | | |
male | mlmm.gwas | Dataset for examples with the mlmm.gwas package, male+female and male+female+interaction models | factor | | |
Balt | msltrend | Ocean water level data for Baltimore, USA | data.frame | 112 | 2 |
s | msltrend | sample 'msl.trend' object | msl.trend | | |
t | msltrend | sample 'msl.forecast' object | msl.forecast | | |
zaki | arulesSequences | Zaki Data Set | transactions | | |
craterA | highriskzone | Bomb crater Point Pattern | ppp | | |
craterB | highriskzone | Bomb crater Point Pattern | ppp | | |
Cancerrate | fds | Breast Cancer Data | fts | | |
ECBYieldcurve | fds | Yield curve data spot rate | fts | | |
Electricityconsumption | fds | Electricity consumption time series | sfts | | |
Fatspectrum | fds | Fat content spectrometric data | fds | | |
Fatvalues | fds | Fat content spectrometric data | numeric | | |
FedYieldcurve | fds | Federal Reserve interest rate | fts | | |
Moisturespectrum | fds | Moisture content spectrometric data | fds | | |
Moisturevalues | fds | Moisture content spectrometric data | numeric | | |
Octanespectrum | fds | Octane content spectrometric data | fds | | |
Octanevalues | fds | Octane content spectrometric data | numeric | | |
Pigweight | fds | Pig weight data | fds | | |
SAelectdemand | fds | Electricity demand in Adelaide | sfts | | |
SOI | fds | Annual Southern Oscillation Index (SOI) for the period 1900-2004. | fts | | |
Satellite | fds | Satellite topex/poseidon | fds | | |
Yieldcurve | fds | US: Treasury bond | fts | | |
aa | fds | Phoneme data | fds | | |
actfemale | fds | Australia and Australian state mortality rates | fts | | |
actmale | fds | Australia and Australian state mortality rates | fts | | |
actotal | fds | Australia and Australian state mortality rates | fts | | |
ao | fds | Phoneme data | fds | | |
ausfemale | fds | Australia and Australian state mortality rates | fts | | |
ausmale | fds | Australia and Australian state mortality rates | fts | | |
austotal | fds | Australia and Australian state mortality rates | fts | | |
dcl | fds | Phoneme data | fds | | |
femalemigration | fds | Spanish migration from 1999 to 2003 | fts | | |
fridaydemand | fds | Electricity demand in Adelaide | sfts | | |
fridaytempairport | fds | Temperatures in South Australia | sfts | | |
fridaytempkent | fds | Temperatures in South Australia | sfts | | |
iy | fds | Phoneme data | fds | | |
labc | fds | Biscuit dough piece data | matrix | 4 | 40 |
labp | fds | Biscuit dough piece data | matrix | 4 | 32 |
malemigration | fds | Spanish migration from 1999 to 2003 | fts | | |
mondaydemand | fds | Electricity demand in Adelaide | sfts | | |
mondaytempairport | fds | Temperatures in South Australia | sfts | | |
mondaytempkent | fds | Temperatures in South Australia | sfts | | |
nirc | fds | Biscuit dough piece data | fds | | |
nirp | fds | Biscuit dough piece data | fds | | |
nswfemale | fds | Australia and Australian state mortality rates | fts | | |
nswmale | fds | Australia and Australian state mortality rates | fts | | |
nswtotal | fds | Australia and Australian state mortality rates | fts | | |
ntfemale | fds | Australia and Australian state mortality rates | fts | | |
ntmale | fds | Australia and Australian state mortality rates | fts | | |
ntotal | fds | Australia and Australian state mortality rates | fts | | |
qldfemale | fds | Australia and Australian state mortality rates | fts | | |
qldmale | fds | Australia and Australian state mortality rates | fts | | |
qldtotal | fds | Australia and Australian state mortality rates | fds | | |
safemale | fds | Australia and Australian state mortality rates | fts | | |
samale | fds | Australia and Australian state mortality rates | fts | | |
satotal | fds | Australia and Australian state mortality rates | fts | | |
saturdaydemand | fds | Electricity demand in Adelaide | sfts | | |
saturdaytempairport | fds | Temperatures in South Australia | sfts | | |
saturdaytempkent | fds | Temperatures in South Australia | sfts | | |
sh | fds | Phoneme data | fds | | |
sundaydemand | fds | Electricity demand in Adelaide | sfts | | |
sundaytempairport | fds | Temperatures in South Australia | sfts | | |
sundaytempkent | fds | Temperatures in South Australia | sfts | | |
tasfemale | fds | Australia and Australian state mortality rates | fts | | |
tasmale | fds | Australia and Australian state mortality rates | fts | | |
tastotal | fds | Australia and Australian state mortality rates | fts | | |
tempairport | fds | Temperatures in South Australia | sfts | | |
tempkent | fds | Temperatures in South Australia | sfts | | |
thursdaydemand | fds | Electricity demand in Adelaide | sfts | | |
thursdaytempairport | fds | Temperatures in South Australia | sfts | | |
thursdaytempkent | fds | Temperatures in South Australia | sfts | | |
tuesdaydemand | fds | Electricity demand in Adelaide | sfts | | |
tuesdaytempairport | fds | Temperatures in South Australia | sfts | | |
tuesdaytempkent | fds | Temperatures in South Australia | sfts | | |
vicfemale | fds | Australia and Australian state mortality rates | fts | | |
vicmale | fds | Australia and Australian state mortality rates | fts | | |
victotal | fds | Australia and Australian state mortality rates | fts | | |
wafemale | fds | Australia and Australian state mortality rates | fts | | |
wamale | fds | Australia and Australian state mortality rates | fts | | |
watotal | fds | Australia and Australian state mortality rates | fts | | |
wednesdaydemand | fds | Electricity demand in Adelaide | sfts | | |
wednesdaytempairport | fds | Temperatures in South Australia | sfts | | |
wednesdaytempkent | fds | Temperatures in South Australia | sfts | | |
es | corTest | A BioConductor ExpressionSet Object Storing Gene Expression Data | ExpressionSet | | |
LEdemo | morph | Landscape Ecology Demo Data (3D Voxels) | array | | |
dissim.lesmis | SOMbrero | Dataset "Les Misérables" | matrix | 77 | 77 |
lesmis | SOMbrero | Dataset "Les Misérables" | igraph | | |
presidentielles2002 | SOMbrero | 2002 French presidential election data set | data.frame | 106 | 16 |
data_timeinv | jlctree | A simulated dataset with time-invariant longitudinal outcome and covariates. | data.frame | 500 | 10 |
data_timevar | jlctree | A simulated dataset with time-varying longitudinal outcome and covariates. | data.frame | 866 | 11 |
cusum_example_data | cusum | Non-Risk-adjusted Performance Data | tbl_df | 2000 | 3 |
gscusum_example_data | cusum | Group-sequential Non-Risk-adjusted Performance Data with Block Identifier | tbl_df | 500 | 4 |
racusum_example_data | cusum | Risk-adjusted Performance Data | tbl_df | 2000 | 4 |
ragscusum_example_data | cusum | Group-sequential Risk-adjusted Performance Data with Block Identifier | tbl_df | 500 | 5 |
fit | intccr | Output of 'ciregic' | ciregic | | |
fit_aipw | intccr | Output of 'ciregic_aipw' | ciregic_aipw | | |
fit_lt | intccr | Output of 'ciregic_lt' | ciregic_lt | | |
longdata | intccr | Simulated interval-censored competing risks data - long format | data.frame | 868 | 5 |
longdata_lt | intccr | Simulated left-truncated and interval-censored competing risks data - long format | data.frame | 1339 | 6 |
pseudo.HIV.long | intccr | Artificial HIV dataset | data.frame | 22710 | 6 |
simdata | intccr | Simulated interval-censored competing risks data with 2 covariates - wide format | data.frame | 200 | 6 |
simdata_aipw | intccr | Simulated interval censored data with 2 covariates in the presence of 50\% of missing cause of failure - wide format | data.frame | 200 | 7 |
simdata_lt | intccr | Simulated left-truncated and interval-censored competing risks data with 2 covariates - wide format | data.frame | 275 | 7 |
credit | mlr3summary | German Credit Dataset (Preprocessed) | data.frame | 522 | 6 |
vY | DMQ | data: Microsoft Corporation logarithmic percentage returns from December 8, 2010 to November 15, 2018 for a total of T = 2000 observation downloaded from Yahoo finance. | xts | 2000 | 1 |
glucose_data | ega | 5072 paired reference and test glucose values. | data.frame | 5072 | 2 |
BUPA | cosso | BUPA Liver Disorder Data | data.frame | 345 | 7 |
ozone | cosso | Ozone pollution data in Los Angels, 1976 | data.frame | 330 | 9 |
veteran | cosso | Veterans' Administration Lung Cancer study | data.frame | 137 | 8 |
simdata | ClusPred | Simulated data | list | | |
Helianthemum | ecespa | Spatial point pattern of Helianthemum squamatum adult plants and seedlings | ppp | | |
fig1 | ecespa | Artificial point data. | data.frame | 87 | 2 |
fig2 | ecespa | Artificial point data. | data.frame | 105 | 2 |
fig3 | ecespa | Artificial point data. | data.frame | 70 | 2 |
gypsophylous | ecespa | Spatial point pattern of a plant community | ppp | | |
quercusvm | ecespa | Alive and dead oak trees | ppp | | |
seedlings1 | ecespa | Cohorts of Helianthemum squamatum seedlings | ppp | | |
seedlings2 | ecespa | Cohorts of Helianthemum squamatum seedlings | ppp | | |
swamp | ecespa | Tree Species in a Swamp Forest | data.frame | 734 | 3 |
syr1 | ecespa | Syrjala test data | ppp | | |
syr2 | ecespa | Syrjala test data | ppp | | |
syr3 | ecespa | Syrjala test data | ppp | | |
data1 | Dtableone | data1 | data.frame | 475 | 3 |
data2 | Dtableone | data2 | data.frame | 475 | 3 |
data3 | Dtableone | data3 | data.frame | 475 | 3 |
data4 | Dtableone | data4 | data.frame | 475 | 3 |
KS.data | iWeigReg | A simulated dataset | data.frame | 1000 | 10 |
LGEWIS.example | LGEWIS | Data example for LGEWIS (tests for genetic association or gene-environment interaction) | list | | |
DataCD | Blendstat | Dataset, peeled cherry coffee. | data.frame | 36 | 12 |
DataNAT | Blendstat | Dataset, natural cherry coffee. | data.frame | 36 | 12 |
liliales | matrixcut | Chloroplast genome sequence similarity matrix for 163 Liliales species | matrix | 163 | 163 |
primates | matrixcut | Mitochondrial genome sequence similarity matrix for 31 primate species | matrix | 37 | 37 |
xenarthra | matrixcut | Mitochondrial genome sequence similarity matrix for 37 Xenarthra species | matrix | 37 | 37 |
dataOvarian | joint.Cox | Survival data of 1003 ovarian cancer patients from 4 independent studies. | data.frame | 1003 | 6 |
dataOvarian1 | joint.Cox | Data on time-to-recurrence and 158 gene expressions for 912 ovarian cancer patients from 4 independent studies. | data.frame | 912 | 162 |
dataOvarian2 | joint.Cox | Data on time-to-death and 128 gene expressions for 912 ovarian cancer patients from 4 independent studies. | data.frame | 912 | 132 |
simul | gte | Simulated Data | data.frame | 100 | 3 |
immigrationconjoint | cjoint | Immigration Conjoint Experiment Dataset from Hainmueller et. al. (2014) | data.frame | 13960 | 16 |
immigrationdesign | cjoint | Conjoint Design for the Immigration Experiment in Hainmueller et. al. (2014) | conjointDesign | | |
japan2014conjoint | cjoint | Japan 2014 Conjoint Experiment Dataset from Horiuchi et. al. (2014) | data.frame | 19220 | 24 |
andy2011params | pRoloc | Class '"AnnotationParams"' | AnnotationParams | | |
dunkley2006params | pRoloc | Class '"AnnotationParams"' | AnnotationParams | | |
agri_studies | NaileR | Agribusiness studies survey | data.frame | 53 | 42 |
atomic_habit | NaileR | Atomic habits survey | data.frame | 167 | 50 |
atomic_habit_clust | NaileR | Atomic habits survey | data.frame | 167 | 51 |
beard | NaileR | Beard descriptions | tbl_df | 494 | 2 |
beard_cont | NaileR | Beard descriptions | data.frame | 8 | 335 |
beard_wide | NaileR | Beard descriptions | data.frame | 8 | 24 |
boss | NaileR | Ideal boss survey | data.frame | 73 | 39 |
car_alone | NaileR | Atomic habits survey | data.frame | 158 | 2 |
fabric | NaileR | Car seat fabrics | data.frame | 567 | 4 |
glossophobia | NaileR | Glossophobia survey | data.frame | 139 | 41 |
local_food | NaileR | Local food systems survey | data.frame | 573 | 63 |
nutriscore | NaileR | Nutri-score survey | data.frame | 112 | 36 |
quality | NaileR | Perception of food quality | tbl_df | 55 | 9 |
rorschach | NaileR | Rorschach inkblots | data.frame | 600 | 5 |
waste | NaileR | Food waste survey | data.frame | 180 | 77 |
X | gpboost | Example data for the GPBoost package | matrix | 500 | |
X_test | gpboost | Example data for the GPBoost package | matrix | 5 | |
agaricus.test | gpboost | Test part from Mushroom Data Set | list | | |
agaricus.train | gpboost | Training part from Mushroom Data Set | list | | |
bank | gpboost | Bank Marketing Data Set | data.table | 4521 | 17 |
coords | gpboost | Example data for the GPBoost package | matrix | 500 | |
coords_test | gpboost | Example data for the GPBoost package | matrix | 5 | |
group_data | gpboost | Example data for the GPBoost package | matrix | 500 | 2 |
group_data_test | gpboost | Example data for the GPBoost package | matrix | 5 | 2 |
y | gpboost | Example data for the GPBoost package | numeric | | |
defaultPrescriptionsBySpecies | medfateland | Default prescriptions by species | data.frame | 27 | 20 |
example_ifn | medfateland | Example of distributed forest inventory stands | sf | 100 | 8 |
example_watershed | medfateland | Example of watershed | sf | 66 | 15 |
example_watershed_burnin | medfateland | Example of watershed | sf | 66 | 15 |
config | snowfall | Internal configuration and test data | data.frame | 6 | 1 |
f1 | snowfall | Internal configuration and test data | function | | |
f2 | snowfall | Internal configuration and test data | function | | |
MortTempPart | dCovTS | Cardiovascular mortality, temperature and pollution data in Los Angeles County | data.frame | 508 | 3 |
ibmSp500 | dCovTS | Monthly returns of IBM and S&P 500 composite index | data.frame | 1032 | 3 |
lung_FDA | mxfda | Multiplex imaging data from a non-small cell lung cancer study | mxFDA | | |
lung_df | mxfda | Multiplex imaging data from a non-small cell lung cancer study. | tbl_df | 285412 | 18 |
ovarian_FDA | mxfda | Multiplex imaging data from an ovarian cancer tumor microarray | mxFDA | | |
failure | evd | Failure Times | numeric | | |
fox | evd | Maximum Annual Flood Discharges of the Fox River | data.frame | 33 | 2 |
lisbon | evd | Annual Maximum Wind Speeds at Lisbon | numeric | | |
lossalae | evd | General Liability Claims | data.frame | 1500 | 2 |
ocmulgee | evd | Maximum Annual Flood Discharges of the Ocmulgee River | data.frame | 40 | 2 |
oldage | evd | Oldest Ages for Swedish Males and Females | data.frame | 66 | 2 |
oxford | evd | Annual Maximum Temperatures at Oxford | numeric | | |
portpirie | evd | Annual Maximum Sea Levels at Port Pirie | numeric | | |
sask | evd | Maximum Annual Flood Discharges of the North Saskachevan River | numeric | | |
sealevel | evd | Annual Sea Level Maxima at Dover and Harwich | data.frame | 81 | 2 |
sealevel2 | evd | Annual Sea Level Maxima at Dover and Harwich with Indicator | data.frame | 81 | 3 |
uccle | evd | Rainfall Maxima at Uccle, Belgium | data.frame | 35 | 4 |
venice | evd | Largest Sea Levels in Venice | data.frame | 51 | 10 |
venice2 | evd | Largest Sea Levels in Venice | data.frame | 125 | 10 |
buffalo | ctmm | African buffalo GPS dataset from Kruger National Park, South Africa. | list | | |
coati | ctmm | Coatis on Barro Colorado Island, Panama. | list | | |
gazelle | ctmm | Mongolian gazelle GPS dataset from the Mongolia's Eastern Steppe. | list | | |
jaguar | ctmm | Jaguar data from the Jaguar movement database. | list | | |
pelican | ctmm | Brown Pelican GPS and ARGOS data. | list | | |
turtle | ctmm | Wood turtle GPS and calibration dataset from Working Land and Seascapes. | list | | |
wolf | ctmm | Maned wolf GPS dataset from The Maned Wolf Conservation Program. | list | | |
mouse.GTDB_220 | chem16S | 'phyloseq-class' objects generated using the DADA2 Pipeline Tutorial | phyloseq | | |
mouse.RDP | chem16S | 'phyloseq-class' objects generated using the DADA2 Pipeline Tutorial | phyloseq | | |
mouse.silva | chem16S | 'phyloseq-class' objects generated using the DADA2 Pipeline Tutorial | phyloseq | | |
Fleiss1993bin | meta | Aspirin after Myocardial Infarction | data.frame | 7 | 6 |
Fleiss1993cont | meta | Mental Health Treatment | data.frame | 5 | 8 |
Fleiss93 | meta | Aspirin after Myocardial Infarction | data.frame | 7 | 6 |
Fleiss93cont | meta | Mental Health Treatment | data.frame | 5 | 8 |
Olkin1995 | meta | Thrombolytic Therapy after Acute Myocardial Infarction | data.frame | 70 | 6 |
Olkin95 | meta | Thrombolytic Therapy after Acute Myocardial Infarction | data.frame | 70 | 6 |
Pagliaro1992 | meta | Meta-analysis on Prevention of First Bleeding in Cirrhosis | data.frame | 28 | 8 |
amlodipine | meta | Amlodipine for Work Capacity | data.frame | 8 | 7 |
caffeine | meta | Caffeine for daytime drowsiness | data.frame | 8 | 12 |
cisapride | meta | Cisapride in Non-Ulcer Dispepsia | data.frame | 13 | 5 |
lungcancer | meta | Smoking example | data.frame | 7 | 6 |
smoking | meta | Smoking example | data.frame | 7 | 6 |
woodyplants | meta | Elevated CO_2 and total biomass of woody plants | data.frame | 102 | 10 |
slesions | ZINARp | Skin lesions dataset | data.frame | 84 | 1 |
Lights | eixport | Spatial distribution example | matrix | 149 | |
edgar | eixport | Emissions EDGAR | data.frame | 58656 | 8 |
emis_opt | eixport | List of WRF emission species | list | | |
emisco | eixport | Emissions from VEIN examples | sf | 1505 | 2 |
gCO | eixport | Gridded emissions from VEIN demo | sf | 437 | 2 |
rawprofile | eixport | Raw profile | matrix | 168 | |
actg181 | MLEcens | Data from the Aids Clinical Trials Group protocol ACTG 181 | matrix | 204 | 4 |
actg181Mod | MLEcens | Modified data from the Aids Clinical Trials Group protocol ACTG 181 | matrix | 204 | 4 |
cosmesis | MLEcens | Breast cosmesis data | matrix | 94 | 3 |
ex | MLEcens | Example data set (artificial) | matrix | 6 | 4 |
menopause | MLEcens | Menopause data | matrix | 2423 | 4 |
menopauseMod | MLEcens | Modified menopause data | matrix | 2423 | 4 |
affluence | affluenceIndex | Equivalised income | data.frame | 2000 | 5 |
rpu_boundaries | nhdplusTools | RPU Boundaries Raster Processing Unit boundaries | sf | 70 | 4 |
vpu_boundaries | nhdplusTools | VPU Boundaries Vector Processing Unit boundaries | sf | 23 | 3 |
UPN_914 | allMT | Sample data for a patient with unique patient number (UPN) 914 | data.frame | 28 | 10 |
UPN_915 | allMT | Sample data for a patient with unique patient number (UPN) 915 | data.frame | 38 | 10 |
UPN_916 | allMT | Sample data for a patient with unique patient number (UPN) 916 | data.frame | 37 | 10 |
zeller14 | lefser | Example dataset for lefser | SummarizedExperiment | | |
df_adverse_events | gtreg | Simulated Adverse Event Database | tbl_df | 100 | 8 |
df_patient_characteristics | gtreg | Simulated Patient Characteristics Database | tbl_df | 100 | 7 |
mastitis | pedigreemm | Mastitis cases in dairy cattle | data.frame | 1675 | 8 |
milk | pedigreemm | Milk production | data.frame | 3397 | 9 |
pedCows | pedigreemm | Pedigree of the cows in milk | pedigree | | |
pedCowsR | pedigreemm | Pedigree of the cows in milk with 0.70 of the information in pedCows | pedigree | | |
pedSires | pedigreemm | Pedigree of the sires from mastitis | pedigree | | |
t2uc | rbcc | Sample data for Risk-based Multivariate Control Chart | data.frame | 50 | 6 |
FEV | coneproj | Forced Expiratory Volume | data.frame | 654 | 5 |
TwoDamat | coneproj | A Two Dimensional Constraint Matrix | matrix | 324 | |
cubic | coneproj | A Data Set for the Example of the Qprog Function | data.frame | 50 | 2 |
feet | coneproj | Foot Measurements for Fourth Grade Children | data.frame | 39 | 8 |
A0 | Rwave | Transient Signal | data.frame | 1023 | 1 |
A4 | Rwave | Transient Signal | data.frame | 1023 | 1 |
B0 | Rwave | Transient Signal | data.frame | 1023 | 1 |
B4 | Rwave | Transient Signal | data.frame | 1023 | 1 |
backscatter.1.000 | Rwave | | data.frame | 7935 | 1 |
backscatter.1.180 | Rwave | | data.frame | 7935 | 1 |
backscatter.1.220 | Rwave | | data.frame | 7935 | 1 |
data_blocks | nipalsMCIA | NCI-60 Multi-Omics Data | list | | |
metadata_NCI60 | nipalsMCIA | NCI-60 Multi-Omics Metadata | data.frame | 21 | 1 |
albatross | distantia | Flight Path Time Series of Albatrosses in The Pacific | sf | 536 | 8 |
cities_coordinates | distantia | Coordinates of 100 Major Cities | sf | 20 | 11 |
cities_temperature | distantia | Long Term Monthly Temperature in 20 Major Cities | data.frame | 8420 | 3 |
covid_counties | distantia | County Coordinates of the Covid Prevalence Dataset | sf | 36 | 9 |
covid_prevalence | distantia | Time Series of Covid Prevalence in California Counties | data.frame | 7092 | 3 |
distances | distantia | Distance Methods | data.frame | 12 | 5 |
eemian_coordinates | distantia | Site Coordinates of Nine Interglacial Sites in Central Europe | sf | 9 | 6 |
eemian_pollen | distantia | Pollen Counts of Nine Interglacial Sites in Central Europe | data.frame | 376 | 24 |
fagus_coordinates | distantia | Site Coordinates of Fagus sylvatica Stands | sf | 3 | 2 |
fagus_dynamics | distantia | Time Series Data from Three Fagus sylvatica Stands | data.frame | 648 | 5 |
honeycomb_climate | distantia | Rainfall and Temperature in The Americas | tbl_df | 9432 | 4 |
honeycomb_polygons | distantia | Hexagonal Grid | sf | 72 | 2 |
census | ale | Census Income | tbl_df | 32561 | 15 |
var_cars | ale | Multi-variable transformation of the mtcars dataset. | tbl_df | 32 | 14 |
acq | tm | 50 Exemplary News Articles from the Reuters-21578 Data Set of Topic acq | VCorpus | | |
crude | tm | 20 Exemplary News Articles from the Reuters-21578 Data Set of Topic crude | VCorpus | | |
EUR_ld.blocks19 | RapidoPGS | LD block architecture for European populations (hg19). | GRanges | | |
EUR_ld.blocks38 | RapidoPGS | LD block architecture for European populations (hg38). | GRanges | | |
michailidou19 | RapidoPGS | Subset of Michailidou BRCA GWAS sumstat dataset. | data.table | 100000 | 10 |
michailidou38 | RapidoPGS | Subset of Michailidou BRCA GWAS sumstat dataset. | data.table | 100000 | 10 |
annotations | annotatr | example_annotations data | GRanges | | |
LG5data | tclust | LG5data data | data.frame | 200 | 11 |
M5data | tclust | M5data data | matrix | 2000 | 3 |
flea | tclust | Flea | data.frame | 74 | 7 |
geyser2 | tclust | Old Faithful Geyser Data | data.frame | 271 | 2 |
pine | tclust | Pinus nigra dataset | data.frame | 362 | 2 |
swissbank | tclust | Swiss banknotes data | data.frame | 200 | 6 |
wholesale | tclust | Wholesale customers dataset | data.frame | 440 | 8 |
hiv.data | Rtreemix | Example of an RtreemixData object | RtreemixData | | |
ex.cliqueGroups | cliqueMS | Example m/z processed data | anClique | | |
negative.adinfo | cliqueMS | Default list of negative charged adducts | data.frame | 11 | 5 |
positive.adinfo | cliqueMS | Default list of positive charged adducts | data.frame | 39 | 5 |
Cetacea | relations | Cetacea Data | data.frame | 36 | 16 |
Felines | relations | Felines Data | data.frame | 30 | 14 |
SVM_Benchmarking_Classification | relations | SVM Benchmarking Data and Consensus Relations | relation_ensemble | | |
SVM_Benchmarking_Classification_Consensus | relations | SVM Benchmarking Data and Consensus Relations | list | | |
SVM_Benchmarking_Regression | relations | SVM Benchmarking Data and Consensus Relations | relation_ensemble | | |
SVM_Benchmarking_Regression_Consensus | relations | SVM Benchmarking Data and Consensus Relations | list | | |
bamExample | scruff | Example GAlignments Object | GAlignments | | |
barcodeExample | scruff | A vector of example cell barcodes. | character | | |
cbtop10000 | scruff | Top 10,000 rows for v1, v2, and v3 cell barcode whitelist files | data.table | 10000 | 3 |
sceExample | scruff | Example SingleCellExperiment Object | SingleCellExperiment | | |
validCb | scruff | Cell barcode whitelist (737K-august-2016.txt) | data.frame | 737280 | 1 |
N | BCEAweb | | numeric | | |
N.outcomes | BCEAweb | | numeric | | |
N.resources | BCEAweb | | numeric | | |
QALYs.adv | BCEAweb | | matrix | 1000 | 2 |
QALYs.death | BCEAweb | | matrix | 1000 | 2 |
QALYs.hosp | BCEAweb | | matrix | 1000 | 2 |
QALYs.inf | BCEAweb | | matrix | 1000 | 2 |
QALYs.pne | BCEAweb | | matrix | 1000 | 2 |
c.pts | BCEAweb | | matrix | 1000 | |
cost | BCEAweb | | matrix | 500 | |
cost | BCEAweb | | matrix | 1000 | 2 |
cost.GP | BCEAweb | | matrix | 1000 | 2 |
cost.hosp | BCEAweb | | matrix | 1000 | 2 |
cost.otc | BCEAweb | | matrix | 1000 | 2 |
cost.time.off | BCEAweb | | matrix | 1000 | 2 |
cost.time.vac | BCEAweb | | matrix | 1000 | 2 |
cost.travel | BCEAweb | | matrix | 1000 | 2 |
cost.trt1 | BCEAweb | | matrix | 1000 | 2 |
cost.trt2 | BCEAweb | | matrix | 1000 | 2 |
cost.vac | BCEAweb | | matrix | 1000 | 2 |
data | BCEAweb | | data.frame | 13 | 9 |
e.pts | BCEAweb | | matrix | 1000 | |
eff | BCEAweb | | matrix | 500 | |
eff | BCEAweb | | matrix | 1000 | 2 |
life.years | BCEAweb | | matrix | 500 | |
pi_post | BCEAweb | | matrix | 500 | |
smoking | BCEAweb | | data.frame | 50 | 6 |
smoking_output | BCEAweb | | matrix | 500 | 76 |
smoking_parameters | BCEAweb | | data.frame | 500 | 1 |
smoking_results | BCEAweb | | data.frame | 500 | 1 |
treats | BCEAweb | | character | | |
treats | BCEAweb | | character | | |
vaccine_mat | BCEAweb | | matrix | 1000 | 77 |
vaccine_parameters | BCEAweb | | data.frame | 1000 | 1 |
vaccine_results | BCEAweb | | data.frame | 1000 | 1 |
ecosent | grafzahl | A Corpus Of Dutch News Headlines | data.frame | 6322 | 4 |
supported_model_types | grafzahl | Supported model types | character | | |
unciviltweets | grafzahl | A Corpus Of Tweets With Incivility Labels | corpus | | |
mtscr_creativity | mtscr | Creativity assessment through semantic distance dataset | tbl_df | 4585 | 4 |
mtscr_self_rank | mtscr | Self-chosen best answers | tbl_df | 3225 | 4 |
simwei | weitrix | Simulated weitrix dataset. | SummarizedExperiment | | |
doseFormToRoute | CodelistGenerator | Equivalence from dose from concept IDs to route categories. | tbl_df | 194 | 2 |
Ktlim | solaR2 | Markov Transition Matrices for the Aguiar etal. procedure | matrix | 2 | |
Ktmtm | solaR2 | Markov Transition Matrices for the Aguiar etal. procedure | numeric | | |
MTM | solaR2 | Markov Transition Matrices for the Aguiar etal. procedure | data.frame | 100 | 10 |
est_SIAR | solaR2 | Data on the stations that make up the SIAR network | data.table | 625 | 7 |
helios | solaR2 | Daily irradiation and ambient temperature from the Helios-IES database | data.frame | 355 | 4 |
prodEx | solaR2 | Productivity of a set of PV systems of a PV plant. | data.table | 493 | 23 |
pumpCoef | solaR2 | Coefficients of centrifugal pumps. | data.table | 124 | 13 |
data_test1 | lmhelprs | Sample Data: For Testing | data.frame | 100 | 8 |
cchain | vistla | Synthetic continuous data representing a simple mediator chain | data.frame | 20 | 6 |
chain | vistla | Synthetic data representing a simple mediator chain | data.frame | 11 | 6 |
junction | vistla | Synthetic data representing a junction | data.frame | 50 | 8 |
ig_fet | gigs | INTERGROWTH-21st Fetal Standards growth curve data | list | | |
ig_nbs | gigs | INTERGROWTH-21st Newborn Size Standards (including very preterm) growth curve data | list | | |
ig_nbs_coeffs | gigs | INTERGROWTH-21st Newborn Size Standards GAMLSS coefficients | list | | |
ig_nbs_ext | gigs | Extended INTERGROWTH-21st Newborn Size Standards (including very preterm) growth curve data | list | | |
ig_nbs_ext_coeffs | gigs | Extended INTERGROWTH-21st Newborn Size Standards GAMLSS coefficients | list | | |
ig_png | gigs | INTERGROWTH-21st Postnatal Growth Standards growth curve data | list | | |
life6mo | gigs | Data extract from the Low birthweight Infant Feeding Exploration (LIFE) study | data.frame | 2191 | 10 |
who_gs | gigs | WHO Child Growth Standards growth curve data | list | | |
who_gs_coeffs | gigs | WHO Child Growth Standards LMS coefficients | list | | |
candles | apexcharter | Candlestick demo data | data.frame | 60 | 5 |
climate_paris | apexcharter | Paris Climate | data.frame | 12 | 3 |
consumption | apexcharter | Electricity consumption and forecasting | data.frame | 120 | 3 |
eco2mix | apexcharter | eco2mix data | data.table | 3033 | 3 |
life_expec | apexcharter | Life expectancy data | data.frame | 10 | 4 |
life_expec_long | apexcharter | Life expectancy data (long format) | data.frame | 20 | 4 |
temperatures | apexcharter | Temperature data | data.frame | 365 | 6 |
unhcr_ts | apexcharter | UNHCR data by continent of origin | data.frame | 913 | 4 |
BER78 | palinsol | Tables supplied by BER78 and BER90 | list | | |
BER90 | palinsol | Tables supplied by BER78 and BER90 | list | | |
LA04 | palinsol | Astronomical elements supplied by Laskar et al. 2004 | list | | |
Ktlim | solaR | Markov Transition Matrices for the Aguiar etal. procedure | matrix | 2 | |
Ktm | solaR | Markov Transition Matrices for the Aguiar etal. procedure | numeric | | |
MTM | solaR | Markov Transition Matrices for the Aguiar etal. procedure | data.frame | 100 | 10 |
helios | solaR | Daily irradiation and ambient temperature from the Helios-IES database | data.frame | 355 | 4 |
prodEx | solaR | Productivity of a set of PV systems of a PV plant. | zoo | 493 | 22 |
pumpCoef | solaR | Coefficients of centrifugal pumps. | data.frame | 124 | 13 |
coriell | biomvRCNS | Array CGH data set of Coriell cell lines | data.frame | 2271 | 5 |
encodeTP53 | biomvRCNS | mapped RNA-seq data from ENCODE | list | | |
variosm | biomvRCNS | Differential methylation data from sequencing | GRanges | | |
COforest | cgam | Colorado Forest Data Set | data.frame | 9167 | 19 |
cubic | cgam | A Data Set for Cgam | data.frame | 50 | 2 |
mental | cgam | Alachua County Study of Mental Impairment | data.frame | 40 | 3 |
plasma | cgam | A Data Set for Cgam | data.frame | 314 | 9 |
mbrain_raw | SpotClean | Example 10x Visium spatial data: raw count matrix | dgCMatrix | | |
deaths2019 | hmsidwR | Dataset: Health Metrics Data - Number of Deaths Due to 9 Causes in 2019 | tbl_df | 2754 | 7 |
deaths9 | hmsidwR | Health Metrics Data - Number of Deaths Due to 9 Causes in 6 Locations for the Years 2011 and 2021. | data.frame | 5112 | 7 |
disweights | hmsidwR | Dataset: Health Metrics Data - Disability Weights and Severity in 2019 and 2021 | tbl_df | 463 | 9 |
g7_hmetrics | hmsidwR | Dataset: Health Metrics Data - G7 Countries | data.frame | 3402 | 9 |
germany_lungc | hmsidwR | Dataset: Health Metrics Data - Germany lungcancer Deaths 2019 | tbl_df | 48 | 8 |
gho_le_hale | hmsidwR | Dataset: Global Health Observatory (GHO) - Countries Life Expectancy and Healthy Life Expectancy(HALE) 2000-2019 | tbl_df | 8784 | 6 |
gho_lifetables | hmsidwR | Dataset: Global Health Observatory (GHO) Life tables: WHO Global Life table values | tbl_df | 1995 | 5 |
idDALY_map_data | hmsidwR | Dataset: Health Metrics Data - Simple Feature Collection Average Disability-Adjusted Life Years (DALYs) per 100,000 population from 1990 to 2021 | sf | 1402 | 4 |
id_affected_countries | hmsidwR | Dataset: Health Metrics Data - Infectious Diseases 1980-2021 | tbl_df | 3066 | 6 |
incprev_stroke | hmsidwR | Global Region Health Metrics Data - Incidence and Prevalence for Stroke 2019 and 2021 Numbers - 5-year age groups from <1 to 85+ and both Location available Global | data.frame | 228 | 7 |
infectious_diseases | hmsidwR | Dataset: Health Metrics Data - Infectious Diseases 1980-2021 | spec_tbl_df | 7470 | 10 |
rabies | hmsidwR | Dataset: Health Metrics Data - Rabies Deaths and DALYs from 1980 to 2021 | tbl_df | 296 | 7 |
sdi90_19 | hmsidwR | Dataset: Health Metrics Data - Socio-Demographic Index (SDI) for 1990 and 2019 | tbl_df | 20010 | 3 |
spatialdalys2021 | hmsidwR | Health Metrics Data - Disability-Adjusted Life Years (DALYs) Estimations for 204 countries in 2021 with spatial information. | tbl_df | 92862 | 7 |
iris2 | crosstable | Modified 'iris' dataset | tbl_df | 150 | 5 |
mtcars2 | crosstable | Modified 'mtcars' dataset | tbl_df | 32 | 14 |
M3_example | bayesRecon | Example of a time series from the M3 forecasting competition | list | | |
M5_CA1_basefc | bayesRecon | Example of hierarchical forecasts for a store from the M5 competition | list | | |
carparts_example | bayesRecon | Example of a time series from carparts | ts | | |
extr_mkt_events | bayesRecon | Extreme market events dataset | mts | 3508 | 6 |
extr_mkt_events_basefc | bayesRecon | Base forecasts for the extreme market events dataset | list | | |
infantMortality | bayesRecon | Infant Mortality grouped time series dataset | list | | |
Egambia | tmod | Gene expression in TB patients and Healthy controls | data.frame | 5547 | 33 |
EgambiaResults | tmod | Gene expression in TB patients and Healthy controls | data.frame | 5547 | 9 |
cell_signatures | tmod | Cell type signatures | tmodGS | | |
modmetabo | tmod | Modules for metabolic profiling | tmodGS | | |
tbmprof | tmod | Modules for metabolic profiling | matrix | 136 | 424 |
tmod | tmod | Default gene expression module data | tmodGS | | |
vaccination | tmod | Transcriptomic responses to vaccination | data.frame | 3000 | 12 |
CigaretteDemand | ivreg | U.S. Cigarette Demand Data | data.frame | 48 | 10 |
Kmenta | ivreg | Partly Artificial Data on the U.S. Economy | data.frame | 20 | 5 |
SchoolingReturns | ivreg | U.S. Returns to Schooling Data | data.frame | 3010 | 22 |
Data_Maize | decompML | Monthly International Maize Price | ts | 252 | 1 |
chicago | fqar | Chicagoland floristic quality assessment data | tbl_df | 877 | 52 |
missouri | fqar | Missouri floristic quality assessment data | tbl_df | 284 | 52 |
book.tee.data | mrds | Golf tee data used in chapter 6 of Advanced Distance Sampling examples | list | | |
lfbcvi | mrds | Black-capped vireo mark-recapture distance sampling analysis | data.frame | 2506 | 13 |
lfgcwa | mrds | Golden-cheeked warbler mark-recapture distance sampling analysis | data.frame | 2430 | 13 |
pronghorn | mrds | Pronghorn aerial survey data from Wyoming | data.frame | 660 | 5 |
ptdata.distance | mrds | Single observer point count data example from Distance | data.frame | 144 | 6 |
ptdata.dual | mrds | Simulated dual observer point count data | data.frame | 420 | 6 |
ptdata.removal | mrds | Simulated removal observer point count data | data.frame | 408 | 6 |
ptdata.single | mrds | Simulated single observer point count data | data.frame | 341 | 4 |
stake77 | mrds | Wooden stake data from 1977 survey | data.frame | 150 | 10 |
stake78 | mrds | Wooden stake data from 1978 survey | data.frame | 150 | 13 |
errBalancedF | dada2 | An empirical error matrix. | matrix | 16 | 41 |
errBalancedR | dada2 | An empirical error matrix. | matrix | 16 | 41 |
tperr1 | dada2 | An empirical error matrix. | matrix | 16 | 41 |
unwpp_countries | popim | List of countries in the UN WPP data 2024 | tbl_df | 237 | 4 |
dat | ecic | Simulated sample data | tbl_df | 60000 | 5 |
heasman_reid_1961_chains | chainbinomial | Common Cold Data | data.frame | 24 | 2 |
heasman_reid_1961_crowding | chainbinomial | Common Cold Data | data.frame | 5 | 4 |
heasman_reid_1961_intro_case_status | chainbinomial | Common Cold Data | data.frame | 5 | 5 |
gasoline | pls | Octane numbers and NIR spectra of gasoline | data.frame | 60 | 2 |
mayonnaise | pls | NIR measurements and oil types of mayonnaise | data.frame | 162 | 4 |
oliveoil | pls | Sensory and physico-chemical data of olive oils | data.frame | 16 | 2 |
yarn | pls | NIR spectra and density measurements of PET yarns | data.frame | 28 | 3 |
kg_3202 | BiocHail | data.frame with metadata about 3202 samples genotyped against T2T reference | data.frame | 3202 | 22 |
pcs_191k | BiocHail | HWE-normalized PCA scores for 3202 thousand-genomes samples genotyped with the telomere-to-telomere reference | data.frame | 3202 | 5 |
pcs_38k | BiocHail | HWE-normalized PCA scores for 3202 thousand-genomes samples genotyped with the telomere-to-telomere reference | data.frame | 3202 | 8 |
CRC_abd | MMUPHin | Species level feature abundance data of five public CRC studies | matrix | 484 | 551 |
CRC_meta | MMUPHin | Sample metadata of five public CRC studies | data.frame | 551 | 28 |
vaginal_abd | MMUPHin | Species level feature abundance data of two public vaginal studies | matrix | 119 | 86 |
vaginal_meta | MMUPHin | Sample metadata of two public vaginal studies | data.frame | 86 | 24 |
candidate_module | ProgModule | candidate_module, candidate module | list | | |
final_candidate_module | ProgModule | final_candidate_module, Final candidate modules | list | | |
local_network | ProgModule | local_network, local network gene set | list | | |
maf_data | ProgModule | maf_data, MAF file | data.frame | 3745 | 10 |
module | ProgModule | module, gene set | character | | |
mut_status | ProgModule | mut_status, mutations matrix | matrix | 338 | 331 |
net | ProgModule | net, network | igraph | | |
plotMutInteract_moduledata | ProgModule | plotMutInteract_moduledata | list | | |
plotMutInteract_mutdata | ProgModule | plotMutInteract_mutdata | matrix | 89 | 430 |
subnet | ProgModule | subnet, network | igraph | | |
univarCox_result | ProgModule | univarCox_result | numeric | | |
HMDB_blood | AlpsNMR | The Human Metabolome DataBase multiplet table: blood metabolites normally found in NMR-based metabolomics | data.frame | 224 | 8 |
HMDB_cell | AlpsNMR | The Human Metabolome DataBase multiplet table: cell metabolites normally found in NMR-based metabolomics | data.frame | 232 | 6 |
HMDB_urine | AlpsNMR | The Human Metabolome DataBase multiplet table: urine metabolites normally found in NMR-based metabolomics | data.frame | 1266 | 9 |
Parameters_blood | AlpsNMR | to rDolphin | data.frame | 14 | 2 |
Parameters_cell | AlpsNMR | Parameters for cell samples profiling | data.frame | 14 | 2 |
Parameters_urine | AlpsNMR | Parameters for urine samples profiling | data.frame | 14 | 2 |
ROI_blood | AlpsNMR | ROIs for blood (plasma/serum) samples | data.frame | 35 | 12 |
ROI_cell | AlpsNMR | ROIs for cell samples | data.frame | 44 | 12 |
ROI_urine | AlpsNMR | ROIs for urine samples | data.frame | 59 | 12 |
hmdb | AlpsNMR | The Human Metabolome DataBase multiplet table | tbl_df | 2629 | 12 |
pbmc3ksub | dreval | Expression profiles for 2,700 PBMCs | SingleCellExperiment | | |
DLBCL | cytometree | Diffuse large B-cell lymphoma data set from the FlowCAP-I challenge. | data.frame | 5524 | 4 |
HIPC | cytometree | HIPC T cell panel data set from HIPC program, patient 1369. The data was analyzed and gated by Stanford. | matrix | 33992 | 7 |
IMdata | cytometree | Influenza vaccine response dataset | matrix | 10000 | 39 |
lgcm | semtree | Simulated Linear Latent Growth Curve Data | data.frame | 400 | 8 |
annotation.mine | MSstatsShiny | Example annotation file for Spectromine | data.frame | 72 | 7 |
annotation.mq | MSstatsShiny | Example annotation file for MaxQuant | data.frame | 150 | 7 |
annotation.pd | MSstatsShiny | Example annotation file for PD | data.frame | 150 | 7 |
dia_skyline_model | MSstatsShiny | Example of Sklyine DDA dataset modeled using MSstats 'groupComparison' function. | list | | |
dia_skyline_summarized | MSstatsShiny | Example of Sklyine DDA dataset processed using MSstats summarization function. | list | | |
evidence | MSstatsShiny | Example evidence file for MaxQuant | data.table | 1075 | 105 |
example_dia_skyline | MSstatsShiny | Example of input Sklyine DDA dataset. | data.frame | 41820 | 16 |
example_skyline_annotation | MSstatsShiny | Example annotation file | data.frame | 30 | 3 |
proteinGroups | MSstatsShiny | Example ProteinGroups file for MaxQuant | data.table | 8 | 611 |
raw.mine | MSstatsShiny | Example output file Spectromine | data.frame | 170 | 28 |
raw.om | MSstatsShiny | Example output file Spectromine | data.frame | 860 | 13 |
raw.pd | MSstatsShiny | Example output file PD | data.table | 2858 | 50 |
tmt_pd_model | MSstatsShiny | Example of TMT dataset modeled using MSstatsTMT 'groupComparisonTMT' function. | list | | |
tmt_pd_summarized | MSstatsShiny | Example of TMT dataset processed using MSstatsTMT summarization function. | list | | |
simulation | GGPA | Simulation dataa for graph-GPA | list | | |
aml | RcppML | Acute Myelogenous Leukemia cells | list | | |
hawaiibirds | RcppML | Bird species frequency in Hawaii | list | | |
movielens | RcppML | Movie ratings | list | | |
COTOR2 | raw | Committee on the Theory of Risk (COTOR) | numeric | | |
COTOR3 | raw | Committee on the Theory of Risk (COTOR) | tbl_df | 490 | 2 |
COTOR4 | raw | Committee on the Theory of Risk (COTOR) | tbl_df | 2500 | 2 |
COTOR5 | raw | Committee on the Theory of Risk (COTOR) | tbl_df | 4849 | 4 |
Hurricane | raw | Hurricane data | tbl_df | 17494 | 8 |
MultiTri | raw | MultiTri | tbl_df | 3000 | 14 |
NJM_WC | raw | New Jersey Manufacturing Company Workers Comp Reserving Data | tbl_df | 100 | 13 |
PPA_AccidentYear | raw | Private Passenger Auto (PPA) Ratemaking Data | tbl_df | 5 | 4 |
PPA_LossDevelopment | raw | Private Passenger Auto (PPA) Ratemaking Data | tbl_df | 25 | 3 |
PPA_LossTrend | raw | Private Passenger Auto (PPA) Ratemaking Data | tbl_df | 20 | 7 |
PPA_PremiumTrend | raw | Private Passenger Auto (PPA) Ratemaking Data | tbl_df | 23 | 3 |
PPA_RateChange | raw | Private Passenger Auto (PPA) Ratemaking Data | tbl_df | 7 | 5 |
PPA_ULAE | raw | Private Passenger Auto (PPA) Ratemaking Data | tbl_df | 3 | 4 |
RegionExperience | raw | Region data | data.frame | 40 | 4 |
StateExperience | raw | State data | data.frame | 500 | 6 |
comauto | raw | NAIC | tbl_df | 15800 | 13 |
medmal | raw | NAIC | tbl_df | 3400 | 13 |
othliab | raw | NAIC | tbl_df | 23900 | 13 |
ppauto | raw | NAIC | tbl_df | 14600 | 13 |
prodliab | raw | NAIC | tbl_df | 7000 | 13 |
wkcomp | raw | NAIC | tbl_df | 13200 | 13 |
swimmers | MagmaClustR | French swimmers performances data on 100m freestyle events | data.frame | 76832 | 4 |
weight | MagmaClustR | Weight follow-up data of children in Singapore | data.frame | 3629 | 4 |
AOD | twang | Subset of Alcohol and Other Drug treatment data | data.frame | 600 | 7 |
egsingle | twang | US Sustaining Effects study | data.frame | 7230 | 12 |
iptwExLong | twang | Example data for iptw function (long version) | list | | |
iptwExWide | twang | Example data for iptw function (wide version) | data.frame | 1000 | 9 |
lalonde | twang | Lalonde's National Supported Work Demonstration data | data.frame | 614 | 10 |
lindner | twang | Lindner Center data on 996 PCI patients analyzed by Kereiakes et al. (2000) | data.frame | 996 | 11 |
mnIptwExLong | twang | Example data for iptw function (long version, more than two treatments). | list | | |
mnIptwExWide | twang | Example data for iptw function (wide version, more than two treatments) | data.frame | 3000 | 9 |
raceprofiling | twang | Traffic stop data | data.frame | 5000 | 10 |
HLA_Type_Table | HIBAG | Four-digit HLA types of a study simulated from HapMap CEU | data.frame | 60 | 13 |
HapMap_CEU_Geno | HIBAG | SNP genotypes of a study simulated from HapMap CEU genotypic data | hlaSNPGenoClass | | |
Athaliana_ODE | GRENITS | Gene expression time series generated with ODE model | data.frame | 5 | 50 |
Athaliana_ODE_4NoiseReps | GRENITS | Gene expression time series generated with ODE model with added noise | data.frame | 5 | 200 |
ex_raster | abmR | Example Environmental Raster | RasterLayer | | |
AllstarFull | Lahman | AllstarFull table | data.frame | 5673 | 8 |
Appearances | Lahman | Appearances table | data.frame | 113720 | 21 |
AwardsManagers | Lahman | AwardsManagers table | data.frame | 193 | 6 |
AwardsPlayers | Lahman | AwardsPlayers table | data.frame | 6797 | 6 |
AwardsShareManagers | Lahman | AwardsShareManagers table | data.frame | 510 | 7 |
AwardsSharePlayers | Lahman | AwardsSharePlayers table | data.frame | 7447 | 7 |
Batting | Lahman | Batting table | data.frame | 113799 | 22 |
BattingPost | Lahman | BattingPost table | data.frame | 16857 | 22 |
CollegePlaying | Lahman | CollegePlaying table | data.frame | 17350 | 3 |
Fielding | Lahman | Fielding table | data.frame | 151507 | 18 |
FieldingOF | Lahman | FieldingOF table | data.frame | 12380 | 6 |
FieldingOFsplit | Lahman | FieldingOFsplit table | data.frame | 35995 | 18 |
FieldingPost | Lahman | FieldingPost data | data.frame | 16006 | 17 |
HallOfFame | Lahman | Hall of Fame Voting Data | data.frame | 6382 | 9 |
HomeGames | Lahman | HomeGames table | data.frame | 3233 | 9 |
LahmanData | Lahman | Lahman Datasets | data.frame | 27 | 5 |
Managers | Lahman | Managers table | data.frame | 3749 | 10 |
ManagersHalf | Lahman | ManagersHalf table | data.frame | 93 | 10 |
Parks | Lahman | Parks table | data.frame | 260 | 6 |
People | Lahman | People table | data.frame | 21010 | 26 |
Pitching | Lahman | Pitching table | data.frame | 51368 | 30 |
PitchingPost | Lahman | PitchingPost table | data.frame | 6757 | 30 |
Salaries | Lahman | Salaries table | data.frame | 26428 | 5 |
Schools | Lahman | Schools table | data.frame | 1241 | 5 |
SeriesPost | Lahman | SeriesPost table | data.frame | 389 | 9 |
Teams | Lahman | Teams table | data.frame | 3045 | 48 |
TeamsFranchises | Lahman | TeamFranchises table | data.frame | 120 | 4 |
TeamsHalf | Lahman | TeamsHalf table | data.frame | 52 | 10 |
battingLabels | Lahman | Variable Labels | data.frame | 22 | 2 |
fieldingLabels | Lahman | Variable Labels | data.frame | 18 | 2 |
pitchingLabels | Lahman | Variable Labels | data.frame | 30 | 2 |
samplebioassay | bioassayR | Sample activity data for use with bioassayR | data.frame | 57 | 3 |
coffee | padr | Coffee Data Set | tbl_df | 4 | 2 |
emergency | padr | Emergency Calls for Montgomery County, PA | tbl_df | 120450 | 6 |
fire | mintyr | fire | data.table | 9794 | 10 |
nedap | mintyr | nedap | data.table | 31863 | 9 |
bre80 | chicane | Bre80 Cell Line | data.table | 47766 | 13 |
frac13 | TORDs | Fractional factorial | data.frame | 2047 | 13 |
frac14 | TORDs | Fractional factorial | data.frame | 4096 | 14 |
frac15 | TORDs | Fractional factorial | data.frame | 4096 | 15 |
dataset1 | MPCI | Simulated data set | matrix | 180 | |
dataset2 | MPCI | Real bivariate data set | matrix | 25 | |
ascii_art | startifyR | Ascii art | list | | |
quotes | startifyR | Quotes | list | | |
annotation | proteinProfiles | IPS sample data | data.frame | 247 | 8 |
ratios | proteinProfiles | IPS sample data | matrix | 247 | 10 |
RankCorr | ggdist | Thinned subset of posterior sample from a Bayesian analysis of perception of correlation. | mcmc.list | | |
RankCorr_u_tau | ggdist | Thinned subset of posterior sample from a Bayesian analysis of perception of correlation. | data.frame | 3000 | 5 |
KEGG_cancer_pathways_descriptions | driveR | KEGG 'Pathways in cancer'-related Pathways - Descriptions | data.frame | 21 | 2 |
MTL_submodel_descriptions | driveR | MTL Sub-model Descriptions | data.frame | 21 | 2 |
example_cohort_features_table | driveR | Example Cohort-level Features Table for Driver Prioritization | data.frame | 362 | 27 |
example_cohort_scna_table | driveR | Example Cohort-level Somatic Copy Number Alteration Table | data.frame | 126147 | 5 |
example_features_table | driveR | Example Features Table for Driver Prioritization | data.frame | 4908 | 27 |
example_gene_scna_table | driveR | Example Gene-level Somatic Copy Number Alteration Table | data.frame | 46270 | 2 |
example_scna_table | driveR | Example Somatic Copy Number Alteration Table | data.frame | 3160 | 4 |
specific_thresholds | driveR | Tumor type specific probability thresholds | numeric | | |
dailytrades | PINstimation | Example of quarterly data | data.frame | 60 | 2 |
hfdata | PINstimation | High-frequency trade-data | data.frame | 100000 | 5 |
bacteria | packcircles | Abundance of bacteria | data.frame | 167 | 3 |
africa | phyloregion | Plants of southern Africa | list | | |
little_guodong | pcutils | My cat | list | | |
metadata | pcutils | test data for pcutils package | data.frame | 18 | 10 |
otutab | pcutils | test data for pcutils package | data.frame | 485 | 18 |
taxonomy | pcutils | test data for pcutils package | data.frame | 485 | 7 |
BatchData | MethylMix | BatchData data set | data.frame | 23263 | 3 |
GEcancer | MethylMix | Cancer Gene expression data of glioblastoma patients from the TCGA project | matrix | 14 | 251 |
METcancer | MethylMix | DNA methylation data from cancer tissue from glioblastoma patients from the TCGA project | matrix | 14 | 251 |
METnormal | MethylMix | DNA methylation data from normal tissue from glioblastoma patients | matrix | 14 | 4 |
ProbeAnnotation | MethylMix | ProbeAnnotation data set | data.frame | 365860 | 2 |
SNPprobes | MethylMix | SNPprobes data set | character | | |
m750 | modeltime | The 750th Monthly Time Series used in the M4 Competition | tbl_df | 306 | 3 |
m750_models | modeltime | Three (3) Models trained on the M750 Data (Training Set) | mdl_time_tbl | 3 | 3 |
m750_splits | modeltime | The results of train/test splitting the M750 Data | rsplit | | |
m750_training_resamples | modeltime | The Time Series Cross Validation Resamples the M750 Data (Training Set) | time_series_cv | 6 | 2 |
arusha_df | ag5Tools | Example dataset for the agera5 package | data.frame | 100 | 4 |
arvicola | secrlinear | Water Vole Capture Dataset | capthist | | |
glymemask | secrlinear | Linear Mask for Water Vole Dataset | linearmask | 326 | 2 |
and_vertebrates | lterdatasampler | Cutthroat trout and salamander length and weights in Mack Creek, Andrews Forest LTER | tbl_df | 32209 | 16 |
arc_weather | lterdatasampler | Daily weather data from Toolik Field Station at Toolik Lake, Alaska (1988 - 2018) from Arctic LTER | tbl_df | 11171 | 5 |
hbr_maples | lterdatasampler | Health of Sugar Maple (Acer saccharum) Seedlings in Response to Calcium Addition (2003-2004), Hubbard Brook LTER | tbl_df | 359 | 11 |
knz_bison | lterdatasampler | Konza Prairie Bison Herd Information, Konza Prairie Biological Station LTER | tbl_df | 8325 | 8 |
luq_streamchem | lterdatasampler | LUQ Stream Chemistry Data for Quebrada Sonadora (QS) site | tbl_df | 317 | 22 |
ntl_airtemp | lterdatasampler | Daily Average Temperature Data in Madison, WI (1869 - 2019), North Temperate Lakes LTER | tbl_df | 55151 | 3 |
ntl_icecover | lterdatasampler | Ice Freeze and Thaw Dates for Madison, WI Area Lakes (1853 - 2019), North Temperate Lakes LTER | tbl_df | 334 | 5 |
nwt_pikas | lterdatasampler | American Pika (Ochotona princeps) Stress and Habitat Measurements (2018), Niwot Ridge LTER | tbl_df | 109 | 8 |
pie_crab | lterdatasampler | Fiddler crab body size in salt marshes from Florida to Massachusetts, USA at PIE and VCR LTER and NOAA NERR sites during summer 2016. | tbl_df | 392 | 9 |
ct47 | BayesMultiMode | X chromosomal macrosatellite repeats ct47 | numeric | | |
cyclone | BayesMultiMode | Tropical cyclones lifetime maximum intensity | tbl_df | 7211 | 4 |
d4z4 | BayesMultiMode | Autosomal macrosatellite repeats d4z4 | numeric | | |
galaxy | BayesMultiMode | Galaxy series | numeric | | |
transactions | bolasso | Customer transaction data | tbl_df | 5000 | 201 |
UCcrown | Toothnroll | example dataset | list | | |
UCroot | Toothnroll | example dataset | list | | |
UI1crown | Toothnroll | example dataset | list | | |
UI1root | Toothnroll | example dataset | list | | |
UI2crown | Toothnroll | example dataset | list | | |
UI2root | Toothnroll | example dataset | list | | |
URI1_tooth | Toothnroll | example dataset | list | | |
gravity_no_zeros | gravity | Gravity dataset without zero trade flows | tbl_df | 17088 | 10 |
gravity_zeros | gravity | Gravity dataset without zero trade flows | tbl_df | 22588 | 10 |
leslie_mpm1 | Rage | Example Leslie matrix population model (MPM) | list | | |
mpm1 | Rage | Example matrix population model (MPM) | list | | |
dates | bfast | A vector with date information (a Datum type) to be linked with each NDVI layer within the modis raster datacube (modisraster data set) | Date | | |
harvest | bfast | 16-day NDVI time series for a Pinus radiata plantation. | ts | | |
ndvi | bfast | A random NDVI time series | ts | | |
simts | bfast | Simulated seasonal 16-day NDVI time series | stl | | |
som | bfast | Two 16-day NDVI time series from the south of Somalia | data.frame | 263 | 3 |
population | NonProbEst | A full population | data.frame | 50000 | 6 |
sampleNP | NonProbEst | A non-probabilistic sample | data.frame | 1000 | 9 |
sampleP | NonProbEst | A probabilistic sample | data.frame | 500 | 5 |
lat.lon.meuse | loa | Example data for use with loa | data.frame | 155 | 14 |
roadmap.meuse | loa | Example data for use with loa | staticMap | | |
nhanes | table.glue | NHANES blood pressure data | data.frame | 29400 | 17 |
nonverbal | mHMMbayes | Nonverbal communication of patients and therapist | matrix | 9000 | 5 |
nonverbal_cov | mHMMbayes | Predictors of nonverbal communication | data.frame | 10 | 3 |
kosovo_aggregate | LCMCR | Killings in the Kosovo war from March 20 to June 22, 1999. | data.frame | 4400 | 4 |
dfASHRAETableG11 | comf | Calibration data for SET | data.frame | 22 | 11 |
dfField | comf | Field data example | data.frame | 156 | 9 |
dfISO7730AppE | comf | Calibration data for PMV | data.frame | 2963 | 6 |
dfISO7730TableD1 | comf | Calibration data for PMV and PPD | data.frame | 13 | 8 |
dfISO7933AppF | comf | Calibration data for Tre, SWtotg, Dlimtre, Dlimloss50, Dlimloss95 | data.frame | 10 | 16 |
dfSolarGainValues | comf | Dataset with Different Combinations of Inputs to Calculate Solar Gain | data.frame | 384 | 10 |
dfUTCIValues | comf | Dataset with Different Combinations of Inputs to Calculate UTCI | data.frame | 81 | 5 |
dfVariables | comf | List of all the variables used in this package | data.frame | 79 | 5 |
WaggaWagga | musicXML | Wagga-Wagga dataset | data.frame | 79 | 3 |
bush10 | rrcovNA | Campbell Bushfire Data with added missing data items (10 percent) | data.frame | 38 | 5 |
ces | rrcovNA | Consumer Expenditure Survey Data | data.frame | 869 | 8 |
E | roben | simulated data for demonstrating the features of roben | matrix | 100 | 4 |
E2 | roben | simulated data for demonstrating the features of roben | matrix | 500 | 5 |
X | roben | simulated data for demonstrating the features of roben | matrix | 100 | 10 |
X2 | roben | simulated data for demonstrating the features of roben | matrix | 500 | 100 |
Y | roben | simulated data for demonstrating the features of roben | matrix | 100 | |
Y2 | roben | simulated data for demonstrating the features of roben | matrix | 500 | |
clin | roben | simulated data for demonstrating the features of roben | matrix | 100 | 2 |
clin2 | roben | simulated data for demonstrating the features of roben | matrix | 500 | 3 |
coeff | roben | simulated data for demonstrating the features of roben | list | | |
coeff2 | roben | simulated data for demonstrating the features of roben | list | | |
author_data | logos | Author Data for Biblical Books | data.frame | 66 | 6 |
new_testament | logos | The Society of Biblical Literature Greek New Testament | data.frame | 7939 | 5 |
old_testament | logos | Old Testament Dataset | data.frame | 23213 | 5 |
rasb_bible | logos | RASB Bible | data.frame | 31102 | 4 |
verses_by_book | logos | Verses by Book | tbl_df | 66 | 2 |
E | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
E.new | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
E2 | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 500 | |
X | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
X.new | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
X2 | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 500 | |
Y | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
Y.new | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
Y2 | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 500 | |
Z | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
Z.new | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
Z2 | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 500 | |
clin | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
clin.new | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 100 | |
clin2 | spinBayes | simulated data for demonstrating the features of BVCfit | matrix | 500 | |
rgn.htr | regnet | Example datasets for demonstrating the features of regnet | list | | |
rgn.logi | regnet | Example datasets for demonstrating the features of regnet | list | | |
rgn.surv | regnet | Example datasets for demonstrating the features of regnet | list | | |
rgn.tcga | regnet | Example datasets for demonstrating the features of regnet | list | | |
lflags | ggflags | List of country flags | list | | |
ds_aat | splithalfr | | data.frame | 6528 | 12 |
ds_gng | splithalfr | | data.frame | 28200 | 7 |
ds_iat | splithalfr | | data.frame | 9696 | 11 |
ds_rapi | splithalfr | | data.frame | 426 | 24 |
ds_sst | splithalfr | | data.frame | 27000 | 7 |
ds_vpt | splithalfr | | data.frame | 19520 | 12 |
Groningen | spatialrisk | Coordinates of houses in Groningen | tbl_df | 25000 | 9 |
insurance | spatialrisk | Sum insured per postal code in the Netherlands | tbl_df | 29990 | 5 |
knmi_stations | spatialrisk | KNMI stations | spec_tbl_df | 50 | 7 |
nl_corop | spatialrisk | Object of class 'sf' for COROP regions in the Netherlands | sf | 40 | 5 |
nl_gemeente | spatialrisk | Object of class 'sf' for municipalities in the Netherlands | sf | 352 | 6 |
nl_postcode2 | spatialrisk | Object of class 'sf' for 2-digit postcode regions in the Netherlands | sf | 90 | 4 |
nl_postcode3 | spatialrisk | Object of class 'sf' for 3-digit postcode regions in the Netherlands | sf | 798 | 4 |
nl_postcode4 | spatialrisk | Object of class 'sf' for 4-digit postcode regions in the Netherlands | sf | 4169 | 7 |
nl_provincie | spatialrisk | Object of class 'sf' for provinces in the Netherlands | sf | 12 | 4 |
voters | fwildclusterboot | Random example data set | data.frame | 300 | 13 |
toy_outbreak_long | o2geosocial | Simulated outbreaks | list | | |
toy_outbreak_short | o2geosocial | Simulated outbreaks | list | | |
murray | SpatialPack | The Murray smelter site dataset | data.frame | 253 | 5 |
radiata | SpatialPack | The Pinus Radiata dataset | data.frame | 468 | 6 |
texmos2 | SpatialPack | USC texture mosaic number 2 | matrix | 512 | |
twelve | SpatialPack | Ishihara plate number 1 | array | | |
wheat | SpatialPack | Brodatz texture image, Straw (D15) | matrix | 512 | |
ExpressionData | VSOLassoBag | Simulated Example Data for VSOLassoBag Application | SummarizedExperiment | | |
COVID19 | HDNRA | HDNRA_data COVID19 | matrix | 87 | |
corneal | HDNRA | HDNRA_data corneal | matrix | 150 | 2000 |
ICAapp | ccmEstimator | Application data | data.frame | 1602 | 14 |
cognition | galamm | Simulated Data with Measurements of Cognitive Abilities | data.frame | 14400 | 7 |
diet | galamm | Diet Data | data.frame | 742 | 8 |
epilep | galamm | Epilepsy Data | data.frame | 236 | 7 |
hsced | galamm | Example Data with Heteroscedastic Residuals | data.frame | 1200 | 5 |
latent_covariates | galamm | Simulated Data with Latent and Observed Covariates Interaction | data.frame | 600 | 5 |
latent_covariates_long | galamm | Simulated Longitudinal Data with Latent and Observed Covariates Interaction | data.frame | 800 | 5 |
lifespan | galamm | Simulated Dataset with Lifespan Trajectories of Three Cognitive Domains | data.frame | 54457 | 10 |
mresp | galamm | Simulated Mixed Response Data | data.frame | 4000 | 4 |
mresp_hsced | galamm | Simulated Mixed Response Data with Heteroscedastic Residuals | data.frame | 4000 | 6 |
ExampleData | surtvep | example data with 2000 observations of 2 continuous variables | list | | |
ExampleDataBinary | surtvep | example data with 2000 observations of 2 binary variables | list | | |
StrataExample | surtvep | example data for stratified model illustration | list | | |
support | surtvep | Study to Understand Prognoses Preferences Outcomes and Risks of Treatment | data.frame | 9104 | 34 |
mako_colours | protti | Viridis colour scheme | character | | |
metal_chebi_uniprot | protti | List of metal-related ChEBI IDs in UniProt | grouped_df | 193 | 10 |
metal_go_slim_subset | protti | Molecular function gene ontology metal subset | tbl_df | 850 | 8 |
metal_list | protti | List of metals | data.frame | 99 | 5 |
protti_colours | protti | Colour scheme for protti | character | | |
ptsi_pgk | protti | Structural analysis example data | tbl_df | 900 | 5 |
rapamycin_10uM | protti | Rapamycin 10 uM example data | tbl_df | 20164 | 8 |
rapamycin_dose_response | protti | Rapamycin dose response example data | tbl_df | 24804 | 7 |
viridis_colours | protti | Viridis colour scheme | character | | |
metaMatMetformin | metadeconfoundR | Documentation for the metaMatMetformin RData in /data | data.frame | 753 | 5 |
reduced_feature | metadeconfoundR | Documentation for the reduced_feature RData in /data | data.frame | 753 | 50 |
APSsystem | bnRep | APSsystem Bayesian Network | bn.fit | | |
BOPfailure1 | bnRep | BOPfailure Bayesian Networks | bn.fit | | |
BOPfailure2 | bnRep | BOPfailure Bayesian Networks | bn.fit | | |
BOPfailure3 | bnRep | BOPfailure Bayesian Networks | bn.fit | | |
GDIpathway1 | bnRep | GDIpathway Bayesian Networks | bn.fit | | |
GDIpathway2 | bnRep | GDIpathway Bayesian Networks | bn.fit | | |
accidents | bnRep | accidents Bayesian Network | bn.fit | | |
adhd | bnRep | adhd Bayesian Network | bn.fit | | |
adversarialbehavior | bnRep | adversarialbehavior Bayesian Network | bn.fit | | |
aerialvehicles | bnRep | aerialvehicles Bayesian Network | bn.fit | | |
agropastoral1 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
agropastoral2 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
agropastoral3 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
agropastoral4 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
agropastoral5 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
aircrash | bnRep | aircrash Bayesian Network | bn.fit | | |
algal1 | bnRep | algal Bayesian Networks | bn.fit | | |
algal2 | bnRep | algal Bayesian Networks | bn.fit | | |
algalactivity1 | bnRep | algalactivity Bayesian Networks | bn.fit | | |
algalactivity2 | bnRep | algalactivity Bayesian Networks | bn.fit | | |
algorithms1 | bnRep | algorithms Bayesian Networks | bn.fit | | |
algorithms2 | bnRep | algorithms Bayesian Networks | bn.fit | | |
algorithms3 | bnRep | algorithms Bayesian Networks | bn.fit | | |
algorithms4 | bnRep | algorithms Bayesian Networks | bn.fit | | |
algorithms5 | bnRep | algorithms Bayesian Networks | bn.fit | | |
algorithms6 | bnRep | algorithms Bayesian Networks | bn.fit | | |
arcticwaters | bnRep | arcticwaters Bayesian Network | bn.fit | | |
argument | bnRep | argument Bayesian Network | bn.fit | | |
asia | bnRep | asia Bayesian Network | bn.fit | | |
aspergillus | bnRep | aspergillus Bayesian Network | bn.fit | | |
augmenting | bnRep | augmenting Bayesian Network | bn.fit | | |
bank | bnRep | bank Bayesian Network | bn.fit | | |
bankruptcy | bnRep | bankruptcy Bayesian Network | bn.fit | | |
beam1 | bnRep | beams Bayesian Network | bn.fit | | |
beam2 | bnRep | beams Bayesian Network | bn.fit | | |
beatles | bnRep | beatles Bayesian Network | bn.fit | | |
blacksea | bnRep | blacksea Bayesian Network | bn.fit | | |
blockchain | bnRep | blockchain Bayesian Network | bn.fit | | |
bnRep_summary | bnRep | BnRep Summary | data.frame | 214 | 17 |
building | bnRep | building Bayesian Network | bn.fit | | |
bullet | bnRep | bullet Bayesian Network | bn.fit | | |
burglar | bnRep | burglar Bayesian Network | bn.fit | | |
cachexia1 | bnRep | cachexia Bayesian Networks | bn.fit | | |
cachexia2 | bnRep | cachexia Bayesian Networks | bn.fit | | |
cardiovascular | bnRep | cardiovascular Bayesian Network | bn.fit | | |
case | bnRep | case Bayesian Network | bn.fit | | |
catchment | bnRep | catchment Bayesian Network | bn.fit | | |
charleston | bnRep | charleston Bayesian Network | bn.fit | | |
chds | bnRep | chds Bayesian Network | bn.fit | | |
cng | bnRep | cng Bayesian Network | bn.fit | | |
compaction | bnRep | compaction Bayesian Network | bn.fit | | |
conasense | bnRep | conasense Bayesian Network | bn.fit | | |
concrete1 | bnRep | concrete Bayesian Networks | bn.fit | | |
concrete2 | bnRep | concrete Bayesian Networks | bn.fit | | |
concrete3 | bnRep | concrete Bayesian Networks | bn.fit | | |
concrete4 | bnRep | concrete Bayesian Networks | bn.fit | | |
concrete5 | bnRep | concrete Bayesian Networks | bn.fit | | |
concrete6 | bnRep | concrete Bayesian Networks | bn.fit | | |
concrete7 | bnRep | concrete Bayesian Networks | bn.fit | | |
consequenceCovid | bnRep | consequenceCovid Bayesian Network | bn.fit | | |
constructionproductivity | bnRep | constructionproductivity Bayesian Network | bn.fit | | |
coral1 | bnRep | coral Bayesian Networks | bn.fit | | |
coral2 | bnRep | coral Bayesian Networks | bn.fit | | |
coral3 | bnRep | coral Bayesian Networks | bn.fit | | |
coral4 | bnRep | coral Bayesian Networks | bn.fit | | |
coral5 | bnRep | coral Bayesian Networks | bn.fit | | |
corical | bnRep | corical Bayesian Network | bn.fit | | |
corrosion | bnRep | corrosion Bayesian Network | bn.fit | | |
corticosteroid | bnRep | corticosteroid Bayesian Network | bn.fit | | |
covid1 | bnRep | covid Bayesian Networks | bn.fit | | |
covid2 | bnRep | covid Bayesian Networks | bn.fit | | |
covid3 | bnRep | covid Bayesian Networks | bn.fit | | |
covidfear | bnRep | covidfear Bayesian Network | bn.fit | | |
covidrisk | bnRep | covidrisk Bayesian Network | bn.fit | | |
covidtech | bnRep | covidtech Bayesian Network | bn.fit | | |
covidtest | bnRep | covidtest Bayesian Network | bn.fit | | |
crimescene | bnRep | crimescene Bayesian Network | bn.fit | | |
criminal1 | bnRep | criminal Bayesian Networks | bn.fit | | |
criminal2 | bnRep | criminal Bayesian Networks | bn.fit | | |
criminal3 | bnRep | criminal Bayesian Networks | bn.fit | | |
criminal4 | bnRep | criminal Bayesian Networks | bn.fit | | |
crypto | bnRep | crypto Bayesian Network | bn.fit | | |
curacao1 | bnRep | curacao Bayesian Networks | bn.fit | | |
curacao2 | bnRep | curacao Bayesian Networks | bn.fit | | |
curacao3 | bnRep | curacao Bayesian Networks | bn.fit | | |
curacao4 | bnRep | curacao Bayesian Networks | bn.fit | | |
curacao5 | bnRep | curacao Bayesian Networks | bn.fit | | |
darktriad | bnRep | darktriad Bayesian Network | bn.fit | | |
diabetes | bnRep | ciabetes Bayesian Network | bn.fit | | |
diagnosis | bnRep | diagnosis Bayesian Network | bn.fit | | |
dioxins | bnRep | dioxins Bayesian Network | bn.tan | | |
disputed1 | bnRep | disputed Bayesian Networks | bn.fit | | |
disputed2 | bnRep | disputed Bayesian Networks | bn.fit | | |
disputed3 | bnRep | disputed Bayesian Networks | bn.fit | | |
disputed4 | bnRep | disputed Bayesian Networks | bn.fit | | |
dragline | bnRep | dragline Bayesian Network | bn.fit | | |
drainage | bnRep | drainage Bayesian Network | bn.fit | | |
dustexplosion | bnRep | dustexplosion Bayesian Network | bn.fit | | |
earthquake | bnRep | earthquake Bayesian Network | bn.fit | | |
ecosystem | bnRep | ecosystem Bayesian Network | bn.fit | | |
electricvehicle | bnRep | electricvehicle Bayesian Network | bn.fit | | |
electrolysis | bnRep | electrolysis Bayesian Network | bn.fit | | |
emergency | bnRep | emergency Bayesian Network | bn.fit | | |
engines | bnRep | engines Bayesian Network | bn.fit | | |
enrollment | bnRep | enrollment Bayesian Network | bn.fit | | |
estuary | bnRep | estuary Bayesian Network | bn.fit | | |
expenditure | bnRep | expenditure Bayesian Network | bn.fit | | |
fingermarks1 | bnRep | fingermarks Bayesian Networks | bn.fit | | |
fingermarks2 | bnRep | fingermarks Bayesian Networks | bn.fit | | |
fire | bnRep | fire Bayesian Network | bn.fit | | |
firealarm | bnRep | firealarm Bayesian Network | bn.fit | | |
firerisk | bnRep | firerisk Bayesian Network | bn.fit | | |
flood | bnRep | flood Bayesian Network | bn.fit | | |
fluids1 | bnRep | fluids Bayesian Networks | bn.fit | | |
fluids2 | bnRep | fluids Bayesian Networks | bn.fit | | |
fluids3 | bnRep | fluids Bayesian Networks | bn.fit | | |
foodallergy1 | bnRep | foodallergy Bayesian Networks | bn.fit | | |
foodallergy2 | bnRep | foodallergy Bayesian Networks | bn.fit | | |
foodallergy3 | bnRep | foodallergy Bayesian Networks | bn.fit | | |
foodsecurity | bnRep | foodsecurity Bayesian Network | bn.fit | | |
fundraising | bnRep | fundraising Bayesian Network | bn.fit | | |
gasexplosion | bnRep | gasexplosion Bayesian Network | bn.fit | | |
gasifier | bnRep | gasifier Bayesian Network | bn.fit | | |
gonorrhoeae | bnRep | gonorrhoeae Bayesian Network | bn.fit | | |
greencredit | bnRep | greencredit Bayesian Network | bn.fit | | |
grounding | bnRep | grounding Bayesian Network | bn.fit | | |
healthinsurance | bnRep | healthinsurance Bayesian Network | bn.fit | | |
humanitarian | bnRep | humanitarian Bayesian Network | bn.fit | | |
hydraulicsystem | bnRep | hydraulicsystem Bayesian Network | bn.fit | | |
income | bnRep | income Bayesian Network | bn.fit | | |
intensification | bnRep | intensification Bayesian Network | bn.fit | | |
intentionalattacks | bnRep | intentionalattacks Bayesian Network | bn.fit | | |
inverters | bnRep | inverters Bayesian Network | bn.fit | | |
knowledge | bnRep | knowledge Bayesian Network | bn.fit | | |
kosterhavet | bnRep | kosterhavet Bayesian Network | bn.fit | | |
lawschool | bnRep | lawschool Bayesian Network | bn.fit | | |
lexical | bnRep | lexical Bayesian Network | bn.fit | | |
lidar | bnRep | lidar Bayesian Network | bn.fit | | |
liquefaction | bnRep | liquefaction Bayesian Network | bn.fit | | |
liquidity | bnRep | liquidity Bayesian Network | bn.fit | | |
lithium | bnRep | lithium Bayesian Network | bn.fit | | |
macrophytes | bnRep | macrophytes Bayesian Network | bn.fit | | |
medicaltest | bnRep | medicaltest Bayesian Network | bn.fit | | |
megacities | bnRep | megacities Bayesian Network | bn.fit | | |
metal | bnRep | metal Bayesian Network | bn.fit | | |
moodstate | bnRep | moodstate Bayesian Network | bn.fit | | |
mountaingoat | bnRep | mountaingoat Bayesian Network | bn.fit | | |
nanomaterials1 | bnRep | nanomaterial Bayesian Networks | bn.fit | | |
nanomaterials2 | bnRep | nanomaterial Bayesian Networks | bn.fit | | |
navigation | bnRep | navigation Bayesian Network | bn.fit | | |
nuclearwaste | bnRep | nuclearwaste Bayesian Network | bn.fit | | |
nuisancegrowth | bnRep | nuisancegrowth Bayesian Network | bn.fit | | |
oildepot | bnRep | oildepot Bayesian Network | bn.fit | | |
onlinerisk | bnRep | onlinerisk Bayesian Network | bn.fit | | |
orbital | bnRep | orbital Bayesian Network | bn.fit | | |
oxygen | bnRep | oxygen Bayesian Network | bn.fit | | |
parkinson | bnRep | parkinson Bayesian Network | bn.fit | | |
perioperative | bnRep | perioperative Bayesian Network | bn.fit | | |
permaBN | bnRep | permaBN Bayesian Network | bn.fit | | |
phdarticles | bnRep | phdarticles Bayesian Network | bn.fit | | |
pilot | bnRep | pilot Bayesian Network | bn.fit | | |
pneumonia | bnRep | pneumonia Bayesian Network | bn.fit | | |
polymorphic | bnRep | polymorphic Bayesian Network | bn.fit | | |
poultry | bnRep | poultry Bayesian Network | bn.fit | | |
project | bnRep | project Bayesian Network | bn.fit | | |
projectmanagement | bnRep | projectmanagement Bayesian Network | bn.fit | | |
propellant | bnRep | propellant Bayesian Network | bn.fit | | |
rainstorm | bnRep | rainstorm Bayesian Network | bn.fit | | |
rainwater | bnRep | rainwater Bayesian Network | bn.fit | | |
redmeat | bnRep | redmeat Bayesian Network | bn.fit | | |
resilience | bnRep | resilience Bayesian Network | bn.fit | | |
ricci | bnRep | ricci Bayesian Network | bn.fit | | |
rockburst | bnRep | rockburst Bayesian Network | bn.fit | | |
rockquality | bnRep | rockquality Bayesian Network | bn.fit | | |
ropesegment | bnRep | ropesegment Bayesian Network | bn.fit | | |
safespeeds | bnRep | safespeeds Bayesian Network | bn.fit | | |
sallyclark | bnRep | sallyclark Bayesian Network | bn.fit | | |
salmonella1 | bnRep | salmonella Bayesian Networks | bn.fit | | |
salmonella2 | bnRep | salmonella Bayesian Networks | bn.fit | | |
seismic | bnRep | seismic Bayesian Network | bn.fit | | |
shipping | bnRep | shipping Bayesian Network | bn.fit | | |
simulation | bnRep | simulation Bayesian Network | bn.fit | | |
softwarelogs1 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
softwarelogs2 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
softwarelogs3 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
softwarelogs4 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
soil | bnRep | soil Bayesian Network | bn.fit | | |
soillead | bnRep | soillead Bayesian Network | bn.fit | | |
soilliquefaction1 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
soilliquefaction2 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
soilliquefaction3 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
soilliquefaction4 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
stocks | bnRep | stocks Bayesian Network | bn.fit | | |
student1 | bnRep | student Bayesian Networks | bn.fit | | |
student2 | bnRep | student Bayesian Networks | bn.fit | | |
suffocation | bnRep | suffocation Bayesian Network | bn.fit | | |
tastingtea | bnRep | tastingtea Bayesian Network | bn.fit | | |
tbm | bnRep | tbm Bayesian Network | bn.fit | | |
theft1 | bnRep | theft Bayesian Networks | bn.fit | | |
theft2 | bnRep | theft Bayesian Networks | bn.fit | | |
titanic | bnRep | titanic Bayesian Network | bn.fit | | |
trajectories | bnRep | trajectories Bayesian Network | bn.fit | | |
transport | bnRep | transport Bayesian Network | bn.fit | | |
tubercolosis | bnRep | tubercolosis Bayesian Network | bn.fit | | |
turbine1 | bnRep | turbine Bayesian Networks | bn.fit | | |
turbine2 | bnRep | turbine Bayesian Networks | bn.fit | | |
twinframework | bnRep | twinframework Bayesian Network | bn.fit | | |
urinary | bnRep | urinary Bayesian Network | bn.fit | | |
vaccine | bnRep | vaccine Bayesian Network | bn.fit | | |
vessel1 | bnRep | vessel Bayesian Networks | bn.fit | | |
vessel2 | bnRep | vessel Bayesian Networks | bn.fit | | |
waterlead | bnRep | waterlead Bayesian Network | bn.tan | | |
wheat | bnRep | wheat Bayesian Network | bn.fit | | |
windturbine | bnRep | windturbine Bayesian Network | bn.fit | | |
witness | bnRep | witness Bayesian Network | bn.fit | | |
yangtze | bnRep | yangtze Bayesian Network | bn.fit | | |
lalonde | plotBart | Lalonde dataset | data.frame | 445 | 12 |
memory_exp | RMediation | Memory Experiment Data Description from MacKinnon et al., 2018 | tbl_df | 369 | 5 |
nhgis_ts_tables | ipumseasyr | NHGIS Time Series Table names | character | | |
usa_states | ipumseasyr | U.S. States Reference data | data.frame | 51 | 4 |
alloauto | AmoudSurv | Leukemia data set | data.frame | 101 | 3 |
bmt | AmoudSurv | Bone Marrow Transplant (bmt) data set | data.frame | 91 | 3 |
gastric | AmoudSurv | Gastric data set | data.frame | 90 | 3 |
larynx | AmoudSurv | Larynx Cancer-Patients data set | data.frame | 90 | 5 |
RProjects | ReplicationSuccess | Data from four large-scale replication projects | data.frame | 143 | 15 |
SSRP | ReplicationSuccess | Data from the Social Sciences Replication Project | data.frame | 21 | 18 |
protzko2020 | ReplicationSuccess | Data from Protzko et al. (2020) | data.frame | 80 | 6 |
tweets | fastNaiveBayes | This data originally came from Crowdflower's Data for Everyone library. | data.frame | 14640 | 2 |
tweetsDTM | fastNaiveBayes | This data originally came from Crowdflower's Data for Everyone library. | data.frame | 14640 | 2214 |
OneCpt_IVInfusionData | Certara.RsNLME | Pharmacokinetic dataset containing 100 subjects with single dose given by infusion | data.frame | 800 | 6 |
pkData | Certara.RsNLME | Pharmacokinetic dataset containing 16 subjects with single bolus dose | spec_tbl_df | 112 | 8 |
pkcovbqlData | Certara.RsNLME | Pharmacokinetic pediatric dataset containing 80 subjects with single bolus dose. | spec_tbl_df | 880 | 8 |
pkpdData | Certara.RsNLME | Pharmacokinetic/Pharmacodynamic dataset containing 200 subjects with single bolus dose | spec_tbl_df | 2600 | 5 |
nishiura | intervalcalc | Serial Interval Data from Nishiura et al | data.table | 28 | 10 |
phytoplankton_acoustic_data | RMBC | Phytoplankton_acoustic_data | list | | |
ACCOUNT | regclass | Predicting whether a customer will open a new kind of account | data.frame | 24242 | 8 |
APPLIANCE | regclass | Appliance shipments | data.frame | 26 | 7 |
ATTRACTF | regclass | Attractiveness Score (female) | data.frame | 70 | 21 |
ATTRACTM | regclass | Attractiveness Score (male) | data.frame | 70 | 23 |
AUTO | regclass | AUTO dataset | data.frame | 82 | 5 |
BODYFAT | regclass | BODYFAT data | data.frame | 252 | 14 |
BODYFAT2 | regclass | Secondary BODYFAT dataset | data.frame | 20 | 4 |
BULLDOZER | regclass | BULLDOZER data | data.frame | 924 | 6 |
BULLDOZER2 | regclass | Modified BULLDOZER data | data.frame | 924 | 6 |
CALLS | regclass | CALLS dataset | data.frame | 579 | 2 |
CENSUS | regclass | CENSUS data | data.frame | 3534 | 39 |
CENSUSMLR | regclass | Subset of CENSUS data | data.frame | 1000 | 7 |
CHARITY | regclass | CHARITY dataset | data.frame | 15283 | 11 |
CHURN | regclass | CHURN dataset | data.frame | 5000 | 18 |
CUSTCHURN | regclass | CUSTCHURN dataset | data.frame | 500 | 8 |
CUSTLOYALTY | regclass | CUSTLOYALTY dataset | data.frame | 500 | 9 |
CUSTREACQUIRE | regclass | CUSTREACQUIRE dataset | data.frame | 500 | 9 |
CUSTVALUE | regclass | CUSTVALUE dataset | data.frame | 500 | 11 |
DIET | regclass | DIET data | data.frame | 35 | 2 |
DONOR | regclass | DONOR dataset | data.frame | 19372 | 50 |
EDUCATION | regclass | EDUCATION data | data.frame | 607 | 18 |
EX2.CENSUS | regclass | CENSUS data for Exercise 5 in Chapter 2 | data.frame | 3534 | 41 |
EX2.TIPS | regclass | TIPS data for Exercise 6 in Chapter 2 | data.frame | 244 | 8 |
EX3.ABALONE | regclass | ABALONE dataset for Exercise D in Chapter 3 | data.frame | 1528 | 7 |
EX3.BODYFAT | regclass | Bodyfat data for Exercise F in Chapter 3 | data.frame | 20 | 4 |
EX3.HOUSING | regclass | Housing data for Exercise E in Chapter 3 | data.frame | 522 | 2 |
EX3.NFL | regclass | NFL data for Exercise A in Chapter 3 | data.frame | 352 | 137 |
EX4.BIKE | regclass | Bike data for Exercise 1 in Chapter 4 | data.frame | 414 | 5 |
EX4.STOCKPREDICT | regclass | Stock data for Exercise 2 in Chapter 4 (prediction set) | data.frame | 5 | 40 |
EX4.STOCKS | regclass | Stock data for Exercise 2 in Chapter 4 | data.frame | 216 | 41 |
EX5.BIKE | regclass | BIKE dataset for Exercise 4 Chapter 5 | data.frame | 413 | 9 |
EX5.DONOR | regclass | DONOR dataset for Exercise 4 in Chapter 5 | data.frame | 8132 | 18 |
EX6.CLICK | regclass | CLICK data for Exercise 2 in Chapter 6 | data.frame | 13594 | 15 |
EX6.DONOR | regclass | DONOR dataset for Exercise 1 in Chapter 6 | data.frame | 8132 | 18 |
EX6.WINE | regclass | WINE data for Exercise 3 Chapter 6 | data.frame | 2700 | 12 |
EX7.BIKE | regclass | BIKE dataset for Exercise 1 Chapters 7 and 8 | data.frame | 410 | 9 |
EX7.CATALOG | regclass | CATALOG data for Exercise 2 in Chapters 7 and 8 | data.frame | 4000 | 7 |
EX9.BIRTHWEIGHT | regclass | Birthweight dataset for Exercise 1 in Chapter 9 | data.frame | 553 | 13 |
EX9.NFL | regclass | NFL data for Exercise 2 Chapter 9 | data.frame | 352 | 26 |
EX9.STORE | regclass | Data for Exercise 3 Chapter 9 | data.frame | 1500 | 68 |
FRIEND | regclass | Friendship Potential vs. Attractiveness Ratings | data.frame | 54 | 2 |
FUMBLES | regclass | Wins vs. Fumbles of an NFL team | data.frame | 352 | 2 |
JUNK | regclass | Junk-mail dataset | data.frame | 4601 | 58 |
LARGEFLYER | regclass | Interest in frequent flier program (large version) | data.frame | 100000 | 2 |
LAUNCH | regclass | New product launch data | data.frame | 652 | 420 |
MOVIE | regclass | Movie grosses | data.frame | 309 | 3 |
NFL | regclass | NFL database | data.frame | 352 | 113 |
OFFENSE | regclass | Some offensive statistics from 'NFL' dataset | data.frame | 352 | 10 |
PIMA | regclass | Pima Diabetes dataset | data.frame | 392 | 8 |
POISON | regclass | Cockroach poisoning data | data.frame | 481 | 2 |
PRODUCT | regclass | Sales of a product one quarter after release | data.frame | 2768 | 4 |
PURCHASE | regclass | PURCHASE data | data.frame | 27723 | 6 |
SALARY | regclass | Harris Bank Salary data | data.frame | 93 | 5 |
SMALLFLYER | regclass | Interest in a frequent flier program (small version) | data.frame | 100 | 2 |
SOLD26 | regclass | Predicting future sales | data.frame | 2768 | 16 |
SPEED | regclass | Speed vs. Fuel Efficiency | data.frame | 40 | 2 |
STUDENT | regclass | STUDENT data | data.frame | 607 | 19 |
SURVEY09 | regclass | Student survey 2009 | data.frame | 579 | 47 |
SURVEY10 | regclass | Student survey 2010 | data.frame | 699 | 20 |
SURVEY11 | regclass | Student survey 2011 | data.frame | 628 | 51 |
TIPS | regclass | TIPS dataset | data.frame | 244 | 8 |
WINE | regclass | WINE data | data.frame | 2700 | 12 |
brandywine | rtide | Brandywine Tide Height Data | tbl_df | 8 | 3 |
harmonics | rtide | Harmonics | tide_harmonics | | |
monterey | rtide | Monterey Tide Height Data | tbl_df | 8 | 3 |
Mtb80 | negenes | Number of insertion sites in each gene in M tb CDC1551 | matrix | 4204 | 2 |
G | webSDM | Simulated environemntal covariates G | igraph | | |
X | webSDM | Simulated environmental covariates X | matrix | 1000 | 3 |
Y | webSDM | Simulated species distribution Y | matrix | 1000 | 6 |
camden_crimes | stppSim | Records of crimes of Camden Borough of London, UK, 2021 (Source: https://data.police.uk/data/) | data.frame | 4578 | 4 |
poly | stppSim | Boundary coordinates | matrix | 831 | 2 |
xyt_data | stppSim | Spatiotemporal point data | matrix | 4267 | 3 |
xyz | stppSim | xyz data | data.frame | 20 | 3 |
simDat | rocTree | Simulated dataset for demonstration | tbl_df | 5050 | 5 |
sleep_emo_con | stdmod | Sample Dataset: Predicting Sleep Duration | tbl_df | 500 | 6 |
test_mod1 | stdmod | Sample Dataset: A Path Model With A Moderator | data.frame | 300 | 6 |
test_mod2 | stdmod | Sample Dataset: A Path Model With A Moderator | data.frame | 300 | 6 |
test_mod3_miss | stdmod | Sample Dataset: A Path Model With A Moderator | data.frame | 500 | 6 |
test_x_1_w_1_v_1_cat1_n_500 | stdmod | Sample Dataset: One IV, One Moderator, Two Covariates | data.frame | 500 | 5 |
test_x_1_w_1_v_1_cat1_xw_cov_n_500 | stdmod | Sample Dataset: One IV, One Moderator, Two Covariates | data.frame | 500 | 5 |
test_x_1_w_1_v_1_cat1_xw_cov_wcat3_n_500 | stdmod | Sample Dataset: One IV, One 3-Category Moderator, Two Covariates | data.frame | 500 | 5 |
test_x_1_w_1_v_2_n_500 | stdmod | Sample Dataset: One IV, One Moderator, Two Covariates | data.frame | 500 | 5 |
latin_data | makemyprior | Latin square experiment data | list | | |
neonatal_data | makemyprior | Neonatal mortality data | list | | |
wheat_data | makemyprior | Genomic wheat breeding model data | list | | |
cichlids | phylopath | Cichlid traits and the evolution of cooperative breeding. | data.frame | 69 | 5 |
cichlids_tree | phylopath | Cichlid phylogeny. | phylo | | |
red_list | phylopath | Data on brain size, life history and vulnerability to extinction | data.frame | 474 | 7 |
red_list_tree | phylopath | Mammalian phylogeny | phylo | | |
rhino | phylopath | Rhinogrades traits. | data.frame | 100 | 6 |
rhino_tree | phylopath | Rhinogrades phylogeny. | phylo | | |
CARE | rddapp | Carolina Abecedarian Project and the Carolina Approach to Responsive Education (CARE), 1972-1992 | data.frame | 81 | 5 |
mouse | clValid | Mouse Mesenchymal Cells | data.frame | 147 | 8 |
AttachmentData | ConNEcT | Attachment-related mother-child interaction dataset | data.frame | 90 | 7 |
FamilyData | ConNEcT | Affective family interaction dataset | data.frame | 540 | 9 |
SymptomData | ConNEcT | Depression symptom dataset | matrix | 145 | 8 |
CATS | TSPred | Time series of the CATS Competition | data.frame | 980 | 5 |
CATS.cont | TSPred | Continuation dataset of the time series of the CATS Competition | data.frame | 20 | 5 |
EUNITE.Loads | TSPred | Electrical loads of the EUNITE Competition | data.frame | 730 | 48 |
EUNITE.Loads.cont | TSPred | Continuation dataset of the electrical loads of the EUNITE Competition | data.frame | 31 | 48 |
EUNITE.Reg | TSPred | Electrical loads regressors of the EUNITE Competition | data.frame | 730 | 2 |
EUNITE.Reg.cont | TSPred | Continuation dataset of the electrical loads regressors of the EUNITE Competition | data.frame | 31 | 2 |
EUNITE.Temp | TSPred | Temperatures of the EUNITE Competition | data.frame | 1461 | 1 |
EUNITE.Temp.cont | TSPred | Continuation dataset of the temperatures of the EUNITE Competition | data.frame | 31 | 1 |
NN3.A | TSPred | Dataset A of the NN3 Competition | data.frame | 126 | 111 |
NN3.A.cont | TSPred | Continuation dataset of the Dataset A of the NN3 Competition | data.frame | 18 | 111 |
NN5.A | TSPred | Dataset A of the NN5 Competition | data.frame | 735 | 111 |
NN5.A.cont | TSPred | Continuation dataset of the Dataset A of the NN5 Competition | data.frame | 56 | 111 |
SantaFe.A | TSPred | Time series A of the Santa Fe Time Series Competition | data.frame | 1000 | 1 |
SantaFe.A.cont | TSPred | Continuation dataset of the time series A of the Santa Fe Time Series Competition | data.frame | 100 | 1 |
SantaFe.D | TSPred | Time series D of the Santa Fe Time Series Competition | data.frame | 100000 | 1 |
SantaFe.D.cont | TSPred | Continuation dataset of the time series D of the Santa Fe Time Series Competition | data.frame | 500 | 1 |
ipeadata_d | TSPred | The Ipea Most Requested Dataset (daily) | data.frame | 8154 | 12 |
ipeadata_d.cont | TSPred | The Ipea Most Requested Dataset (daily) | data.frame | 30 | 12 |
ipeadata_m | TSPred | The Ipea Most Requested Dataset (monthly) | data.frame | 1019 | 23 |
ipeadata_m.cont | TSPred | The Ipea Most Requested Dataset (monthly) | data.frame | 12 | 23 |
austria_boundary | Poly4AT | Austria Boundary Dataset | sf | 1 | 2 |
abronia | tenm | Occurrence records of Abronia graminea | data.frame | 106 | 5 |
colors | tenm | Colors for plotting | character | | |
x.cod | scape | Cod Assessment | scape | | |
x.ling | scape | Ling Assessment | scape | | |
x.oreo | scape | Oreo Assessment | scape | | |
x.saithe | scape | Saithe Assessment | scape | | |
x.sbw | scape | Whiting Assessment | scape | | |
xmcmc | scape | MCMC Results from Cod Assessment | list | | |
xproj | scape | MCMC Projections from Cod Assessment | list | | |
acc_running | adeptdata | Outdoor Run Raw Accelerometry Data | data.frame | 300000 | 5 |
acc_walking_IU | adeptdata | Outdoor Continuous Walking Raw Accelerometry Data | data.frame | 2590448 | 6 |
stride_template | adeptdata | Walking Stride Pattern Templates | list | | |
available_tr8 | TR8 | A dataframe containing the traits available for download. | data.frame | 208 | 3 |
biolflor_lookup | TR8 | biolflor_lookup | data.frame | 3661 | 8 |
column_list | TR8 | column_list | list | | |
imkerbond_check | TR8 | Set of plant species names to be corrected. | character | | |
leda_lookup | TR8 | List with reference variables needed to download traits from LEDA Traitbase | list | | |
list_of_traits_Biolflor | TR8 | list_of_traits_Biolflor | character | | |
pignatti | TR8 | pignatti | data.frame | 5770 | 14 |
ref_PLANTS | TR8 | ref_PLANTS | data.frame | 2178 | 4 |
traits_eco | TR8 | Set of functional traits to be retrieved by Ecoflora. | list | | |
traits_pollen_Biolflor | TR8 | traits_pollen_Biolflor | character | | |
traits_special_Biolflor | TR8 | traits_special_Biolflor | character | | |
green.data | MCMC.OTU | Symbiodinium sp. ITS2 OTUs from Orbicella franksi and O. faveolata | data.frame | 58 | 156 |
HsReferenceDB | arrayQuality | Reference slides for Mouse oligos hybridizations | matrix | 15 | 27 |
MmDEGenes | arrayQuality | Known DE genes for Mouse quality hybridizations. | data.frame | 121 | 9 |
MmReferenceDB | arrayQuality | Reference slides for Mouse oligos hybridizations | matrix | 15 | 16 |
SampleFunregData | funreg | Sample dataset for funreg | data.frame | 8109 | 13 |
ozone1 | aqfig | Daily maximum ozone concentrations | data.frame | 23 | 3 |
ozone2 | aqfig | Daily maximum ozone concentrations | data.frame | 23 | 3 |
Covariates | MatrixEQTL | Artificial data for Matrix eQTL sample code: Covariates. | data.frame | 2 | 17 |
GE | MatrixEQTL | Artificial data for Matrix eQTL sample code: Gene expression. | data.frame | 10 | 17 |
SNP | MatrixEQTL | Artificial data for Matrix eQTL sample code: Genotype. | data.frame | 15 | 17 |
geneloc | MatrixEQTL | Artificial data for Matrix eQTL sample code: Gene location file. | data.frame | 10 | 4 |
snpsloc | MatrixEQTL | Artificial data for Matrix eQTL sample code: SNP location file. | data.frame | 15 | 3 |
CPS | synthdid | CPS | data.frame | 2000 | 8 |
PENN | synthdid | PENN | data.frame | 5328 | 5 |
california_prop99 | synthdid | California proposition 99 | data.frame | 1209 | 4 |
ndvi_AK10000 | remotePARTS | NDVI remote sensing data for 10,000 random pixels from Alaska, with rare land classes removed. | data.frame | 10000 | 37 |
partGLS_ndviAK | remotePARTS | partitioned GLS results | partGLS | | |
alerce_data | HBV.IANIGLA | Alerce's glacier data for modeling | list | | |
glacio_hydro_hbv | HBV.IANIGLA | Synthetic glacio-hydrological data for modeling | list | | |
lumped_hbv | HBV.IANIGLA | Lumped HBV catchment data | data.frame | 5310 | 5 |
semi_distributed_hbv | HBV.IANIGLA | Semi-distributed HBV model data | list | | |
tupungato_data | HBV.IANIGLA | Tupungato River basin data | list | | |
eleveld_pd | tci | Eleveld et al. pharmacodynamic data | data.frame | 122 | 15 |
eleveld_pk | tci | Eleveld et al. pharmacokinetic data | data.frame | 1033 | 16 |
Missouri | RcppCensSpatial | TCDD concentration data | data.frame | 127 | 5 |
M | SFSI | Wheat dataset | matrix | 776 | |
VI_E1 | SFSI | Wheat dataset | matrix | 776 | 2 |
X_E1 | SFSI | Wheat dataset | matrix | 776 | 250 |
Y | SFSI | Wheat dataset | data.frame | 776 | 7 |
genCOV_xy | SFSI | Wheat dataset | matrix | 250 | 4 |
genCOV_yy | SFSI | Wheat dataset | list | | |
resCOV_yy | SFSI | Wheat dataset | list | | |
banknote_authentication | nonet | Bank Note Authentication Data Set | data.frame | 1372 | 5 |
selects2015 | glm.predict | Swiss Electoral Studies (Selects) 2015 - Post-electoral study | data.frame | 5337 | 23 |
AU_adj | treesliceR | Adjacency matrix for focal and adjacent assemblages | matrix | 720 | 720 |
AU_grid | treesliceR | Australia grid map | sf | 720 | 2 |
pass_mat | treesliceR | Presence-absence matrix of Australian passeriformes | data.frame | 720 | 308 |
pass_trees | treesliceR | Phylogenetic tree of passerines from Australia | list | | |
F1_miRNA_count | sRNAGenetic | Sequences of miRNAs from one species | data.table | 333 | 3 |
F1_miRNA_rpm | sRNAGenetic | Sequences of miRNAs from one species | data.table | 333 | 3 |
F1_sRNA_seq | sRNAGenetic | Sequences of sRNAs from one species | data.frame | 399 | 3 |
P1_miRNA_count | sRNAGenetic | Sequences of miRNAs from one species | data.table | 331 | 3 |
P1_miRNA_rpm | sRNAGenetic | Sequences of miRNAs from one species | data.table | 331 | 3 |
P1_sRNA_seq | sRNAGenetic | Sequences of sRNAs from one species | data.frame | 399 | 3 |
P2_miRNA_count | sRNAGenetic | Sequences of miRNAs from one species | data.table | 371 | 3 |
P2_miRNA_rpm | sRNAGenetic | Sequences of miRNAs from one species | data.table | 371 | 3 |
P2_sRNA_seq | sRNAGenetic | Sequences of sRNAs from one species | data.frame | 399 | 3 |
dichotomousItemParameters | DFIT | Sets of focal and reference item parameters from Wright (2011). | list | | |
polytomousItemParameters | DFIT | Sets of focal and reference item parameters from Raju et al. (2009) | list | | |
iso_map | Wavelength | ISO Code Mapping Table | tbl_df | 70 | 3 |
opm_holiday | Wavelength | OPM Holiday | tbl_df | 68 | 2 |
template_cols_long | Wavelength | Column Headers for HFR Long Template | character | | |
template_cols_meta | Wavelength | Column Headers for HFR Indicator Meta Data | character | | |
template_cols_wide | Wavelength | Column Headers for HFR Wide Template | character | | |
template_cols_wide_lim | Wavelength | Column Headers for HFR Wide - Limited Template | character | | |
hcp_1200_scanning_info | neurohcp | Scanning Information for HCP 1200 Data | tbl_df | 69615 | 18 |
hcp_900_scanning_info | neurohcp | Scanning Information for HCP 900 Data | tbl_df | 34406 | 18 |
hcp_scanning_info | neurohcp | Scanning Information for HCP Data | tbl_df | 34406 | 18 |
LSP | xergm.common | Longitudinal international defense alliance network, 1981-2000 | list | | |
advice | xergm.common | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | 1 |
alcohol | xergm.common | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | 3 |
allyNet | xergm.common | Longitudinal international defense alliance network, 1981-2000 | list | | |
committee | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 20 |
contigMat | xergm.common | Longitudinal international defense alliance network, 1981-2000 | matrix | 164 | 164 |
delinquency | xergm.common | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | 4 |
demographics | xergm.common | Longitudinal classroom friendship network and behavior (Andrea Knecht) | data.frame | 26 | 4 |
friendship | xergm.common | Longitudinal classroom friendship network and behavior (Andrea Knecht) | list | | |
infrep | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
intpos | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 6 |
lNet | xergm.common | Longitudinal international defense alliance network, 1981-2000 | list | | |
pol | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
primary | xergm.common | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | |
scifrom | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
scito | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
types | xergm.common | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | data.frame | 30 | 1 |
warNet | xergm.common | Longitudinal international defense alliance network, 1981-2000 | list | | |
banknote | andrews | Swiss banknotes data | data.frame | 200 | 7 |
arbuthnot_tbl | sampledatasets | Male and female births in London | tbl_df | 82 | 3 |
cards_tbl | sampledatasets | Standard Deck of Playing Cards | tbl_df | 52 | 4 |
cars_df | sampledatasets | Speed and Stopping Distances of Cars | data.frame | 50 | 2 |
mtcars_df | sampledatasets | Motor Trend Car Road Tests | data.frame | 32 | 11 |
swiss_df | sampledatasets | Swiss Fertility and Socioeconomic Indicators (1888) | data.frame | 47 | 6 |
imports85 | randomForest | The Automobile Data | data.frame | 205 | 26 |
example_matrix | mvvg | Example Matrix | matrix | 5 | |
toydata_dvs | MIAmaxent | Derived variables and transformation functions, from toy data. | list | | |
toydata_seldvs | MIAmaxent | Selected derived variables accompanied by selection trails, from toy data. | list | | |
toydata_selevs | MIAmaxent | Selected explanatory variables accompanied by selection trails, from toy data. | list | | |
toydata_sp1po | MIAmaxent | Occurrence and environmental toy data. | data.frame | 40 | 5 |
arsenic | investr | Concentrations of arsenic in water samples | data.frame | 32 | 2 |
beetle | investr | Dobson's Beetle Data | data.frame | 8 | 3 |
bladder | investr | Bladder volume data | data.frame | 184 | 3 |
crystal | investr | Crystal weight data | data.frame | 14 | 2 |
nasturtium | investr | Bioassay on Nasturtium | data.frame | 42 | 2 |
whisky | investr | Whisky data | data.frame | 10 | 2 |
cancer | simule | Microarray data set for breast cancer | list | | |
exampleData | simule | A simulated toy dataset that includes 2 data matrices (from 2 related tasks). | list | | |
exampleDataGraph | simule | A simulated toy dataset that includes 3 igraph objects | list | | |
nip_37_data | simule | NIPS word count dataset | list | | |
heart | extendedFamily | Heart Attack Data | data.frame | 4483 | 11 |
data_mrs | Rbent | MRS data | data.frame | 107 | 3 |
data_transport | Rbent | bedload transport data | data.frame | 76 | 2 |
lyme.svs.eco0.dat | SpatialVS | The Lyme disease dataset with Eco id=0 | list | | |
lyme.svs.eco1.dat | SpatialVS | The Lyme disease dataset with Eco id=1 | list | | |
small.test.dat | SpatialVS | A small dataset for fast testing of functions | list | | |
bobcat | multimark | Bobcat data | matrix | 46 | 8 |
bobcatSCR | multimark | Bobcat spatial capture-recapture data | list | | |
tiger | multimark | Tiger data | list | | |
sample_data | amanida | Example input data for the amanida function | tbl_df | 143 | 6 |
mprefix | fedz1 | Meaning of prefix | data.frame | 12 | 2 |
prds | fedz1 | Title of series with a short description | data.frame | 11291 | 2 |
table_detail | fedz1 | Title of tables | tbl_df | 198 | 3 |
Data1 | MBMethPred | Training data | data.frame | 910 | 400 |
Data2 | MBMethPred | Data2 | data.frame | 10000 | 50 |
Data3 | MBMethPred | Data3 | data.frame | 21641 | 50 |
RLabels | MBMethPred | RLabels | factor | | |
MU284 | surveybootstrap | The MU284 Population dataset | data.frame | 284 | 11 |
MU284.boot.res.summ | surveybootstrap | Benchmarks for unit tests | data.frame | 10 | 4 |
MU284.complex.surveys | surveybootstrap | Simulated sample surveys drawn from the MU284 Population using a complex design | list | | |
MU284.surveys | surveybootstrap | Simulated sample surveys drawn from the MU284 Population | list | | |
mini_diamond | baizer | Minimal tibble dataset adjusted from diamond | tbl_df | 100 | 7 |
cricket_batting | weird | Cricket batting data for international test players | tbl_df | 3754 | 15 |
fr_mortality | weird | French mortality rates by age and sex | tbl_df | 31648 | 4 |
n01 | weird | Multivariate standard normal data | tbl_df | 1000 | 10 |
oldfaithful | weird | Old faithful eruption data | tbl_df | 2261 | 3 |
ow | lax | Oxford and Worthing annual maximum temperatures | data.frame | 160 | 4 |
PID.db | CePa | pathway catalogues from Pathway Interaction Database(PID) | list | | |
gene.list | CePa | Differential gene list and background gene list | list | | |
lusweavedata | lusweave | lusweavedata | list | | |
co2_ml | ggpointless | Monthly CO2 records taken at Mauna Loa, since March 1958 | data.frame | 767 | 5 |
covid_vac | ggpointless | Rates of COVID-19 Cases and Deaths by Vaccination Status | data.frame | 146 | 4 |
female_leaders | ggpointless | Female leaders of independent states. | data.frame | 131 | 5 |
AbsoluteTemperature | archetypal | Global Absolute Temperature data set for Northern Hemisphere 1969-2013 | data.frame | 155862 | 18 |
gallupGPS6 | archetypal | Gallup Global Preferences Study processed data set of six variables | data.frame | 76132 | 6 |
wd2 | archetypal | 2D data set for demonstration purposes | data.frame | 100 | 2 |
wd25 | archetypal | 2D data set created by 5 points for demonstration purposes | matrix | 600 | 2 |
wd3 | archetypal | 3D data set for demonstration purposes | data.frame | 100 | 3 |
CondA | multiDimBio | Treatment condition for animals contained in the data set Nuclei | factor | | |
CondB | multiDimBio | Stress condition for animals contained in the data set Nuclei | factor | | |
Dyad | multiDimBio | Housing dyad for animals contained in the data set Nuclei | factor | | |
Groups | multiDimBio | The group ID for animals contained in the data set Nuclei | integer | | |
Nuclei | multiDimBio | Brain activity in 14 brain regions for 71 individuals | matrix | 71 | 14 |
Scores | multiDimBio | Principle component scores based on the data in Nuclei | matrix | 71 | 4 |
ExampleData | prnsamplr | ExampleData | data.frame | 40000 | 6 |
lgd.ds.c | LGDtoolkit | Synthetic modeling dataset | data.frame | 1200 | 20 |
BinormCircle | MSG | Random numbers containing a "circle" | data.frame | 20000 | 2 |
ChinaLifeEdu | MSG | Life Expectancy and the Number of People with Higher Education in China (2005) | data.frame | 31 | 2 |
Export.USCN | MSG | Export of US and China from 1999 to 2004 in US dollars | data.frame | 13 | 3 |
PlantCounts | MSG | Number of plants corresponding to altitude | data.frame | 600 | 2 |
assists | MSG | Assists between players in CLE and LAL | table | 22 | 22 |
canabalt | MSG | The scores of the game Canabalt from Twitter | data.frame | 1208 | 3 |
gov.cn.pct | MSG | Percentage data in some government websites | data.frame | 10000 | 4 |
murcia | MSG | Composition of Soil from Murcia Province, Spain | data.frame | 88 | 4 |
music | MSG | Attributes of some music clips | data.frame | 36 | 12 |
quake6 | MSG | Earth quakes from 1973 to 2010 | data.frame | 4999 | 9 |
t.diff | MSG | The differences of P-values in t test assuming equal or unequal variances | data.frame | 1000 | 99 |
tukeyCount | MSG | Results of a Simulation to Tukey's Fast Test | data.frame | 10000 | 3 |
tvearn | MSG | Top TV earners | data.frame | 72 | 10 |
CLELAL09 | animation | The NBA game between CLE Cavaliers and LAL Lakers on Dec 25, 2009 | data.frame | 455 | 7 |
HuSpeech | animation | Word counts of a speech by the Chinese President Hu | integer | | |
ObamaSpeech | animation | Word counts of a speech by the US President Obama | integer | | |
iatemp | animation | Average yearly temperatures in central Iowa | ts | | |
pageview | animation | Page views from Sep 21, 2007 to Dec 2, 2007 of Yihui's website | data.frame | 73 | 5 |
pollen | animation | Synthetic dataset about the geometric features of pollen grains | data.frame | 3848 | 5 |
vanke1127 | animation | Stock prices of Vanke Co., Ltd on 2009/11/27 | data.frame | 2831 | 2 |
LRB | binford | Binford's data | tbl_df | 339 | 507 |
LRBfact | binford | Binford's data - labeled version | tbl_df | 339 | 507 |
LRBkey | binford | Key to Binford's data | tbl_df | 506 | 16 |
pvModels | tdr | Error Statistics | data.frame | 493 | 22 |
pvObs | tdr | Error Statistics | numeric | | |
truemodel | eicm | A parameterized EICM model for simulation | eicm | | |
seattledmi | microsynth | Data for a crime intervention in Seattle, Washington | data.frame | 154272 | 22 |
asti | hlt | asti data | data.frame | 1129 | 27 |
aristolochia | AgroR | Dataset: Germination of seeds of _Aristolochia_ sp. as a function of temperature. | tbl_df | 80 | 2 |
bean | AgroR | Dataset: Bean | data.frame | 25 | 2 |
cloro | AgroR | Dataset: Sodium dichloroisocyanurate in soybean | tbl_df | 40 | 4 |
corn | AgroR | Dataset: Corn | data.frame | 24 | 3 |
covercrops | AgroR | Dataset: Covercrops | data.frame | 36 | 4 |
emerg | AgroR | Dataset: Emergence of passion fruit seeds over time . | tbl_df | 64 | 2 |
enxofre | AgroR | Dataset: Sulfur data | tbl_df | 108 | 5 |
eucalyptus | AgroR | Dataset: Eucaliptus grandis Barbin (2013) | tbl_df | 72 | 4 |
laranja | AgroR | Dataset: Orange plants under different rootstocks | tbl_df | 27 | 3 |
mirtilo | AgroR | Dataset: Cutting blueberry data | tbl_df | 36 | 4 |
orchard | AgroR | Dataset: Orchard | data.frame | 60 | 4 |
passiflora | AgroR | Dataset: Substrate data in the production of passion fruit seedlings | data.frame | 20 | 4 |
pepper | AgroR | Dataset: Pepper | data.frame | 20 | 3 |
phao | AgroR | Dataset: Osmocote in _Phalaenopsis_ sp. | data.frame | 25 | 2 |
pomegranate | AgroR | Dataset: Pomegranate data | tbl_df | 24 | 5 |
porco | AgroR | Dataset: Pig development and production | tbl_df | 16 | 4 |
sensorial | AgroR | Dataset: Sensorial data | tbl_df | 25 | 3 |
simulate1 | AgroR | Dataset: Simulated data dict | tbl_df | 120 | 3 |
simulate2 | AgroR | Dataset: Simulated data dbct | tbl_df | 120 | 4 |
simulate3 | AgroR | Dataset: Simulated data dqlt | tbl_df | 80 | 5 |
soybean | AgroR | Dataset: Soybean | data.frame | 40 | 3 |
tomate | AgroR | Dataset: Tomato data | tbl_df | 96 | 4 |
weather | AgroR | Dataset: Weather data | tbl_df | 152 | 6 |
mx_sample | mxnorm | Sample multiplexed dataset for 'mxnorm'. | data.frame | 3000 | 6 |
example.kfa | kfa | kfa results from simulated data example | kfa | | |
ier | bridgesampling | Standardized International Exchange Rate Changes from 1975 to 1986 | matrix | 143 | 6 |
turtles | bridgesampling | Turtles Data from Janzen, Tucker, and Paukstis (2000) | data.frame | 244 | 3 |
example_data | Mmcsd | A longitudinal example dataset. | tbl_df | 6700 | 9 |
de.bioRxiv.240846 | DTAT | Simulated '3+3/PC' dose-titration study from bioRxiv paper no. 240846 | list | | |
dtat1000 | DTAT | Precomputed neutrophil-guided chemotherapy dose titration for 1000 simulated subjects. | data.frame | 10000 | 14 |
macleish_layers | macleish | MacLeish spatial data | list | | |
maple_sap | macleish | Maple sap collection at MacLeish | tbl_df | 118 | 4 |
orchard_2015 | macleish | Weather data from Macleish Field Stations | tbl_df | 52547 | 9 |
tree_diameter1 | macleish | MacLeish Data Plot 1 | tbl_df | 1020 | 8 |
tree_diameter2 | macleish | MacLeish Data Plot 2 | tbl_df | 850 | 7 |
whately_2015 | macleish | Weather data from Macleish Field Stations | tbl_df | 52560 | 8 |
RI | spacejamr | Geographical boundary of Rhode Island. | spacejamr | | |
Maryland | readsdr | Influenza in Maryland during the 1918 pandemic | spec_tbl_df | 91 | 6 |
bma | microdiluteR | Absorption values from six broth microdilution assays conducted on 96-well plates | list | | |
Canto_pesticides | CalcThemAll.PRM | Canto Region Pesticide Concentration Values (Example Data Set) | spec_tbl_df | 808 | 24 |
pesticide_info | CalcThemAll.PRM | Pesticide Information for Pesticide Risk Metric Calculations (Reference Table) | data.frame | 22 | 9 |
medreg | vegclust | Regeneration of Mediterranean vegetation data set | stratifiedvegdata | | |
treedata | vegclust | Synthetic vegetation data set with tree data | data.frame | 33 | 5 |
wetland | vegclust | Wetland vegetation data set | data.frame | 41 | 33 |
FI_test | FastImputation | Imputation Test Data | data.frame | 250 | 9 |
FI_train | FastImputation | Imputation Training Data | data.frame | 10000 | 9 |
FI_true | FastImputation | Imputation "True" Data | data.frame | 250 | 9 |
paletteer_packages | paletteer | Names and version information for all packages included | tbl_df | 77 | 5 |
palettes_c_names | paletteer | Names of all continuous palettes | tbl_df | 319 | 3 |
palettes_d | paletteer | Complete list of fixed discrete palettes | list | | |
palettes_d_names | paletteer | Names of all fixed discrete palettes | tbl_df | 2415 | 5 |
palettes_dynamic | paletteer | Complete list of dynamic palettes | list | | |
palettes_dynamic_names | paletteer | Names of all fixed discrete palettes | data.frame | 25 | 4 |
Andrew | googleVis | Hurricane Andrew: googleVis example data set | data.frame | 47 | 8 |
Cairo | googleVis | Daily temperature data for Cairo | data.frame | 1091 | 2 |
Cats | googleVis | Cat Search Terms | data.frame | 18 | 3 |
CityPopularity | googleVis | CityPopularity: googleVis example data set | data.frame | 6 | 2 |
Exports | googleVis | Exports: googleVis example data set | data.frame | 10 | 3 |
Fruits | googleVis | Fruits: googleVis example data set | data.frame | 9 | 7 |
OpenClose | googleVis | OpenClose: googleVis example data set | data.frame | 5 | 5 |
Population | googleVis | Population: googleVis example data set | data.frame | 195 | 7 |
Regions | googleVis | Regions: googleVis example data set | data.frame | 11 | 4 |
Stock | googleVis | Stock: googleVis example data set | data.frame | 12 | 5 |
dino | googleVis | Dinosaur data | data.frame | 28 | 2 |
sim | MultisiteMediation | Simulated data list | data.frame | 500 | 9 |
inflation_mean | murphydiagram | Data sets with forecasts and realizations | data.frame | 129 | 4 |
recession_probability | murphydiagram | Data sets with forecasts and realizations | data.frame | 183 | 4 |
vocab_growth | huttenlocher1991 | Vocabulary growth for 22 toddlers | tbl_df | 126 | 9 |
ex_K | oscar | Example data from TYKS / HUSLAB | matrix | 22 | 38 |
ex_X | oscar | Example data from TYKS / HUSLAB | matrix | 650 | 38 |
ex_Y | oscar | Example data from TYKS / HUSLAB | matrix | 650 | 2 |
ex_c | oscar | Example data from TYKS / HUSLAB | numeric | | |
ef_data | ef | Counts of salmon fry and parr from electrofishing. | data.frame | 90 | 9 |
glass | kknn | Glass Identification Database | data.frame | 214 | 11 |
ionosphere | kknn | Johns Hopkins University Ionosphere Database | data.frame | 351 | 35 |
miete | kknn | Munich Rent Standard Database (1994) | data.frame | 1082 | 17 |
wind_sensit_2007 | monoClust | Existence of Microorganisms Carried in Wind | tbl_df | 671 | 3 |
wind_sensit_2008 | monoClust | Existence of Microorganisms Carried in Wind | tbl_df | 673 | 3 |
metric | mapi | 'metric' test dataset | data.table | 19900 | 3 |
samples | mapi | 'samples' test dataset | data.table | 200 | 4 |
cgd1 | mma | cgd1 Data Set | data.frame | 128 | 20 |
weight_behavior | mma | Weight_Behavior Data Set | data.frame | 691 | 15 |
loans | PDtoolkit | German Credit Data | data.frame | 1000 | 21 |
ptk | standardize | Duration and voicing measures of voiceless plosives in Spanish | data.frame | 751 | 11 |
reuters_docs | ldaPrototype | A Snippet of the Reuters Dataset | list | | |
reuters_vocab | ldaPrototype | A Snippet of the Reuters Dataset | character | | |
bn1_data | MOTE | Between Subjects One-way ANOVA Example Data | data.frame | 11 | 2 |
bn2_data | MOTE | Between Subjects Two-way ANOVA Example Data | data.frame | 1000 | 3 |
chisq_data | MOTE | Chi-Square Example Data | data.frame | 60 | 2 |
dept_data | MOTE | Dependent t Example Data | data.frame | 7 | 2 |
indt_data | MOTE | Independent t Example Data | data.frame | 8 | 2 |
mix2_data | MOTE | Mixed Two-way ANOVA Example Data | data.frame | 158 | 3 |
rm1_data | MOTE | Repeated Measures Oneway ANOVA Example Data | data.frame | 18 | 3 |
rm2_data | MOTE | Repeated Measures Two-way ANOVA Example Data | data.frame | 158 | 6 |
singt_data | MOTE | Single Sample t Example Data | data.frame | 15 | 1 |
AlberMorgan | scdhlm | Alber-Morgan, et al. (2007) | data.frame | 119 | 4 |
Anglesea | scdhlm | Example 2 from Hedges, Pustejovsky, & Shadish (2012) | data.frame | 55 | 5 |
BartonArwood | scdhlm | Barton-Arwood, Wehby, & Falk (2005) | data.frame | 143 | 4 |
Bryant2018 | scdhlm | Bryant et al. (2018) | data.frame | 570 | 9 |
Carson | scdhlm | Carson (2008) | data.frame | 47 | 5 |
CaseHarrisGraham | scdhlm | Case, Harris, and Graham (1992) | data.frame | 56 | 4 |
Datchuk | scdhlm | Datchuk (2016) | data.frame | 74 | 4 |
DelemereDounavi | scdhlm | Delemere & Dounavi (2018) | data.frame | 187 | 5 |
GunningEspie | scdhlm | Gunning & Espie (2003) | data.frame | 301 | 4 |
Lambert | scdhlm | Example 1 from Hedges, Pustejovsky, & Shadish (2012) | data.frame | 461 | 6 |
Laski | scdhlm | Example 2 from Hedges, Pustejovsky, & Shadish (2013) | data.frame | 128 | 4 |
MB1results | scdhlm | MB1 simulation results | cast_df | 8320 | 7 |
MB1time | scdhlm | MB1 simulation time | proc_time | | |
MB2results | scdhlm | MB2 simulation results | cast_df | 9600 | 9 |
MB2time | scdhlm | MB2 simulation time | proc_time | | |
MB4results | scdhlm | MB4 simulation results | cast_df | 14400 | 9 |
MB4time | scdhlm | MB4 simulation time | proc_time | | |
Musser | scdhlm | Musser (2001) | data.frame | 120 | 4 |
Peltier | scdhlm | Peltier et al. (2020) | data.frame | 232 | 4 |
Rodgers | scdhlm | Rodgers et al. (2021) | data.frame | 83 | 4 |
Rodriguez | scdhlm | Rodriguez & Anderson (2014) | data.frame | 148 | 4 |
Romaniuk | scdhlm | Romaniuk (2002) | data.frame | 191 | 6 |
Ruiz | scdhlm | Ruiz, et al. (2020) | tbl_df | 840 | 5 |
Saddler | scdhlm | Example 1 from Hedges, Pustejovsky, & Shadish (2013) | data.frame | 124 | 5 |
Salazar | scdhlm | Salazar, et al. (2020) | data.frame | 324 | 5 |
Schutte | scdhlm | Example from Pustejovsky, Hedges, & Shadish (2014) | data.frame | 136 | 4 |
Thiemann2001 | scdhlm | Thiemann & Goldstein (2001) | data.frame | 363 | 8 |
Thiemann2004 | scdhlm | Thiemann & Goldstein (2004) | data.frame | 408 | 8 |
Thorne | scdhlm | Thorne (2005) | data.frame | 776 | 7 |
city_data | Cluster.OBeu | city data example | data.frame | 196 | 5 |
alon | dglars | Data from the microarray experiment done by Alon et al. (1999) | data.frame | 62 | 2001 |
breast | dglars | Breast Cancer microarray experiment | data.frame | 52 | 288 |
duke | dglars | Duke breast cancer microarray experiment | data.frame | 46 | 7130 |
Spellman | minerva | CDC15 Yeast Gene Expression Dataset | data.frame | 23 | 4382 |
ex_IC | ALassoSurvIC | Virtual data set for interval censored data | data.frame | 100 | 8 |
ex_ICLT | ALassoSurvIC | Virtual data set for interval censored and left truncated data | data.frame | 100 | 9 |
reader_1 | ORFID | ORFID data samples | spec_tbl_df | 460 | 9 |
reader_2 | ORFID | ORFID data samples | spec_tbl_df | 8590 | 9 |
reader_3 | ORFID | ORFID data samples | spec_tbl_df | 1027 | 9 |
reader_ds | ORFID | ORFID data samples | spec_tbl_df | 824 | 16 |
reader_us | ORFID | ORFID data samples | spec_tbl_df | 781 | 16 |
gcd | monobin | Excerpt from German Credit Data | data.frame | 1000 | 4 |
auxdat_mecont | bndovb | A simulated auxiliary data to show how to use 'bndovbme' function with continuous proxy variables | data.frame | 3000 | 5 |
auxdat_medisc | bndovb | A simulated auxiliary data to show how to use 'bndovbme' function with discrete proxy variables | data.frame | 3000 | 5 |
auxdat_nome | bndovb | A simulated auxiliary data to show how to use 'bndovb' function | data.frame | 5000 | 3 |
maindat_mecont | bndovb | A simulated main data to show how to use 'bndovbme' function with continuous proxy variables | data.frame | 3000 | 3 |
maindat_medisc | bndovb | A simulated main data to show how to use 'bndovbme' function with discrete proxy variables | data.frame | 3000 | 3 |
maindat_nome | bndovb | A simulated main data to show how to use 'bndovb' function | data.frame | 5000 | 3 |
TBBPA | enviGCMS | Demo data for TBBPA metabolism in Pumpkin | list | | |
list | enviGCMS | Demo data | list | | |
matrix | enviGCMS | Demo raw data matrix | matrix | 4001 | 1278 |
sccp | enviGCMS | Short-Chain Chlorinated Paraffins(SCCPs) peaks information for quantitative analysis | data.frame | 24 | 8 |
MaConDa | pmd | mass spectrometry contaminants database for PMD check | data.frame | 308 | 5 |
hmdb | pmd | A dataframe containing HMDB with unique accurate mass pmd with three digits frequency larger than 1 and accuracy percentage larger than 0.9. | data.frame | 114824 | 9 |
keggrall | pmd | A dataframe containing reaction related accurate mass pmd and related reaction formula with KEGG ID | data.frame | 22336 | 14 |
omics | pmd | A dataframe containing multiple reaction database ID and their related accurate mass pmd and related reactions | data.frame | 58050 | 12 |
sda | pmd | A dataset containing common Paired mass distances of substructure, ions replacements, and reaction | tbl_df | 94 | 4 |
spmeinvivo | pmd | A peaks list dataset containing 9 samples from 3 fish with triplicates samples for each fish from LC-MS. | list | | |
clinical | fSRM | Data set on negativity in problematic and nonproblematic families A simulated dataset, mimicking the study performed by Eichelsheim et al. (2011) who investigated whether there are differences in patterns of negativity between families with and without an adolescent with externalizing problem behavior. In the study of Eichelsheim et al. (2011), four members of the same family (mother, father, target adolescent and sibling) reported on the amount of negativity they experienced in relation to each other. In total, these authors studied 120 Dutch four-person families with a target adolescent scoring above the externalizing behavior clinical norm scores on either the Child Behavior Check List (N = 47; CBCL; Achenbach, 1991) or the Youth Self Report (N = 73; YSR; Achenbach, 1991). Because of confidentiality reasons, not the original data but mimicked data are used here, so results of social relations analyses will deviate from the original paper. | data.frame | 1440 | 4 |
clinical.wide | fSRM | Data set on negativity in problematic and nonproblematic families A simulated dataset, mimicking the study performed by Eichelsheim et al. (2011) who investigated whether there are differences in patterns of negativity between families with and without an adolescent with externalizing problem behavior. In the study of Eichelsheim et al. (2011), four members of the same family (mother, father, target adolescent and sibling) reported on the amount of negativity they experienced in relation to each other. In total, these authors studied 120 Dutch four-person families with a target adolescent scoring above the externalizing behavior clinical norm scores on either the Child Behavior Check List (N = 47; CBCL; Achenbach, 1991) or the Youth Self Report (N = 73; YSR; Achenbach, 1991). Because of confidentiality reasons, not the original data but mimicked data are used here, so results of social relations analyses will deviate from the original paper. | data.frame | 120 | 13 |
four.person | fSRM | Data set on attachment anxiety in four person families (Cook, 2000) | data.frame | 2496 | 4 |
three.person | fSRM | Data set on attachment anxiety in 3-person families (based on Cook, 2000) | data.frame | 1248 | 5 |
two.groups | fSRM | Data set on negative interactions A simulated dataset, mimicking the study performed by Eichelsheim et al. (2011) who investigated whether there are differences in patterns of negativity between families with and without an adolescent with externalizing problem behavior. The problematic and nonproblematic group consist of 120 and 153 four-person families, respectively. This dataset contains a measures of negativity for each of the 12 relationships. Four roles are present: Mothers "M", fathers "F", the asolescent with externalizing problem behavior "T", and the adolescent sibling without problem behavior "S". A wide version of the same data set is in 'two.groups.wide'. | data.frame | 3276 | 6 |
two.groups.wide | fSRM | Data set on negative interactions A simulated dataset, mimicking the study performed by Eichelsheim et al. (2011) who investigated whether there are differences in patterns of negativity between families with and without an adolescent with externalizing problem behavior. The problematic and nonproblematic group consist of 120 and 153 four-person families, respectively. This dataset contains a measures of negativity for each of the 12 relationships. Four roles are present: Mothers "M", fathers "F", the asolescent with externalizing problem behavior "T", and the adolescent sibling without problem behavior "S". A wide version of the same data set is in 'two.groups.wide'. | data.frame | 273 | 13 |
two.indicators | fSRM | Data set on attachment dependency (Cook, 2000) The classic Cook (2000) dataset consists of measurements on security of attachment within families. Only the variable measuring attachment dependency in family relationships is included in this dataset. Four roles are present (i.e. two parents and two children): mothers "m", fathers "f", the older child as "c", and the younger child "y". | data.frame | 2496 | 5 |
ChronoModelEvents | ArchaeoData | Ksar'Akil Dates Calibrated by ChronoModel | data.frame | 30000 | 17 |
ChronoModelPhases | ArchaeoData | Ksar'Akil Phases Calibrated by ChronoModel | data.frame | 30000 | 9 |
burials | ArchaeoData | Anglo-Saxon Female Burials with Beads | data.frame | 5000 | 77 |
fishpond | ArchaeoData | Calibration of a Fishpond Chronology | data.frame | 55964 | 11 |
ksarakil | ArchaeoData | Ksar'Akil Dates Calibrated by OxCal | data.frame | 1000 | 27 |
JanMayenBirds | Gendis2unmix | Fulmarin petrels with unknown sex from Jan Mayen | data.frame | 194 | 8 |
fulmarin | Gendis2unmix | Fulmarin petrels data | data.frame | 379 | 6 |
hue | lcc | Hue color data | data.frame | 554 | 4 |
simulated_hue | lcc | Hue color simulated data | data.frame | 6000 | 4 |
simulated_hue_block | lcc | Hue color simulated data in a randomized block design | data.frame | 24000 | 5 |
GCAM2ISO_MAPPING | edgeTrpLib | | data.table | 249 | 2 |
L1mapping | edgeTrpLib | | data.table | 15 | 2 |
example_btm | textplot | Example Biterm Topic Model | BTM | | |
example_embedding | textplot | Example word embedding matrix | matrix | 835 | |
example_embedding_clusters | textplot | Example words emitted in a ETM text clustering model | data.frame | 2000 | 4 |
example_udpipe | textplot | Example annotation of text using udpipe | data.frame | 13 | 17 |
centromeres | PureCN | A list of data.frames containing centromere positions. | list | | |
purecn.DNAcopy.bdry | PureCN | DNAcopy boundary data | integer | | |
purecn.example.output | PureCN | Example output | list | | |
cdnowElog | BTYD | CDNOW event log data | data.frame | 6919 | 1 |
cdnowSummary | BTYD | CDNOW repeat transaction data summary | list | | |
discreteSimElog | BTYD | Discrete simulated annual event log data | data.frame | 52432 | 1 |
donationsSummary | BTYD | Discrete donation data summary | list | | |
pineneedles | litterfitter | decomposition trajectory for pine needles | data.frame | 6 | 2 |
HAO | ontoFAST | Hymenoptera Anatomy Ontology (HAO) | ontology_index | | |
Scarab | ontoFAST | A modified Hymenoptera Anatomy Ontology (HAO) to accommodate anatomy of dung beetles | ontology_index | | |
Sharkey_2011 | ontoFAST | Hymenoptera character statements | data.frame | 392 | 9 |
Sharkey_2011_annot | ontoFAST | Hymenoptera characters annotated with ontology terms | list | | |
Tarasov_2017_annot | ontoFAST | Dung beetle characters annotated with ontology terms | list | | |
exclude_terms | ontoFAST | Ontology terms to exclude for sunburst plot | character | | |
formats | uniprotREST | (Dataset) UniProt download formats | data.frame | 43 | 3 |
from_to_dbs | uniprotREST | (Dataset) From/to databases for ID mapping | data.frame | 100 | 5 |
from_to_rules | uniprotREST | (Dataset) From/to rules for ID mapping | list | | |
return_fields | uniprotREST | (Dataset) UniProt return fields | data.frame | 389 | 4 |
bike_log | conformalInference.fd | Log of all bike rentals in Milan in 2016 form January to March. | list | | |
bike_regressors | conformalInference.fd | Regressors to model the log of all bike rentals in Milan in 2016. | list | | |
adm2021 | IPEDS | Admissions and Test Scores 2021 Data | data.frame | 1981 | 39 |
complete2021 | IPEDS | Completions 2021 Data | data.frame | 15980 | 19 |
conference | IPEDS | Conferences Data | tbl_df | 497 | 3 |
dir_info2021 | IPEDS | Directory Info 2021 Data | data.frame | 6129 | 47 |
fall_enroll2021 | IPEDS | Fall Enrollment 2021 Data | tbl_df | 6049 | 31 |
fin_aid1920 | IPEDS | Student Financial Aid Data 2019 - 2020 | data.frame | 5859 | 91 |
offerings2021 | IPEDS | Offerings Data | data.frame | 6179 | 114 |
relig_aff | IPEDS | Religious Affiliations Data | tbl_df | 64 | 2 |
staff2021 | IPEDS | Staff 2021 Data | data.frame | 63625 | 34 |
staff_cat | IPEDS | Staff Categories Data | tbl_df | 65 | 2 |
example_data_long | washi | Example data in long (tidy) format | tbl_df | 1800 | 14 |
example_data_wide | washi | Example data in wide format | spec_tbl_df | 30 | 72 |
synr_exampledf_large | synr | Raw consistency test data example, long format | data.frame | 645 | 4 |
synr_exampledf_long_small | synr | Raw consistency test data example, long format (small) | data.frame | 18 | 4 |
synr_exampledf_wide_small | synr | Raw consistency test data example, wide format (small) | data.frame | 3 | 19 |
ottawa_db_shp | pseudohouseholds | 2021 Statistics Canada Dissemination Block Boundaries and Populations for Ottawa, Ontario | sf | 8559 | 3 |
ottawa_roads_shp | pseudohouseholds | 2021 Statistics Canada Road Network for Ottawa, Ontario | sf | 33983 | 5 |
region_shp | pseudohouseholds | Synthetic region shapefile for testing | sf | 1 | 3 |
road_shp | pseudohouseholds | Synthetic road shapefile for testing | sf | 1 | 2 |
example_dataset | STMotif | Example of dataset | matrix | 20 | |
d.bfsrg | bfsMaps | Swiss Federal Statistical Office (SFSO) Spatial Divisions | data.frame | 2136 | 27 |
kt | bfsMaps | Abbreviations for Swiss Cantons | factor | | |
tl_example1 | tealeaves | tealeaves example output 1 | tbl_df | 100 | 20 |
bp_children | bp | B-Proact1v Children Data | tbl_df | 6181 | 15 |
bp_ghana | bp | Task Shifting and Blood Pressure Control in Ghana Data | tbl_df | 2271 | 21 |
bp_hypnos | bp | HYPNOS Study - 5 Subject Sample | data.frame | 250 | 12 |
bp_jhs | bp | Blood Pressure - 1 Subject - John Schwenck | data.frame | 222 | 11 |
bp_preg | bp | Pregnancy Day Assessment Clinic Data | tbl_df | 4750 | 55 |
bp_rats | bp | Blood Pressure in Salt-Sensitive Dahl Rats Data | grouped_df | 360000 | 5 |
bioact | PepSAVIms | Bioactivity data | list | | |
mass_spec | PepSAVIms | Mass spectrometry data | data.frame | 30799 | 38 |
degelder | kohonen | Powder pattern data by Rene de Gelder | list | | |
nir | kohonen | Near-infrared data with temperature effects | list | | |
peppaPic | kohonen | Synthetic image of a pepper plant with peppers | matrix | 480000 | 4 |
vintages | kohonen | Wine data | factor | | |
wines | kohonen | Wine data | matrix | 177 | 13 |
yeast | kohonen | Yeast cell-cycle data | list | | |
lifelines_sample | LexisPlotR | Data for 300 random lifelines | data.frame | 300 | 2 |
data_sim1 | LCCR | Simulated data 1 | matrix | 177 | |
data_sim2 | LCCR | Simulated data 2 | list | | |
data_sim3 | LCCR | Simulated data 3 | list | | |
data_sim4 | LCCR | Simulated data 4 | list | | |
dat.exp | immunoClust | immunoClust Meta-clustering Sample | list | | |
dat.fcs | immunoClust | immunoClust Cell-clustering Sample | flowFrame | | |
dat.meta | immunoClust | immunoClust Meta-clustering Results Sample | immunoMeta | | |
PDXMI | Xeva | PDX-MI data | data.frame | 45 | 4 |
brca | Xeva | PDXE breast cancer dataset | XevaSet | | |
repdx | Xeva | Example PDX dataset | XevaSet | | |
app | mnem | Processed scRNAseq from pooled CRISPR screens | list | | |
feature.matrix | openPrimeR | Data Sets. | data.frame | 908 | 32 |
primer.data | openPrimeR | Data Sets. | list | | |
primer.data | openPrimeR | Data Sets. | list | | |
primer.df | openPrimeR | Data Sets. | Primers | 8 | 151 |
ref.data | openPrimeR | Data Sets. | data.frame | 940 | 3 |
settings | openPrimeR | Data Sets. | DesignSettings | | |
settings | openPrimeR | Data Sets. | DesignSettings | | |
template.data | openPrimeR | Data Sets. | list | | |
template.df | openPrimeR | Data Sets. | Templates | 147 | 35 |
tiller.primer.df | openPrimeR | Data Sets. | Primers | 4 | 151 |
tiller.settings | openPrimeR | Data Sets. | DesignSettings | | |
tiller.template.df | openPrimeR | Data Sets. | Templates | 147 | 35 |
X.clust | MDMR | Simulated clustered predictor data to illustrate the Mixed-MDMR function | data.frame | 250 | 3 |
X.mdmr | MDMR | Simulated predictor data to illustrate the MDMR package. | matrix | 500 | |
Y.clust | MDMR | Simulated clustered outcome data to illustrate the Mixed-MDMR function | data.frame | 250 | 12 |
Y.mdmr | MDMR | Simulated outcome data to illustrate the MDMR package. | matrix | 500 | |
IrishDirectoratesData | IrishDirectorates | Board Composition For Companies Quoted On The Irish Stock Exchange From 2003 To 2013 | list | | |
IrishDirectoratesFit | IrishDirectorates | Fitted Dynamic Bipartite Latent Position Model. | list | | |
GMWL | isoWater | Global Meteoric Water Line | numeric | | |
data_temp | vaxpmx | Example of a hypothetical vaccine clinical trial data set | data.frame | 600 | 8 |
breast_cancer | bcn | The breast cancer wisconsin dataset. | data.frame | 569 | 31 |
digits | bcn | The digits dataset. | data.frame | 1797 | 65 |
penguins | bcn | Size measurements for adult foraging penguins near Palmer Station, Antarctica | data.frame | 344 | 8 |
wine | bcn | The wine dataset. | data.frame | 178 | 14 |
implants | turboEM | Fetal Death in Mice | data.frame | 523 | 2 |
parties | turboEM | Political Parties | numeric | | |
psychfactors | turboEM | Psychiatric Test Correlations | matrix | 10 | |
rats | turboEM | Population Growth of Rats | data.frame | 300 | 9 |
votes | turboEM | Roll Call Votes | matrix | 401 | |
sample_data | Counternull | Sample Data | data.frame | 156 | 2 |
sample_matrix | Counternull | Sample Randomization Matrix | data.frame | 156 | 1000 |
CNOlistToy_Gene | CNORfeeder | | CNOlist | | |
PPINigraph | CNORfeeder | Protein-protein interaction netwrok | igraph | | |
UniprotIDdream | CNORfeeder | Uniprot identifiers for proteins in DreamModel | list | | |
cnolist | CNORfeeder | CNOlist | CNOlist | | |
database | CNORfeeder | OmniPath PPI | matrix | 12336 | |
feederObject | CNORfeeder | Feeder Object | list | | |
indices | CNORfeeder | Mis-fit indices | list | | |
integratedModel | CNORfeeder | Integrated Model | list | | |
model | CNORfeeder | Prior Knowledge Network | list | | |
model | CNORfeeder | Prior Knowledge Network | list | | |
simData | CNORfeeder | CNORode simuation data | list | | |
MyPortfolio | ELT | MyPortfolio used for the exemple. | data.frame | 87090 | 6 |
ReferenceFemale | ELT | ReferenceFemale used for the exemple. | data.frame | 66 | 54 |
ReferenceMale | ELT | ReferenceMale used for the exemple. | data.frame | 66 | 54 |
misspellings | fuzzyjoin | A corpus of common misspellings, for examples and practice | tbl_df | 4505 | 2 |
map_pictures | CSGo | Maps Images | data.frame | 34 | 2 |
support | CSGo | Categories and Descriptions of the Statistics Data | tbl_df | 133 | 4 |
weapon_pictures | CSGo | Weapon Images | data.frame | 40 | 2 |
admin1.map | choroplethrAdmin1 | An Administrative Level 1 map of every country in the world | data.frame | 1253216 | 9 |
admin1.regions | choroplethrAdmin1 | Names of all (country, region) pairs on the admin1.map data.frame. Here "region" means "Administrative Level 1 Region". | data.frame | 4399 | 2 |
truck | PFLR | Truck emissions data | matrix | 108 | 91 |
lifedata | bayesmlogit | Simplified Data for generating life tables. | data.frame | 8198 | 15 |
sir_example | simode | Example dataset for a multi-group SIR model | list | | |
covid | estimateW | Covid incidences data | tbl_df | 513 | 9 |
hp_survey | comperes | Results of Harry Potter Books Survey | tbl_df | 657 | 3 |
ncaa2005 | comperes | Example competition results from 2005 NCAA football season | longcr | 20 | 3 |
NelPlo | dlm | Nelson-Plosser macroeconomic time series | mts | 43 | 2 |
USecon | dlm | US macroeconomic time series | mts | 40 | 2 |
stdGrid | SCEPtER | Standard grid for mass and radius estimation | matrix | 204930 | 9 |
pima | reglogit | Pima Indian Data | data.frame | 768 | 9 |
Sachs | BGGM | Data: Sachs Network | data.frame | 7466 | 11 |
asd_ocd | BGGM | Data: Autism and Obssesive Compulsive Disorder | data.frame | 17 | 17 |
bfi | BGGM | Data: 25 Personality items representing 5 factors | data.frame | 2800 | 27 |
csws | BGGM | Data: Contingencies of Self-Worth Scale (CSWS) | data.frame | 680 | 36 |
depression_anxiety_t1 | BGGM | Data: Depression and Anxiety (Time 1) | data.frame | 403 | 16 |
depression_anxiety_t2 | BGGM | Data: Depression and Anxiety (Time 2) | data.frame | 403 | 16 |
gss | BGGM | Data: 1994 General Social Survey | data.frame | 1002 | 7 |
ifit | BGGM | Data: ifit Intensive Longitudinal Data | data.frame | 197 | 8 |
iri | BGGM | Data: Interpersonal Reactivity Index (IRI) | data.frame | 1973 | 29 |
ptsd | BGGM | Data: Post-Traumatic Stress Disorder | data.frame | 221 | 20 |
ptsd_cor1 | BGGM | Data: Post-Traumatic Stress Disorder (Sample # 1) | data.frame | 16 | 16 |
ptsd_cor2 | BGGM | Data: Post-Traumatic Stress Disorder (Sample # 2) | data.frame | 16 | 16 |
ptsd_cor3 | BGGM | Data: Post-Traumatic Stress Disorder (Sample # 3) | data.frame | 16 | 16 |
ptsd_cor4 | BGGM | Data: Post-Traumatic Stress Disorder (Sample # 4) | data.frame | 16 | 16 |
rsa | BGGM | Data: Resilience Scale of Adults (RSA) | data.frame | 675 | 34 |
tas | BGGM | Data: Toronto Alexithymia Scale (TAS) | data.frame | 1925 | 21 |
women_math | BGGM | Data: Women and Mathematics | matrix | 1190 | 6 |
covariate_mat | wISAM | Example covariate matrix | matrix | 200 | |
kinship_mat | wISAM | Example kinship matrix | matrix | 200 | 200 |
locus_list | wISAM | Example locus list | list | | |
phenotype | wISAM | Example phenotype | numeric | | |
se_mean_per_strain | wISAM | Example standard error of the mean per strain | numeric | | |
dataArea | geoSAE | Dataset on Area Level | data.frame | 15 | 17 |
dataUnit | geoSAE | Dataset on Unit Level | data.frame | 210 | 6 |
zspline | geoSAE | Z-Spline | data.frame | 210 | 10 |
Canada | vars | Canada: Macroeconomic time series | mts | 84 | 4 |
site1 | ppmHR | Example individual-level data of data-contributing site 1 | data.frame | 800 | 10 |
site2 | ppmHR | Example individual-level data of data-contributing site 2 | data.frame | 1000 | 10 |
site3 | ppmHR | Example individual-level data of data-contributing site 3 | data.frame | 1200 | 10 |
sumstatsColHeaders | MungeSumstats | Summary Statistics Column Headers | data.frame | 410 | 2 |
gom | revdbayes | Storm peak significant wave heights from the Gulf of Mexico | numeric | | |
newlyn | revdbayes | Newlyn sea surges | numeric | | |
oxford | revdbayes | Annual Maximum Temperatures at Oxford | numeric | | |
portpirie | revdbayes | Annual Maximum Sea Levels at Port Pirie, South Australia | numeric | | |
rainfall | revdbayes | Daily Aggregate Rainfall | numeric | | |
venice | revdbayes | Largest Sea Levels in Venice | data.frame | 51 | 10 |
PROCTCAE_table | ProAE | PRO-CTCAE variable / label crosswalk | data.frame | 124 | 2 |
tox_acute | ProAE | PRO-CTCAE data reflecting acute drug toxicity | data.frame | 1400 | 7 |
tox_chronic | ProAE | PRO-CTCAE data reflecting chronic drug toxicity | data.frame | 1400 | 6 |
tox_cumulative | ProAE | PRO-CTCAE data reflecting cumulative drug toxicity | data.frame | 1400 | 6 |
tox_cyclic | ProAE | PRO-CTCAE data reflecting cyclical drug toxicity | data.frame | 1400 | 6 |
tox_late | ProAE | PRO-CTCAE data reflecting late incipient drug toxicity | data.frame | 1400 | 6 |
testData | normalr | Test dataset for the paper | data.frame | 957 | 9 |
airports | flightplot | The Information of All Aiports | spec_tbl_df | 7698 | 14 |
sample_trips | flightplot | Sample Trip Dataset | spec_tbl_df | 47 | 2 |
world | flightplot | World Map Vector Data | sf | 1420 | 4 |
hotel_rooms | levitate | Hotel room listings | spec_tbl_df | 85 | 2 |
helix_data | LUCIDus | HELIX data | list | | |
sim_data | LUCIDus | A simulated dataset for LUCID | list | | |
epidural_c | BayesCACE | Meta-analysis data with full compliance information | data.frame | 10 | 10 |
epidural_ic | BayesCACE | Meta-analysis data without full compliance information | data.frame | 27 | 14 |
skills | radarchart | Skills in a team | data.frame | 6 | 4 |
skillsByName | radarchart | Rotated version of skills data | data.frame | 3 | 7 |
station_codes | trainR | National Rail Enquiries (NRE) Station Codes dataset | tbl_df | 2579 | 2 |
data | micss | Data used in the examples | tbl_df | 7705 | 2 |
hypothyroid | bagged.outliertrees | Hypothyroid | data.frame | 2772 | 23 |
lubisch | amap | Dataset Lubischew | data.frame | 74 | 8 |
california.tract10 | RapidPolygonLookup | Census Tract spatial polygons for the state of California | SpatialPolygonsDataFrame | | |
sf.crime.2012 | RapidPolygonLookup | Sample data with lat/long information | data.frame | 20000 | 4 |
sf.polys | RapidPolygonLookup | Spatial polygons of San Francisco | list | | |
Piggott | nortestARMA | Daily gas demand study by Piggott | data.frame | 366 | 2 |
potato | nortestARMA | Prince Edward Island; Average Yield, Potatoes (Hundredweight per harvested acres (1957-2014)) | data.frame | 58 | 2 |
resid.temp | nortestARMA | Daily gas demand study by Piggott | numeric | | |
Apo | daewr | apolipoprotein survey varaince component study | data.frame | 30 | 2 |
BPmonitor | daewr | blood pressure monitor experiment | data.frame | 12 | 3 |
Bdish | daewr | Confounded Block Dishwashing Experiment | design | 16 | 6 |
Bff | daewr | Confounded block fractional mouse growth experiment | design | 16 | 10 |
BoxM | daewr | Box and Meyer's unreplicated 2^4 from Chapter 3 | data.frame | 16 | 5 |
COdata | daewr | CO emmisions experiment data from Chapter 3 | data.frame | 18 | 3 |
MPV | daewr | mixture process variable experiment with mayonnaise | data.frame | 35 | 6 |
Naph | daewr | Yields of naphthalene black | data.frame | 30 | 2 |
Rations | daewr | Cattle rations design experiment data from Table 10.16 | data.frame | 45 | 4 |
SPMPV | daewr | Split-plot mixture process variable experiment with vinyl | data.frame | 28 | 7 |
Smotor | daewr | Single array for starting motor experiment | data.frame | 18 | 6 |
Tet | daewr | Tetracycline concentration in plasma | data.frame | 9 | 2 |
Treb | daewr | Box-Behnken design for trebuchet experiment | coded.data | 15 | 4 |
WeldS | daewr | Table 12.24 Experiment with Weld Tensile Strength | data.frame | 16 | 16 |
antifungal | daewr | Two-period crossover study of antifungal agent | data.frame | 34 | 5 |
apple | daewr | Confounded apple slice browning experiment | data.frame | 24 | 4 |
arso | daewr | 2^{(7-3)} arsenic removal experiment | design | 8 | 8 |
augm | daewr | 2^{(7-3)} arsenic removal experiment augmented with mirror image | design | 16 | 9 |
bha | daewr | mouse liver enzyme experiment | data.frame | 16 | 4 |
bioequiv | daewr | Extra-period crossover bioequivalence study | data.frame | 108 | 6 |
bioeqv | daewr | Latin Square bioequivalence experiment | data.frame | 9 | 4 |
blood | daewr | Variance component study of calcium in blood serum | data.frame | 27 | 3 |
bread | daewr | Bread rise experiment data from Chapter 2 | data.frame | 12 | 3 |
cakeb | daewr | Split-Plot response surface for cake baking experiment | data.frame | 11 | 6 |
cement | daewr | CCD design for cement workability experiment | coded.data | 20 | 5 |
chem | daewr | Chemical process experiment data from Chapter 3 | data.frame | 16 | 5 |
chipman | daewr | Williams' crossover design for sprinting experiment | data.frame | 36 | 7 |
connector | daewr | Table 12.21 Experiment with Elastometric Connector | data.frame | 32 | 8 |
cont | daewr | Control factor array and summary statistics for controller circuit design experiment | data.frame | 18 | 6 |
cpipe | daewr | Split-plot response surface for ceramic pipe experiment | data.frame | 48 | 6 |
culture | daewr | paecilomyces variotii culture experiment | design | 16 | 9 |
dairy | daewr | Repeated measures study with dairy cow diets | data.frame | 120 | 5 |
drug | daewr | Data from rat behavior experiment in Chapter 4 | data.frame | 50 | 3 |
eptaxr | daewr | Single array and raw response for silicon layer growth experiment | data.frame | 64 | 9 |
eptaxs2 | daewr | Control array and variance of response for silicon layer growth experiment | data.frame | 16 | 9 |
eptaxyb | daewr | Control array and mean response for silicon layer growth experiment | data.frame | 16 | 9 |
gagerr | daewr | Gauge R&R Study | data.frame | 60 | 3 |
gear | daewr | Unreplicated split-plot fractional-factorial experiment on geometric distortion of drive gears | design | 16 | 6 |
hardwood | daewr | low grade hardwood conjoint study | data.frame | 12 | 5 |
inject | daewr | Single array for injection molding experiment | data.frame | 20 | 8 |
pastry | daewr | Blocked response surface design for pastry dough experiment | data.frame | 28 | 5 |
pest | daewr | Pesticide formulation experiment | data.frame | 13 | 4 |
pesticide | daewr | pesticide application experiment | data.frame | 16 | 4 |
plasma | daewr | Unreplicated split-plot 2^5 experiment on plasma treatment of paper | design | 32 | 6 |
polvdat | daewr | Polvoron mixture experiment | data.frame | 12 | 4 |
polymer | daewr | polymerization strength variability study | data.frame | 120 | 5 |
prodstd | daewr | Complete control factor array and noise factor array for connector experiment | data.frame | 72 | 8 |
qsar | daewr | Library of substituted hydroxyphenylurea compounds | data.frame | 36 | 4 |
rcb | daewr | generalized RCB golf driving experiment | data.frame | 135 | 3 |
residue | daewr | Herbicide degradation experiment | data.frame | 16 | 8 |
rubber | daewr | Rubber Elasticity data | data.frame | 96 | 4 |
sausage | daewr | Split-plot experiment on sausage casing with RCB in whole plot | data.frame | 32 | 7 |
soup | daewr | dry mix soup experiment | design | 16 | 6 |
soupmx | daewr | dry soup mix variance component study | data.frame | 12 | 2 |
splitPdes | daewr | Split-plot cookie baking experiment | data.frame | 24 | 5 |
strung | daewr | Repeated measures study with dairy cow diets | data.frame | 120 | 4 |
strungtile | daewr | Strung out control factor array and raw response data for Ina tile experiment | data.frame | 16 | 16 |
sugarbeet | daewr | Sugarbeet data from Chapter 2 | data.frame | 18 | 2 |
taste | daewr | taste test panel experiment | data.frame | 24 | 3 |
teach | daewr | Teaching experiment data from Chapter 2 | data.frame | 30 | 4 |
tile | daewr | Control factor array and summary statistics for Ina tile experiment | data.frame | 8 | 11 |
vinyl | daewr | Vinysl plasticizer formulations experiment data | data.frame | 40 | 7 |
virus | daewr | Assay of Viral Contamination experiment data from Chapter 3 | data.frame | 18 | 3 |
volt | daewr | Volt meter experiment data from Chapter 3 | data.frame | 16 | 4 |
web | daewr | Web page design experiment data from Chapter 3 | data.frame | 36 | 6 |
Pen_Data | priorityelasticnet | Simulated Patient Data for Binary Classification | data.frame | 406 | 325 |
whas500 | smoothHR | Worcester Heart Attack Study WHAS500 Data | data.frame | 500 | 22 |
clust_test | clr | Electricity load example: clusters on test set | list | | |
clust_train | clr | Electricity load example: clusters on train set | list | | |
gb_load | clr | Electricity load from Great Britain | data.frame | 105216 | 7 |
MPG | sharpData | Mileage Data | data.frame | 15 | 10 |
burnRate | sharpData | Firebrand Burning Properties | data.frame | 33 | 6 |
whale | sharpData | Whale data | data.frame | 228 | 3 |
LFQRatio2 | cp4p | Dataset "LFQRatio2" | data.frame | 1481 | 9 |
LFQRatio25 | cp4p | Dataset "LFQRatio25" | data.frame | 1472 | 9 |
gdd_data | pollen | Exemplary dataset for GDD calculations | data.frame | 100 | 3 |
pollen_count | pollen | Pollen count of alder, birch, and hazel | data.frame | 8352 | 5 |
example_data | BipartiteModularityMaximization | Example dataset of a bipartite network. | data.frame | 798 | 8 |
UTIdata | skewlmm | Data set for Unstructured Treatment Interruption Study | data.frame | 373 | 5 |
miceweight | skewlmm | Data set for clinical trial measuring mice weight | data.frame | 572 | 4 |
countrycode | learningtower | Country iso3c and name mapping for PISA OECD countries participants. | spec_tbl_df | 109 | 2 |
school | learningtower | Subset of the School data available for the years 2000-2022 from the PISA OECD database | tbl_df | 131385 | 13 |
student_subset_2000 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1550 | 22 |
student_subset_2003 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1550 | 22 |
student_subset_2006 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1850 | 22 |
student_subset_2009 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1900 | 22 |
student_subset_2012 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1900 | 22 |
student_subset_2015 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1900 | 22 |
student_subset_2018 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1900 | 22 |
student_subset_2022 | learningtower | Processed and Sampled PISA Student Data (2000-2022) | tbl_df | 1850 | 22 |
openseadragon_areas | recogito | A dataset of annotations using openseadragon | data.frame | 3 | 9 |
state_bird | jsTree | Character vector of state birds | character | | |
states | jsTree | State data | data.frame | 400 | 5 |
pigs1 | scrutiny | Means and sample sizes for GRIM-testing | tbl_df | 12 | 2 |
pigs2 | scrutiny | Percentages and sample sizes for GRIM-testing | tbl_df | 6 | 2 |
pigs3 | scrutiny | Binary means and standard deviations for using DEBIT | tbl_df | 7 | 3 |
pigs4 | scrutiny | Data with duplications | tbl_df | 5 | 3 |
pigs5 | scrutiny | Means, SDs, and sample sizes for GRIMMER-testing | tbl_df | 12 | 3 |
SimData | jrSiCKLSNMF | A simulated dataset for use with jrSiCKLSNMF | list | | |
SimSickleJrSmall | jrSiCKLSNMF | A small SickleJr object containing a subset of data from the SimData data object. Contains the completed analysis from the 'Getting Started' vignette for a small subset of 10 cells with 150 genes and 700 peaks. The clusters derived from this dataset are not accurate; this dataset is intended for use with code examples. | SickleJr | | |
liquor_sales | ialiquor | Iowa Class E Liquor Sales Summary | tbl_df | 280736 | 10 |
wheatds | Phenotype | Stripe rust disease severity (leaf areas infected, DS) of the wheat RIL population | data.frame | 280 | 4 |
concat.test | splitstackshape | Example Dataset with Concatenated Cells | data.frame | 25 | 4 |
mist_data | MiSTr | mist_data | list | | |
KSData | KSEAapp | Kinase-Substrate (K-S) Relationship Dataset | data.frame | 19757 | 14 |
KSEA.Scores.1 | KSEAapp | One of the 3 datasets for heatmap plotting | data.frame | 127 | 7 |
KSEA.Scores.2 | KSEAapp | One of the 3 datasets for heatmap plotting | data.frame | 127 | 7 |
KSEA.Scores.3 | KSEAapp | One of the 3 datasets for heatmap plotting | data.frame | 105 | 7 |
PX | KSEAapp | PX dataset for KSEA calculations | data.frame | 3444 | 6 |
Cryp.soui | NatureSounds | Acoustic recording of _Crypturellus soui_ (Little Tinamou). | Wave | | |
Phae.long.est | NatureSounds | Extended selection table of _Phaethornis longirostris_ songs | extended_selection_table | 50 | 8 |
Phae.long1 | NatureSounds | Audio recording #1 of _Phaethornis longirostris_ | Wave | | |
Phae.long2 | NatureSounds | Audio recording #2 of _Phaethornis longirostris_ | Wave | | |
Phae.long3 | NatureSounds | Audio recording #3 of _Phaethornis longirostris_ | Wave | | |
Phae.long4 | NatureSounds | Audio recording #4 of _Phaethornis longirostris_ | Wave | | |
lbh.est | NatureSounds | Extended selection table of _Phaethornis longirostris_ songs | extended_selection_table | 50 | 8 |
monk.parakeet.est | NatureSounds | Extended selection table of monk parakeet contact calls | extended_selection_table | 52 | 6 |
thyroptera.est | NatureSounds | Extended selection table of Spix's disc-winged bat social calls | extended_selection_table | 38 | 8 |
ov.cgh | Oncotree | Ovarian cancer CGH data | data.frame | 87 | 7 |
hmeq | mob | Credit attributes of 5,960 home equity loans | data.frame | 5960 | 13 |
county_bins | binsmooth | ACS County Income Data, 2006-2010 | data.frame | 51536 | 6 |
county_true | binsmooth | ACS County Income Statistics, 2006-2010 | data.frame | 3221 | 4 |
lateradata | Laterality | lateradata: data.frame for later-package examples | data.frame | 674 | 5 |
laterdata | Laterality | laterdata: data.frame for later-package examples | data.frame | 674 | 5 |
acfail | npsurv | Air Conditioner Failure Data | numeric | | |
ap | npsurv | Angina Pectoris Survival Data | data.frame | 30 | 3 |
cancer | npsurv | Breast Retraction Times after Beast Cancer Treatments. | data.frame | 94 | 3 |
gastric | npsurv | Gastric Cancer Survival Data | data.frame | 45 | 2 |
leukemia | npsurv | Remission Times for Acute Leukemia Patients | data.frame | 42 | 3 |
marijuana | npsurv | Angina Pectoris Survival Data | matrix | 21 | 3 |
nzmort | npsurv | New Zealand Mortality in 2000 | data.frame | 210 | 3 |
expression_txt | geisha | GEISHA Expression Data (expression.txt) | tbl_df | 5594 | 7 |
expression_xml | geisha | GEISHA Expression Data (expression.xml) | tbl_df | 93000 | 5 |
last_updates | geisha | Date of last update of download files | tbl_df | 4 | 2 |
ex.a | PolyTrend | Site 1 | numeric | | |
ex.b | PolyTrend | Site 2 | numeric | | |
ex.c | PolyTrend | Site 3 | numeric | | |
ex.d | PolyTrend | Site 4 | numeric | | |
ex.e | PolyTrend | Site 5 | numeric | | |
ex.f | PolyTrend | Site 6 | numeric | | |
ex.g | PolyTrend | Site 7 | numeric | | |
ex.h | PolyTrend | Site 8 | numeric | | |
ex.k | PolyTrend | Site 9 | numeric | | |
ex.m | PolyTrend | Site 10 | numeric | | |
ex.n | PolyTrend | Site 11 | numeric | | |
cohortDefinitionJson | CirceR | An example cohort definition | character | | |
conceptSetJson | CirceR | An example concept set | character | | |
conceptSetListJson | CirceR | An example concept set list | character | | |
Ushape | r2d2 | U-Shaped Cloud | matrix | 1000 | 2 |
saithe | r2d2 | MCMC Results from Saithe Assessment | data.frame | 1000 | 2 |
vgLargeParam | VarianceGamma | Parameter Sets for Variance Gamma Distribution | matrix | 625 | |
vgLargeShape | VarianceGamma | Parameter Sets for Variance Gamma Distribution | matrix | 25 | |
vgSmallParam | VarianceGamma | Parameter Sets for Variance Gamma Distribution | matrix | 81 | |
vgSmallShape | VarianceGamma | Parameter Sets for Variance Gamma Distribution | matrix | 9 | |
ordmvnorm | mildsvm | Sample ordinal MIL data using mvnorm | mi_df | 1000 | 7 |
bcd | prozor | Data frame as produced by COMET-MS search engine | data.frame | 4557 | 18 |
fdrSample | prozor | Data frame score and proteinID | data.frame | 40000 | 3 |
masses | prozor | MS masses A dataset containing approx 150000 MS1 precursor masses | numeric | | |
mm | prozor | | dgCMatrix | | |
pepprot | prozor | Table containing peptide information | data.frame | 4938 | 4 |
protpepmetashort | prozor | Small version of pepprot dataset to speed up computation | data.frame | 423 | 7 |
countries | pubDashboard | List of countries taken from the package 'countrycode' | character | | |
journal_field | pubDashboard | List of academic journals and corresponding fields | tbl_df | 60 | 5 |
universities | pubDashboard | A data frame of university and corresponding country | data.frame | 9420 | 2 |
us_states | pubDashboard | List of US states taken from the package 'countrycode' | data.frame | 50 | 3 |
world_capitals | pubDashboard | List of world capitals taken from the package 'maps' | data.frame | 259 | 6 |
charnes1981 | Benchmarking | Data: Charnes et al. (1981): Program follow through | data.frame | 70 | 11 |
milkProd | Benchmarking | Data: Milk producers | data.frame | 108 | 5 |
norWood2004 | Benchmarking | Data: Forestry in Norway | data.frame | 113 | 7 |
pigdata | Benchmarking | Data: Multi-output pig producers | data.frame | 248 | 19 |
projekt | Benchmarking | Data: Milk producers | data.frame | 101 | 14 |
sheepData | FFD | Simulated test data frame of Austrian sheep holdings. | data.frame | 15287 | 3 |
Clust.exampleE | puma | The example data of the mean gene expression levels | matrix | 700 | 20 |
Clust.exampleStd | puma | The example data of the standard deviation for gene expression levels | matrix | 700 | 20 |
Clustii.exampleE | puma | The example data of the mean gene expression levels | data.frame | 600 | 80 |
Clustii.exampleStd | puma | The example data of the standard deviation for gene expression levels | data.frame | 600 | 80 |
eset_mmgmos | puma | An example ExpressionSet created from the Dilution data with mmgmos | exprReslt | | |
exampleE | puma | The example data of the mean gene expression levels | matrix | 200 | 6 |
exampleStd | puma | The example data of the standard deviation for gene expression levels | matrix | 200 | 6 |
hgu95aphis | puma | Estimated parameters of the distribution of phi | numeric | | |
long2 | jmBIG | longitudinal data | tbl_df | 5639 | 13 |
longsurv | jmBIG | longitudinal- survival dataset | tbl_df | 5639 | 13 |
surv2 | jmBIG | survival data | tbl_df | 1000 | 13 |
tkr.dat | resilience | Pre-post stressor response data | data.frame | 900 | 6 |
ExampleDb | scoper | | tbl_df | 2000 | 16 |
Example10x | alakazam | | tbl_df | 194 | 57 |
ExampleDb | alakazam | | tbl_df | 1999 | 19 |
ExampleDbChangeo | alakazam | | tbl_df | 1999 | 15 |
ExampleTrees | alakazam | | list | | |
SingleDb | alakazam | | spec_tbl_df | 1 | 32 |
ahChainFiles | circRNAprofiler | ahChainFile | data.frame | 1113 | 2 |
ahRepeatMasker | circRNAprofiler | ahRepeatMasker | data.frame | 84 | 3 |
attractSpecies | circRNAprofiler | attractSpecies | data.frame | 37 | 1 |
backSplicedJunctions | circRNAprofiler | backSplicedJunctions | data.frame | 63521 | 16 |
gtf | circRNAprofiler | gtf | data.frame | 4401 | 9 |
gwasTraits | circRNAprofiler | gwasTraits | data.frame | 653 | 1 |
iupac | circRNAprofiler | iupac | data.frame | 18 | 4 |
memeDB | circRNAprofiler | memeDB | data.frame | 25 | 2 |
mergedBSJunctions | circRNAprofiler | mergedBSJunctions | data.frame | 41558 | 16 |
miRspeciesCodes | circRNAprofiler | miRspeciesCodes | data.frame | 271 | 2 |
hav_be_1993_1994 | serosv | Hepatitis A serological data from Belgium in 1993 and 1994 (aggregated) | data.frame | 86 | 3 |
hav_be_2002 | serosv | Hepatitis A serological data from Belgium in 2002 (line listing) | data.frame | 2259 | 2 |
hav_bg_1964 | serosv | Hepatitis A serological data from Bulgaria in 1964 (aggregated) | data.frame | 83 | 3 |
hbv_ru_1999 | serosv | Hepatitis B serological data from Russia in 1999 (aggregated) | data.frame | 182 | 4 |
hcv_be_2006 | serosv | Hepatitis C serological data from Belgium in 2006 (line listing) | data.frame | 421 | 2 |
mumps_uk_1986_1987 | serosv | Mumps serological data from the UK in 1986 and 1987 (aggregated) | data.frame | 44 | 3 |
parvob19_be_2001_2003 | serosv | Parvo B19 serological data from Belgium from 2001-2003 (line listing) | data.frame | 3080 | 5 |
parvob19_ew_1996 | serosv | Parvo B19 serological data from England and Wales in 1996 (line listing) | data.frame | 2821 | 5 |
parvob19_fi_1997_1998 | serosv | Parvo B19 serological data from Finland from 1997-1998 (line listing) | data.frame | 1117 | 5 |
parvob19_it_2003_2004 | serosv | Parvo B19 serological data from Italy from 2003-2004 (line listing) | data.frame | 2513 | 5 |
parvob19_pl_1995_2004 | serosv | Parvo B19 serological data from Poland from 1995-2004 (line listing) | data.frame | 2493 | 5 |
rubella_mumps_uk | serosv | Rubella - Mumps data from the UK (aggregated) | data.frame | 44 | 5 |
rubella_uk_1986_1987 | serosv | Rubella serological data from the UK in 1986 and 1987 (aggregated) | data.frame | 44 | 3 |
tb_nl_1966_1973 | serosv | Tuberculosis serological data from the Netherlands 1966-1973 (aggregated) | data.frame | 110 | 5 |
vzv_be_1999_2000 | serosv | VZV serological data from Belgium (Flanders) from 1999-2000 (aggregated) | data.frame | 44 | 3 |
vzv_be_2001_2003 | serosv | VZV serological data from Belgium from 2001-2003 (line listing) | data.frame | 2657 | 4 |
vzv_parvo_be | serosv | VZV and Parvovirus B19 serological data in Belgium (line listing) | data.frame | 3374 | 7 |
Quantiles.TA | rt.test | Quantile values of the robustified statistic, TA. | matrix | 97 | 500 |
Quantiles.TB | rt.test | Quantile values of the robustified statistic, TB. | matrix | 97 | 500 |
ENSO.dat | BINCOR | Equatorial Pacific SST anomalies from El Niño 3 region. | data.frame | 125 | 2 |
ID31.dat | BINCOR | Unevenly-spaced pollen record from the marine sediments core (MD04-2845) collected on the southwestern European margin. | data.frame | 77 | 2 |
ID32.dat | BINCOR | Unevenly-spaced pollen record from the marine sediments core (MD95-2039) collected on the southwestern European margin. | data.frame | 141 | 2 |
NHSST.dat | BINCOR | Northern Hemisphere (NH) sea surface temperature (SST) anomalies. | data.frame | 125 | 2 |
MICS2014Ch | PakPMICS2014Ch | Multiple Indicator Cluster Survey (MICS) 2014 Child Questionnaire Data for Punjab, Pakistan | data.table | 31083 | 270 |
dff4 | tsrsa | Daily Fama French 4 Factor Returns | xts | 24795 | 5 |
dvix | tsrsa | Daily VIX Close | xts | 7809 | 1 |
gw | tsrsa | Goyal Welch Equity Premium Data. | xts | 215 | 23 |
mff4 | tsrsa | Monthly Fama French 4 Factor Returns | xts | 1128 | 5 |
mff6 | tsrsa | Monthly Fama French 6 Factor Returns | xts | 690 | 7 |
mind10 | tsrsa | Monthly Fama French 10 Industry Returns | xts | 1128 | 10 |
mind5 | tsrsa | Monthly Fama French 5 Industry Returns | xts | 1128 | 5 |
CancerMenopause | qeML | Swedish breast cancer. | data.frame | 99 | 2 |
EPIWgProduct | qeML | EPI Growth Data | data.frame | 74 | 10 |
ThyroidDisease | qeML | Thyroid Disease | data.frame | 2751 | 28 |
courseRecords | qeML | Records from several offerings of a certain course. | list | | |
currency | qeML | Pre-Euro Era Currency Fluctuations | data.frame | 762 | 5 |
day | qeML | Bike sharing data. | data.frame | 731 | 16 |
day1 | qeML | Bike sharing data. | data.frame | 731 | 16 |
day2 | qeML | Bike sharing data. | data.frame | 731 | 15 |
empAttrition | qeML | Employee Attrition Data | data.frame | 1470 | 32 |
english | qeML | English vocabulary data | data.frame | 5498 | 13 |
forest500 | qeML | Subset of the Covertype data. | data.frame | 500 | 55 |
iranChurn | qeML | Iranian Customer Churn Data | data.frame | 10000 | 11 |
lsa | qeML | Law School Admissions Data | data.frame | 20800 | 11 |
ltrfreqs | qeML | Letter Frequencies | data.frame | 26 | 2 |
mlb | qeML | Major Leage Baseball player data set. | data.frame | 1015 | 7 |
mlb1 | qeML | Major Leage Baseball player data set. | data.frame | 1015 | 4 |
mlensSideInfo | qeML | MovieLens User Summary Data | data.frame | 943 | 7 |
nyctaxi | qeML | New York City Taxi Data | data.frame | 10000 | 5 |
oliveoils | qeML | Italian olive oils data set. | data.frame | 572 | 10 |
prgeng | qeML | Silicon Valley programmers and engineers data | data.frame | 20090 | 6 |
quizDocs | qeML | Course quiz documents | list | | |
quizzes | qeML | Course quiz documents | data.frame | 143 | 2 |
svcensus | qeML | Silicon Valley programmers and engineers data | data.frame | 20090 | 6 |
weatherTS | qeML | Weather Time Series | data.frame | 4017 | 10 |
goals | GWRM | Goals scored by footballers in the first division of the Spanish league | data.frame | 1224 | 4 |
pop.geno | simer | Raw genotype matrix from outside in simdata | matrix | 100 | |
pop.map | simer | Map file from outside in simdata | data.frame | 10000 | 5 |
dietox | geepack | Growth curves of pigs in a 3x3 factorial experiment | data.frame | 861 | 8 |
koch | geepack | Ordinal Data from Koch | data.frame | 288 | 4 |
muscatine | geepack | Data on Obesity from the Muscatine Coronary Risk Factor Study. | data.frame | 14568 | 7 |
ohio | geepack | Ohio Children Wheeze Status | data.frame | 2148 | 4 |
respdis | geepack | Clustered Ordinal Respiratory Disorder | data.frame | 111 | 5 |
respiratory | geepack | Data from a clinical trial comparing two treatments for a respiratory illness | data.frame | 444 | 8 |
seizure | geepack | Epiliptic Seizures | data.frame | 59 | 7 |
sitka89 | geepack | Growth of Sitka Spruce Trees | data.frame | 632 | 4 |
spruce | geepack | Log-size of 79 Sitka spruce trees | data.frame | 1027 | 6 |
yeast | trigger | A yeast data set for Transcriptional Regulation Inference from Genetics of Gene ExpRession | list | | |
boats | imager | Photograph of sailing boats from Kodak set | cimg | | |
sim | Harshlight | blemish simulations from 100.000 random chips | matrix | 8 | 6 |
sim.int | Harshlight | blemish simulations from 100.000 random chips | matrix | 2 | 12 |
country_continent_mapping | rincewind | Country to continent mapping | spec_tbl_df | 210 | 2 |
population_by_age | rincewind | | tbl_df | 256 | 26 |
BF_sim | SimDesign | Example simulation from Brown and Forsythe (1974) | SimDesign | 28 | 12 |
BF_sim_alternative | SimDesign | (Alternative) Example simulation from Brown and Forsythe (1974) | SimDesign | 16 | 29 |
pems | MetricGraph | Traffic speed data from San Jose, California | list | | |
pems_repl | MetricGraph | Traffic speed data with replicates from San Jose, California | list | | |
starnames | qs | Official list of IAU Star Names | data.frame | 336 | 9 |
dictionary_afltables | fitzRoy | AFL Tables Data Dictionary: AFL Tables Column Formats | data.frame | 81 | 2 |
mapping_afltables | fitzRoy | AFL Tables Mapping: AFL Tables Naming Convention Mapping | character | | |
simData | BayesSurvive | Simulated survival data | list | | |
pearson1000 | ssdtests | Pearson 1000 Data | tbl_df | 1000 | 1 |
mu_est_long | nichetools | A 'data.frame' containing posterior estimates of mu | tbl_df | 8000 | 7 |
niw_fish_post | nichetools | A 'list' of the posterior estimates of mu and Sigma from '{nicheROVER}' | list | | |
over_stat | nichetools | A 'data.frame' containing the estimates of percentage of overlap among groups | array | | |
post_sam_siber | nichetools | A 'list' of the posterior estimates of mu and Sigma from '{SIBER}' | list | | |
sigma_est_wide | nichetools | A 'data.frame' containing posterior estimates of Sigma | tbl_df | 8000 | 6 |
blog.data | packageRank | Blog post data. | list | | |
rstudio.logs | packageRank | Eight RStudio Download Logs to Fix Duplicate Logs Errors in 'cranlogs'. | list | | |
all_neon_tick_data | mvgam | NEON Amblyomma and Ixodes tick abundance survey data | grouped_df | 3505 | 24 |
portal_data | mvgam | Portal Project rodent capture survey data | data.frame | 320 | 5 |
elements | Rpdb | Periodic Table of the Elements | data.frame | 117 | 15 |
universalConstants | Rpdb | Universal Constants | data.frame | 23 | 3 |
NIRmilk | doBy | NIRmilk | data.frame | 17 | 158 |
beets | doBy | beets data | data.frame | 30 | 5 |
breastcancer | doBy | Gene expression signatures for p53 mutation status in 250 breast cancer samples | data.frame | 250 | 1001 |
budworm | doBy | Budworm data | data.frame | 12 | 4 |
cad1 | doBy | Coronary artery disease data | data.frame | 236 | 14 |
cad2 | doBy | Coronary artery disease data | data.frame | 67 | 14 |
carcass | doBy | Lean meat contents of 344 pig carcasses | data.frame | 344 | 7 |
carcassall | doBy | Lean meat contents of 344 pig carcasses | data.frame | 344 | 18 |
codstom | doBy | Diet of Atlantic cod in the Gulf of St. Lawrence (Canada) | data.frame | 10000 | 9 |
crickets | doBy | crickets data | data.frame | 30 | 3 |
crimeRate | doBy | crimeRate | data.frame | 50 | 8 |
crime_rate | doBy | crimeRate | data.frame | 50 | 7 |
cropyield | doBy | Yield from Danish agricultural production of grain and root crop. | data.frame | 97 | 7 |
dietox | doBy | Growth curves of pigs in a 3x3 factorial experiment | data.frame | 861 | 8 |
fatacid | doBy | Fish oil in pig food | data.frame | 26 | 5 |
fev | doBy | Forced expiratory volume in children | data.frame | 654 | 5 |
haldCement | doBy | Heat development in cement under hardening. | data.frame | 13 | 5 |
income | doBy | income data | data.frame | 80 | 3 |
math | doBy | Mathematics marks for students | data.frame | 88 | 5 |
mathmark | doBy | Mathematics marks for students | data.frame | 88 | 5 |
milkman | doBy | Milk yield data for manually milked cows. | data.frame | 161836 | 12 |
milkman_rdm1 | doBy | Milk yield data for manually milked cows. | data.frame | 12026 | 12 |
nir_milk | doBy | nir_milk | list | | |
personality | doBy | Personality traits | data.frame | 240 | 32 |
potatoes | doBy | Weight and size of 20 potatoes | data.frame | 20 | 3 |
prostate | doBy | Prostate Tumor Gene Expression Dataset | list | | |
shoes | doBy | shoes | data.frame | 10 | 4 |
wine | doBy | Chemical composition of wine | data.frame | 178 | 14 |
ex_cc | smcfcs | Simulated case cohort data | data.frame | 1565 | 7 |
ex_coarsening | smcfcs | Simulated example data with a coarsened factor covariate | data.frame | 100 | 4 |
ex_compet | smcfcs | Simulated example data with competing risks outcome and partially observed covariates | data.frame | 1000 | 4 |
ex_coxquad | smcfcs | Simulated example data with time to event outcome and quadratic covariate effects | data.frame | 1000 | 5 |
ex_dtsam | smcfcs | Simulated discrete time survival data set | data.frame | 1000 | 4 |
ex_finegray | smcfcs | Simulated example data with competing risks outcome and partially observed covariates | data.frame | 1000 | 4 |
ex_flexsurv | smcfcs | Simulated example data with time-to-event Weibull outcome and two covariates | data.frame | 1000 | 4 |
ex_lininter | smcfcs | Simulated example data with continuous outcome and interaction between two partially observed covariates | data.frame | 1000 | 3 |
ex_linquad | smcfcs | Simulated example data with continuous outcome and quadratic covariate effects | data.frame | 1000 | 4 |
ex_logisticquad | smcfcs | Simulated example data with binary outcome and quadratic covariate effects | data.frame | 1000 | 4 |
ex_ncc | smcfcs | Simulated nested case-control data | data.frame | 700 | 8 |
ex_poisson | smcfcs | Simulated example data with count outcome, modelled using Poisson regression | data.frame | 1000 | 3 |
WHONET | AMR | Data Set with 500 Isolates - WHONET Example | tbl_df | 500 | 53 |
antimicrobials | AMR | Data Sets with 616 Antimicrobial Drugs | tbl_df | 496 | 14 |
antivirals | AMR | Data Sets with 616 Antimicrobial Drugs | tbl_df | 120 | 11 |
clinical_breakpoints | AMR | Data Set with Clinical Breakpoints for SIR Interpretation | tbl_df | 34376 | 14 |
dosage | AMR | Data Set with Treatment Dosages as Defined by EUCAST | tbl_df | 503 | 9 |
example_isolates | AMR | Data Set with 2 000 Example Isolates | tbl_df | 2000 | 46 |
example_isolates_unclean | AMR | Data Set with Unclean Data | tbl_df | 3000 | 8 |
intrinsic_resistant | AMR | Data Set with Bacterial Intrinsic Resistance | tbl_df | 301583 | 2 |
microorganisms | AMR | Data Set with 78 678 Taxonomic Records of Microorganisms | tbl_df | 78678 | 26 |
microorganisms.codes | AMR | Data Set with 4 971 Common Microorganism Codes | tbl_df | 4971 | 2 |
microorganisms.groups | AMR | Data Set with 521 Microorganisms In Species Groups | tbl_df | 521 | 4 |
bandwidth.chrisproba | PEcAnRTM | | data.frame | 62 | 4 |
dataSpec_prospectd | PEcAnRTM | | matrix | 2101 | 8 |
fwhm.aviris.classic | PEcAnRTM | | data.frame | 224 | 2 |
fwhm.aviris.ng | PEcAnRTM | | data.frame | 480 | 5 |
fwhm.hyperion | PEcAnRTM | | list | | |
model.list | PEcAnRTM | | data.frame | 6 | 5 |
rsr.avhrr | PEcAnRTM | | matrix | 503 | 4 |
rsr.landsat5 | PEcAnRTM | | matrix | 914 | 7 |
rsr.landsat7 | PEcAnRTM | | matrix | 914 | 7 |
rsr.landsat8 | PEcAnRTM | | matrix | 825 | 8 |
rsr.modis | PEcAnRTM | | matrix | 1820 | 8 |
rsr.viirs | PEcAnRTM | | matrix | 2500 | 11 |
sensor.rsr | PEcAnRTM | | list | | |
testspec_ACRU | PEcAnRTM | | matrix | 2101 | 78 |
iris_dataset | dataset | Edgar Anderson's Iris Data | dataset_df | 150 | 6 |
orange_df | dataset | Growth of Orange Trees | dataset_df | 35 | 4 |
epilepsy | brms | Epileptic seizure counts | data.frame | 236 | 9 |
inhaler | brms | Clarity of inhaler instructions | data.frame | 572 | 5 |
kidney | brms | Infections in kidney patients | data.frame | 76 | 7 |
loss | brms | Cumulative Insurance Loss Payments | data.frame | 55 | 4 |
dogfish | sdmTMB | Example fish survey data | tbl_df | 1458 | 9 |
hbll_s_grid | sdmTMB | Example fish survey data | data.frame | 2802 | 3 |
pcod | sdmTMB | Example fish survey data | tbl_df | 2143 | 12 |
pcod_2011 | sdmTMB | Example fish survey data | tbl_df | 969 | 12 |
pcod_mesh_2011 | sdmTMB | Example fish survey data | sdmTMBmesh | | |
qcs_grid | sdmTMB | Example fish survey data | data.frame | 7314 | 5 |
wcvi_grid | sdmTMB | Example fish survey data | data.frame | 2689 | 3 |
yelloweye | sdmTMB | Example fish survey data | data.frame | 1559 | 9 |
pecan_releases | PEcAn.all | Dates, tags, and versions of all PEcAn releases | data.frame | 33 | 3 |
pecan_version_history | PEcAn.all | Versions of all PEcAn packages in each release of PEcAn | data.frame | 53 | 34 |
nessi | LaMa | Loch Ness Monster Acceleration Data | data.frame | 5000 | 3 |
trex | LaMa | T-Rex Movement Data | data.frame | 10000 | 4 |
df_estimated_velocities_gmwmx | gmwmx2 | Estimated northward and eastward velocity and their standard deviation using the GMWMX estimator | data.frame | 1202 | 12 |
ensemble.output | PEcAn.uncertainty | | list | | |
ensemble.samples | PEcAn.uncertainty | | list | | |
sa.samples | PEcAn.uncertainty | | list | | |
sensitivity.output | PEcAn.uncertainty | | list | | |
settings | PEcAn.uncertainty | | list | | |
trait.samples | PEcAn.uncertainty | | list | | |
standard_vars | PEcAn.utils | Standardized variable names and units for PEcAn | data.frame | 117 | 12 |
trait.dictionary | PEcAn.utils | | data.frame | 101 | 4 |
BLOSUM | DECIPHER | BLOSUM Amino Acid Substitution Matrices | array | | |
HEC_MI1 | DECIPHER | Mutual Information for Protein Secondary Structure Prediction | array | | |
HEC_MI2 | DECIPHER | Mutual Information for Protein Secondary Structure Prediction | array | | |
MIQS | DECIPHER | MIQS Amino Acid Substitution Matrix | matrix | 25 | 25 |
MMLSUM | DECIPHER | MMLSUM Amino Acid Substitution Matrices | array | | |
NonCodingRNA_Archaea | DECIPHER | NonCoding Models for Common Non-Coding RNA Families | list | | |
NonCodingRNA_Bacteria | DECIPHER | NonCoding Models for Common Non-Coding RNA Families | list | | |
NonCodingRNA_Eukarya | DECIPHER | NonCoding Models for Common Non-Coding RNA Families | list | | |
PAM | DECIPHER | PAM Amino Acid Substitution Matrices | array | | |
PFASUM | DECIPHER | PFASUM Amino Acid Substitution Matrices | array | | |
RESTRICTION_ENZYMES | DECIPHER | Common Restriction Enzyme's Cut Sites | character | | |
TrainingSet_16S | DECIPHER | Training Set for Classification of 16S rRNA Gene Sequences | Taxa | | |
deltaGrules | DECIPHER | Free Energy of Hybridization of Probe/Target Quadruplets on Microarrays | array | | |
deltaGrulesRNA | DECIPHER | Pseudoenergy Parameters for RNA Quadruplets | array | | |
deltaHrules | DECIPHER | Change in Enthalpy of Hybridization of DNA/DNA Quadruplets in Solution | array | | |
deltaHrulesRNA | DECIPHER | Change in Enthalpy of Hybridization of RNA/RNA Quadruplets in Solution | array | | |
deltaSrules | DECIPHER | Change in Entropy of Hybridization of DNA/DNA Quadruplets in Solution | array | | |
deltaSrulesRNA | DECIPHER | Change in Entropy of Hybridization of RNA/RNA Quadruplets in Solution | array | | |
PROSYM | ontoProc | PROSYM: HGNC symbol synonyms for PR (protein ontology) entries identified in Cell Ontology | data.frame | 209947 | 2 |
allGOterms | ontoProc | allGOterms: data.frame with ids and terms | data.frame | 44541 | 2 |
humrna | ontoProc | humrna: a data.frame of SRA metadata related to RNA-seq in humans | data.frame | 513 | 12 |
minicorpus | ontoProc | minicorpus: a vector of annotation strings found in 'study title' of SRA metadata. | character | | |
packDesc2019 | ontoProc | packDesc2019: overview of ontoProc resources | data.frame | 13 | 8 |
packDesc2021 | ontoProc | packDesc2021: overview of ontoProc resources | data.frame | 12 | 6 |
packDesc2022 | ontoProc | packDesc2022: overview of ontoProc resources | data.frame | 14 | 7 |
packDesc2023 | ontoProc | packDesc2023: overview of ontoProc resources | data.frame | 15 | 7 |
stopWords | ontoProc | stopWords: vector of stop words from xpo6.com | character | | |
BADM | PEcAn.data.land | Biomass and soil data from FluxNet sites | data.frame | 12300 | 13 |
iscn_soc | PEcAn.data.land | Soil organic carbon (SOC) density based on eco-region level 2 code from the ISCN database. | matrix | 200 | 43 |
soil_class | PEcAn.data.land | Default parameters for calculating soil properties from sand & clay content | list | | |
FLUXNET.sitemap | PEcAn.data.atmosphere | | data.frame | 698 | 2 |
Lat | PEcAn.data.atmosphere | | array | | |
Lat | PEcAn.data.atmosphere | | array | | |
Lon | PEcAn.data.atmosphere | | array | | |
Lon | PEcAn.data.atmosphere | | array | | |
cruncep | PEcAn.data.atmosphere | | data.table | 8736 | 10 |
cruncep_landmask | PEcAn.data.atmosphere | | data.table | 259200 | 3 |
ebifarm | PEcAn.data.atmosphere | | data.table | 8390 | 10 |
landmask | PEcAn.data.atmosphere | | data.table | 18048 | 3 |
narr | PEcAn.data.atmosphere | | data.table | 8760 | 10 |
narr3h | PEcAn.data.atmosphere | | data.table | 8736 | 10 |
ads | dst | The Captain's Problem. 'ads': Relation between variables Arrival (A), Departure delay (D) and Sailing delay (S) | matrix | 18 | 17 |
captain_result | dst | The Captain's Problem. 'swr': Result of the evaluation of the Hypergraph at node Arrival (A) | list | | |
dlfm | dst | The Captain's Problem. 'dlfm': Relation between variables Departure delay (D), Loading delay (L), Forecast of the weather (F), Maintenance delay (M) | data.frame | 10 | 12 |
fw | dst | The Captain's Problem. 'fw': Relation between variables Forecast of the weather (F) and Weather at sea (W) | matrix | 4 | 6 |
mrf | dst | The Captain's Problem. 'mrf': Relation between variables No Maintenance (M = false) and Repairs at sea (R) | matrix | 8 | 6 |
mrt | dst | The Captain's Problem. 'mrt': Relation between variables Maintenance done (M = true) and Repairs at sea (R) | matrix | 8 | 6 |
swr | dst | The Captain's Problem. 'swr': Relation between variables Sailing delay (S), Weather at sea (W), and Repairs at sea (R) | matrix | 6 | 10 |
TMT10 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT10ETD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT10HCD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT11 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT11HCD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT16 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT16HCD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT6 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT6b | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT7 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT7b | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
iTRAQ4 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
iTRAQ5 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
iTRAQ8 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
iTRAQ9 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
itraqdata | MSnbase | Example 'MSnExp' and 'MSnSet' data sets | MSnExp | | |
msnset | MSnbase | Example 'MSnExp' and 'MSnSet' data sets | MSnSet | | |
msnset2 | MSnbase | Example 'MSnExp' and 'MSnSet' data sets | MSnSet | | |
naset | MSnbase | Quantitative proteomics data imputation | MSnSet | | |
Jenkins2004_Table9 | PEcAn.allometry | | data.frame | 174 | 1 |
Table3_GTR-NE-319.v2 | PEcAn.allometry | | data.frame | 2642 | 1 |
allom.components | PEcAn.allometry | | data.frame | 41 | 3 |
. | onion | Class "dot" | dot | | |
bunny | onion | The Stanford Bunny | matrix | 35947 | 3 |
co2.1850.2020 | PEcAn.LPJGUESS | | data.frame | 171 | 2 |
pftmapping | PEcAn.ED2 | Mapping of PEcAn PFT names to ED2 PFT numbers | data.frame | 73 | 2 |
sibcasa_output_vars | PEcAn.SIBCASA | Output variables for SIBCASA | data.frame | 131 | 6 |
friend_data | networksem | Friendship network data | list | | |
GMAT | difNLR | Dichotomous dataset based on GMAT with the same total score distribution for groups. | data.frame | 2000 | 22 |
GMAT2 | difNLR | Dichotomous dataset based on GMAT. | data.frame | 1000 | 21 |
GMAT2key | difNLR | Key of correct answers for 'GMAT2test' dataset. | factor | | |
GMAT2test | difNLR | Dataset based on GMAT. | data.frame | 1000 | 21 |
GMATkey | difNLR | Key of correct answers for 'GMATtest' dataset. | character | | |
GMATtest | difNLR | Dataset based on GMAT with the same total score distribution for groups. | data.frame | 2000 | 22 |
MSATB | difNLR | Dichotomous dataset of Medical School Admission Test in Biology. | data.frame | 1407 | 21 |
MSATBkey | difNLR | Key of correct answers for 'MSATBtest' dataset. | character | | |
MSATBtest | difNLR | Dataset of School Admission Test in Biology. | data.frame | 1407 | 21 |
bonapersona | pema | Data from 'The behavioral phenotype of early life adversity' | escalc | 734 | 65 |
curry | pema | Data from 'Happy to Help?' | data.frame | 56 | 18 |
GRset | epimutacions | GRset | GenomicRatioSet | | |
res.epi.manova | epimutacions | res.epi.manova | data.frame | 6 | 16 |
Human_HK_genes | CAESAR.Suite | Human housekeeping genes database | data.frame | 2833 | 4 |
Mouse_HK_genes | CAESAR.Suite | Mouse housekeeping genes database | data.frame | 3984 | 5 |
toydata | CAESAR.Suite | A toy dataset to run examples | list | | |
bem_dfmdata | bvartools | FRED-QD data | mts | 225 | 196 |
e1 | bvartools | West German economic time series data | mts | 92 | 3 |
e6 | bvartools | German interest and inflation rate data | mts | 107 | 2 |
us_macrodata | bvartools | US macroeconomic data | mts | 195 | 3 |
DE_PCBC_stemSig | TCGAbiolinks | A numeric vector with SC-derived definitive endoderm (DE) signature trained on PCBC's dataset | numeric | | |
EB_PCBC_stemSig | TCGAbiolinks | A numeric vector with stem cell (SC)-derived embryoid bodies (EB) signature trained on PCBC's dataset | numeric | | |
ECTO_PCBC_stemSig | TCGAbiolinks | A numeric vector with SC-derived ectoderm (ECTO) signature trained on PCBC's dataset | numeric | | |
MESO_PCBC_stemSig | TCGAbiolinks | A numeric vector with SC-derived mesoderm (MESO) signature trained on PCBC's dataset | numeric | | |
SC_PCBC_stemSig | TCGAbiolinks | A numeric vector with stem cell-like signature trained on PCBC's dataset | numeric | | |
TabSubtypesCol_merged | TCGAbiolinks | TCGA samples with their Pam50 subtypes | data.frame | 4768 | 3 |
Tumor.purity | TCGAbiolinks | TCGA samples with their Tumor Purity measures | data.frame | 9364 | 7 |
bcgsc.ca_CHOL.IlluminaHiSeq_DNASeq.1.somatic.maf | TCGAbiolinks | TCGA CHOL MAF | tbl_df | 3555 | 34 |
chol_maf | TCGAbiolinks | TCGA CHOL MAF transformed to maftools object | MAF | | |
clinical.biotab | TCGAbiolinks | A list of data frames with clinical data parsed from XML (code in vignettes) | list | | |
dataBRCA | TCGAbiolinks | TCGA data matrix BRCA | data.frame | 20530 | 10 |
dataDEGsFiltLevel | TCGAbiolinks | TCGA data matrix BRCA DEGs | data.frame | 3649 | 6 |
dataREAD | TCGAbiolinks | TCGA data SummarizedExperiment READ | RangedSummarizedExperiment | | |
dataREAD_df | TCGAbiolinks | TCGA data matrix READ | data.frame | 20531 | 2 |
gbm.exp.harmonized | TCGAbiolinks | A RangedSummarizedExperiment two samples with gene expression data from vignette aligned against hg38 | RangedSummarizedExperiment | | |
gbm.exp.legacy | TCGAbiolinks | A RangedSummarizedExperiment two samples with gene expression data from vignette aligned against hg19 | RangedSummarizedExperiment | | |
geneInfo | TCGAbiolinks | geneInfo for normalization of RNAseq data | matrix | 20531 | 3 |
geneInfoHT | TCGAbiolinks | geneInfoHT for normalization of HTseq data | data.frame | 68016 | 2 |
met.gbm.27k | TCGAbiolinks | A DNA methylation RangedSummarizedExperiment for 8 samples (only first 20 probes) aligned against hg19 | RangedSummarizedExperiment | | |
msi_results | TCGAbiolinks | MSI data for two samples | data.frame | 2 | 4 |
tabSurvKMcompleteDEGs | TCGAbiolinks | tabSurvKMcompleteDEGs | data.frame | 200 | 7 |
finalData | eventPred | | tbl_df | 300 | 9 |
interimData1 | eventPred | | tbl_df | 224 | 9 |
interimData2 | eventPred | | tbl_df | 300 | 9 |
exampleExpressionMatrix | fgsea | Example of expression values obtained for GSE14308. | matrix | 10000 | 12 |
examplePathways | fgsea | Example list of mouse Reactome pathways. | list | | |
exampleRanks | fgsea | Example vector of gene-level statistics obtained for Th1 polarization. | numeric | | |
LIBD_subset | DESpace | Subset from the human DLPFC 10x Genomics Visium dataset of the 'spatialLIBD' package | SpatialExperiment | | |
results_individual_svg | DESpace | Results from 'individual_svg' function | list | | |
results_svg_test | DESpace | Results from 'svg_test' function | list | | |
maits | MAST | MAITs data set, RNASeq | list | | |
predicted_sig | MAST | Predicted signatures | data.table | 100 | 8 |
vbeta | MAST | Vbeta Data Set | data.frame | 34200 | 11 |
vbetaFA | MAST | Vbeta Data Set, FluidigmAssay | FluidigmAssay | | |
df_pathway_statistics | dce | Biological pathway information. | data.frame | 100909 | 5 |
clothianidin | melt | Clothianidin concentration in maize plants | data.frame | 102 | 3 |
thiamethoxam | melt | Thiamethoxam applications in squash crops | data.frame | 165 | 11 |
gsAnnotation_df | sSNAPPY | gsAnnotation_df: Categorization of KEGG pathways used for community annotation | tbl_df | 549 | 2 |
logCPM_example | sSNAPPY | logCPM_example: Normalised logCPM of patient-derived explant models obtained from 5 ER-positive primamry breast cancer tumours (GSE80098) | matrix | 7672 | 15 |
metadata_example | sSNAPPY | metadata_example: Sample metadata for malignant breast cancer tumours PDE from 5 ER+ breast cancer tumour (GSE80098) | tbl_df | 15 | 4 |
synapterTiny | synapter | Loads a small test data for the 'synapter' package | Synapter | | |
B.dk | Epi | Births in Denmark by year and month of birth and sex | data.frame | 1416 | 4 |
BrCa | Epi | Clinical status, relapse, metastasis and death in 2982 women with breast cancer. | data.frame | 2982 | 17 |
DMconv | Epi | Conversion to diabetes | data.frame | 1519 | 6 |
DMepi | Epi | Epidemiological rates for diabetes in Denmark 1996-2015 | data.frame | 4200 | 8 |
DMlate | Epi | The Danish National Diabetes Register. | data.frame | 10000 | 7 |
DMrand | Epi | The Danish National Diabetes Register. | data.frame | 10000 | 7 |
M.dk | Epi | Mortality in Denmark 1974 ff. | data.frame | 7800 | 6 |
N.dk | Epi | Population size in Denmark | data.frame | 8600 | 4 |
S.typh | Epi | Salmonella Typhimurium outbreak 1996 in Denmark. | data.frame | 136 | 15 |
Y.dk | Epi | Population risk time in Denmark | data.frame | 16800 | 6 |
bdendo | Epi | A case-control study of endometrial cancer | data.frame | 315 | 13 |
bdendo11 | Epi | A case-control study of endometrial cancer | data.frame | 126 | 13 |
births | Epi | Births in a London Hospital | data.frame | 500 | 8 |
blcaIT | Epi | Bladder cancer mortality in Italian males | data.frame | 55 | 4 |
brv | Epi | Bereavement in an elderly cohort | data.frame | 399 | 11 |
diet | Epi | Diet and heart data | data.frame | 337 | 15 |
ewrates | Epi | Rates of lung and nasal cancer mortality, and total mortality. | data.frame | 150 | 5 |
gmortDK | Epi | Population mortality rates for Denmark in 5-years age groups. | data.frame | 418 | 21 |
hivDK | Epi | hivDK: seroconversion in a cohort of Danish men | data.frame | 297 | 7 |
lep | Epi | An unmatched case-control study of leprosy incidence | data.frame | 1370 | 7 |
lungDK | Epi | Male lung cancer incidence in Denmark | data.frame | 220 | 9 |
mortDK | Epi | Population mortality rates for Denmark in 1-year age-classes. | data.frame | 1820 | 21 |
nickel | Epi | A Cohort of Nickel Smelters in South Wales | data.frame | 679 | 7 |
occup | Epi | A small occupational cohort | data.frame | 13 | 4 |
pr | Epi | Diabetes prevance as of 2010-01-01 in Denmark | data.frame | 200 | 4 |
st2alb | Epi | Clinical trial: Steno2 baseline and follow-up. | data.frame | 563 | 3 |
st2clin | Epi | Clinical trial: Steno2 baseline and follow-up. | data.frame | 750 | 5 |
steno2 | Epi | Clinical trial: Steno2 baseline and follow-up. | data.frame | 160 | 14 |
testisDK | Epi | Testis cancer incidence in Denmark, 1943-1996 | data.frame | 4860 | 4 |
thoro | Epi | Thorotrast Study | data.frame | 2470 | 14 |
FCV | abn | Dataset related to Feline calicivirus infection among cats in Switzerland. | data.frame | 300 | 15 |
adg | abn | Dataset related to average daily growth performance and abattoir findings in pigs commercial production. | data.frame | 341 | 9 |
ex0.dag.data | abn | Synthetic validation data set for use with abn library examples | data.frame | 300 | 30 |
ex1.dag.data | abn | Synthetic validation data set for use with abn library examples | data.frame | 10000 | 10 |
ex2.dag.data | abn | Synthetic validation data set for use with abn library examples | data.frame | 10000 | 18 |
ex3.dag.data | abn | Validation data set for use with abn library examples | data.frame | 1000 | 14 |
ex4.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 2000 | 11 |
ex5.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 434 | 19 |
ex6.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 800 | 8 |
ex7.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 10648 | 3 |
g2b2c_data | abn | Toy Data Set for Examples in README | data.frame | 1000 | 5 |
g2pbcgrp | abn | Toy Data Set for Examples in README | data.frame | 10000 | 6 |
pigs.vienna | abn | Dataset related to diseases present in 'finishing pigs', animals about to enter the human food chain at an abattoir. | data.frame | 25000 | 11 |
var33 | abn | simulated dataset from a DAG comprising of 33 variables | data.frame | 250 | 33 |
SierraLeone2014 | fitode | Data from 2014 Sierra Leone Ebola epidemic | data.frame | 67 | 2 |
blowfly | fitode | Nicholson's blowfly data | data.frame | 361 | 5 |
bombay | fitode | Data from 1905 plague outbreak in Mumbai (formerly called Bombay) | data.frame | 30 | 2 |
phila1918 | fitode | 1918 Philadelphia flu data set | data.frame | 122 | 2 |
tumorgrowth | fitode | Tumor growth data | data.frame | 14 | 2 |
MMRcoverageDE | surveillance | MMR coverage levels in the 16 states of Germany | data.frame | 19 | 5 |
abattoir | surveillance | Abattoir Data | sts | | |
campyDE | surveillance | Campylobacteriosis and Absolute Humidity in Germany 2002-2011 | data.frame | 522 | 12 |
deleval | surveillance | Surgical Failures Data | sts | | |
fluBYBW | surveillance | Influenza in Southern Germany | sts | | |
fooepidata | surveillance | Toy Data for 'twinSIR' | epidata | 17800 | 13 |
h1_nrwrp | surveillance | RKI SurvStat Data | disProg | | |
ha | surveillance | Hepatitis A in Berlin | disProg | | |
ha.sts | surveillance | Hepatitis A in Berlin | sts | | |
hagelloch | surveillance | 1861 Measles Epidemic in the City of Hagelloch, Germany | epidata | 70500 | 16 |
hagelloch.df | surveillance | 1861 Measles Epidemic in the City of Hagelloch, Germany | data.frame | 188 | 26 |
hepatitisA | surveillance | Hepatitis A in Germany | disProg | | |
husO104Hosp | surveillance | Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011 | data.frame | 630 | 2 |
imdepi | surveillance | Occurrence of Invasive Meningococcal Disease in Germany | epidataCS | | |
imdepifit | surveillance | Example 'twinstim' Fit for the 'imdepi' Data | twinstim | | |
influMen | surveillance | Influenza and meningococcal infections in Germany, 2001-2006 | disProg | | |
k1 | surveillance | RKI SurvStat Data | disProg | | |
m1 | surveillance | RKI SurvStat Data | disProg | | |
m2 | surveillance | RKI SurvStat Data | disProg | | |
m3 | surveillance | RKI SurvStat Data | disProg | | |
m4 | surveillance | RKI SurvStat Data | disProg | | |
m5 | surveillance | RKI SurvStat Data | disProg | | |
measles.weser | surveillance | Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002 | disProg | | |
measlesDE | surveillance | Measles in the 16 states of Germany | sts | | |
measlesWeserEms | surveillance | Measles in the Weser-Ems region of Lower Saxony, Germany, 2001-2002 | sts | | |
meningo.age | surveillance | Meningococcal infections in France 1985-1997 | disProg | | |
momo | surveillance | Danish 1994-2008 all-cause mortality data for eight age groups | sts | | |
n1 | surveillance | RKI SurvStat Data | disProg | | |
n2 | surveillance | RKI SurvStat Data | disProg | | |
q1_nrwh | surveillance | RKI SurvStat Data | disProg | | |
q2 | surveillance | RKI SurvStat Data | disProg | | |
rotaBB | surveillance | Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories | sts | | |
s1 | surveillance | RKI SurvStat Data | disProg | | |
s2 | surveillance | RKI SurvStat Data | disProg | | |
s3 | surveillance | RKI SurvStat Data | disProg | | |
salmAllOnset | surveillance | Salmonella cases in Germany 2001-2014 by data of symptoms onset | sts | | |
salmHospitalized | surveillance | Hospitalized Salmonella cases in Germany 2004-2014 | sts | | |
salmNewport | surveillance | Salmonella Newport cases in Germany 2004-2013 | sts | | |
salmonella.agona | surveillance | Salmonella Agona cases in the UK 1990-1995 | disProg | | |
shadar | surveillance | Salmonella Hadar cases in Germany 2001-2006 | disProg | | |
stsNewport | surveillance | Salmonella Newport cases in Germany 2001-2015 | sts | | |
telework_data | OPSR | Telework Data | data.frame | 1584 | 35 |
timeuse_data | OPSR | TimeUse+ Data | data.frame | 824 | 40 |
example_maze | samc | | matrix | 20 | |
example_split_corridor | samc | | list | | |
example_toy_res | samc | | matrix | 10 | |
edgelist | netgsa | A data frame of edges, each row corresponding to one edge | data.frame | 2959 | 4 |
group | netgsa | The vector of class indicators | numeric | | |
nonedgelist | netgsa | A data frame of nonedges, each row corresponding to one negative edge | data.frame | 20 | 4 |
pathways | netgsa | A list of KEGG pathways | list | | |
pathways_mat | netgsa | Matrix with pathway indicators | matrix | 100 | 2598 |
x | netgsa | Data matrix p by n | matrix | 2598 | 520 |
mapmaker_example_f2 | onemap | Simulated data from a F2 population | onemap | | |
onemap_example_bc | onemap | Simulated data from a backcross population | onemap | | |
onemap_example_f2 | onemap | Simulated data from a F2 population | onemap | | |
onemap_example_out | onemap | Data from a full-sib family derived from two outbred parents | onemap | | |
onemap_example_riself | onemap | Simulated data from a RIL population produced by selfing. | onemap | | |
simu_example_bc | onemap | Simulated data from a backcross population | onemap | | |
simu_example_f2 | onemap | Simulated data from a f2 intercross population | onemap | | |
simu_example_out | onemap | Simulated data from a outcrossing population | onemap | | |
vcf_example_bc | onemap | Data generated from VCF file with biallelic markers from a f2 backcross population | onemap | | |
vcf_example_f2 | onemap | Data generated from VCF file with biallelic markers from a f2 intercross population | onemap | | |
vcf_example_out | onemap | Data generated from VCF file with biallelic markers from a full-sib family derived from two outbred parents | onemap | | |
vcf_example_riself | onemap | Data generated from VCF file with biallelic markers from a RIL population produced by selfing | onemap | | |
MRC.1000 | stepR | Values of the MRC statistic for 1,000 observations (all intervals) | numeric | | |
MRC.asymptotic | stepR | "Asymptotic" values of the MRC statistic (all intervals) | numeric | | |
MRC.asymptotic.dyadic | stepR | "Asymptotic" values of the MRC statistic (dyadic intervals) | numeric | | |
filtered.regulon6.1 | qpgraph | Preprocessed microarray oxygen deprivation data and filtered RegulonDB data | data.frame | 3283 | 5 |
gds680.eset | qpgraph | Preprocessed microarray oxygen deprivation data and filtered RegulonDB data | ExpressionSet | | |
subset.filtered.regulon6.1 | qpgraph | Preprocessed microarray oxygen deprivation data and filtered RegulonDB data | data.frame | 681 | 5 |
subset.gds680.eset | qpgraph | Preprocessed microarray oxygen deprivation data and filtered RegulonDB data | ExpressionSet | | |
spike_in_de_res | PRONE | Example data.table of DE results of a spike-in proteomics data set | data.table | 7500 | 10 |
spike_in_se | PRONE | Example SummarizedExperiment of a spike-in proteomics data set | SummarizedExperiment | | |
tuberculosis_TMT_de_res | PRONE | Example data.table of DE results of a real-world proteomics data set | data.table | 9030 | 9 |
tuberculosis_TMT_se | PRONE | Example SummarizedExperiment of a real-world proteomics data set | SummarizedExperiment | | |
APA | Rankcluster | Rank data: APA | list | | |
big4 | Rankcluster | Rank data: big4 | list | | |
eurovision | Rankcluster | Multidimensional partial rank data: eurovision | list | | |
quiz | Rankcluster | Multidimensional rank data: quiz | list | | |
sports | Rankcluster | Rank data: sports | list | | |
words | Rankcluster | Rank data: words | list | | |
acceptor.m | SPLINTER | acceptor.m | matrix | 4 | 15 |
compatible_cds | SPLINTER | compatible_cds | list | | |
compatible_tx | SPLINTER | compatible_tx | list | | |
donor.m | SPLINTER | donor.m | matrix | 4 | 9 |
pcr_result1 | SPLINTER | pcr_result1 | data.frame | 1 | 2 |
primers | SPLINTER | primers | data.frame | 5 | 28 |
region_minus_exon | SPLINTER | region_minus_exon | CompressedGRangesList | | |
roi | SPLINTER | roi | list | | |
splice_data | SPLINTER | splice_data | list | | |
splice_fasta | SPLINTER | splice_fasta | data.frame | 5 | 2 |
thecds | SPLINTER | thecds | CompressedGRangesList | | |
theexons | SPLINTER | theexons | CompressedGRangesList | | |
valid_cds | SPLINTER | valid_cds | CompressedGRangesList | | |
valid_tx | SPLINTER | valid_tx | CompressedGRangesList | | |
biogrid_hs | rTRM | Network dataset of class 'igraph' | igraph | | |
biogrid_mm | rTRM | Network dataset of class 'igraph' | igraph | | |
DAdata | hipathia | Wilcoxon and limma comparison object for nodes, pathways and functional annotations | list | | |
brca | hipathia | BRCA gene expression dataset as SummarizedExperiment | SummarizedExperiment | | |
brca_data | hipathia | BRCA gene expression dataset | matrix | 3187 | 40 |
brca_design | hipathia | BRCA experimental design | data.frame | 40 | 1 |
comp | hipathia | Wilcoxon comparison of pathways object | data.frame | 60 | 5 |
exp_data | hipathia | Normalized BRCA gene expression dataset | SummarizedExperiment | | |
go_vals | hipathia | Gene Ontology matrix of the BRCA gene expression dataset | SummarizedExperiment | | |
hidata | hipathia | Results object | MultiAssayExperiment | | |
path_vals | hipathia | Pathways matrix of the BRCA gene expression dataset | SummarizedExperiment | | |
pathways | hipathia | Pathways object including pathways has03320 and hsa04012. | list | | |
results | hipathia | Results object | MultiAssayExperiment | | |
aml | trtswitch | | data.frame | 23 | 3 |
heart | trtswitch | | data.frame | 172 | 8 |
immdef | trtswitch | | data.frame | 1000 | 9 |
ingots | trtswitch | | tbl_df | 25 | 4 |
rawdata | trtswitch | | data.frame | 4910 | 7 |
sexagg | trtswitch | | data.frame | 36 | 9 |
shilong | trtswitch | | data.frame | 602 | 19 |
six | trtswitch | | tbl_df | 64 | 6 |
tobin | trtswitch | | data.frame | 20 | 3 |
dendata_epichannel | boldenr | Dengue cases by states for epidemiological channel. | data.frame | 1545 | 5 |
ACTG175 | BART | AIDS Clinical Trials Group Study 175 | data.frame | 2139 | 27 |
alligator | BART | American alligator Food Choice | data.frame | 80 | 5 |
arq | BART | NHANES 2009-2010 Arthritis Questionnaire | data.frame | 4747 | 10 |
bladder | BART | Bladder Cancer Recurrences | data.frame | 340 | 7 |
bladder1 | BART | Bladder Cancer Recurrences | data.frame | 294 | 11 |
bladder2 | BART | Bladder Cancer Recurrences | data.frame | 178 | 8 |
leukemia | BART | Bone marrow transplantation for leukemia and multi-state models | data.frame | 137 | 22 |
lung | BART | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
transplant | BART | Liver transplant waiting list | data.frame | 815 | 6 |
xdm20.test | BART | A data set used in example of 'recur.bart'. | matrix | 79800 | 84 |
xdm20.train | BART | A real data example for 'recur.bart'. | matrix | 39450 | 84 |
ydm20.test | BART | A data set used in example of 'recur.bart'. | integer | | |
ydm20.train | BART | A data set used in example of 'recur.bart'. | integer | | |
Example_TCGA_LGG_FPKM_data | CPSM | Example TCGA LGG FPKM data | SummarizedExperiment | | |
Key_Clin_feature_list | CPSM | Key Clin feature list | data.frame | 8 | 1 |
Key_Clin_features_with_PI_list | CPSM | Key Clin features with PI list | data.frame | 9 | 1 |
Key_PI_list | CPSM | Key PI list | data.frame | 1 | 1 |
Key_univariate_features_list | CPSM | Key univariate features list | data.frame | 2391 | 1 |
Key_univariate_features_with_Clin_list | CPSM | Key univariate features with Clin list | data.frame | 2399 | 1 |
New_data | CPSM | New data | data.frame | 176 | 2025 |
Test_Clin | CPSM | Test Clin | data.frame | 18 | 20 |
Test_Norm_data | CPSM | Test Norm data | data.frame | 18 | 2025 |
Test_PI_data | CPSM | Test PI data | data.frame | 18 | 57 |
Test_Uni_sig_data | CPSM | Test Uni sig data | data.frame | 18 | 209 |
Train_Clin | CPSM | Train Clin | data.frame | 158 | 20 |
Train_Data_Nomogram_input | CPSM | Train Data Nomogram input | data.frame | 158 | 34 |
Train_Norm_data | CPSM | Train Norm data | data.frame | 158 | 2025 |
Train_PI_data | CPSM | Train PI data | data.frame | 158 | 57 |
Train_Uni_sig_data | CPSM | Train Uni sig data | data.frame | 158 | 209 |
feature_list_for_Nomogram | CPSM | feature list for Nomogram | data.frame | 6 | 1 |
mean_median_survival_time_data | CPSM | mean median survival time data | data.frame | 18 | 3 |
survCurves_data | CPSM | survCurves data | data.frame | 15 | 19 |
test_FPKM | CPSM | test FPKM | data.frame | 18 | 2025 |
train_FPKM | CPSM | train FPKM | data.frame | 158 | 2025 |
knownSignatures | SigCheck | Previously identified gene signatures for use in 'sigCheckKnown' | list | | |
nkiResults | SigCheck | Precomputed list of results for a call to 'sigCheckAll' using the 'breastCancerNKI' dataset. | list | | |
houses | markets | | data.frame | 144 | 7 |
example_diff_result | pairedGSEA | Output of running paired_diff on example_se. | DFrame | | |
example_gene_sets | pairedGSEA | MSigDB gene sets from humans, category C5 with ensemble gene IDs | list | | |
example_ora_results | pairedGSEA | Output of running paired_ora on example_diff_result and gene sets extracted from MSigDB | DFrame | | |
example_se | pairedGSEA | A small subset of the GEO:GSE61220 data set. | SummarizedExperiment | | |
aus_fertility | vital | Australian fertility data | vital | 3010 | 5 |
aus_mortality | vital | Australian mortality data | vital | 300879 | 8 |
norway_births | vital | Norwegian mortality and births data | vital | 369 | 3 |
norway_fertility | vital | Norwegian mortality and births data | tbl_ts | 2464 | 4 |
norway_mortality | vital | Norwegian mortality and births data | vital | 40959 | 6 |
fibonacci_table | cubs | lebedev_table | integer | | |
gl_table | cubs | lebedev_table | integer | | |
grid_table | cubs | lebedev_table | integer | | |
is_valid_GL | cubs | is_valid_GL | function | | |
is_valid_fibonacci | cubs | is_valid_GL | function | | |
is_valid_grid | cubs | is_valid_GL | function | | |
lebedev | cubs | lebedev | list | | |
lebedev_table | cubs | lebedev_table | data.frame | 32 | 2 |
qmc_table | cubs | lebedev_table | integer | | |
random_table | cubs | lebedev_table | integer | | |
sphericaldesigns | cubs | spherical designs | list | | |
sphericaldesigns_table | cubs | lebedev_table | data.frame | 100 | 2 |
Stamp | mixR | 1872 Hidalgo Stamp Data | numeric | | |
Stamp2 | mixR | 1872 Hidalgo Stamp Data (Binned) | matrix | 62 | 3 |
nlsw88 | Counterfactual | NLSW, 1988 extract | data.frame | 2246 | 17 |
isohedron | mapmisc | Country boundaries | matrix | 6762 | 2 |
meuse | mapmisc | Data from the Netherlands | PackedSpatVector | | |
nldCities | mapmisc | Data from the Netherlands | PackedSpatVector | | |
nldElev | mapmisc | Data from the Netherlands | PackedSpatRaster | | |
nldTiles | mapmisc | Data from the Netherlands | PackedSpatRaster | | |
worldMap | mapmisc | Country boundaries | PackedSpatVector | | |
namesDE | mapping | Statistical Unit Names | list | | |
namesEU | mapping | Statistical Unit Names | list | | |
namesFR | mapping | Statistical Unit Names | list | | |
namesIT | mapping | Statistical Unit Names | list | | |
namesUK | mapping | Statistical Unit Names | list | | |
namesUS | mapping | Statistical Unit Names | list | | |
namesWR | mapping | Statistical Unit Names | data.frame | 251 | 19 |
popDE | mapping | German Population | tbl_df | 16 | 4 |
popEU | mapping | European population | data.frame | 2252 | 5 |
popEUnuts2 | mapping | European population | data.frame | 327 | 5 |
popFR | mapping | French Population | tbl_df | 13 | 2 |
popIT | mapping | Italian Population | data.frame | 107 | 4 |
popUK | mapping | United Kingdom Population | tbl_df | 420 | 3 |
popUS | mapping | USA population | data.frame | 52 | 2 |
popWR | mapping | World population | data.frame | 269 | 5 |
tax_wedge_ocde | mapping | OCDE tax wedge | data.frame | 74 | 7 |
usa_election | mapping | Usa Election | data.frame | 51 | 19 |
bmd | lava | Longitudinal Bone Mineral Density Data (Wide format) | data.frame | 112 | 7 |
bmidata | lava | Data | data.frame | 552 | 10 |
brisa | lava | Simulated data | data.frame | 500 | 21 |
calcium | lava | Longitudinal Bone Mineral Density Data | data.frame | 501 | 6 |
hubble | lava | Hubble data | data.frame | 36 | 3 |
hubble2 | lava | Hubble data | data.frame | 24 | 3 |
indoorenv | lava | Data | data.frame | 200 | 13 |
missingdata | lava | Missing data example | list | | |
nldata | lava | Example data (nonlinear model) | data.frame | 50 | 3 |
nsem | lava | Example SEM data (nonlinear) | data.frame | 500 | 7 |
semdata | lava | Example SEM data | data.frame | 500 | 21 |
serotonin | lava | Serotonin data | data.frame | 250 | 20 |
serotonin2 | lava | Data | data.frame | 250 | 19 |
twindata | lava | Twin menarche data | data.frame | 4000 | 7 |
drc_error_1 | dr4pl | Single High Outlier | data.frame | 162 | 2 |
drc_error_2 | dr4pl | Multiple High Outliers at Different measurements | data.frame | 10 | 2 |
drc_error_3 | dr4pl | Support Problem and Outliers at a Single Dose Level | data.frame | 132 | 2 |
drc_error_4 | dr4pl | Support Problem | data.frame | 132 | 2 |
sample_data_1 | dr4pl | sample_data_1 | data.frame | 40 | 2 |
sample_data_10 | dr4pl | sample_data_10 | data.frame | 12 | 2 |
sample_data_11 | dr4pl | sample_data_11 | data.frame | 12 | 2 |
sample_data_12 | dr4pl | sample_data_12 | data.frame | 12 | 2 |
sample_data_13 | dr4pl | sample_data_13 | data.frame | 12 | 2 |
sample_data_2 | dr4pl | sample_data_2 | data.frame | 40 | 2 |
sample_data_3 | dr4pl | sample_data_3 | data.frame | 40 | 2 |
sample_data_4 | dr4pl | sample_data_4 | data.frame | 40 | 2 |
sample_data_5 | dr4pl | sample_data_5 | data.frame | 40 | 2 |
sample_data_6 | dr4pl | sample_data_6 | data.frame | 40 | 2 |
sample_data_7 | dr4pl | sample_data_7 | data.frame | 40 | 2 |
sample_data_8 | dr4pl | sample_data_8 | data.frame | 40 | 2 |
sample_data_9 | dr4pl | sample_data_9 | data.frame | 12 | 2 |
SBS2000 | validate | | data.frame | 60 | 11 |
nace_rev2 | validate | | data.frame | 996 | 10 |
retailers | validate | | data.frame | 60 | 10 |
samplonomy | validate | | data.frame | 1199 | 5 |
apfp | ChemmineR | Frequent Atom Pairs | data.frame | 4096 | 2 |
apset | ChemmineR | Atom pairs stored in 'APset' object | APset | | |
atomprop | ChemmineR | Standard atomic weights | data.frame | 118 | 6 |
pubchemFPencoding | ChemmineR | Enncoding of PubChem Fingerprints | data.frame | 881 | 2 |
sdfsample | ChemmineR | SD file in 'SDFset' object | SDFset | | |
smisample | ChemmineR | SMILES file in 'SMIset' object | SMIset | | |
coffee | ssc | Time series data set | data.frame | 56 | 287 |
wine | ssc | Wine recognition data | data.frame | 178 | 14 |
mainz7g | survcomp | Subset of MAINZ dataset containing gene expression, annotations and clinical data. | ExpressionSet | | |
nki7g | survcomp | Subset of NKI dataset containing gene expression, annotations and clinical data. | ExpressionSet | | |
transbig7g | survcomp | Subset of the TRANSBIG dataset containing gene expression, annotations and clinical data. | ExpressionSet | | |
unt7g | survcomp | Subset of UNT dataset containing gene expression, annotations and clinical data. | ExpressionSet | | |
upp7g | survcomp | Subset of UPP dataset containing gene expression, annotations and clinical data. | ExpressionSet | | |
vdx7g | survcomp | Subset of VDX dataset containing gene expression, annotations and clinical data. | ExpressionSet | | |
gmB | pcalg | Graphical Model 5-Dim Binary Example Data | list | | |
gmD | pcalg | Graphical Model Discrete 5-Dim Example Data | list | | |
gmG | pcalg | Graphical Model 8-Dimensional Gaussian Example Data | list | | |
gmG8 | pcalg | Graphical Model 8-Dimensional Gaussian Example Data | list | | |
gmI | pcalg | Graphical Model 7-dim IDA Data Examples | list | | |
gmI7 | pcalg | Graphical Model 7-dim IDA Data Examples | list | | |
gmInt | pcalg | Graphical Model 8-Dimensional Interventional Gaussian Example Data | list | | |
gmL | pcalg | Latent Variable 4-Dim Graphical Model Data Example | list | | |
ETn_example | IWTomics | ETn Recombination hotspots data | IWTomicsData | | |
features_example | IWTomics | Example of features | list | | |
regionsFeatures_center | IWTomics | Example of '"IWTomicsData"' object with center alignment | IWTomicsData | | |
regionsFeatures_scale | IWTomics | Example of '"IWTomicsData"' object with scale alignment | IWTomicsData | | |
regions_example | IWTomics | Example of regions | CompressedGRangesList | | |
dutch | longevity | Dutch survival data | tbl_df | 305143 | 11 |
ewsim | longevity | England and Wales simulated supercentenarian data | data.frame | 179 | 3 |
idlmetadata | longevity | IDL metadata | tbl_df | 21 | 4 |
japanese | longevity | Japanese survival data | tbl_df | 1038 | 4 |
japanese2 | longevity | Japanese survival data (2) | tbl_df | 216 | 4 |
ArringtonEtAl2002 | ANOPA | Arrington et al. (2002) dataset | data.frame | 21 | 5 |
ArticleExample1 | ANOPA | ArticleExample1 | data.frame | 4 | 3 |
ArticleExample2 | ANOPA | ArticleExample2 | data.frame | 6 | 4 |
ArticleExample3 | ANOPA | ArticleExample3 | data.frame | 30 | 4 |
minimalBSExample | ANOPA | A collection of minimal Examples from various designs with one or two factors. | data.frame | 3 | 3 |
minimalMxExample | ANOPA | A collection of minimal Examples from various designs with one or two factors. | data.frame | 27 | 5 |
minimalMxExampleCompiled | ANOPA | A collection of minimal Examples from various designs with one or two factors. | data.frame | 4 | 5 |
minimalWSExample | ANOPA | A collection of minimal Examples from various designs with one or two factors. | data.frame | 19 | 3 |
twoWayExample | ANOPA | A collection of minimal Examples from various designs with one or two factors. | data.frame | 6 | 4 |
twoWayWithinExample | ANOPA | A collection of minimal Examples from various designs with one or two factors. | data.frame | 30 | 6 |
income | ordinal | Income distribution (percentages) in the Northeast US | data.frame | 14 | 3 |
soup | ordinal | Discrimination study of packet soup | data.frame | 1847 | 12 |
wine | ordinal | Bitterness of wine | data.frame | 72 | 6 |
A.1nm | colorSpec | Standard Illuminants A, B, and C (1931) | colorSpec | | |
ACES.RGB | colorSpec | Theoretical RGB Cameras - BT.709.RGB, Adobe.RGB, and ACES.RGB | colorSpec | 471 | 3 |
Adobe.RGB | colorSpec | Theoretical RGB Cameras - BT.709.RGB, Adobe.RGB, and ACES.RGB | colorSpec | 471 | 3 |
B.5nm | colorSpec | Standard Illuminants A, B, and C (1931) | colorSpec | | |
BT.709.RGB | colorSpec | Theoretical RGB Cameras - BT.709.RGB, Adobe.RGB, and ACES.RGB | colorSpec | 471 | 3 |
C.5nm | colorSpec | Standard Illuminants A, B, and C (1931) | colorSpec | | |
D50.5nm | colorSpec | Standard Illuminant D50 (1964) | colorSpec | | |
D65.1nm | colorSpec | Standard Illuminant D65 (1964) | colorSpec | | |
D65.5nm | colorSpec | Standard Illuminant D65 (1964) | colorSpec | | |
F96T12 | colorSpec | Photon Irradiance of F96T12 Fluorescent Bulb | colorSpec | | |
Flea2.RGB | colorSpec | Flea2 Camera FL2-14S3C from Point Grey | colorSpec | 45 | 3 |
Fs.5nm | colorSpec | Standard series F Illuminants F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11, and F12 | colorSpec | 81 | 12 |
HigherPasserines | colorSpec | Cone Fundamentals for the Higher Passerines | colorSpec | 401 | 4 |
Hoya | colorSpec | standard Hoya filters | colorSpec | 4 | 4 |
atmosphere2003 | colorSpec | Standard Solar Irradiance - Extraterrestrial and Terrestrial | colorSpec | | |
daylight1964 | colorSpec | Standard Daylight Components | colorSpec | 107 | 3 |
daylight2013 | colorSpec | Standard Daylight Components | colorSpec | 531 | 3 |
lms1971.5nm | colorSpec | Cone Fundamentals - 2-degree (1971) | colorSpec | 81 | 3 |
lms2000.1nm | colorSpec | Cone Fundamentals - 2-degree (2000) | colorSpec | 441 | 3 |
luminsivity.1nm | colorSpec | Luminous Efficiency Functions (photopic and scotopic) | colorSpec | 4 | 2 |
scanner.ACES | colorSpec | standard RGB scanners | colorSpec | 181 | 3 |
solar.irradiance | colorSpec | Standard Solar Irradiance - Extraterrestrial and Terrestrial | colorSpec | 721 | 3 |
xyz1931.1nm | colorSpec | CIE Color Matching Functions - 2-degree (1931) | colorSpec | 471 | 3 |
xyz1931.5nm | colorSpec | CIE Color Matching Functions - 2-degree (1931) | colorSpec | 81 | 3 |
xyz1964.1nm | colorSpec | CIE Color Matching Functions - 10-degree (1964) | colorSpec | 471 | 3 |
xyz1964.5nm | colorSpec | CIE Color Matching Functions - 10-degree (1964) | colorSpec | 81 | 3 |
ColonFinal | EndoMineR | Fake Lower GI Endoscopy Set | data.frame | 2000 | 1 |
Myendo | EndoMineR | Fake Endoscopies | data.frame | 2000 | 13 |
Mypath | EndoMineR | Fake Pathology report | data.frame | 2000 | 10 |
PathDataFrameFinal | EndoMineR | Fake Upper GI Pathology Set | data.frame | 2000 | 1 |
PathDataFrameFinalColon | EndoMineR | Fake Lower GI Pathology Set | data.frame | 2000 | 1 |
TheOGDReportFinal | EndoMineR | Fake Upper GI Endoscopy Set | data.frame | 2000 | 1 |
vColon | EndoMineR | Fake Lower GI Endoscopy Set including Pathology | data.frame | 2105 | 26 |
dataXY | PPtreeregViz | Simulated data | data.frame | 100 | 5 |
insurance | PPtreeregViz | Insurance Data | data.frame | 1338 | 7 |
Athenstaedt | psychTools | Gender Role Self Concept data from Athenstaedt (2003) | data.frame | 576 | 117 |
Athenstaedt.dictionary | psychTools | Gender Role Self Concept data from Athenstaedt (2003) | data.frame | 80 | 2 |
Athenstaedt.keys | psychTools | Gender Role Self Concept data from Athenstaedt (2003) | list | | |
Damian | psychTools | Project Talent data set from Marion Spengler and Rodica Damian | matrix | 19 | 19 |
GERAS.dictionary | psychTools | Data from Gruber et al, 2020, Study 2: Gender Related Attributes Survey | data.frame | 107 | 4 |
GERAS.items | psychTools | Data from Gruber et al, 2020, Study 2: Gender Related Attributes Survey | data.frame | 471 | 51 |
GERAS.keys | psychTools | Data from Gruber et al, 2020, Study 2: Gender Related Attributes Survey | list | | |
GERAS.scales | psychTools | Data from Gruber et al, 2020, Study 2: Gender Related Attributes Survey | matrix | 471 | 13 |
Pollack | psychTools | Pollack et al (2012) correlation matrix for mediation example | matrix | 9 | 9 |
Schutz | psychTools | The Schutz correlation matrix example from Shapiro and ten Berge | matrix | 9 | 9 |
Spengler | psychTools | Project Talent data set from Marion Spengler and Rodica Damian | matrix | 25 | 25 |
Spengler.stat | psychTools | Project Talent data set from Marion Spengler and Rodica Damian | data.frame | 25 | 3 |
USAF | psychTools | 17 anthropometric measures from the USAF showing a general factor | matrix | 19 | 19 |
ability | psychTools | 16 ability items scored as correct or incorrect. | matrix | 1525 | 16 |
ability.keys | psychTools | 16 ability items scored as correct or incorrect. | list | | |
affect | psychTools | Two data sets of affect and arousal scores as a function of personality and movie conditions | data.frame | 330 | 20 |
all.income | psychTools | US family income from US census 2008 | data.frame | 100 | 4 |
bfi | psychTools | 25 Personality items representing 5 factors | data.frame | 2800 | 28 |
bfi.adjectives.dictionary | psychTools | Dictionary for the 100 Big Five Adjectives | data.frame | 100 | 2 |
bfi.adjectives.keys | psychTools | 100 adjectives describing the "big 5" for 502 subjects | list | | |
bfi.dictionary | psychTools | 25 Personality items representing 5 factors | data.frame | 28 | 7 |
bfi.keys | psychTools | 25 Personality items representing 5 factors | list | | |
big5.100.adjectives | psychTools | 100 adjectives describing the "big 5" for 502 subjects | data.frame | 554 | 102 |
big5.adjectives.keys | psychTools | 100 adjectives describing the "big 5" for 502 subjects | list | | |
blant | psychTools | A 29 x 29 matrix that produces weird factor analytic results | data.frame | 29 | 29 |
blot | psychTools | Bond's Logical Operations Test - BLOT | data.frame | 150 | 35 |
burt | psychTools | 11 emotional variables from Burt (1915) | matrix | 11 | 11 |
cities | psychTools | Distances between 11 US cities | data.frame | 11 | 11 |
city.location | psychTools | Distances between 11 US cities | data.frame | 11 | 2 |
colom | psychTools | Correlations of 14 ability tests from the Spanish version of the WAIS (taken from Colom et al. 2002.) | matrix | 14 | 14 |
colom.ed0 | psychTools | Correlations of 14 ability tests from the Spanish version of the WAIS (taken from Colom et al. 2002.) | data.frame | 14 | 14 |
colom.ed1 | psychTools | Correlations of 14 ability tests from the Spanish version of the WAIS (taken from Colom et al. 2002.) | matrix | 14 | 14 |
colom.ed2 | psychTools | Correlations of 14 ability tests from the Spanish version of the WAIS (taken from Colom et al. 2002.) | data.frame | 14 | 14 |
colom.ed3 | psychTools | Correlations of 14 ability tests from the Spanish version of the WAIS (taken from Colom et al. 2002.) | matrix | 14 | 14 |
cubits | psychTools | Galton's example of the relationship between height and 'cubit' or forearm length | data.frame | 9 | 8 |
cushny | psychTools | A data set from Cushny and Peebles (1905) on the effect of three drugs on hours of sleep, used by Student (1908) | data.frame | 10 | 7 |
eminence | psychTools | Eminence of 69 American Psychologists | data.frame | 69 | 9 |
epi | psychTools | Eysenck Personality Inventory (EPI) data for 3570 participants | data.frame | 3570 | 57 |
epi.bfi | psychTools | 13 personality scales from the Eysenck Personality Inventory and Big 5 inventory | data.frame | 231 | 13 |
epi.dictionary | psychTools | Eysenck Personality Inventory (EPI) data for 3570 participants | data.frame | 57 | 1 |
epi.keys | psychTools | Eysenck Personality Inventory (EPI) data for 3570 participants | list | | |
epiR | psychTools | Eysenck Personality Inventory (EPI) data for 3570 participants | data.frame | 948 | 60 |
galton | psychTools | Galton's Mid parent child height data | data.frame | 928 | 2 |
globalWarm | psychTools | 7 attitude items about Global Warming policy from Erik Nisbet | data.frame | 815 | 7 |
heights | psychTools | A data.frame of the Galton (1888) height and cubit data set. | data.frame | 348 | 2 |
holzinger.dictionary | psychTools | The raw and transformed data from Holzinger and Swineford, 1939 | data.frame | 33 | 2 |
holzinger.raw | psychTools | The raw and transformed data from Holzinger and Swineford, 1939 | data.frame | 301 | 33 |
holzinger.swineford | psychTools | The raw and transformed data from Holzinger and Swineford, 1939 | data.frame | 301 | 33 |
income | psychTools | US family income from US census 2008 | data.frame | 44 | 4 |
iqitems | psychTools | 16 multiple choice IQ items | data.frame | 1525 | 16 |
msq | psychTools | 75 mood items from the Motivational State Questionnaire for 3896 participants | data.frame | 3896 | 92 |
msq.keys | psychTools | 75 mood items from the Motivational State Questionnaire for 3032 unique participants | list | | |
msqR | psychTools | 75 mood items from the Motivational State Questionnaire for 3032 unique participants | data.frame | 6411 | 88 |
neo | psychTools | NEO correlation matrix from the NEO_PI_R manual | matrix | 30 | 30 |
peas | psychTools | Galton's Peas | data.frame | 700 | 2 |
sai | psychTools | State Anxiety data from the PMC lab over multiple occasions. | data.frame | 5378 | 23 |
sai.dictionary | psychTools | State Anxiety data from the PMC lab over multiple occasions. | data.frame | 26 | 2 |
salary | psychTools | Salary example from Cohen, Cohen, Aiken and West (2003) | data.frame | 62 | 5 |
sat.act | psychTools | 3 Measures of ability: SATV, SATQ, ACT | data.frame | 700 | 6 |
spi | psychTools | A sample from the SAPA Personality Inventory including an item dictionary and scoring keys. | data.frame | 4000 | 145 |
spi.dictionary | psychTools | A sample from the SAPA Personality Inventory including an item dictionary and scoring keys. | data.frame | 145 | 6 |
spi.keys | psychTools | A sample from the SAPA Personality Inventory including an item dictionary and scoring keys. | list | | |
tai | psychTools | State Anxiety data from the PMC lab over multiple occasions. | data.frame | 3032 | 23 |
veg | psychTools | Paired comparison of preferences for 9 vegetables | data.frame | 9 | 9 |
zola | psychTools | Correlation matrix of 135 self report and 30 peer report personality items | matrix | 165 | 165 |
zola.dictionary | psychTools | Correlation matrix of 135 self report and 30 peer report personality items | data.frame | 175 | 6 |
zola.keys | psychTools | Correlation matrix of 135 self report and 30 peer report personality items | list | | |
geneLists | RankAggreg | Ordered Gene Lists from 5 microarray studies | matrix | 5 | 25 |
df1 | sure | Simulated quadratic data | data.frame | 2000 | 2 |
df2 | sure | Simulated heteroscedastic data | data.frame | 2000 | 2 |
df3 | sure | Simulated Gumbel data | data.frame | 2000 | 2 |
df4 | sure | Simulated proportionality data | data.frame | 4000 | 2 |
df5 | sure | Simulated interaction data | data.frame | 2000 | 3 |
NYCdata | gasper | NYC Taxi Network Dataset | list | | |
SuiteSparseData | gasper | Matrix Data from SuiteSparse Matrix Collection | data.frame | 2893 | 8 |
grid1 | gasper | Grid1 Graph from AG-Monien Graph Collection | list | | |
minnesota | gasper | Minnesota Road Network | list | | |
pittsburgh | gasper | Pittsburgh Census Tracts Network. | list | | |
rlogo | gasper | R logo graph. | list | | |
pisa.psa.cols | PSAboot | Character vector representing the list of covariates used for estimating propensity scores. | character | | |
pisalux | PSAboot | Programme of International Student Assessment (PISA) results from the Luxembourg in 2009. | data.frame | 4622 | 65 |
pisausa | PSAboot | Programme of International Student Assessment (PISA) results from the United States in 2009. | data.frame | 5233 | 65 |
cmap | RGSEA | Data from Connectivity map build 01 | matrix | 22268 | 6 |
e1 | RGSEA | Data from GDS4102 | matrix | 54675 | 2 |
e2 | RGSEA | Data from GDS4100 | matrix | 54675 | 4 |
CKME | clue | Cassini Data Partitions Obtained by K-Means | cl_partition_ensemble | | |
Cassini | clue | Cassini Data | mlbench.cassini | | |
GVME | clue | Gordon-Vichi Macroeconomic Partition Ensemble Data | cl_partition_ensemble | | |
GVME_Consensus | clue | Gordon-Vichi Macroeconomic Consensus Partition Data | cl_partition_ensemble | | |
Kinship82 | clue | Rosenberg-Kim Kinship Terms Partition Data | cl_partition_ensemble | | |
Kinship82_Consensus | clue | Gordon-Vichi Kinship82 Consensus Partition Data | cl_partition_ensemble | | |
Phonemes | clue | Miller-Nicely Consonant Phoneme Confusion Data | matrix | 16 | 16 |
evapo_p | flextreat.hydrus1d | DWD: Potential Evaporation, Daily | data.frame | 2436 | 10 |
irrigation | flextreat.hydrus1d | Irrigation: Monthly | tbl_df | 84 | 8 |
materials | flextreat.hydrus1d | Materials | tbl_df | 12 | 7 |
precipitation_daily | flextreat.hydrus1d | Precipitation: Daily | tbl_df | 9566 | 2 |
precipitation_hourly | flextreat.hydrus1d | Precipitation: Hourly | data.frame | 229149 | 2 |
greek_stop_words | rperseus | A dictionary of Greek stop words | tbl_df | 223 | 1 |
perseus_catalog | rperseus | Metadata for texts available via the Perseus Digital Library. | tbl_df | 2291 | 5 |
archery1 | qcr | Target archery dataset in the ranking round (used as Phase I) | array | | |
circuit | qcr | Circuit boards data | data.frame | 46 | 4 |
counters | qcr | The performance of the counters data | data.frame | 180 | 3 |
dowel1 | qcr | Dowel pin dataset | data.frame | 40 | 2 |
employment | qcr | Level of employment data | data.frame | 288 | 3 |
orangejuice | qcr | Orange juice data | data.frame | 54 | 4 |
oxidation | qcr | Oxidation Onset Temperature | data.frame | 250 | 2 |
pcmanufact | qcr | Personal computer manufacturer data | data.frame | 20 | 3 |
pistonrings | qcr | Piston rings data | data.frame | 200 | 3 |
plates | qcr | Vickers hardness data | data.frame | 250 | 2 |
presion | qcr | Level of pressure data | data.frame | 180 | 3 |
arthritis | multgee | Rheumatoid Arthritis Clinical Trial | data.frame | 906 | 7 |
housing | multgee | Homeless Data | data.frame | 1448 | 4 |
e3mg | multivator | Output from computer model e3mg | matrix | 843 | 7 |
e3mg_LoF | multivator | Output from computer model e3mg | list | | |
eigenmaps | multivator | Dataset due to McNeall | matrix | 2048 | |
landmask | multivator | Dataset due to McNeall | matrix | 64 | |
mcneall_pc | multivator | Dataset due to McNeall | matrix | 92 | 20 |
mcneall_temps | multivator | Dataset due to McNeall | matrix | 2048 | |
mean_temp | multivator | Dataset due to McNeall | numeric | | |
opt_mcneall | multivator | Dataset due to McNeall | mhp | | |
toy_LoF | multivator | Toy datasets | list | | |
toy_beta | multivator | Toy datasets | numeric | | |
toy_d | multivator | Toy datasets | numeric | | |
toy_d2 | multivator | Toy datasets | numeric | | |
toy_expt | multivator | Toy datasets | experiment | | |
toy_mhp | multivator | Toy datasets | mhp | | |
toy_mm | multivator | Toy datasets | mdm | | |
toy_mm2 | multivator | Toy datasets | mdm | | |
toy_mm3 | multivator | Toy datasets | mdm | | |
toy_mm4 | multivator | Toy datasets | mdm | | |
toy_point | multivator | Toy datasets | mdm | | |
.Random.seed | FLBEIA | | integer | | |
SRs | FLBEIA | | list | | |
advice | FLBEIA | | list | | |
advice.ctrl.fixed | FLBEIA | | list | | |
advice.ctrl.ices | FLBEIA | | list | | |
advice.ctrl.ices_lo | FLBEIA | | list | | |
advice.ctrl.msmsy_lo | FLBEIA | | list | | |
advice.default | FLBEIA | | list | | |
advice.msmsy | FLBEIA | | list | | |
advice.steps | FLBEIA | | list | | |
assess.ctrl | FLBEIA | | list | | |
biols | FLBEIA | | FLBiols | | |
biols.ctrl | FLBEIA | | list | | |
catch | FLBEIA | FLBEIA datasets | data.frame | 40 | 8 |
covars | FLBEIA | | list | | |
covars.ctrl | FLBEIA | | list | | |
evhoe | FLBEIA | FLBEIA datasets | data.frame | 18 | 4 |
fleets | FLBEIA | | list | | |
fleets.ctrl.mpro | FLBEIA | | list | | |
fleets.ctrl.mpro_lo | FLBEIA | | list | | |
fleets.ctrl.trad | FLBEIA | | list | | |
fleets.ctrl.trad_lo | FLBEIA | | list | | |
main.ctrl | FLBEIA | | list | | |
multiAdv | FLBEIA | FLBEIA datasets | list | | |
multiAdvC | FLBEIA | FLBEIA datasets | list | | |
multiAssC | FLBEIA | FLBEIA datasets | list | | |
multiBD | FLBEIA | FLBEIA datasets | list | | |
multiBio | FLBEIA | FLBEIA datasets | FLBiols | | |
multiBioC | FLBEIA | FLBEIA datasets | list | | |
multiCv | FLBEIA | FLBEIA datasets | list | | |
multiCvC | FLBEIA | FLBEIA datasets | list | | |
multiFl | FLBEIA | FLBEIA datasets | FLFleetsExt | | |
multiFlC | FLBEIA | FLBEIA datasets | list | | |
multiMainC | FLBEIA | FLBEIA datasets | list | | |
multiObsC | FLBEIA | FLBEIA datasets | list | | |
multiRes | FLBEIA | FLBEIA datasets | list | | |
multiSR | FLBEIA | FLBEIA datasets | list | | |
multistk | FLBEIA | | FLStock | | |
obs.ctrl | FLBEIA | | list | | |
oneAdv | FLBEIA | FLBEIA datasets | list | | |
oneAdvC | FLBEIA | FLBEIA datasets | list | | |
oneAssC | FLBEIA | FLBEIA datasets | list | | |
oneBio | FLBEIA | FLBEIA datasets | FLBiols | | |
oneBioC | FLBEIA | FLBEIA datasets | list | | |
oneCv | FLBEIA | FLBEIA datasets | list | | |
oneCvC | FLBEIA | FLBEIA datasets | list | | |
oneFl | FLBEIA | FLBEIA datasets | FLFleetsExt | | |
oneFlC | FLBEIA | FLBEIA datasets | list | | |
oneIndAge | FLBEIA | FLBEIA datasets | list | | |
oneIndBio | FLBEIA | FLBEIA datasets | list | | |
oneItAdv | FLBEIA | FLBEIA datasets | list | | |
oneItAdvC | FLBEIA | FLBEIA datasets | list | | |
oneItAssC | FLBEIA | FLBEIA datasets | list | | |
oneItBio | FLBEIA | FLBEIA datasets | FLBiols | | |
oneItBioC | FLBEIA | FLBEIA datasets | list | | |
oneItCv | FLBEIA | FLBEIA datasets | list | | |
oneItCvC | FLBEIA | FLBEIA datasets | list | | |
oneItFl | FLBEIA | FLBEIA datasets | FLFleetsExt | | |
oneItFlC | FLBEIA | FLBEIA datasets | list | | |
oneItIndAge | FLBEIA | FLBEIA datasets | list | | |
oneItIndBio | FLBEIA | FLBEIA datasets | list | | |
oneItMainC | FLBEIA | FLBEIA datasets | list | | |
oneItObsC | FLBEIA | FLBEIA datasets | list | | |
oneItObsCIndAge | FLBEIA | FLBEIA datasets | list | | |
oneItObsCIndBio | FLBEIA | FLBEIA datasets | list | | |
oneItRes | FLBEIA | FLBEIA datasets | list | | |
oneItSR | FLBEIA | FLBEIA datasets | list | | |
oneMainC | FLBEIA | FLBEIA datasets | list | | |
oneObsC | FLBEIA | FLBEIA datasets | list | | |
oneObsCIndAge | FLBEIA | FLBEIA datasets | list | | |
oneObsCIndBio | FLBEIA | FLBEIA datasets | list | | |
oneRes | FLBEIA | FLBEIA datasets | list | | |
oneSR | FLBEIA | FLBEIA datasets | list | | |
expressionSignal | MBCB | MBCB - Bayesian Background Correction for Illumina Beadarray | data.frame | 1033 | 4 |
negativeControl | MBCB | MBCB - Bayesian Background Correction for Illumina Beadarray | data.frame | 1655 | 4 |
companies_r3k16 | qmj | A list of all companies in the Russell 3000 Index | data.frame | 3004 | 2 |
financials_r3k16 | qmj | Financial statements of all companies in the Russell 3000 index for the past four years | data.frame | 11488 | 23 |
prices_r3k16 | qmj | A dataframe of price returns and closing prices for companies in the Russell 3000 Index | tbl_df | 757605 | 4 |
quality_r3k16 | qmj | A dataframe of quality scores for companies listed in the Russell 3000 | data.frame | 2662 | 7 |
iso639 | glosario | List of languages and their iso639-1 and iso639-2 codes | tbl_df | 507 | 7 |
dataDART | DART | Example data for DART package | list | | |
Intervals | tracktables | Example genomic intervals | GRanges | | |
barbiturates | hyperSpec | | list | | |
flu | hyperSpec | | hyperSpec | | |
laser | hyperSpec | | hyperSpec | | |
palette_colorblind | hyperSpec | | character | | |
paracetamol | hyperSpec | | hyperSpec | | |
MathAnxiety | likert | Pre-summarized results from an administration of the Math Anxiety Scale Survey. | data.frame | 14 | 6 |
MathAnxietyGender | likert | Pre-summarized results from an administration of the Math Anxiety Scale Survey grouped by gender. | data.frame | 28 | 7 |
gap | likert | Fictitious dataset with importance and satisfaction results across five different offices. | data.frame | 68 | 11 |
mass | likert | Results from an administration of the Math Anxiety Scale Survey. | data.frame | 20 | 15 |
pisaitems | likert | Programme of International Student Assessment | data.frame | 66690 | 81 |
sasr | likert | Results from the Survey of Academic Self-Regulation (SASR). | data.frame | 860 | 63 |
kinomics_exmaple | kinograte | an example of Kinome data | rowwise_df | 234 | 2 |
ppi_network_example | kinograte | an example of ppi network | spec_tbl_df | 176205 | 3 |
proteomics_exmaple | kinograte | an example of proteomics data | tbl_df | 141 | 2 |
rnaseq_example | kinograte | an example of RNA differential gene expression data | spec_tbl_df | 3207 | 3 |
mat | lpcover | | matrix | 100 | 207 |
amyloid | survivalVignettes | Survival with amyloidosis | data.frame | 1005 | 9 |
amyloidmodel | survivalVignettes | Survival with amyloidosis | data.frame | 837 | 4 |
contingencytable.csv | revengc | Contingency table example | data.frame | 11 | 12 |
univariatetable.csv | revengc | Univariate frequency table example | data.frame | 5 | 2 |
SpikeIn | affy | SpikeIn Experiment Data: ProbeSet Example | ProbeSet | | |
cdfenv.example | affy | Example cdfenv | environment | | |
mapCdfName | affy | Clean Affymetrix's CDF name | data.frame | 3 | 3 |
opcodes | c64asm | Reference list of opcode information | tbl_df | 217 | 15 |
got | upsetjs | | data.frame | 21 | 6 |
KRSA_Mapping_PTK_PamChip_86402_v1 | KRSA | KRSA kinase-substrate mapping file for PamChip 86402 PTK (v1 mapping) | spec_tbl_df | 193 | 2 |
KRSA_Mapping_STK_PamChip_87102_v1 | KRSA | KRSA kinase-substrate mapping file for PamChip 87102 STK (v1 mapping) | spec_tbl_df | 141 | 2 |
KRSA_coverage_PTK_PamChip_86402_v1 | KRSA | KRSA kinase coverage file for PamChip 86402 PTK (v1 mapping) | data.frame | 1278 | 2 |
KRSA_coverage_STK_PamChip_87102_v1 | KRSA | KRSA kinase coverage file for PamChip 87102 STK (v1 mapping) | data.frame | 2423 | 2 |
KRSA_coverage_STK_PamChip_87102_v2 | KRSA | KRSA kinase coverage file for PamChip 87102 STK (v2 mapping, removed PDK kinase) | data.frame | 2422 | 2 |
KRSA_layout_PTK_PamChip_86402_v1 | KRSA | | spec_tbl_df | 204 | 1 |
KRSA_layout_STK_PamChip_87102_v1 | KRSA | | spec_tbl_df | 152 | 1 |
KRSA_uka_mapping_PTK_PamChip_86402_v1 | KRSA | | spec_tbl_df | 1438 | 11 |
KRSA_uka_mapping_STK_PamChip_87102_v1 | KRSA | | spec_tbl_df | 2106 | 11 |
ballModel_edges | KRSA | Protein-Protein Interactions based on PhosphositePlus database | data.frame | 592 | 2 |
ballModel_nodes | KRSA | Protein-Protein Interactions based on PhosphositePlus database | tbl_df | 179 | 2 |
ptk_pamchip_86402_mapping | KRSA | CDRL Complete mapping of peptides to HGNC symbols (PTK PamChip 86402) | tbl_df | 193 | 2 |
stk_pamchip_87102_mapping | KRSA | CDRL Complete mapping of peptides to HGNC symbols (STK PamChip 87102) | tbl_df | 141 | 2 |
Y_RM | RestoreNet | Rhesus Macaque clonal tracking dataset | list | | |
zzbr | demogsurv | DHS Model Births Recode Dataset | data.frame | 23666 | 105 |
zzir | demogsurv | DHS Model Individual Recode Dataset | data.frame | 8348 | 819 |
DAX | PMwR | Deutscher Aktienindex (DAX) | data.frame | 505 | 1 |
REXP | PMwR | REXP | data.frame | 502 | 1 |
gap | RGAP | gap data set | list | | |
indicator | RGAP | Indicators fo CUBS | list | | |
QMwind | plotdap | QMwind Data | griddap_nc | | 2 |
murSST | plotdap | murSST Data | griddap_nc | | 2 |
sardines | plotdap | sardine Data | tabledap | 56 | 5 |
stocks | mistr | Log-returns of Five Stocks | data.frame | 2726 | 5 |
genotypes | hsphase | Example of Genotype Data Set | matrix | 90 | 9601 |
map | hsphase | Example Map File for Genetic Data | data.frame | 11333 | 3 |
pedigree | hsphase | Example Pedigree Data Set | data.frame | 90 | 2 |
SNR.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
amplicon.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
chromosome.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
clone.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
control.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
dapi.snr.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
dyn.x.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
dyn.y.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
dynamics.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
edge | MANOR | Examples of array-CGH data with spatial artifacts | arrayCGH | | |
edge.norm | MANOR | Examples of array-CGH data with spatial artifacts | arrayCGH | | |
global.spatial.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
gradient | MANOR | Examples of array-CGH data with spatial artifacts | arrayCGH | | |
gradient.norm | MANOR | Examples of array-CGH data with spatial artifacts | arrayCGH | | |
intensity.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
local.spatial.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
num.chromosome.flag | MANOR | Internal Functions for MANOR Package | flag | | |
pct.clone.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
pct.replicate.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
pct.spot.before.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
pct.spot.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
position.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
ref.snr.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
rep.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
replicate.aux | MANOR | Internal Functions for MANOR Package | function | | |
replicate.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
smoothness.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
spatial.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
spot.corr.flag | MANOR | Internal Functions for MANOR Package | flag | | |
spot.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
unique.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
val.mark.flag | MANOR | Examples of flag objects to apply to CGH arrays | flag | | |
var.replicate.qscore | MANOR | Examples of qscore objects (quality scores) to apply to CGH arrays | qscore | | |
GSE36594Expr | MM2Sdata | Normalized gene expression data for Mouse MB samples (from GSE36594). | ExpressionSet | | |
GSE37418Expr | MM2Sdata | Normalized gene expression data for Human MB (GSE37418). | ExpressionSet | | |
spateMCMC | spate | 'spateMCMC' object output obtained from 'spate.mcmc'. | spateMCMC | | |
spateMLE | spate | Maximum likelihood estimate for SPDE model with Gaussian observations. | list | | |
brokentrans | paircompviz | Artificial dataset that suffers with broken transitivity of the pairwise t-test comparisons | data.frame | 166 | 2 |
achievement | LearnBayes | School achievement data | data.frame | 109 | 7 |
baseball.1964 | LearnBayes | Team records in the 1964 National League baseball season | data.frame | 45 | 4 |
bermuda.grass | LearnBayes | Bermuda grass experiment data | data.frame | 64 | 4 |
birdextinct | LearnBayes | Bird measurements from British islands | data.frame | 62 | 5 |
birthweight | LearnBayes | Birthweight regression study | data.frame | 24 | 3 |
breastcancer | LearnBayes | Survival experience of women with breast cancer under treatment | data.frame | 45 | 3 |
calculus.grades | LearnBayes | Calculus grades dataset | data.frame | 100 | 3 |
cancermortality | LearnBayes | Cancer mortality data | data.frame | 20 | 2 |
chemotherapy | LearnBayes | Chemotherapy treatment effects on ovarian cancer | data.frame | 26 | 5 |
darwin | LearnBayes | Darwin's data on plants | data.frame | 15 | 1 |
donner | LearnBayes | Donner survival study | data.frame | 45 | 3 |
election | LearnBayes | Florida election data | data.frame | 67 | 5 |
election.2008 | LearnBayes | Poll data from 2008 U.S. Presidential Election | data.frame | 51 | 4 |
footballscores | LearnBayes | Game outcomes and point spreads for American football | data.frame | 672 | 8 |
hearttransplants | LearnBayes | Heart transplant mortality data | data.frame | 94 | 2 |
iowagpa | LearnBayes | Admissions data for an university | data.frame | 40 | 4 |
jeter2004 | LearnBayes | Hitting data for Derek Jeter | data.frame | 154 | 10 |
marathontimes | LearnBayes | Marathon running times | data.frame | 20 | 1 |
puffin | LearnBayes | Bird measurements from British islands | data.frame | 38 | 5 |
schmidt | LearnBayes | Batting data for Mike Schmidt | data.frame | 18 | 14 |
sluggerdata | LearnBayes | Hitting statistics for ten great baseball players | data.frame | 199 | 13 |
soccergoals | LearnBayes | Goals scored by professional soccer team | data.frame | 35 | 1 |
stanfordheart | LearnBayes | Data from Stanford Heart Transplanation Program | data.frame | 82 | 4 |
strikeout | LearnBayes | Baseball strikeout data | data.frame | 438 | 4 |
studentdata | LearnBayes | Student dataset | data.frame | 657 | 11 |
ex_sales | hpiR | Subset of Seattle Home Sales | data.frame | 5348 | 16 |
seattle_sales | hpiR | Seattle Home Sales | data.frame | 43313 | 16 |
mozilla_id_path | voice | Sample IDs and paths | tbl_df | 34425 | 2 |
envData | ICDS | The variables in the environment include an example expression profile,an methylation profile,an copy number variation data,amplified genes,deleted genes,A numeric vector of z_scores,p-values,A vector of 0/1s, indicating the class of samples,interested subpathways,Optimized subpathway,and the statistical significance p value and FDR for these optimal subpathways | environment | | |
llraDat1 | eRm | An Artificial LLRA Data Set | data.frame | 150 | 26 |
llraDat2 | eRm | An Artificial LLRA Data Set | data.frame | 70 | 21 |
llradat3 | eRm | An Artificial LLRA Data Set | data.frame | 60 | 6 |
lltmdat1 | eRm | Data for Computing Extended Rasch Models | data.frame | 100 | 30 |
lltmdat2 | eRm | Data for Computing Extended Rasch Models | data.frame | 15 | 5 |
lpcmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 6 |
lrsmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 6 |
pcmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 7 |
pcmdat2 | eRm | Data for Computing Extended Rasch Models | data.frame | 300 | 4 |
raschdat1 | eRm | Data for Computing Extended Rasch Models | data.frame | 100 | 30 |
raschdat1_RM_fitted | eRm | Data for Computing Extended Rasch Models | dRm | | |
raschdat1_RM_lrres2 | eRm | Data for Computing Extended Rasch Models | LR | | |
raschdat1_RM_plotDIF | eRm | Data for Computing Extended Rasch Models | list | | |
raschdat2 | eRm | Data for Computing Extended Rasch Models | data.frame | 25 | 6 |
raschdat3 | eRm | Data for Computing Extended Rasch Models | data.frame | 500 | 6 |
raschdat4 | eRm | Data for Computing Extended Rasch Models | data.frame | 500 | 6 |
rsmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 6 |
xmpl | eRm | Example Data | matrix | 300 | 30 |
xmplbig | eRm | Example Data | matrix | 4096 | |
pphpc_diff | micompr | Data from two implementations of the PPHPC model, one of which setup with a different parameter | grpoutputs | | |
pphpc_noshuff | micompr | Data from two implementations of the PPHPC model, one of which has agent list shuffling deactivated | grpoutputs | | |
pphpc_ok | micompr | Data from two similar implementations of the PPHPC model | grpoutputs | | |
pphpc_testvlo | micompr | Data for testing variable length outputs | grpoutputs | | |
EC | GOplot | Transcriptomic information of endothelial cells. | list | | |
binarydb | RWsearch | CRAN Matrix Of Available Binary Packages (archivedb.rda) | matrix | 94 | 17 |
crandb | RWsearch | CRAN Packages (crandb.rda) | data.frame | 94 | 69 |
tvdb | RWsearch | Task Views (tvdb.rda) | list | | |
filmy_taxonomy | taxastand | Taxonomy of filmy ferns (family Hymenophyllaceae) | spec_tbl_df | 2729 | 31 |
scores | nopaco | Hypothetical data of outcomes for two risk models | list | | |
paleta_epe | epe4md | | character | | |
ne_countries | necountries | Countries of naturalearth | sf | 295 | 21 |
ne_towns | necountries | Populated places of naturalearth | sf | 7342 | 6 |
slave_trade | necountries | Slave trade and economic development | tbl_df | 52 | 24 |
sp_solow | necountries | Solow's growth model with spatial correlation | tbl_df | 91 | 6 |
Toronto | SSIMmap | Sample polygon data of Toronto | sf | 3577 | 5 |
tropicalsound | soundecology | tropicalsound sound example | Wave | | |
caldana | HDANOVA | Arabidopsis thaliana growth experiment | data.frame | 140 | 3 |
candies | HDANOVA | Sensory assessment of candies. | data.frame | 165 | 3 |
HMSPCI | apComplex | High-Throughput Mass Spectromic Protein Complex Identification (HMS-PCI) Data from Ho, et al. (2002) | matrix | 493 | 1578 |
HMSPCIgraph | apComplex | High-Throughput Mass Spectromic Protein Complex Identification (HMS-PCI) Data from Ho, et al. (2002) | graphNEL | | |
Krogan | apComplex | High-Definition Macromolecular Composition of Yeast RNA-Processing Complexes (2004) | matrix | 153 | 485 |
MBMEcHMSPCI | apComplex | HMSPCI data complex estimates | matrix | 397 | 242 |
MBMEcKrogan | apComplex | Krogan data complex estimates | matrix | 200 | 82 |
MBMEcTAP | apComplex | TAP data complex estimates | matrix | 669 | 260 |
SBMHcHMSPCI | apComplex | HMSPCI data complex estimates | matrix | 1578 | 437 |
SBMHcTAP | apComplex | TAP data complex estimates | matrix | 1364 | 325 |
TAP | apComplex | Tandem Affinity Purification (TAP) Data from Gavin et al. (2002) | matrix | 455 | 1364 |
TAPgraph | apComplex | Tandem Affinity Purification (TAP) Data from Gavin et al. (2002) | graphNEL | | |
UnRBBcHMSPCI | apComplex | HMSPCI data complex estimates | matrix | 1578 | 329 |
UnRBBcTAP | apComplex | TAP data complex estimates | matrix | 1364 | 123 |
apEX | apComplex | Example data set for apComplex package | matrix | 4 | 8 |
apEXG | apComplex | Example data set for apComplex package | graphNEL | | |
gavin06FilteredEstimates | apComplex | AP-MS data complex estimates | list | | |
gavinBP2006 | apComplex | Tandem Affinity Purification (TAP) Data from Gavin et al. (2006) | matrix | 1752 | 2551 |
krogan06FilteredEstimates | apComplex | AP-MS data complex estimates | list | | |
kroganBPMat2006 | apComplex | Tandem Affinity Purification (TAP) Data from Krogan et al. (2006) | matrix | 2264 | 5361 |
yNameTAP | apComplex | TAP data complex estimates using standard gene names | matrix | 1363 | 260 |
yTAP | apComplex | yTAP Complexes published Gavin, et al. (2002). | list | | |
frbsData | frbs | Data set of the package | list | | |
agres | dlsem | European agricultural data | data.frame | 350 | 18 |
industry | dlsem | Industrial development | data.frame | 320 | 7 |
data_cr | mvord | Simulated credit ratings | data.frame | 690 | 10 |
data_cr_panel | mvord | Simulated panel of credit ratings | data.frame | 11320 | 9 |
data_mvord | mvord | Simulated credit ratings | data.frame | 3000 | 9 |
data_mvord2 | mvord | Simulated credit ratings | data.frame | 1000 | 10 |
data_mvord_panel | mvord | Simulated panel of credit ratings | data.frame | 10000 | 9 |
data_mvord_toy | mvord | Data set toy example | data.frame | 100 | 6 |
essay_data | mvord | Essay data | data.frame | 198 | 6 |
Mrk421 | RobPer | Data: Light curve from Mrk 421 | data.frame | 655 | 3 |
Mrk501 | RobPer | Data: Light curve from Mrk 501 | data.frame | 210 | 3 |
star_groj0422.32 | RobPer | Data: Light curve from GROJ0422+32 | matrix | 729 | |
exfdr | twilight | Example of twilight result | twilight | | |
expval | twilight | Example of twilight.pval result | twilight | | |
voropm2 | drda | Vorinostat in OPM-2 cell-line dataset | data.frame | 45 | 4 |
NBdata | unifiedWMWqPCR | Documentation for the dataset NBdata | data.frame | 19703 | 4 |
NBgroups | unifiedWMWqPCR | Documentation for the dataset NBdata | factor | | |
NBmat | unifiedWMWqPCR | Documentation for the dataset NBdata | matrix | 323 | 61 |
hospiceALPHA | medicare | Sample Medicare Hospice Cost Report 2014 data | data.frame | 61820 | 5 |
hospiceNMRC | medicare | Sample Medicare Hospice Cost Report 2014 data | data.frame | 200202 | 5 |
hospiceRPT | medicare | Sample Medicare Hospice Cost Report 2014 data | data.frame | 500 | 18 |
pos2010 | medicare | Sample Medicare Provider of Service 2010 data for hospices | data.frame | 402 | 530 |
finData | HAC | Financial data | matrix | 283 | 4 |
PER_2015_magn | vismeteor | | list | | |
PER_2015_rates | vismeteor | | list | | |
D16 | knotR | Optimized knots | knot | | |
T20 | knotR | Optimized knots | knot | | |
amphichiral15 | knotR | Optimized knots | knot | | |
celtic3 | knotR | Optimized knots | knot | | |
fiveloops | knotR | Optimized knots | knot | | |
flower | knotR | Optimized knots | knot | | |
fourloops | knotR | Optimized knots | knot | | |
hexknot | knotR | Optimized knots | knot | | |
hexknot2 | knotR | Optimized knots | knot | | |
hexknot3 | knotR | Optimized knots | knot | | |
k10_1 | knotR | Optimized knots | knot | | |
k10_123 | knotR | Optimized knots | knot | | |
k10_47 | knotR | Optimized knots | knot | | |
k10_61 | knotR | Optimized knots | knot | | |
k11a1 | knotR | Optimized knots | knot | | |
k11a179 | knotR | Optimized knots | knot | | |
k11a361 | knotR | Optimized knots | knot | | |
k11n157 | knotR | Optimized knots | knot | | |
k11n157_morenodes | knotR | Optimized knots | knot | | |
k11n22 | knotR | Optimized knots | knot | | |
k12a1202 | knotR | Optimized knots | knot | | |
k12n838 | knotR | Optimized knots | knot | | |
k12n_0242 | knotR | Optimized knots | knot | | |
k12n_0411 | knotR | Optimized knots | knot | | |
k3_1 | knotR | Optimized knots | knot | | |
k3_1a | knotR | Optimized knots | knot | | |
k4_1 | knotR | Optimized knots | knot | | |
k4_1a | knotR | Optimized knots | knot | | |
k5_1 | knotR | Optimized knots | knot | | |
k5_2 | knotR | Optimized knots | knot | | |
k6_1 | knotR | Optimized knots | knot | | |
k6_2 | knotR | Optimized knots | knot | | |
k6_3 | knotR | Optimized knots | knot | | |
k7_1 | knotR | Optimized knots | knot | | |
k7_2 | knotR | Optimized knots | knot | | |
k7_3 | knotR | Optimized knots | knot | | |
k7_4 | knotR | Optimized knots | knot | | |
k7_5 | knotR | Optimized knots | knot | | |
k7_6 | knotR | Optimized knots | knot | | |
k7_7 | knotR | Optimized knots | knot | | |
k7_7a | knotR | Optimized knots | knot | | |
k8_1 | knotR | Optimized knots | knot | | |
k8_10 | knotR | Optimized knots | knot | | |
k8_11 | knotR | Optimized knots | knot | | |
k8_11_90deg_crossing | knotR | Optimized knots | knot | | |
k8_12 | knotR | Optimized knots | knot | | |
k8_13 | knotR | Optimized knots | knot | | |
k8_14 | knotR | Optimized knots | knot | | |
k8_15 | knotR | Optimized knots | knot | | |
k8_16 | knotR | Optimized knots | knot | | |
k8_17 | knotR | Optimized knots | knot | | |
k8_18 | knotR | Optimized knots | knot | | |
k8_19 | knotR | Optimized knots | knot | | |
k8_19a | knotR | Optimized knots | knot | | |
k8_19b | knotR | Optimized knots | knot | | |
k8_2 | knotR | Optimized knots | knot | | |
k8_20 | knotR | Optimized knots | knot | | |
k8_21 | knotR | Optimized knots | knot | | |
k8_3 | knotR | Optimized knots | knot | | |
k8_3_90deg_crossing | knotR | Optimized knots | knot | | |
k8_4 | knotR | Optimized knots | knot | | |
k8_4a | knotR | Optimized knots | knot | | |
k8_5 | knotR | Optimized knots | knot | | |
k8_5_90deg_crossing | knotR | Optimized knots | knot | | |
k8_6 | knotR | Optimized knots | knot | | |
k8_6_90deg_crossing | knotR | Optimized knots | knot | | |
k8_7 | knotR | Optimized knots | knot | | |
k8_8 | knotR | Optimized knots | knot | | |
k8_9 | knotR | Optimized knots | knot | | |
k9_1 | knotR | Optimized knots | knot | | |
k9_10 | knotR | Optimized knots | knot | | |
k9_11 | knotR | Optimized knots | knot | | |
k9_12 | knotR | Optimized knots | knot | | |
k9_13 | knotR | Optimized knots | knot | | |
k9_14 | knotR | Optimized knots | knot | | |
k9_15 | knotR | Optimized knots | knot | | |
k9_16 | knotR | Optimized knots | knot | | |
k9_17 | knotR | Optimized knots | knot | | |
k9_18 | knotR | Optimized knots | knot | | |
k9_19 | knotR | Optimized knots | knot | | |
k9_2 | knotR | Optimized knots | knot | | |
k9_20 | knotR | Optimized knots | knot | | |
k9_21 | knotR | Optimized knots | knot | | |
k9_22 | knotR | Optimized knots | knot | | |
k9_23 | knotR | Optimized knots | knot | | |
k9_23a | knotR | Optimized knots | knot | | |
k9_24 | knotR | Optimized knots | knot | | |
k9_25 | knotR | Optimized knots | knot | | |
k9_26 | knotR | Optimized knots | knot | | |
k9_27 | knotR | Optimized knots | knot | | |
k9_28 | knotR | Optimized knots | knot | | |
k9_29 | knotR | Optimized knots | knot | | |
k9_3 | knotR | Optimized knots | knot | | |
k9_30 | knotR | Optimized knots | knot | | |
k9_31 | knotR | Optimized knots | knot | | |
k9_32 | knotR | Optimized knots | knot | | |
k9_33 | knotR | Optimized knots | knot | | |
k9_34 | knotR | Optimized knots | knot | | |
k9_35 | knotR | Optimized knots | knot | | |
k9_36 | knotR | Optimized knots | knot | | |
k9_37 | knotR | Optimized knots | knot | | |
k9_38 | knotR | Optimized knots | knot | | |
k9_39 | knotR | Optimized knots | knot | | |
k9_4 | knotR | Optimized knots | knot | | |
k9_40 | knotR | Optimized knots | knot | | |
k9_41 | knotR | Optimized knots | knot | | |
k9_42 | knotR | Optimized knots | knot | | |
k9_43 | knotR | Optimized knots | knot | | |
k9_44 | knotR | Optimized knots | knot | | |
k9_45 | knotR | Optimized knots | knot | | |
k9_46 | knotR | Optimized knots | knot | | |
k9_47 | knotR | Optimized knots | knot | | |
k9_48 | knotR | Optimized knots | knot | | |
k9_49 | knotR | Optimized knots | knot | | |
k9_5 | knotR | Optimized knots | knot | | |
k9_6 | knotR | Optimized knots | knot | | |
k9_7 | knotR | Optimized knots | knot | | |
k9_8 | knotR | Optimized knots | knot | | |
k9_9 | knotR | Optimized knots | knot | | |
k_infinity | knotR | Optimized knots | knot | | |
longthin | knotR | Optimized knots | knot | | |
ochiai | knotR | Optimized knots | knot | | |
ornamental20 | knotR | Optimized knots | knot | | |
perko_A | knotR | Optimized knots | knot | | |
perko_B | knotR | Optimized knots | knot | | |
pretzel_2_3_7 | knotR | Optimized knots | knot | | |
pretzel_2_3_7_90deg_crossing | knotR | Optimized knots | knot | | |
pretzel_7_3_7 | knotR | Optimized knots | knot | | |
pretzel_7_3_7_90deg_crossing | knotR | Optimized knots | knot | | |
pretzel_p3_p5_p7_m3_m5 | knotR | Optimized knots | knot | | |
reefknot | knotR | Optimized knots | knot | | |
satellite | knotR | Optimized knots | knot | | |
sum_31_41 | knotR | Optimized knots | knot | | |
three_figure_eights | knotR | Optimized knots | knot | | |
trefoil_of_trefoils | knotR | Optimized knots | knot | | |
triloop | knotR | Optimized knots | knot | | |
unknot | knotR | Optimized knots | knot | | |
CCASAnetData | survSpearman | CCASAnetData | data.frame | 6691 | 5 |
data123 | survSpearman | data123 | data.frame | 300 | 4 |
DSraw | DSjobtracker | Data Scientists/Data Analyst/ Statistician Job Tracker | spec_tbl_df | 551 | 152 |
DStidy | DSjobtracker | Data scientists, data analyst, and statistician job advertisements from 2020 to 2023 | spec_tbl_df | 1172 | 109 |
DStidy_2020 | DSjobtracker | Data scientists, data Analyst, and statistician related job advertisements in 2020 | tbl_df | 430 | 115 |
DStidy_2021 | DSjobtracker | Data Scientists/Data Analyst/ Statistician Job Advertisements in the year 2021 | tbl_df | 382 | 115 |
stackedSpectra | IDSL.FSA | Example for a stacked spectra | matrix | 58 | 3 |
google_example_1_data | localsolver | Data set for google_machine_reassignment demo. | list | | |
FortytwoSTR | KINSIMU | Information of 42 autosomal STRs | list | | |
pediexample | KINSIMU | Example of pedi matrix | data.frame | 8 | 3 |
Bookspan | scuba | Tissue data from Bookspan's book | list | | |
BuehlmannL16A | scuba | Decompression model ZH-L16A | data.frame | 17 | 4 |
Workman65 | scuba | Decompression model of Workman 1965 | data.frame | 9 | 3 |
baron | scuba | Real Scuba Dive Profile | data.frame | 606 | 6 |
deepmine | scuba | Extremely Deep Decompression Dive | dive | | |
pedro902 | scuba | Real Scuba Dive Profiles | dive | | |
pedro903 | scuba | Real Scuba Dive Profiles | dive | | |
pedro904 | scuba | Real Scuba Dive Profiles | dive | | |
pedro922 | scuba | Real Scuba Dive Profiles | dive | | |
pedro943 | scuba | Real Scuba Dive Profiles | dive | | |
pedro944 | scuba | Real Scuba Dive Profiles | dive | | |
pedro945 | scuba | Real Scuba Dive Profiles | dive | | |
pedro946 | scuba | Real Scuba Dive Profiles | dive | | |
pedro948 | scuba | Real Scuba Dive Profiles | dive | | |
pedro949 | scuba | Real Scuba Dive Profiles | dive | | |
pedro950 | scuba | Real Scuba Dive Profiles | dive | | |
bgt.data | uroot | BGT-data Sample Data Set | list | | |
ch.data | uroot | CH-data Sample Data Set | list | | |
LoadCurve | extremefit | Load curve of an habitation | list | | |
dataOyster | extremefit | High-frequency noninvasive valvometry data | list | | |
dataWind | extremefit | Wind speed for Brest (France) | data.frame | 10903 | 4 |
windEchirolles | gumbel | Daily Climatological data between August 2005 and May 2007 | data.frame | 669 | 15 |
windStMartin | gumbel | Daily Climatological data between August 2005 and May 2007 | data.frame | 638 | 15 |
cloudms2 | cloudUtil | Benchmark data set for cloudUtil | data.frame | 10969 | 15 |
waterlevels | Tides | Observed water levels of the tides in the Lippenbroek Flood Control Area with controled reduced tide (FCA-CRT) | data.frame | 8915 | 3 |
superheroes_supertbl | REDCapTidieR | Superheroes Data | redcap_supertbl | 2 | 9 |
CLF | publishTC | Classical Moving Average | finite_filters | | |
CLF_CN | publishTC | Classical Moving Average | finite_filters | | |
cars_registrations | publishTC | Data set examples | ts | | |
fred | publishTC | Data set examples | mts | 766 | 2 |
french_ipi | publishTC | Data set examples | mts | 416 | 3 |
henderson | publishTC | Classical Moving Average | list | | |
local_param_est | publishTC | Classical Moving Average | list | | |
simulated_data | publishTC | Data set examples | mts | 84 | 6 |
dt_potato | flexFitR | Drone-derived data from a potato breeding trial | tbl_df | 1568 | 8 |
DoignonFalmagne7 | pks | Artificial Responses from Doignon and Falmagne (1999) | list | | |
chess | pks | Responses to Chess Problems and Knowledge Structures | list | | |
density97 | pks | Responses and Knowledge Structures from Taagepera et al. (1997) | list | | |
endm | pks | Responses and Knowledge Structures from Heller and Wickelmaier (2013) | list | | |
fraction17 | pks | Arithmetic Problems for Elementary and Middle School Students | matrix | 191 | 23 |
matter97 | pks | Responses and Knowledge Structures from Taagepera et al. (1997) | list | | |
probability | pks | Problems in Elementary Probability Theory | data.frame | 504 | 68 |
subtraction13 | pks | Arithmetic Problems for Elementary and Middle School Students | data.frame | 294 | 5 |
achim | Rnest | A list of seven correlation matrices. | list | | |
achim24 | Rnest | A correlation matrix composed of six factors. | matrix | 18 | 18 |
briggs_maccallum2003 | Rnest | A list of three correlation matrices. | list | | |
caron2016 | Rnest | A list of six correlation matrices composed of nine variables with three factors and different levels of correlations between factors. | list | | |
caron2019 | Rnest | A list of 15 correlation matrices composed of nine variables with three factors and different levels of correlations between factors. | list | | |
cormat | Rnest | A list containing 120 correlation matrices. | list | | |
cormat.l | Rnest | A list containing 120 lists of correlation matrices and their underlying characteristics. | list | | |
ex_2factors | Rnest | A correlation matrix composed of 2 factors. | matrix | 10 | 10 |
ex_3factors_doub_unique | Rnest | A correlation matrix composed of two factors, a doublet factor and a unique variable. | matrix | 10 | 10 |
ex_4factors_corr | Rnest | A correlation matrix composed of 4 correlated factors. | matrix | 12 | 12 |
ex_mqr | Rnest | A correlation matrix from chapter 19 Explorer of Méthodes quantitatives avec R (MQR). | matrix | 6 | 6 |
meek_bouchard | Rnest | A correlation matrix from Meek-Bouchard. | data.frame | 44 | 44 |
tabachnick_fidell2019 | Rnest | A covariance matrix composed of 11 variables. | matrix | 11 | 11 |
scenarioA | StratPal | example data, scenario A from Hohmann et al. (2024) | list | | |
cpm_models | tectonicr | Global model of current plate motions | vctrs_list_of | | |
iceland | tectonicr | Example crustal stress dataset | sf | 490 | 10 |
nuvel1 | tectonicr | NUVEL-1 Global model of current plate motions | data.frame | 14 | 7 |
nuvel1_plates | tectonicr | Plate Boundaries on the Earth | sf | 36 | 4 |
pb2002 | tectonicr | Global model of current plate motions | data.frame | 52 | 7 |
plates | tectonicr | Plate Boundaries on the Earth | sf | 157 | 9 |
san_andreas | tectonicr | Example crustal stress dataset | sf | 1126 | 10 |
tibet | tectonicr | Example crustal stress dataset | sf | 1165 | 10 |
data_acq_consonants | wisclabmisc | Acquisition and developmental descriptions of consonants and vowels | tbl_df | 24 | 10 |
data_example_intelligibility_by_length | wisclabmisc | Simulated intelligibility scores by utterance length | tbl_df | 694 | 5 |
data_fake_intelligibility | wisclabmisc | Fake intelligibility data | tbl_df | 200 | 2 |
data_fake_rates | wisclabmisc | Fake speaking rate data | tbl_df | 200 | 2 |
data_features_consonants | wisclabmisc | Phonetic features of consonants and vowels | tbl_df | 24 | 11 |
data_features_vowels | wisclabmisc | Phonetic features of consonants and vowels | tbl_df | 17 | 13 |
safety | eudract | Example of safety data | data.frame | 116 | 8 |
soc_code | eudract | System Organ Class coding | data.frame | 27 | 3 |
MBsst | rerddapXtracto | MBsst Data | list | | |
Marlintag38606 | rerddapXtracto | Marlin Tag Data | data.frame | 152 | 7 |
Marlintag38606 | rerddapXtracto | Marlin Tag Data | data.frame | 152 | 7 |
PB_Argos | rerddapXtracto | Polar Bear Track Data | spec_tbl_df | 1919 | 4 |
cmocean | rerddapXtracto | cmocean colors The cmocean color palette by Kristen Thyng | list | | |
colors | rerddapXtracto | cmocean colors The cmocean color palette by Kristen Thyng as implemented in the R package "oce" | list | | |
dataInfo | rerddapXtracto | dataInfo Data | info | | |
mbnms | rerddapXtracto | MBNMS Boundaries | data.frame | 6666 | 2 |
swchl | rerddapXtracto | swchl Data | list | | |
fire_comm | mobr | Fire data set | data.frame | 52 | 21 |
fire_plot_attr | mobr | Fire data set | data.frame | 52 | 3 |
inv_comm | mobr | Invasive plants dataset | matrix | 100 | 111 |
inv_plot_attr | mobr | Invasive plants dataset | data.frame | 100 | 3 |
tank_comm | mobr | Cattle tank data set | data.frame | 30 | 47 |
tank_plot_attr | mobr | Cattle tank data set | data.frame | 30 | 3 |
cog_2023 | happign | COG 2023 | data.frame | 34990 | 2 |
com_2024 | happign | COG 2024 | data.frame | 34980 | 2 |
northplatte | thunder | Exemplary sounding dataset - sample from LBF North Platte (WMO ID: 72562) - 03 July 1999, 00:00 UTC | data.frame | 71 | 6 |
sounding_vienna | thunder | Examplary sounding dataset - sample from Vienna (WMO ID: 11035) - 23 August 2011, 1200 UTC | data.frame | 88 | 6 |
gisco_coastallines | giscoR | World coastal lines 'POLYGON' object | sf | 2129 | 3 |
gisco_countries | giscoR | World countries 'POLYGON' 'sf' object | sf | 257 | 6 |
gisco_countrycode | giscoR | Data frame with different country code schemes and world regions | data.frame | 249 | 13 |
gisco_db | giscoR | GISCO database | data.frame | 46 | 10 |
gisco_nuts | giscoR | All NUTS 'POLYGON' object | sf | 2016 | 11 |
cross_blended_hypsometric_tints_db | tidyterra | Cross-blended hypsometric tints | tbl_df | 41 | 6 |
grass_db | tidyterra | GRASS color tables | tbl_df | 2920 | 6 |
hypsometric_tints_db | tidyterra | Hypsometric palettes database | tbl_df | 1102 | 6 |
princess_db | tidyterra | Princess palettes database | tbl_df | 75 | 5 |
volcano2 | tidyterra | Updated topographic information on Auckland's Maungawhau volcano | matrix | 174 | |
dax | fHMM | Deutscher Aktienindex (DAX) index data | data.frame | 9012 | 7 |
dax_model_2n | fHMM | DAX 2-state HMM with normal distributions | fHMM_model | | |
dax_model_3t | fHMM | DAX 3-state HMM with t-distributions | fHMM_model | | |
dax_vw_model | fHMM | DAX/VW hierarchical HMM with t-distributions | fHMM_model | | |
sim_model_2gamma | fHMM | Simulated 2-state HMM with gamma distributions | fHMM_model | | |
spx | fHMM | Standard & Poor’s 500 (S&P 500) index data | data.frame | 23864 | 7 |
unemp | fHMM | Unemployment rate data USA | data.frame | 23710 | 3 |
unemp_spx_model_3_2 | fHMM | Unemployment rate and S&P 500 hierarchical HMM | fHMM_model | | |
vw | fHMM | Volkswagen AG (VW) stock data | data.frame | 6260 | 7 |
ba1986 | SimplyAgree | reps | data.frame | 17 | 5 |
recpre_long | SimplyAgree | Data | tbl_df | 30 | 6 |
reps | SimplyAgree | reps | data.frame | 20 | 3 |
temps | SimplyAgree | Data | tbl_df | 60 | 10 |
admix | plmmr | Admix: Semi-simulated SNP data | list | | |
bbsData | spAbundance | Count data for six warbler species in Pennsylvania, USA | list | | |
bbsPredData | spAbundance | Covariates and coordinates for prediction of relative warbler abundance in Pennsylvania, USA | data.frame | 816 | 7 |
dataNMixSim | spAbundance | Simulated repeated count data of 6 species across 225 sites | list | | |
hbefCount2015 | spAbundance | Count data of 12 foliage gleaning bird species in 2015 in the Hubbard Brook Experimental Forest | list | | |
neonDWP | spAbundance | Distance sampling data of 16 bird species observed in the Disney Wilderness Preserve in 2018 in Florida, USA | list | | |
neonPredData | spAbundance | Land cover covariates and coordinates at a 1ha resolution across Disney Wilderness Preserve | data.frame | 4838 | 4 |
nice | ulrb | V4-V5 16S rRNA gene amplicons, clean OTU table (N-ICE, 2015) | data.frame | 524 | 17 |
nice_env | ulrb | Metadata of samples from OTU tables (N-ICE, 2015) | data.frame | 9 | 8 |
nice_raw | ulrb | V4-V5 16S rRNA gene amplicons, raw OTU table (N-ICE, 2015) | data.frame | 1003 | 19 |
nice_tidy | ulrb | V4-V5 16S rRNA gene amplicons, clean OTU table in tidy/long format (N-ICE, 2015) | tbl_df | 4716 | 10 |
NewHavenResidential | barcode | New Haven, CT Residential Property Data | data.frame | 18221 | 8 |
frt_attr | scfmutils | Attributes and fire metrics of the 15 Fire Regime Units (FRTs) | data.frame | 15 | 10 |
fru_attr | scfmutils | Attributes and fire metrics of the 60 Fire Regime Units (FRUs) | data.frame | 60 | 10 |
MOats | emmeans | Oats data in multivariate form | data.frame | 18 | 3 |
auto.noise | emmeans | Auto Pollution Filter Noise | data.frame | 36 | 4 |
feedlot | emmeans | Feedlot data | data.frame | 67 | 4 |
fiber | emmeans | Fiber data | data.frame | 15 | 3 |
neuralgia | emmeans | Neuralgia data | data.frame | 60 | 5 |
nutrition | emmeans | Nutrition data | data.frame | 107 | 4 |
oranges | emmeans | Sales of oranges | data.frame | 36 | 6 |
pigs | emmeans | Effects of dietary protein on free plasma leucine concentration in pigs | data.frame | 29 | 3 |
ubds | emmeans | Unbalanced dataset | data.frame | 100 | 5 |
avengers | billboarder | Power ratings for The Avengers. | data.frame | 24 | 4 |
avengers_wide | billboarder | Power ratings for The Avengers. | data.frame | 6 | 5 |
cdc_prod_filiere | billboarder | French electricity generation by power source for the day of 2017-06-12. | data.frame | 48 | 11 |
equilibre_mensuel | billboarder | Monthly supply / demand balance (january 2007 to june 2017) | data.frame | 126 | 5 |
prod_filiere_long | billboarder | French electricity generation by year and branch. | data.frame | 45 | 3 |
prod_par_filiere | billboarder | French electricity generation by year and branch. | data.frame | 5 | 11 |
battles | messydates | Dates of battles in 2001 | tbl_df | 20 | 5 |
LondonYorke | pomp | Historical childhood disease incidence data | data.frame | 1968 | 7 |
blowflies | pomp | Nicholson's blowflies. | data.frame | 858 | 3 |
bsflu | pomp | Influenza outbreak in a boarding school | data.frame | 14 | 4 |
ebolaWA2014 | pomp | Ebola outbreak, West Africa, 2014-2016 | data.frame | 75 | 4 |
ewcitmeas | pomp | Historical childhood disease incidence data | data.frame | 14588 | 3 |
ewmeas | pomp | Historical childhood disease incidence data | data.frame | 991 | 2 |
parus | pomp | Parus major population dynamics | data.frame | 27 | 2 |
BaFe2As2 | GeDS | Barium-Ferrum-Arsenide Powder Diffraction Data | data.frame | 1151 | 2 |
CrystalData10k | GeDS | Crystallographic Scattering Data | data.frame | 1721 | 2 |
CrystalData300k | GeDS | Crystallographic Scattering Data | data.frame | 1721 | 2 |
EWmortality | GeDS | Death counts in England and Wales | data.frame | 109 | 3 |
coalMining | GeDS | Coal Mining Disasters data | data.frame | 112 | 2 |
data_disa_uid_map | sismar | Mapa de UID para ligação entre DISA, SISMA e DATIM | tbl_df | 2065 | 3 |
data_partner_pepfar_clinical | sismar | | tbl_df | 658 | 2 |
data_service_hiv_mq | sismar | | tbl_df | 786 | 2 |
data_sisma_geo_above_site | sismar | Mapa para ligar os códigos de forma da província e do distrito aos dados SISMA | tbl_df | 161 | 4 |
data_sisma_geo_sites | sismar | Mapa para ligar as coordenadas geográficas aos dados das unidades de saúde SISMA | tbl_df | 1646 | 3 |
data_sisma_sitelist | sismar | Lista de unidades sanitárias SISMA | tbl_df | 2241 | 5 |
example_scan | bioRad | Scan ('scan') example | scan | | |
example_vp | bioRad | Vertical profile ('vp') example | vp | | |
example_vpts | bioRad | Time series of vertical profiles ('vpts') example | vpts | | |
vpts_schema | bioRad | ENRAM-defined VPTS schema | list | | |
arabian_tractors | biogrowth | Number of tractors in the Arab World according to the World Bank | tbl_df | 40 | 2 |
conditions_pH_temperature | biogrowth | Conditions during a dynamic growth experiment | tbl_df | 4 | 3 |
example_cardinal | biogrowth | Growth rates obtained for several growth experiments | data.frame | 64 | 3 |
example_coupled_onestep | biogrowth | Example data for two-steps fitting of the Baranyi-Ratkowsky model | tbl_df | 162 | 3 |
example_coupled_twosteps | biogrowth | Example data for two-steps fitting of the Baranyi-Ratkowsky model | tbl_df | 4 | 3 |
example_dynamic_growth | biogrowth | Microbial growth under dynamic conditions | spec_tbl_df | 30 | 2 |
example_env_conditions | biogrowth | Environmental conditions during a dynamic experiment | tbl_df | 3 | 3 |
greek_tractors | biogrowth | Number of tractors in Greece according to the World Bank | spec_tbl_df | 46 | 14 |
growth_pH_temperature | biogrowth | Example of dynamic growth | tbl_df | 20 | 2 |
growth_salmonella | biogrowth | Growth of Salmonella spp in broth | spec_tbl_df | 21 | 2 |
multiple_conditions | biogrowth | Environmental conditions during several dynamic experiments | list | | |
multiple_counts | biogrowth | Population growth observed in several dynamic experiments | list | | |
multiple_experiments | biogrowth | A set of growth experiments under dynamic conditions | list | | |
refrigeratorSpain | biogrowth | Temperature recorded in refrigerators | tbl_df | 145 | 3 |
baseline_1_iso | trps | Stable isotope data for amphipods (baseline 1) | tbl_df | 14 | 5 |
baseline_2_iso | trps | Stable isotope data for dreissenids (baseline 2) | tbl_df | 12 | 4 |
combined_iso | trps | Stable isotope data for lake trout, amphipods (benthic baseline; baseline 1) and dreissenids (pelagic baseline; baseline 2), | tbl_df | 117 | 13 |
consumer_iso | trps | Stable isotope data for lake trout (consumer) | tbl_df | 30 | 4 |
colors | rerddap | cmocean colors The cmocean color palette by Kristen Thyng as implemented in the R package "oce" | list | | |
institutions | rerddap | institutions | character | | |
ioos_categories | rerddap | ioos_categories | character | | |
keywords | rerddap | keywords | character | | |
longnames | rerddap | longnames | character | | |
standardnames | rerddap | standardnames | character | | |
variablenames | rerddap | variablenames | character | | |
breastcancer | risks | Breast Cancer Data | tbl_df | 192 | 3 |
example_data | eq5dsuite | example_data | data.frame | 10000 | 13 |
Blavet | transfR | Blavet French river dataset | list | | |
Oudon | transfR | Oudon French river dataset | list | | |
airstrikes | geocausal | airstrikes | data.frame | 3938 | 4 |
airstrikes_base | geocausal | airstrikes_base | data.frame | 808 | 3 |
insurgencies | geocausal | insurgencies | data.frame | 68573 | 4 |
iraq_window | geocausal | iraq_window | owin | | |
ABdata | pdynmc | Employment, wages, capital, and output for companies based in the UK | data.frame | 1031 | 7 |
cigDemand | pdynmc | Cigarette consumption in the US | data.frame | 528 | 9 |
trial | gtsummary | Results from a simulated study of two chemotherapy agents | tbl_df | 200 | 8 |
spiders | cotram | Bavarian Forest Spider Data | data.frame | 190 | 9 |
rotif.mods | modEvA | Rotifer distribution models | list | | |
ECON85 | fPortfolio | Assets Data Sets | data.frame | 304 | 12 |
ECON85LONG | fPortfolio | Assets Data Sets | data.frame | 304 | 19 |
GCCINDEX | fPortfolio | Assets Data Sets | timeSeries | 825 | 11 |
GCCINDEX.RET | fPortfolio | Assets Data Sets | timeSeries | 824 | 11 |
LPP2005 | fPortfolio | Assets Data Sets | timeSeries | 377 | 9 |
LPP2005.RET | fPortfolio | Assets Data Sets | timeSeries | 377 | 9 |
SMALLCAP | fPortfolio | Assets Data Sets | timeSeries | 60 | 22 |
SMALLCAP.RET | fPortfolio | Assets Data Sets | timeSeries | 60 | 22 |
SPISECTOR | fPortfolio | Assets Data Sets | timeSeries | 2216 | 10 |
SPISECTOR.RET | fPortfolio | Assets Data Sets | timeSeries | 2198 | 10 |
SWX | fPortfolio | Assets Data Sets | timeSeries | 1917 | 6 |
SWX.RET | fPortfolio | Assets Data Sets | timeSeries | 1916 | 6 |
bmwRet | fExtremes | Time Series Data Sets | data.frame | 6146 | 2 |
danishClaims | fExtremes | Time Series Data Sets | data.frame | 2167 | 2 |
ais | SkewHyperbolic | Australian Institute of Sport data | data.frame | 202 | 13 |
lrdji | SkewHyperbolic | Dow Jones Log Return Data | matrix | 1132 | 1 |
lrnokeur | SkewHyperbolic | Log Returns of the NOK/EUR Exchange Rate | matrix | 1647 | 1 |
skewhypLargeParam | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 400 | 4 |
skewhypLargeShape | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 25 | 4 |
skewhypSmallParam | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 16 | 4 |
skewhypSmallShape | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 4 | 4 |
ArkansasRiver | GeneralizedHyperbolic | Soil Electrical Conductivity | list | | |
SandP500 | GeneralizedHyperbolic | S&P 500 | numeric | | |
ghypLargeParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 1440 | |
ghypLargeShape | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 90 | |
ghypSmallParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 84 | |
ghypSmallShape | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 21 | |
gigLargeParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Inverse Gaussian Distribution | matrix | 1100 | |
gigSmallParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Inverse Gaussian Distribution | matrix | 125 | |
hyperbLargeParam | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 240 | |
hyperbLargeShape | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 15 | |
hyperbSmallParam | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 28 | |
hyperbSmallShape | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 7 | |
mamquam | GeneralizedHyperbolic | Size of Gravels from Mamquam River | data.frame | 16 | 2 |
nervePulse | GeneralizedHyperbolic | Intervals Between Pulses Along a Nerve Fibre | numeric | | |
nigLargeParam | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 240 | |
nigLargeShape | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 15 | |
nigSmallParam | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 28 | |
nigSmallShape | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 7 | |
resistors | GeneralizedHyperbolic | Resistance of One-half-ohm Resistors | data.frame | 28 | 2 |
traffic | GeneralizedHyperbolic | Intervals Between Vehicles on a Road | numeric | | |
amexListing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 1553 | 4 |
h15Listing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 39 | 2 |
nasdaqListing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 3206 | 4 |
nyseListing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 3387 | 4 |
oandaListing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 191 | 2 |
stoxxListing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 2328 | 8 |
swxListing | fImport | Provider Listing of Symbols and Descriptions | data.frame | 2085 | 4 |
E_nc1 | inlabru | 1-Dimensional Homogeneous Poisson example. | numeric | | |
E_nc2 | inlabru | 1-Dimensional NonHomogeneous Poisson example. | numeric | | |
E_nc3a | inlabru | 1-Dimensional NonHomogeneous Poisson example. | numeric | | |
E_nc3b | inlabru | 1-Dimensional NonHomogeneous Poisson example. | numeric | | |
countdata1 | inlabru | 1-Dimensional Homogeneous Poisson example. | data.frame | 11 | 3 |
countdata2 | inlabru | 1-Dimensional NonHomogeneous Poisson example. | data.frame | 11 | 3 |
countdata3a | inlabru | 1-Dimensional NonHomogeneous Poisson example. | data.frame | 10 | 3 |
countdata3b | inlabru | 1-Dimensional NonHomogeneous Poisson example. | data.frame | 20 | 3 |
cov2_1D | inlabru | 1-Dimensional NonHomogeneous Poisson example. | function | | |
gorillas_sf | inlabru | Gorilla nesting sites in sf format | list | | |
lambda1_1D | inlabru | 1-Dimensional Homogeneous Poisson example. | function | | |
lambda2_1D | inlabru | 1-Dimensional NonHomogeneous Poisson example. | function | | |
lambda3_1D | inlabru | 1-Dimensional NonHomogeneous Poisson example. | function | | |
mexdolphin_sf | inlabru | Pan-tropical spotted dolphins in the Gulf of Mexico | list | | |
mrsea | inlabru | Marine renewables strategic environmental assessment | list | | |
pts1 | inlabru | 1-Dimensional Homogeneous Poisson example. | data.frame | 116 | 1 |
pts2 | inlabru | 1-Dimensional NonHomogeneous Poisson example. | data.frame | 130 | 1 |
pts3 | inlabru | 1-Dimensional NonHomogeneous Poisson example. | data.frame | 408 | 1 |
robins_subset | inlabru | robins_subset | data.frame | 5807 | 10 |
shrimp | inlabru | Blue and red shrimp in the Western Mediterranean Sea | list | | |
toygroups | inlabru | Simulated 1D animal group locations and group sizes | list | | |
toypoints | inlabru | Simulated 2D point process data | list | | |
rhinos | rhino | Population of rhinos | tbl_df | 58 | 3 |
TD | Hmsc | Simulated data and a fitted Hmsc model for a small species community. | list | | |
. | freegroup | Class "dot" | dot | | |
nsher | FLCore | FLCore datasets | FLSR | | |
ple4 | FLCore | FLCore datasets | FLStock | | |
ple4.biol | FLCore | FLCore datasets | FLBiol | | |
ple4.index | FLCore | FLCore datasets | FLIndex | | |
ple4.indices | FLCore | FLCore datasets | FLIndices | | |
ple4sex | FLCore | FLCore datasets | FLStock | | |
stomata | kanova | Stomata patterns | hyperframe | | |
abouheif.eg | ade4 | Phylogenies and quantitative traits from Abouheif | list | | |
acacia | ade4 | Spatial pattern analysis in plant communities | data.frame | 32 | 15 |
aminoacyl | ade4 | Codon usage | list | | |
apis108 | ade4 | Allelic frequencies in ten honeybees populations at eight microsatellites loci | data.frame | 180 | 10 |
aravo | ade4 | Distribution of Alpine plants in Aravo (Valloire, France) | list | | |
ardeche | ade4 | Fauna Table with double (row and column) partitioning | list | | |
arrival | ade4 | Arrivals at an intensive care unit | list | | |
atlas | ade4 | Small Ecological Dataset | list | | |
atya | ade4 | Genetic variability of Cacadors | list | | |
avijons | ade4 | Bird species distribution | list | | |
avimedi | ade4 | Fauna Table for Constrained Ordinations | list | | |
aviurba | ade4 | Ecological Tables Triplet | list | | |
bacteria | ade4 | Genomes of 43 Bacteria | list | | |
banque | ade4 | Table of Factors | data.frame | 810 | 21 |
baran95 | ade4 | African Estuary Fishes | list | | |
bf88 | ade4 | Cubic Ecological Data | list | | |
bordeaux | ade4 | Wine Tasting | data.frame | 5 | 4 |
bsetal97 | ade4 | Ecological and Biological Traits | list | | |
buech | ade4 | Buech basin | list | | |
butterfly | ade4 | Genetics-Ecology-Environment Triple | list | | |
capitales | ade4 | Road Distances | list | | |
carni19 | ade4 | Phylogeny and quantative trait of carnivora | list | | |
carni70 | ade4 | Phylogeny and quantitative traits of carnivora | list | | |
carniherbi49 | ade4 | Taxonomy, phylogenies and quantitative traits of carnivora and herbivora | list | | |
casitas | ade4 | Enzymatic polymorphism in Mus musculus | data.frame | 74 | 15 |
chatcat | ade4 | Qualitative Weighted Variables | list | | |
chats | ade4 | Pair of Variables | data.frame | 8 | 8 |
chazeb | ade4 | Charolais-Zebus | list | | |
chevaine | ade4 | Enzymatic polymorphism in Leuciscus cephalus | list | | |
chickenk | ade4 | Veterinary epidemiological study to assess the risk factors for losses in broiler chickens | list | | |
clementines | ade4 | Fruit Production | data.frame | 15 | 20 |
cnc2003 | ade4 | Frequenting movie theaters in France in 2003 | data.frame | 94 | 12 |
coleo | ade4 | Table of Fuzzy Biological Traits | list | | |
corvus | ade4 | Corvus morphology | data.frame | 28 | 4 |
deug | ade4 | Exam marks for some students | list | | |
doubs | ade4 | Pair of Ecological Tables | list | | |
dunedata | ade4 | Dune Meadow Data | list | | |
ecg | ade4 | Electrocardiogram data | ts | | |
ecomor | ade4 | Ecomorphological Convergence | list | | |
elec88 | ade4 | Electoral Data | list | | |
escopage | ade4 | K-tables of wine-tasting | list | | |
euro123 | ade4 | Triangular Data | list | | |
fission | ade4 | Fission pattern and heritable morphological traits | list | | |
friday87 | ade4 | Faunistic K-tables | list | | |
fruits | ade4 | Pair of Tables | list | | |
ggtortoises | ade4 | Microsatellites of Galapagos tortoises populations | list | | |
granulo | ade4 | Granulometric Curves | list | | |
hdpg | ade4 | Genetic Variation In Human Populations | list | | |
houmousr | ade4 | Morphometric data set | list | | |
housetasks | ade4 | Contingency Table | data.frame | 13 | 4 |
humDNAm | ade4 | human mitochondrial DNA restriction data | list | | |
ichtyo | ade4 | Point sampling of fish community | list | | |
irishdata | ade4 | Geary's Irish Data | list | | |
julliot | ade4 | Seed dispersal | list | | |
jv73 | ade4 | K-tables Multi-Regions | list | | |
kcponds | ade4 | Ponds in a nature reserve | list | | |
lascaux | ade4 | Genetic/Environment and types of variables | list | | |
lizards | ade4 | Phylogeny and quantitative traits of lizards | list | | |
macaca | ade4 | Landmarks | list | | |
macon | ade4 | Wine Tasting | data.frame | 8 | 25 |
macroloire | ade4 | Assemblages of Macroinvertebrates in the Loire River (France) | list | | |
mafragh | ade4 | Phyto-Ecological Survey | list | | |
maples | ade4 | Phylogeny and quantitative traits of flowers | list | | |
mariages | ade4 | Correspondence Analysis Table | data.frame | 9 | 9 |
meau | ade4 | Ecological Data : sites-variables, sites-species, where and when | list | | |
meaudret | ade4 | Ecological Data : sites-variables, sites-species, where and when | list | | |
microsatt | ade4 | Genetic Relationships between cattle breeds with microsatellites | list | | |
mjrochet | ade4 | Phylogeny and quantitative traits of teleos fishes | list | | |
mollusc | ade4 | Faunistic Communities and Sampling Experiment | list | | |
monde84 | ade4 | Global State of the World in 1984 | data.frame | 48 | 5 |
morphosport | ade4 | Athletes' Morphology | list | | |
newick.eg | ade4 | Phylogenetic trees in Newick format | list | | |
njplot | ade4 | Phylogeny and trait of bacteria | list | | |
olympic | ade4 | Olympic Decathlon | list | | |
oribatid | ade4 | Oribatid mite | list | | |
ours | ade4 | A table of Qualitative Variables | data.frame | 38 | 10 |
palm | ade4 | Phylogenetic and quantitative traits of amazonian palm trees | list | | |
pap | ade4 | Taxonomy and quantitative traits of carnivora | list | | |
pcw | ade4 | Distribution of of tropical trees along the Panama canal | list | | |
perthi02 | ade4 | Contingency Table with a partition in Molecular Biology | list | | |
piosphere | ade4 | Plant traits response to grazing | list | | |
presid2002 | ade4 | Results of the French presidential elections of 2002 | list | | |
procella | ade4 | Phylogeny and quantitative traits of birds | list | | |
rankrock | ade4 | Ordination Table | data.frame | 10 | 51 |
rhizobium | ade4 | Genetic structure of two nitrogen fixing bacteria influenced by geographical isolation and host specialization | list | | |
rhone | ade4 | Physico-Chemistry Data | list | | |
rpjdl | ade4 | Avifauna and Vegetation | list | | |
santacatalina | ade4 | Indirect Ordination | data.frame | 11 | 10 |
sarcelles | ade4 | Array of Recapture of Rings | list | | |
seconde | ade4 | Students and Subjects | data.frame | 22 | 8 |
skulls | ade4 | Morphometric Evolution | data.frame | 150 | 4 |
steppe | ade4 | Transect in the Vegetation | list | | |
syndicats | ade4 | Two Questions asked on a Sample of 1000 Respondents | data.frame | 5 | 4 |
t3012 | ade4 | Average temperatures of 30 French cities | list | | |
tarentaise | ade4 | Mountain Avifauna | list | | |
taxo.eg | ade4 | Examples of taxonomy | list | | |
tintoodiel | ade4 | Tinto and Odiel estuary geochemistry | list | | |
tithonia | ade4 | Phylogeny and quantitative traits of flowers | list | | |
tortues | ade4 | Morphological Study of the Painted Turtle | data.frame | 48 | 4 |
toxicity | ade4 | Homogeneous Table | list | | |
trichometeo | ade4 | Pair of Ecological Data | list | | |
ungulates | ade4 | Phylogeny and quantitative traits of ungulates. | list | | |
vegtf | ade4 | Vegetation in Trois-Fontaines | list | | |
veuvage | ade4 | Example for Centring in PCA | list | | |
westafrica | ade4 | Freshwater fish zoogeography in west Africa | list | | |
woangers | ade4 | Plant assemblages in woodlands of the conurbation of Angers (France) | list | | |
worksurv | ade4 | French Worker Survey (1970) | data.frame | 319 | 4 |
yanomama | ade4 | Distance Matrices | list | | |
zealand | ade4 | Road distances in New-Zealand | list | | |
ag_data | lpirfs | Data to estimate fiscal multipliers | tbl_df | 248 | 7 |
interest_rules_var_data | lpirfs | Data to estimate the effects of interest rate rules for monetary policy | tbl_df | 193 | 3 |
monetary_var_data | lpirfs | Data to estimate a standard monetary VAR | tbl_df | 494 | 6 |
Psurveys | fitPS | Number of Groups of Glass Data | list | | |
Ssurveys | fitPS | Size of Groups of Glass Data | list | | |
n3_poly | potential | Points and Polygons Layers of European Statistical Units (NUTS3) | sf | 1506 | 4 |
n3_pt | potential | Points and Polygons Layers of European Statistical Units (NUTS3) | sf | 1506 | 4 |
ev_pib | TractorTsbox | Évolution du PIB français jusqu'au T1 2022 | ts | | |
fish_data | kernopt | Fish dataset from the SIMTAP project | data.frame | 200 | 2 |
contig_list | scRepertoire | A list of 8 single-cell T cell receptor sequences runs. | list | | |
mini_contig_list | scRepertoire | Processed subset of 'contig_list' | list | | |
scRep_example | scRepertoire | A Seurat object of 500 single T cells, | Seurat | | |
Data.Incomes | LorenzRegression | Simulated income data | data.frame | 200 | 7 |
BlueprintEncode.xCell2Ref | xCell2 | Blueprint and ENCODE Projects Reference | xCell2Object | | |
DICE_demo.xCell2Ref | xCell2 | Demo xCell2 Reference Object | xCell2Object | | |
ImmGenData.xCell2Ref | xCell2 | Immunologic Genome Project Reference | xCell2Object | | |
ImmuneCompendium.xCell2Ref | xCell2 | Immune Compendium Reference | xCell2Object | | |
LM22.xCell2Ref | xCell2 | LM22 Reference | xCell2Object | | |
MouseRNAseqData.xCell2Ref | xCell2 | Mouse RNA-Seq Data Reference | xCell2Object | | |
PanCancer.xCell2Ref | xCell2 | PanCancer Reference | xCell2Object | | |
TMECompendium.xCell2Ref | xCell2 | Tumor Microenvironment Compendium Reference | xCell2Object | | |
TabulaMurisBlood.xCell2Ref | xCell2 | Tabula Muris Blood Reference | xCell2Object | | |
TabulaSapiensBlood.xCell2Ref | xCell2 | Tabula Sapiens Blood Reference | xCell2Object | | |
dice_demo_ref | xCell2 | Subset of the DICE Reference | SummarizedExperiment | | |
mix_demo | xCell2 | Demo Bulk Gene Expression Data | matrix | 1000 | 3 |
aeles | animbook | Australian election study data | tbl_df | 1468 | 4 |
cat_change | animbook | Simulated data with some change (category) | tbl_df | 400 | 4 |
dbl_change | animbook | Simulated data with some change (numerical) | tbl_df | 400 | 4 |
osiris | animbook | Osiris firm sales data | tbl_df | 10270 | 5 |
Ausmale | bigdatadist | Australian Male Mortality Rates | list | | |
merval | bigdatadist | Merval Index | data.frame | 5269 | 5 |
Data2D | clusTransition | Synthetic Datasets (Two Dimensional) | list | | |
Data3D | clusTransition | Synthetic Datasets (Three Dimensional) | list | | |
ex_data_JoBS | EDNE.EQ | Example dataset from Hoffelder et al. (2015) | tbl_df | 24 | 4 |
result_global | SpaCCI | result_global data for SpaCCI | list | | |
result_local | SpaCCI | result_local data and spatial coordinates for SpaCCI Local Regional Data | list | | |
result_local_spatial_coords_df | SpaCCI | Local Spatial Coordinates Data Frame | data.frame | 782 | 3 |
result_regional | SpaCCI | result_regional data for SpaCCI | list | | |
test_data | SpaCCI | Test data for SpaCCI | list | | |
testcase1 | refineR | Simulated Testcase 1. | numeric | | |
testcase2 | refineR | Simulated Testcase 2. | numeric | | |
testcase3 | refineR | Simulated Testcase 3. | numeric | | |
testcase4 | refineR | Simulated Testcase 4. | numeric | | |
testcase5 | refineR | Simulated Testcase 5. | numeric | | |
france | factorplot | Example data for factorplot function | data.frame | 542 | 5 |
follic | cmprskcoxmsm | Follicular cell lymphoma study | data.frame | 541 | 7 |
exampleLIBD | SRTsim | Data used for creating vignettes | list | | |
toyData | SRTsim | A toyExample to showcase reference-based simulations | list | | |
toyShiny | SRTsim | A toyExample to showcase reference-free simulations | list | | |
sim2 | HDclust | Synthetic dataset used in section 5.1.2 of the reference paper. | data.frame | 5000 | 6 |
sim3 | HDclust | Synthetic dataset used in section 5.1.3 of the reference paper | data.frame | 1000 | 41 |
Concrete | MAVE | Concrete Compressive Strength Data Set | data.frame | 1030 | 9 |
kc_house_data | MAVE | House price in King County, USA | tbl_df | 21613 | 21 |
spam | MAVE | 4601 email record | data.frame | 4601 | 58 |
Feldspar | ggtern | Elkin and Groves Feldspar Data | data.frame | 40 | 7 |
FeldsparRaster | ggtern | Elkin and Groves Feldspar Data | array | | |
Fragments | ggtern | Grantham and Valbel Rock Fragment Data | data.frame | 72 | 13 |
SkyeLava | ggtern | Aichisons Skye Lavas | data.frame | 23 | 4 |
USDA | ggtern | USDA Textural Classification Data | data.frame | 53 | 4 |
WhiteCells | ggtern | Aichisons White Cells | data.frame | 60 | 5 |
newws | moderate.mediation | NEWWS Riverside data | data.frame | 694 | 12 |
HCD | MCPAN | Hell Creek Dinosaur Data | data.frame | 3 | 9 |
bronch | MCPAN | Rodent bronchial carcinoma data | data.frame | 200 | 3 |
liarozole | MCPAN | Marked improvement of psoriasis after application of liarozole | data.frame | 137 | 2 |
methyl | MCPAN | NTP bioassay data: effect of methyleugenol on skin fibroma | data.frame | 200 | 3 |
Virkler25 | HMMRel | Fatigue crack growth in materials: Virkler dataset (tests 1 to 25) | data.frame | 164 | 26 |
prematurity | pm3 | A data on indicators for premature newborns. | data.frame | 189 | 11 |
mcl_sll | FeaLect | MCL and SLL lymphoma subtypes | data.frame | 22 | 237 |
Pinus | PLORN | Transcriptomes of Pinus roots under a Temperature Gradient | list | | |
circtest | tripack | circtest / sample data | list | | |
circtest2 | tripack | circtest / sample data | list | | |
tritest | tripack | tritest / sample data | list | | |
tritest2 | tripack | tritest / sample data | list | | |
london | bayeslist | The 2017 London List Experiment | data.frame | 3189 | 18 |
srilanka | bayeslist | The Sri Lanka List Experiment on Wartime Sexual Violence | data.frame | 247 | 9 |
Y | VBLPCM | simulated.network | matrix | 30 | 30 |
aids.net | VBLPCM | aids blogs data as a "network" object | network | | |
samplike | VBLPCM | Cumulative network of positive affection within a monastery as a "network" object | network | | |
Dados1 | Tratamentos.ad | Dados de exemplo (1 fator e 1 testemunha). | data.frame | 20 | 4 |
Dados2 | Tratamentos.ad | Dados de exemplo (1 fator e 3 testemunhas). | data.frame | 28 | 4 |
Dados3 | Tratamentos.ad | Dados de exemplo (2 fatores e 2 testemunhas). | data.frame | 28 | 5 |
Dados4 | Tratamentos.ad | Dados de exemplo (2 fatores e 1 testemunha). | data.frame | 36 | 5 |
Dados5 | Tratamentos.ad | Dados de exemplo (2 fatores e 3 testemunha). | data.frame | 44 | 5 |
Dados6 | Tratamentos.ad | Dados de exemplo (3 fatores e 3 testemunha). | data.frame | 76 | 6 |
Dados7 | Tratamentos.ad | Dados de exemplo de DQL (4 tratamentos comuns e 2 testemunhas). | data.frame | 36 | 5 |
Dados8 | Tratamentos.ad | Dados de exemplo de DQL (5 tratamentos comuns e 1 testemunha). | data.frame | 36 | 5 |
ips.16S | ips | Bark Beetle 16S Sequences | DNAbin | 42 | |
ips.28S | ips | Bark Beetle 28S Sequences | DNAbin | 28 | |
ips.cox1 | ips | Bark Beetle COX1 Sequences | DNAbin | 26 | |
ips.tree | ips | Ips Phylogeny | phylo | | |
log_list | ips | Internal IPS Functions | data.frame | 65 | 4 |
operator_list | ips | Internal IPS Functions | data.frame | 149 | 4 |
dataRaw | GESE | dataRaw - a data frame containing the pedigree, phenotype and genotype information | data.frame | 198 | 16 |
database | GESE | database file in example | data.frame | 10 | 3 |
mapInfo | GESE | mafInfo - example data | data.frame | 10 | 2 |
pednew | GESE | pednew - an example pedigree structure | data.frame | 1700 | 5 |
data | MRmediation | This is the data for examples | data.frame | 407 | 16 |
pos | MRmediation | This is the data for examples | integer | | |
FFPE_dat | RCRnorm | FFPE data on 83 regular genes and 28 patients. | list | | |
relgoods | CUB | Relational goods and Leisure time dataset | data.frame | 2459 | 50 |
univer | CUB | Evaluation of the Orientation Services 2002 | data.frame | 2179 | 12 |
being_processed | ccoptimalmatch | Data for matching cases with controls | data.frame | 10664 | 11 |
not_processed | ccoptimalmatch | Not-processed data for matching cases with controls | data.frame | 46019 | 9 |
datasim | ZIPBayes | Toy example data - main study only | data.frame | 500 | 9 |
datasimExt | ZIPBayes | Toy example data - main study and external validation study | list | | |
datasimInt | ZIPBayes | Toy example data - main study and internal validation study | list | | |
curdies | GFD | Curdies river data set | data.frame | 36 | 3 |
pizza | GFD | Pizza delivery times | data.frame | 16 | 6 |
startup | GFD | Startup Costs of five different companies | data.frame | 60 | 2 |
example | CalibratR | example | list | | |
ANmodulation | IDmeasurer | Little owl, _Athene noctua_ - frequency modulation | data.frame | 330 | 12 |
ANspec | IDmeasurer | Little owl, _Athene noctua_ - spectrum properties | data.frame | 330 | 8 |
CCformants | IDmeasurer | Corncrake, _Crex crex_ - formants | data.frame | 330 | 5 |
CCspec | IDmeasurer | Corncrake, _Crex crex_ - spectrum properties | data.frame | 330 | 8 |
LAhighweewoo | IDmeasurer | Yellow-breasted boubou, _Laniarius atroflavus_ - spectrum properties | data.frame | 330 | 7 |
SSgrunts | IDmeasurer | Domestic pig, _Sus scrofa domestica_ - piglet grunts | data.frame | 330 | 11 |
Wmatrix | NSAE | Proximity matrix | data.frame | 71 | 71 |
headcount | NSAE | Head count data | data.frame | 71 | 11 |
paddy | NSAE | Yield data of paddy | data.frame | 70 | 9 |
paddysample | NSAE | Yield data of paddy for sample area | data.frame | 58 | 8 |
emg95306000 | biosignalEMG | Sample EMG data from a decorticate cat | data.frame | 1999 | 1 |
emg96627009 | biosignalEMG | Sample EMG data from a decorticate cat (4 channels) | data.frame | 31979 | 4 |
DeBiasi_COVID_CD8_samp | treekoR | COVID-19 Sample data | SingleCellExperiment | | |
bcm | oppar | Breast cancer metastases from different anatomical sites | ExpressionSet | | |
eset | oppar | Tomlins et al. Prostate Cancer data (GEO: GSE6099) | ExpressionSet | | |
maupin | oppar | Maupin's TGFb data and a TGFb gene signature | list | | |
bison | TwoStepCLogit | Bison Dataset | data.frame | 16818 | 9 |
dataList | SIBERG | Simulated Data From 2-component Mixture Models | list | | |
parList | SIBERG | Simulated Data From 2-component Mixture Models | list | | |
phoneme | SCBmeanfd | Phoneme data | data.frame | 2000 | 151 |
plasma | SCBmeanfd | Plasma citrate data | matrix | 10 | |
sp500 | LSWPlib | Daily log-returns for the S&P 500 stock index. | numeric | | |
GCaMP | GCalcium | Pre-filtered GCaMP calcium activity waveforms | matrix | 11 | |
SNPhood.o | SNPhood | SNPhood example data | SNPhood | | |
covid | MSinference | Number of daily new cases of infections of COVID-19 per country. | matrix | 148 | 42 |
temperature | MSinference | Hadley Centre Central England Temperature (HadCET) dataset, Monthly Mean Central England Temperature (Degrees C) | numeric | | |
wage.rates | SMNCensReg | Wage Rates of 753 Women | data.frame | 753 | 16 |
epiGenomics | bioCancer | Default dataset of bioCancer | data.frame | 48 | 7 |
user_CNA | bioCancer | Example of Copy Number Alteration (CNA) dataset | data.frame | 579 | 13 |
user_MetHM27 | bioCancer | Example of Methylation HM27 dataset | data.frame | 600 | 13 |
user_MetHM450 | bioCancer | Example of Methylation HM450 dataset | data.frame | 10 | 13 |
user_Mut | bioCancer | Example of Mutation dataset | data.frame | 37 | 23 |
user_mRNA | bioCancer | Example of mRNA expression dataset | data.frame | 307 | 13 |
Data | ChannelAttribution | Customer journeys data. | data.table | 10000 | 4 |
spreadn4t2a | IsoCheck | Data: A cyclic 1-spread of PG(3,2) | array | | |
spreadn4t2b | IsoCheck | Data: A cyclic 1-spread of PG(3,2) | array | | |
spreadn6t2a | IsoCheck | Data: A cyclic 1-spread of PG(5,2) | array | | |
spreadn6t2b | IsoCheck | Data: A cyclic 1-spread of PG(5,2) | array | | |
spreadn6t2c | IsoCheck | Data: A cyclic 1-spread of PG(5,2) | array | | |
spreadn6t3a | IsoCheck | Data: A cyclic 2-spread of PG(5,2) | array | | |
spreadn6t3b | IsoCheck | Data: A cyclic 2-spread of PG(5,2) | array | | |
starn5t3a | IsoCheck | Data: A 2-star of PG(4,2) | array | | |
starn5t3b | IsoCheck | Data: A 2-star of PG(4,2) | array | | |
starn8t5a | IsoCheck | Data: A 4-star of PG(7,2) | array | | |
starn8t5b | IsoCheck | Data: A 4-star of PG(7,2) | array | | |
efc | datawizard | Sample dataset from the EFC Survey | data.frame | 100 | 5 |
nhanes_sample | datawizard | Sample dataset from the National Health and Nutrition Examination Survey | tbl_df | 2992 | 7 |
pseudo.data | GSMX | Pseudo dataset | numeric | | |
pseudo.kin | GSMX | Pseudo kinship matrix | matrix | 50 | 50 |
candidate_data | align | Synthetic Candidate Data | data.frame | 50 | 2 |
target_data | align | Synthetic Target Data | data.frame | 251 | 2 |
Hedenfalk | Equalden.HD | Hedenfalk data | matrix | 3226 | 15 |
Rat | Equalden.HD | Rat data | data.frame | 8038 | 5 |
small_graph_example | rlemon | A small network graph example | list | | |
ENSR_subset.hg19 | biscuiteer | ENSR_subset data from hg19 genome | GRanges | | |
ENSR_subset.hg38 | biscuiteer | ENSR_subset data from hg38 genome | GRanges | | |
GRCh37.chromArm | biscuiteer | GRCh37.chromArm | GRanges | | |
GRCh38.chromArm | biscuiteer | GRCh38.chromArm | GRanges | | |
H9state23unmeth.hg19 | biscuiteer | H9state23unmeth.hg19 | GRanges | | |
H9state23unmeth.hg38 | biscuiteer | H9state23unmeth.hg38 | GRanges | | |
HMM_CpG_islands.hg19 | biscuiteer | HMM_CpG_islands.hg19 | GRanges | | |
HMM_CpG_islands.hg38 | biscuiteer | HMM_CpG_islands.hg38 | GRanges | | |
clocks | biscuiteer | clocks | data.frame | 730 | 14 |
hg19.chromArm | biscuiteer | hg19.chromArm | GRanges | | |
hg38.chromArm | biscuiteer | hg38.chromArm | GRanges | | |
seqinfo.hg19 | biscuiteer | seqinfo.hg19 | Seqinfo | | |
seqinfo.hg38 | biscuiteer | seqinfo.hg38 | Seqinfo | | |
seqinfo.mm10 | biscuiteer | seqinfo.mm10 | Seqinfo | | |
arc_categories | arcgeocoder | ArcGIS REST API category data base | tbl_df | 376 | 3 |
arc_spatial_references | arcgeocoder | ESRI (ArcGIS) Spatial Reference data base | tbl_df | 9364 | 8 |
Ancona | MethComp | Data from a rating experiment of recorgnizing point counts. | data.frame | 510 | 4 |
CardOutput | MethComp | Measurements of Cardiac output. | data.frame | 15 | 8 |
Enzyme | MethComp | Enzyme activity data | data.frame | 72 | 3 |
PEFR | MethComp | Peak Expiratory Flow Rate (PEFR) measurements with Wright peak flow and mini Wright peak flow meter. | data.frame | 68 | 4 |
VitCap | MethComp | Merits of two instruments designed to measure certain aspects of human lung function (Vital Capacity) | data.frame | 288 | 5 |
cardiac | MethComp | Measurement of cardiac output by two different methods. | data.frame | 120 | 4 |
fat | MethComp | Measurements of subcutaneous and visceral fat | data.frame | 258 | 5 |
glucose | MethComp | Glucose measurements by different methods | data.frame | 1302 | 6 |
hba.MC | MethComp | A MCmcmc object from the hba1c data | MethComp | | |
hba1c | MethComp | Measurements of HbA1c from Steno Diabetes Center | data.frame | 835 | 6 |
milk | MethComp | Measurement of fat content of human milk by two different methods. | data.frame | 90 | 3 |
ox | MethComp | Measurement of oxygen saturation in blood | data.frame | 354 | 4 |
ox.MC | MethComp | A MCmcmc object from the oximetry data. | MCmcmc | | |
plvol | MethComp | Measurements of plasma volume measured by two different methods. | data.frame | 198 | 3 |
rainman | MethComp | Perception of points in a swarm | data.frame | 30 | 6 |
sbp | MethComp | Systolic blood pressure measured by three different methods. | data.frame | 765 | 4 |
sbp.MC | MethComp | A MCmcmc object from the sbp data | MCmcmc | | |
scint | MethComp | Relative renal function by Scintigraphy | data.frame | 222 | 5 |
LqG_SimuData | LqG | An Example of Simulated Data for LqG | list | | |
Laurasiatherian | phangorn | Laurasiatherian data (AWCMEE) | phyDat | | |
chloroplast | phangorn | Chloroplast alignment | phyDat | | |
mites | phangorn | Morphological characters of Mites (Schäffer et al. 2010) | phyDat | | |
yeast | phangorn | Yeast alignment (Rokas et al.) | phyDat | | |
casen | calidad | Encuesta de Caracterización Socioeconómica Nacional 2020 - CASEN en Pandemia 2020 | tbl_df | 185437 | 10 |
ene | calidad | Encuesta Nacional de Empleo - ENE. 2020-efm | data.frame | 87842 | 6 |
enusc | calidad | Encuesta Nacional Urbana de Seguridad Ciudadana 2019 - ENUSC 2019 | tbl_df | 24465 | 22 |
enusc_2023 | calidad | Encuesta Nacional Urbana de Seguridad Ciudadana 2023 - ENUSC 2023 | tbl_df | 49813 | 15 |
epf_personas | calidad | VIII Encuesta de Presupuestos Familiares | data.frame | 48308 | 9 |
DMRs | BiSeq | The output of 'findDMRs' | GRanges | | |
betaResults | BiSeq | The output of 'betaRegression' | data.frame | 344 | 10 |
betaResultsNull | BiSeq | The output of 'betaRegression' for resampled data | data.frame | 344 | 10 |
predictedMeth | BiSeq | The output of 'predictMeth' | BSrel | | |
promoters | BiSeq | A 'GRanges' of promoters of the human genome | GRanges | | |
rrbs | BiSeq | RRBS data of APL patient samples and controls. | BSraw | | |
vario | BiSeq | Output of 'makeVariogram' | list | | |
london | osmplotr | london | list | | |
Lee_etal2020qPCR | rtpcr | Sample data (with technical replicates) | data.frame | 72 | 8 |
Taylor_etal2019 | rtpcr | Sample qPCR data (one target and two reference genes under two different conditions) | data.frame | 18 | 4 |
data_1factor | rtpcr | Sample data (one factor three levels) | data.frame | 9 | 6 |
data_2factor | rtpcr | Sample data (two factor) | data.frame | 18 | 7 |
data_2factorBlock | rtpcr | Sample data (two factor with blocking factor) | data.frame | 18 | 8 |
data_3factor | rtpcr | Sample data (three factor) | data.frame | 36 | 8 |
data_efficiency | rtpcr | Sample qPCR data: amplification efficiency | data.frame | 21 | 4 |
data_repeated_measure_1 | rtpcr | Repeated measure sample data | data.frame | 9 | 6 |
data_repeated_measure_2 | rtpcr | Repeated measure sample data | data.frame | 18 | 7 |
data_ttest | rtpcr | Sample qPCR data from an experiment conducted under two different conditions | data.frame | 24 | 4 |
data_withTechRep | rtpcr | Sample data (with technical replicates) | data.frame | 18 | 9 |
commodity | backtestGraphics | Commodity Futures Data from 2003 to 2005. | tbl_df | 63382 | 11 |
credit | backtestGraphics | Credit Default Swap Data from 2007 to 2009. | tbl_df | 60382 | 7 |
equity | backtestGraphics | Equity Data from 2005 to 20014. | tbl_df | 7440 | 5 |
gastadj | surrosurv | Individual data from the adjuvant GASTRIC meta-analysis | data.frame | 3288 | 7 |
gastadv | surrosurv | Individual data from the advanced GASTRIC meta-analysis | data.frame | 4069 | 7 |
res_enrich_macrophage_cluPro | mosdef | A sample enrichment object | enrichResult | | |
res_enrich_macrophage_goseq | mosdef | A sample enrichment object | data.frame | 14789 | 10 |
res_enrich_macrophage_topGO | mosdef | A sample enrichment object | data.frame | 6116 | 9 |
res_macrophage_IFNg_vs_naive | mosdef | A sample 'DESeqResults' object | DESeqResults | | |
AADT | AID | Average Annual Daily Traffic Data | data.frame | 121 | 8 |
grades | AID | Student Grades Data | data.frame | 42 | 1 |
textile | AID | Textile Data | data.frame | 27 | 1 |
ad | cnmap | the code and name of China's administrative divisions | data.frame | 3212 | 2 |
cetesb_aqs | qualR | CETESB AQS station latitude and longitude | data.frame | 75 | 5 |
cetesb_param | qualR | CETESB Parameters | data.frame | 20 | 3 |
monitor_ar_aqs | qualR | Monitor Ar AQS stations. | data.frame | 8 | 6 |
monitor_ar_param | qualR | Monitor Ar Parameters | data.frame | 18 | 3 |
asr_sae | SwissASR | Demonstration data set | data.frame | 20 | 22 |
ELEVATION | RMAWGEN | Trentino Dataset | array | | |
LOCATION | RMAWGEN | Trentino Dataset | array | | |
PRECIPITATION | RMAWGEN | Trentino Dataset | data.frame | 18262 | 62 |
PRECIPITATION_MEASUREMENT_END_DAY | RMAWGEN | Trentino Dataset | array | | |
PRECIPITATION_MEASUREMENT_START_DAY | RMAWGEN | Trentino Dataset | array | | |
STATION_LATLON | RMAWGEN | Trentino Dataset | matrix | 59 | |
STATION_NAMES | RMAWGEN | Trentino Dataset | array | | |
TEMPERATURE_MAX | RMAWGEN | Trentino Dataset | data.frame | 18262 | 62 |
TEMPERATURE_MEASUREMENT_END_DAY | RMAWGEN | Trentino Dataset | array | | |
TEMPERATURE_MEASUREMENT_START_DAY | RMAWGEN | Trentino Dataset | array | | |
TEMPERATURE_MIN | RMAWGEN | Trentino Dataset | data.frame | 18262 | 62 |
data1 | MethodCompare | Simulated dataset 1 | tbl_df | 1468 | 3 |
data2 | MethodCompare | Simulated dataset 2 | tbl_df | 1680 | 3 |
data3 | MethodCompare | Simulated dataset 3 | tbl_df | 1682 | 3 |
GDPsatis | robCompositions | GDP satisfaction | data.frame | 31 | 8 |
ageCatWorld | robCompositions | child, middle and eldery population | data.frame | 195 | 4 |
alcohol | robCompositions | alcohol consumptions by country and type of alcohol | data.frame | 193 | 6 |
alcoholreg | robCompositions | regional alcohol per capita (15+) consumption by WHO region | data.frame | 6 | 4 |
arcticLake | robCompositions | arctic lake sediment data | data.frame | 39 | 3 |
cancer | robCompositions | hospital discharges on cancer and distribution of age | data.frame | 24 | 6 |
cancerMN | robCompositions | malignant neoplasms cancer | data.frame | 35 | 5 |
chorizonDL | robCompositions | C-horizon of the Kola data with rounded zeros | data.frame | 606 | 62 |
coffee | robCompositions | coffee data set | data.frame | 30 | 7 |
economy | robCompositions | economic indicators | data.frame | 30 | 7 |
educFM | robCompositions | education level of father (F) and mother (M) | data.frame | 31 | 7 |
efsa | robCompositions | efsa nutrition consumption | data.frame | 87 | 22 |
election | robCompositions | election data | data.frame | 16 | 8 |
electionATbp | robCompositions | Austrian presidential election data | data.frame | 2202 | 11 |
employment | robCompositions | employment in different countries by gender and status. | array | | |
employment2 | robCompositions | Employment in different countries by Sex, Age, Contract, Value | data.frame | 504 | 5 |
employment_df | robCompositions | Employment in different countries by gender and status. | data.frame | 132 | 4 |
expenditures | robCompositions | synthetic household expenditures toy data set | data.frame | 20 | 5 |
expendituresEU | robCompositions | mean consumption expenditures data. | data.frame | 27 | 12 |
foodbalance | robCompositions | country food balances | data.frame | 184 | 116 |
gemas | robCompositions | GEMAS geochemical data set | data.frame | 2108 | 30 |
gjovik | robCompositions | gjovik | data.frame | 615 | 63 |
govexp | robCompositions | government spending | data.frame | 5140 | 4 |
haplogroups | robCompositions | haplogroups data. | data.frame | 38 | 12 |
honey | robCompositions | honey compositions | data.frame | 429 | 17 |
instw | robCompositions | value added, output and input for different ISIC codes and countries. | data.frame | 1555 | 7 |
isic32 | robCompositions | ISIC codes by name | data.frame | 24 | 2 |
laborForce | robCompositions | labour force by status in employment | data.frame | 124 | 9 |
landcover | robCompositions | European land cover | data.frame | 28 | 7 |
lifeExpGdp | robCompositions | life expectancy and GDP (2008) for EU-countries | data.frame | 27 | 9 |
machineOperators | robCompositions | machine operators | data.frame | 27 | 4 |
manu_abs | robCompositions | Distribution of manufacturing output | data.frame | 630 | 4 |
mcad | robCompositions | metabolomics mcad data set | data.frame | 50 | 279 |
mortality | robCompositions | mortality and life expectancy in the EU | data.frame | 60 | 12 |
mortality_tab | robCompositions | mortality table | table | 8 | 2 |
nutrients | robCompositions | nutrient contents | tbl_df | 965 | 50 |
nutrients_branded | robCompositions | nutrient contents (branded) | data.frame | 9618 | 18 |
payments | robCompositions | special payments | data.frame | 535 | 11 |
phd | robCompositions | PhD students in the EU | data.frame | 31 | 11 |
phd_totals | robCompositions | PhD students in the EU (totals) | data.frame | 28 | 5 |
precipitation | robCompositions | 24-hour precipitation | matrix | 6 | 4 |
production | robCompositions | production splitted by nationality on enterprise level | data.frame | 535 | 9 |
rcodes | robCompositions | codes for UNIDO tables | data.frame | 2717 | 6 |
saffron | robCompositions | saffron compositions | data.frame | 53 | 36 |
skyeLavas | robCompositions | aphyric skye lavas data | data.frame | 23 | 3 |
socExp | robCompositions | social expenditures | data.frame | 20 | 8 |
teachingStuff | robCompositions | teaching stuff | data.frame | 1216 | 4 |
trondelagC | robCompositions | regional geochemical survey of soil C in Norway | data.frame | 754 | 70 |
trondelagO | robCompositions | regional geochemical survey of soil O in Norway | data.frame | 756 | 73 |
unemployed | robCompositions | unemployed of young people | data.frame | 5459 | 4 |
mulsam.test | hgwrr | Simulated Spatial Multisampling Data For Test (DataFrame) | list | | |
multisampling | hgwrr | Large Scale Simulated Spatial Multisampling Data (DataFrame) | list | | |
wuhan.hp | hgwrr | Wuhan Second-hand House Price and POI Data (DataFrame) | sf | 19599 | 23 |
CASCrefmicrodata | sdcMicro | Census data set | data.frame | 1080 | 13 |
EIA | sdcMicro | EIA data set | data.frame | 4092 | 15 |
Tarragona | sdcMicro | Tarragona data set | data.frame | 834 | 13 |
casc1 | sdcMicro | Small Artificial Data set | matrix | 13 | 7 |
francdat | sdcMicro | data from the casc project | data.frame | 8 | 8 |
free1 | sdcMicro | Demo data set from mu-Argus | matrix | 4000 | 34 |
microData | sdcMicro | microData | matrix | 13 | 5 |
testdata | sdcMicro | A real-world data set on household income and expenditures | data.frame | 4580 | 15 |
testdata2 | sdcMicro | A real-world data set on household income and expenditures | data.frame | 93 | 19 |
iHMP | BaHZING | iHMP data | phyloseq | | |
iHMP_Reduced | BaHZING | iHMP_Reduced data | phyloseq | | |
FELLA.sample | FELLA | FELLA.DATA sample data | FELLA.DATA | | |
input.sample | FELLA | A randomly generated list of affected metabolites | character | | |
monkeys | PhylogeneticEM | New World Monkeys dataset | list | | |
GS_table | bakR | Example cB data frame | tbl_df | 300 | 63 |
cB_small | bakR | Example cB data frame | grouped_df | 5788 | 5 |
fns | bakR | Example fraction news (fns) data frame | tbl_df | 1800 | 5 |
metadf | bakR | Example meatdf data frame | data.frame | 6 | 2 |
BRCA_genes | miRSM | BRCA genes | SummarizedExperiment | | |
ceRExp | miRSM | ceRNA expression data | SummarizedExperiment | | |
mRExp | miRSM | mRNA expression data | SummarizedExperiment | | |
miRExp | miRSM | miRNA expression data | SummarizedExperiment | | |
miRTarget | miRSM | miRNA-target ineractions | SummarizedExperiment | | |
miami | shapviz | Miami-Dade County House Prices | data.frame | 13932 | 17 |
sample_cov_data | dynamicSDM | Sample projection covariates three variables across for southern Africa. | data.frame | 225 | 6 |
sample_events_data | dynamicSDM | Sample e-Bird sampling event records | data.frame | 1000 | 5 |
sample_explan_data | dynamicSDM | Sample species occurrence records with associated dynamic explanatory variables | data.frame | 330 | 17 |
sample_extent_data | dynamicSDM | MULTIPOLYGON object for the extent of southern Africa | sf | 10 | 2 |
sample_filt_data | dynamicSDM | Sample of filtered species occurrence records | data.frame | 330 | 12 |
sample_occ_data | dynamicSDM | Sample species occurrence records | data.frame | 600 | 7 |
oddata | dpseg | Escherichia coli growth curves. | data.frame | 229 | 97 |
mdb_tbl | selfdestructin5 | mdb_tbl: Example Data Set | tbl_df | 16 | 6 |
PlethMorph | RRPP | Plethodon comparative morphological data | rrpp.data.frame | | |
Pupfish | RRPP | Landmarks on pupfish | rrpp.data.frame | | |
PupfishHeads | RRPP | Landmarks on pupfish heads | rrpp.data.frame | | |
fishy | RRPP | Simulated fish data for measurement error analysis | list | | |
motionpaths | RRPP | Simulated motion paths | list | | |
birth | EgoCor | Spatially correlated birthweight data with artificial coordinates | data.frame | 903 | 8 |
dbbmmstack | move | Dynamic brownian bridges | DBBMMStack | | |
duplicatedDataExample | move | Tracking data example with duplicated timestamps | data.frame | 10 | 5 |
fishers | move | A MoveStack | MoveStack | | |
leroy | move | GPS track data from a fisher | Move | | |
leroydbbmm | move | Dynamic brownian bridges | DBBMM | | |
leroydbgb | move | dynamic Bivariate Gausian Bridge example object | dynBGB | | |
simul_data | MixedPsy | A simulated psychophysical dataset | data.frame | 72 | 6 |
vibro_exp3 | MixedPsy | Data from tactile discrimination task - (Dallmann et al., 2015). | data.frame | 126 | 5 |
dta_file | labelled | Datasets for testing | data.frame | 47 | 6 |
spss_file | labelled | Datasets for testing | list | | |
x_haven_2.0 | labelled | Datasets for testing | haven_labelled | | |
x_spss_haven_2.0 | labelled | Datasets for testing | haven_labelled_spss | | |
incR_envdata | incR | An example data set of environmental temperatures to test the use of 'link{incRenv}'. | data.frame | 1570 | 2 |
incR_procdata | incR | An example of incubation temperature time-series after the use of 'incRprep' and 'incRenv'. | data.frame | 899 | 12 |
incR_rawdata | incR | An example of incubation temperature time-series | data.frame | 954 | 2 |
ExpressionMatrix | sincell | Single-cell expression data for genes differentially expressed in differentiating human skeletal muscle myoblasts cells | matrix | 575 | 271 |
geneset.list | sincell | Example of a geneset collection | list | | |
cardSim | LocalControl | Simulated cardiac medication data for survival analysis | data.frame | 1000 | 6 |
framingham | LocalControl | Framingham heart study data extract on smoking and hypertension. | data.frame | 2316 | 11 |
lindner | LocalControl | Lindner Center for Research and Education study on Abciximab cost-effectiveness and survival | data.frame | 996 | 10 |
bees3sp | BeeBDC | A flagged dataset of 105 random bee occurrence records from the three species | spec_tbl_df | 105 | 124 |
beesFlagged | BeeBDC | A flagged dataset of 100 random bee occurrence records | spec_tbl_df | 100 | 124 |
beesRaw | BeeBDC | A dataset of 100 random bee occurrence records without flags or filters applied | spec_tbl_df | 100 | 90 |
testChecklist | BeeBDC | An example of the beesChecklist file | tbl_df | 1105 | 24 |
testTaxonomy | BeeBDC | An example of the beesTaxonomy file | spec_tbl_df | 79 | 24 |
AllopolyTutorialData | polysat | Simulated Allotetraploid Data | genambig | | |
FCRinfo | polysat | Additional Data on Rubus Samples | data.frame | 20 | 2 |
simgen | polysat | Randomly Generated Data for Learning polysat | genambig | | |
testgenotypes | polysat | Rubus Genotype Data for Learning polysat | genambig | | |
depmap_22q1_TPM | CRISPRball | DepMap expression data | tbl_df | 1393 | 9 |
depmap_22q1_cn | CRISPRball | DepMap copy number data | tbl_df | 1754 | 9 |
depmap_22q1_crispr | CRISPRball | DepMap CRISPR screen data | tbl_df | 1070 | 9 |
depmap_22q1_crispr_rnai | CRISPRball | DepMap CRISPR & RNAi screen data | tbl_df | 1782 | 10 |
depmap_22q1_rnai | CRISPRball | DepMap RNAi screen data | tbl_df | 712 | 9 |
BTdata | MCMCglmm | Blue Tit Data for a Quantitative Genetic Experiment | data.frame | 828 | 7 |
BTped | MCMCglmm | Blue Tit Pedigree | data.frame | 1040 | 3 |
PlodiaPO | MCMCglmm | Phenoloxidase measures on caterpillars of the Indian meal moth. | data.frame | 511 | 3 |
PlodiaR | MCMCglmm | Resistance of Indian meal moth caterpillars to the granulosis virus PiGV. | data.frame | 50 | 5 |
PlodiaRB | MCMCglmm | Resistance (as a binary trait) of Indian meal moth caterpillars to the granulosis virus PiGV. | data.frame | 874 | 4 |
SShorns | MCMCglmm | Horn type and genders of Soay Sheep | data.frame | 666 | 3 |
SE | SEtools | Example dataset | SummarizedExperiment | | |
UPS1.Case4 | LimROTS | Spectronaut and ScaffoldDIA UPS1 Spiked Dataset case 4 | SummarizedExperiment | | |
breweries | mapview | Selected breweries in Franconia | sf | 224 | 9 |
franconia | mapview | Administrative district borders of Franconia | sf | 37 | 7 |
trails | mapview | Selected hiking trails in Franconia | sf | 543 | 4 |
bike | holiglm | Bike Sharing Dataset | data.frame | 731 | 12 |
BuettnerFlorian | SIMLR | test dataset for SIMLR | list | | |
ZeiselAmit | SIMLR | test dataset for SIMLR large scale | list | | |
pig60K | rMVP | Genotyped by pig 60k chip | data.frame | 44580 | 6 |
eons | deeptime | Eon data from the International Commission on Stratigraphy (v2023/06) | data.frame | 3 | 6 |
epochs | deeptime | Epoch data from the International Commission on Stratigraphy (v2023/06) | data.frame | 34 | 6 |
eras | deeptime | Era data from the International Commission on Stratigraphy (v2023/06) | data.frame | 10 | 6 |
periods | deeptime | Period data from the International Commission on Stratigraphy (v2023/06) | data.frame | 22 | 6 |
stages | deeptime | Stage data from the International Commission on Stratigraphy (v2023/06) | data.frame | 102 | 6 |
rainfall | extrememix | Monthly Maxima Daily Rainfall in Madrid | numeric | | |
rainfall_ggpd | extrememix | Rainfall FGGPD Output | ggpd | | |
rainfall_mgpd | extrememix | Rainfall FMGPD Output | mgpd | | |
exp_mixed_data | GrowthCurveME | Sample exponential growth dataset | data.frame | 240 | 3 |
gomp_mixed_data | GrowthCurveME | Sample Gompertz growth dataset | data.frame | 210 | 3 |
lin_mixed_data | GrowthCurveME | Sample linear growth dataset | data.frame | 110 | 3 |
log_mixed_data | GrowthCurveME | Sample logistic growth dataset | data.frame | 800 | 3 |
cal_res | enmpa | Example of results obtained from GLM calibration using enmpa | enmpa_calibration | | |
enm_data | enmpa | Example data used to run model calibration exercises | data.frame | 5627 | 3 |
sel_fit | enmpa | Example of selected models fitted | enmpa_fitted_models | | |
test | enmpa | Example data used to test models | data.frame | 100 | 3 |
GUM.H.2 | errors | Datasets from the Guide to the Expression of Uncertainty in Measurement (GUM) | data.frame | 5 | 3 |
GUM.H.3 | errors | Datasets from the Guide to the Expression of Uncertainty in Measurement (GUM) | data.frame | 11 | 2 |
holes | cocons | Holes Data Set | list | | |
holes_bm | cocons | Holes with trend + multiple realizations Data Set | list | | |
stripes | cocons | Stripes Data Set | list | | |
DF_Seg_Chile | mutualinf | Segregation data in southern Chile | data.frame | 191495 | 11 |
DT_Seg_Chile | mutualinf | Segregation data in southern Chile | data.table | 55960 | 11 |
DT_test | mutualinf | Segregation data in southern Chile | data.table | 6703 | 5 |
DSAM_test_largeData | DSAM | large test dataset | data.frame | 3650 | 5 |
DSAM_test_modData | DSAM | Moderate test dataset | data.frame | 1000 | 5 |
DSAM_test_smallData | DSAM | Small test dataset | data.frame | 200 | 5 |
CentroidsISCCNBS | munsellinterpol | Centroid Notations for the Revised ISCC-NBS Color-Name Blocks | data.frame | 267 | 3 |
Munsell2xy | munsellinterpol | The Munsell HVC to xy 3D Lookup Table | data.frame | 4995 | 6 |
Challenge1Data | fxl | Twitter chart challenge data 1 | data.frame | 226 | 11 |
Challenge2Data | fxl | Twitter chart challenge data 2 | data.frame | 113 | 5 |
Challenge4Data | fxl | Twitter chart challenge data 4 | data.frame | 189 | 11 |
GelinoEtAl2022 | fxl | Plotting data from Koffarnus et al. (2011) | data.frame | 11 | 9 |
Gilroyetal2015 | fxl | Plotting data from Gilroy et al. (2015) | data.frame | 40 | 6 |
Gilroyetal2019 | fxl | Plotting data from Gilroy et al. (2019) - FA | data.frame | 15 | 9 |
Gilroyetal2019Tx | fxl | Plotting data from Gilroy et al. (2019) - Treatment | data.frame | 86 | 8 |
Gilroyetal2021 | fxl | Plotting data from Gilroy et al. (2015) - Treatment | data.frame | 69 | 7 |
KoffarnusEtAl2011 | fxl | Plotting data from Koffarnus et al. (2011) | data.frame | 14979 | 3 |
LozyEtAl2020 | fxl | Plotting data from Lozy et al. (2020) | data.frame | 91 | 5 |
SimulatedAcademicFluency | fxl | Plotting data for Hypothetical Academic MTSS | data.frame | 168 | 7 |
dark | Dark | Dark adaptation data. | dark | | |
X | bapred | Covariate matrix of dataset 'autism' | matrix | 250 | |
batch | bapred | batch variable of dataset 'autism' | factor | | |
y | bapred | Target variable of dataset 'autism' | factor | | |
mvmapit_data | mvMAPIT | Multivariate MAPIT analysis and exhaustive search analysis. | list | | |
phillips_data | mvMAPIT | Multivariate MAPIT analysis of binding affinities in broadly neutralizing antibodies. | list | | |
simulated_data | mvMAPIT | Genotype and trait data with epistasis. | list | | |
data.restricted | fnets | Simulated data from the restricted factor-adjusted vector autoregression model | mts | 500 | 50 |
data.unrestricted | fnets | Simulated data from the unrestricted factor-adjusted vector autoregression model | mts | 500 | 50 |
deg_counts | HybridExpress | Data frame with frequencies (absolute and relative) of DEGs per contrast | data.frame | 4 | 7 |
deg_list | HybridExpress | List of differentially expressed genes for all contrasts | list | | |
go_chlamy | HybridExpress | Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii | data.frame | 34680 | 2 |
se_chlamy | HybridExpress | Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) | SummarizedExperiment | | |
liverData | Scale4C | Example 4C-seq data set of fetal liver data | Scale4C | | |
liverDataVP | Scale4C | Example 4C-seq data set of fetal liver data, with added VP | Scale4C | | |
example.data.for.calculate.p.values | OutSeekR | example.data.for.calculate.p.values | list | | |
outliers | OutSeekR | Example data set for outlier testing | data.frame | 500 | 50 |
alba | TropFishR | Length-frequency data of the clam Abra alba | lfq | | |
bream | TropFishR | bream data | list | | |
emperor | TropFishR | Emperor data | data.frame | 16 | 3 |
gillnet | TropFishR | Gillnet data | list | | |
goatfish | TropFishR | Yellowstriped goatfish data | list | | |
haddock | TropFishR | Haddock data | list | | |
hake | TropFishR | Hake data | list | | |
shrimps | TropFishR | Shrimp data | list | | |
synCAA1 | TropFishR | Synthetic Catch-at-age data I | list | | |
synCAA2 | TropFishR | Synthetic Catch-at-age data II | list | | |
synCPUE | TropFishR | Synthetical catch per unit of effort (CPUE) dataset | data.frame | 6 | 3 |
synLFQ1 | TropFishR | Synthetic length-frequency data I | list | | |
synLFQ2 | TropFishR | Synthetic length frequency data II | list | | |
synLFQ3 | TropFishR | Synthetic length frequency data III | list | | |
synLFQ4 | TropFishR | Synthetic length-frequency data IV (with seasonal oscillation) | lfq | | |
synLFQ5 | TropFishR | Synthetic length-frequency data V (without seasonal oscillation) | lfq | | |
synLFQ6 | TropFishR | Synthetic length-frequency data VI (without seasonal oscillation) | lfq | | |
synLFQ7 | TropFishR | Synthetic length-frequency data VII with seasonal oscillation | lfq | | |
synLFQ8 | TropFishR | Synthetic length-frequency data VIII with variable harvest rate | lfq | | |
tilapia | TropFishR | Tilapia data | list | | |
trammelnet | TropFishR | Trammel net data | list | | |
trawl_fishery_Java | TropFishR | Data from the trawl fishery off the North coast of Java | data.frame | 9 | 3 |
whiting | TropFishR | Whiting data | list | | |
meta_aes | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics charts for the ae domain. One record per unique data mapping | tbl_df | 10 | 10 |
meta_cm | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics for the Concomitant medications Domain. One record per unique data mapping | tbl_df | 7 | 10 |
meta_dm | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics charts for the dm domain. One record per unique data mapping | tbl_df | 7 | 10 |
meta_ecg | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics charts for the ecg domain. One record per unique data mapping | tbl_df | 22 | 10 |
meta_ex | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics for the Exposure Domain. One record per unique data mapping | tbl_df | 8 | 10 |
meta_hepExplorer | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics for the hepExplorer Chart. One record per unique data mapping | tbl_df | 6 | 10 |
meta_labs | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics charts for the labs domain. One record per unique data mapping | tbl_df | 9 | 10 |
meta_mh | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics for the Medical History Domain. One record per unique data mapping | tbl_df | 6 | 10 |
meta_vitals | safetyCharts | Metadata data frame containing information about the data mapping used to configure safetyGraphics charts for the vital signs domain. One record per unique data mapping | tbl_df | 7 | 10 |
Ind_AWAP.2.5 | WASP | Sample data: Index of AWAP grids with no missing data | integer | | |
SPI.12 | WASP | Sample data: Standardized Precipitation Index with 12 month accumulation period. | mts | 1200 | 252 |
aus.coast | WASP | Sample data: Australia map | SpatialPolygons | | |
data.AWAP.2.5 | WASP | Sample data: AWAP rainfall data over Australia | matrix | 1320 | |
data.CI | WASP | Sample data: Climate indices strongly influencing Australia climate | mts | 1332 | 4 |
data.HL | WASP | Sample data: Hysteresis loop | list | | |
data.SW1 | WASP | Sample data: Sinewave model 1 (SW1) | list | | |
data.SW3 | WASP | Sample data: Sinewave model 3 (SW3) | list | | |
lat_lon.2.5 | WASP | Sample data: Latitude and longitude of AWAP grids | data.frame | 252 | 2 |
obs.mon | WASP | Sample data: NCEP reanalysis data averaged over Sydney region | mts | 720 | 7 |
rain.mon | WASP | Sample data: Rainfall station data over Sydney region | mts | 732 | 15 |
Arkansas_eList | EGRET | Example eList | egret | | |
Choptank_eList | EGRET | Example eList | egret | | |
Olympic | OlympicRshiny | Olympic data | spec_tbl_df | 271116 | 14 |
Alcohol_data | fitODBOD | Alcohol data | data.frame | 8 | 3 |
Chromosome_data | fitODBOD | Chromosome Data | data.frame | 4 | 2 |
Course_data | fitODBOD | Course Data | data.frame | 9 | 2 |
Epidemic_Cold | fitODBOD | Family Epidemics | data.frame | 5 | 6 |
Exam_data | fitODBOD | Exam Data | data.frame | 10 | 2 |
Male_Children | fitODBOD | Male children data | data.frame | 13 | 2 |
Plant_DiseaseData | fitODBOD | Plant Disease Incidence data | data.frame | 10 | 2 |
Terror_data_ARG | fitODBOD | Terror Data ARG | data.frame | 7 | 2 |
Terror_data_USA | fitODBOD | Terror Data USA | data.frame | 6 | 2 |
cont.isl | hierfstat | A genetic dataset from a diploid organism in a continent-island model | data.frame | 150 | 6 |
cont.isl99 | hierfstat | A genetic dataset from a diploid organism in a continent-island model | data.frame | 150 | 6 |
crocrussula | hierfstat | Genotypes and sex of 140 shrews Crocidura russula | list | | |
diploid | hierfstat | A genetic dataset from a diploid organism | data.frame | 44 | 6 |
exhier | hierfstat | Example data set with 4 levels, one diploid and one haploid locus | data.frame | 500 | 6 |
gtrunchier | hierfstat | Genotypes at 6 microsatellite loci of Galba truncatula from different patches in Western Switzerland | data.frame | 370 | 8 |
yangex | hierfstat | Example data set from Yang (1998) appendix | data.frame | 232 | 4 |
PC | SC.MEB | simulated PCs | matrix | 25 | 5 |
sce | SC.MEB | A simulated SingleCellExperiment | SingleCellExperiment | | |
capes_synthetic_df | capesR | Synthetic CAPES Data | grouped_df | 118954 | 8 |
years_osf | capesR | Identifiers (IDs) on OSF for the annual data of the Catalog of Theses and Dissertations from the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) | data.frame | 36 | 2 |
brain | DRIP | Brain Image | matrix | 217 | |
circles | DRIP | Image of Circles | matrix | 256 | |
kid | DRIP | Image of a Kid | matrix | 387 | |
lena | DRIP | Lena Image | matrix | 512 | |
peppers | DRIP | Image of Peppers | matrix | 512 | |
sar | DRIP | Synthetic Aperture Radar Image | matrix | 250 | |
stopsign | DRIP | Stop Sign Image | matrix | 160 | |
h1n1_100 | GD | Spatial datasets of H1N1 flu incidences | data.frame | 987 | 11 |
h1n1_150 | GD | Spatial datasets of H1N1 flu incidences | data.frame | 443 | 11 |
h1n1_50 | GD | Spatial datasets of H1N1 flu incidences | data.frame | 3977 | 11 |
ndvi_10 | GD | Spatial datasets of vegetation index changes. | data.frame | 11567 | 7 |
ndvi_20 | GD | Spatial datasets of vegetation index changes. | data.frame | 2892 | 7 |
ndvi_30 | GD | Spatial datasets of vegetation index changes. | data.frame | 1290 | 7 |
ndvi_40 | GD | Spatial datasets of vegetation index changes. | data.frame | 713 | 7 |
ndvi_5 | GD | Spatial datasets of vegetation index changes. | data.frame | 46295 | 7 |
ndvi_50 | GD | Spatial datasets of vegetation index changes. | data.frame | 469 | 7 |
global_regions | sdmtools | Global regions | long_tibble | 249 | 6 |
raster_to_terra | sdmtools | 'raster' to 'terra' equivalence table | long_tibble | 42 | 3 |
KETPch4 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
KETPex481 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
KETPex497 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
KETPex498 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
example1 | dvir | DVI dataset: Generational trio | dviData | | |
example2 | dvir | DVI dataset: Two reference families | dviData | | |
exclusionExample | dvir | Dataset: Exclusion example | dviData | | |
fire | dvir | DVI dataset: Family of fire victims | dviData | | |
grave | dvir | DVI dataset: Family grave | dviData | | |
icmp | dvir | DVI dataset: A large reference pedigree | dviData | | |
planecrash | dvir | DVI dataset: Simulated plane crash | dviData | | |
symmetricSibs | dvir | Dataset: Symmetry examples | dviData | | |
Ex1_genedata | ZIM4rv | An example dataset of genedata | data.frame | 200 | 5 |
Ex1_phenodata | ZIM4rv | An example dataset of phenodata | data.frame | 200 | 8 |
Ex2_covar | ZIM4rv | An example dataset of covariate file | data.frame | 15 | 4 |
Ex2_dosage | ZIM4rv | An example dataset of dosage file | data.frame | 7 | 21 |
Ex2_fam | ZIM4rv | An example dataset of .fam file | data.frame | 15 | 6 |
Ex2_pheno | ZIM4rv | An example dataset of pheno file | data.frame | 15 | 4 |
Ex2_region | ZIM4rv | An example dataset of genetic region file | data.frame | 3 | 4 |
NTDs | gdverse | NTDs data | tbl_df | 185 | 6 |
ndvi | gdverse | dataset of NDVI changes and its influencing factors | tbl_df | 713 | 7 |
sim | gdverse | Simulation data. | tbl_df | 80 | 6 |
srs_table | gdverse | example of spatial information system table | tbl_df | 11 | 5 |
srs_wt | gdverse | example of spatial information system spatial adjacency matrix | matrix | 11 | |
nzmort | poputils | Mortality Data for New Zealand | tbl_df | 84 | 6 |
nzmort_rvec | poputils | Mortality Data and Probabilistic Rates for New Zealand | tbl_df | 84 | 4 |
west_lifetab | poputils | Coale-Demeny West Model Life Tables | tbl_df | 1050 | 10 |
birds | MultiNMix | Birds Dataset - Subset of the North American Breeding Bird Survey Dataset | data.frame | 600 | 13 |
birds_ZI | MultiNMix | Zero-Inflated Birds Dataset - Subset of the North American Breeding Bird Survey Dataset | data.frame | 2880 | 13 |
gDataDropsRestr | statgenQTLxT | Subset of DROPS data for use in examples | gData | | |
SimulatedData | MLModelSelection | Simulated data | list | | |
cbPalette13 | Ternary | Palettes compatible with colour blindness | character | | |
cbPalette15 | Ternary | Palettes compatible with colour blindness | character | | |
cbPalette8 | Ternary | Palettes compatible with colour blindness | character | | |
holdridge | Ternary | Random sample of points for Holdridge plotting | data.frame | 39 | 4 |
holdridgeClasses | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
holdridgeClassesUp | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
holdridgeLifeZones | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
holdridgeLifeZonesUp | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
mauna_loa | data.io | Temperature and atmospheric CO2 at Mauna Loa, Hawai | mts | 768 | 4 |
urchin_bio | data.io | Sea urchins biometry | data.frame | 421 | 19 |
urchin_growth | data.io | Sea urchins growth | data.frame | 7024 | 3 |
zooplankton | data.io | Zooplankton image analysis | data.frame | 1262 | 20 |
pell | pell | Pell Grant Data | tbl_df | 100474 | 6 |
berlinbears | surveyexplorer | Bears bears dataframe | data.frame | 500 | 27 |
COVID19 | refund | The US weekly all-cause mortality and COVID19-associated deaths in 2020 | list | | |
DTI | refund | Diffusion Tensor Imaging: tract profiles and outcomes | data.frame | 382 | 9 |
DTI2 | refund | Diffusion Tensor Imaging: more fractional anisotropy profiles and outcomes | data.frame | 340 | 5 |
PEER.Sim | refund | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function | data.frame | 400 | 4 |
Q | refund | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function | matrix | 7 | |
cd4 | refund | Observed CD4 cell counts | matrix | 366 | 61 |
content | refund | The CONTENT child growth study | data.frame | 4405 | 10 |
gasoline | refund | Octane numbers and NIR spectra of gasoline | data.frame | 60 | 2 |
sofa | refund | SOFA (Sequential Organ Failure Assessment) Data | data.frame | 520 | 7 |
drug_response_w12 | ODT | drug_response_w12 data | matrix | 247 | 119 |
drug_response_w34 | ODT | drug_response_w34 data | matrix | 142 | 119 |
expression_w12 | ODT | expression_w12 Data Set | matrix | 247 | 1000 |
expression_w34 | ODT | expression_w34 Data Set | matrix | 142 | 1000 |
mutations_w12 | ODT | mutations_w12 Data Set | matrix | 247 | 70 |
mutations_w34 | ODT | mutations_w34 Data Set | matrix | 142 | 70 |
data.HT12 | LAM | Datasets from Heck and Thomas (2015) | data.frame | 120 | 11 |
G.in_ex | PopVar | An example barley dataset | data.frame | 246 | 743 |
G.in_ex_imputed | PopVar | An example barley dataset | data.frame | 246 | 743 |
G.in_ex_mat | PopVar | An example barley dataset | matrix | 245 | 742 |
cross.tab_ex | PopVar | An example barley dataset | data.frame | 150 | 2 |
map.in_ex | PopVar | An example barley dataset | data.frame | 742 | 3 |
y.in_ex | PopVar | An example barley dataset | data.frame | 245 | 5 |
E | marble | simulated data for demonstrating the features of marble. | matrix | 80 | |
X | marble | simulated data for demonstrating the features of marble. | matrix | 80 | |
Y | marble | simulated data for demonstrating the features of marble. | matrix | 80 | |
clin | marble | simulated data for demonstrating the features of marble. | matrix | 80 | |
nudge | autodb | Nudge meta-analysis data | data.frame | 447 | 25 |
harmonization_lookup_tables | iidda | Harmonization Lookup Tables | list | | |
standards | iidda | Standards | list | | |
exprs | les | Spike-in ChIP-chip data set | matrix | 452 | 6 |
pos | les | Spike-in ChIP-chip data set | integer | | |
pval | les | Spike-in ChIP-chip data set | numeric | | |
reference | les | Spike-in ChIP-chip data set | data.frame | 1 | 6 |
biomass | VisCollin | Biomass Production in the Cape Fear Estuary | data.frame | 45 | 17 |
cars | VisCollin | Cars Data | data.frame | 406 | 10 |
consumption | VisCollin | Consumption Function Dataset | data.frame | 28 | 5 |
dat | pttstability | Microcosm experimental data | data.frame | 1084 | 4 |
flood | MLEce | The flood events data of the Madawaska basin. | data.frame | 77 | 2 |
fossil_pollen | MLEce | The counts data of the frequency of occurrence of different kinds of fossil pollen grains. | data.frame | 73 | 4 |
StackData | Sstack | Sample Stack Data | list | | |
cass | testCompareR | Coronary Artery Surgery Study data | data.frame | 871 | 3 |
cfpr | testCompareR | US Cystic Fibrosis Patient Registry data | data.frame | 11960 | 3 |
UQGDBiogas | biogas | Biogas Volume and Mass Loss from BMP bottles | data.frame | 135 | 5 |
UQGDSetup | biogas | BMP Bottle Setup Information | data.frame | 9 | 8 |
UQGravBiogas | biogas | Mass Loss and Biogas Composition from BMP bottles | data.frame | 135 | 5 |
UQGravSetup | biogas | BMP Bottle Setup Information | data.frame | 9 | 9 |
comp | biogas | Methane Content of Biogas | data.frame | 132 | 4 |
comp2 | biogas | Methane Content of Biogas | data.frame | 135 | 3 |
feedSetup | biogas | Setup Details for Batch Reactors | data.frame | 12 | 4 |
feedVol | biogas | Biogas Volume from Batch Reactors | data.frame | 44 | 13 |
mass | biogas | Mass Change of Batch Reactors | data.frame | 18 | 5 |
massw | biogas | Mass Change of Batch Reactors | data.frame | 9 | 3 |
s3compl | biogas | Methane Content of Biogas from Batch Reactors | data.frame | 14 | 3 |
s3compw | biogas | Methane Content of Biogas from Batch Reactors | data.frame | 5 | 4 |
s3lcombo | biogas | Biogas Volume and Methane Content from Batch Bottles | data.frame | 21 | 4 |
s3voll | biogas | Biogas Volume from Batch Bottles | data.frame | 21 | 4 |
s3volw | biogas | Biogas Volume from Batch Reactors | data.frame | 7 | 4 |
setup | biogas | Setup Details for Batch Reactors | data.frame | 12 | 11 |
setup2 | biogas | Setup Details for Some Batch Reactors | data.frame | 15 | 4 |
sludgeTwoBiogas | biogas | Headspace Pressure, Mass measurements, and Methane and Carbondioxid Content from Batch Bottles | data.frame | 324 | 8 |
sludgeTwoSetup | biogas | Setup Details for Batch Reactors | data.frame | 18 | 5 |
strawComp | biogas | Methane Content of Biogas | data.frame | 63 | 4 |
strawMass | biogas | Mass Change of Batch Reactors | data.frame | 89 | 4 |
strawPressure | biogas | Headspace Pressure in Batch Reactors | data.frame | 72 | 5 |
strawSetup | biogas | Setup Details for Batch Reactors | data.frame | 12 | 6 |
vol | biogas | Biogas Volume from Batch Reactors | data.frame | 288 | 4 |
vol2 | biogas | Biogas Volume from Batch Reactors | data.frame | 360 | 3 |
cell_type_df | tidyseurat | Cell types of 80 PBMC single cells | tbl_df | 80 | 2 |
pbmc_small_nested_interactions | tidyseurat | Intercellular ligand-receptor interactions for 38 ligands from a single cell RNA-seq cluster. | tbl_df | 100 | 9 |
CharTraj | dtwclust | Subset of character trajectories data set | list | | |
CharTrajLabels | dtwclust | Subset of character trajectories data set | factor | | |
CharTrajMV | dtwclust | Subset of character trajectories data set | list | | |
dtwclustTimings | dtwclust | Results of timing experiments | list | | |
aristolochia | AgroReg | Dataset: Aristolochia | tbl_df | 80 | 2 |
granada | AgroReg | Dataset: Granada | data.frame | 64 | 2 |
common | latexSymb | Common latex_symb objects | list | | |
Data_Series | TrendTM | Example of data | matrix | 30 | |
us16 | ineq.2d | Test dataset for the ineq.2d package | data.frame | 1000 | 8 |
climbing_birds | afpt | Climbing birds | data.frame | 15 | 11 |
fra_sf | fakir | Map of France | sf | 96 | 6 |
H3K36me3.AM.immune.19 | PeakSegDP | Several ChIP-seq profiles, some of which have few data points | list | | |
H3K36me3.TDH.other.chunk3.cluster4 | PeakSegDP | 8 profiles of H3K36me3 data | data.frame | 36914 | 4 |
H3K4me3.TDH.immune.chunk12.cluster4 | PeakSegDP | Histone ChIP-seq data, 26 samples, chr1 subset | data.frame | 20199 | 4 |
chr11ChIPseq | PeakSegDP | ChIP-seq aligned read coverage for 4 samples on a subset of chr11 | list | | |
chr11first | PeakSegDP | Counts of first base of aligned reads | data.frame | 23252 | 4 |
colon | MiPP | Gene expression data for colon cancer | data.frame | 200 | 62 |
leuk1 | MiPP | Gene expression data for leukemia | data.frame | 713 | 38 |
leuk2 | MiPP | Gene expression data for leukemia | data.frame | 713 | 34 |
clean1000.df | learnrbook | Subset of RNAseq gene expression data | data.frame | 1000 | 5 |
clean5000.df | learnrbook | Subset of RNAseq gene expression data | data.frame | 5000 | 5 |
pkgs_all_1ed | learnrbook | Packages used in book "Learn R: As a Language" (1ed) | character | | |
pkgs_all_2ed | learnrbook | Packages used in book "Learn R: As a Language" (2ed) | character | | |
pkgs_ch10_2ed | learnrbook | Packages used in ch. 10 of book "Learn R: As a Language" (2ed) | character | | |
pkgs_ch6_1ed | learnrbook | Packages used in ch. 6 of book "Learn R: As a Language" (1ed) | character | | |
pkgs_ch7_1ed | learnrbook | Packages used in ch. 7 of book "Learn R: As a Language" (1ed) | character | | |
pkgs_ch8_1ed | learnrbook | Packages used in ch. 8 of book "Learn R: As a Language" (1ed) | character | | |
pkgs_ch8_2ed | learnrbook | Packages used in ch. 8 of book "Learn R: As a Language" (2ed) | character | | |
pkgs_ch9_2ed | learnrbook | Packages used in ch. 9 of book "Learn R: As a Language" (2ed) | character | | |
viikki_d29.dat | learnrbook | Wind direction and speed data | tbl_df | 1440 | 3 |
weather_wk_25_2019.tb | learnrbook | Weather data | spec_tbl_df | 10080 | 21 |
exampleData | closeloop | This is a simulated data | data.frame | 4 | 4 |
Swallows | GrapheR | Swallows dataset | data.frame | 120 | 6 |
beryllium | EMD | Solar Irradiance Proxy Data | list | | |
kospi200 | EMD | Korea Stock Price Index 200 | list | | |
lena | EMD | Gray Lena image | matrix | 512 | |
lennon | EMD | Gray John Lennon image | matrix | 256 | |
solar.hs | EMD | Solar Irradiance Proxy Data | list | | |
solar.lean | EMD | Solar Irradiance Proxy Data | list | | |
sunspot | EMD | Sunspot Data | list | | |
BCG | HSAUR | BCG Vaccine Data | data.frame | 13 | 7 |
BtheB | HSAUR | Beat the Blues Data | data.frame | 100 | 8 |
CYGOB1 | HSAUR | CYG OB1 Star Cluster Data | data.frame | 47 | 2 |
Forbes2000 | HSAUR | The Forbes 2000 Ranking of the World's Biggest Companies (Year 2004) | data.frame | 2000 | 8 |
GHQ | HSAUR | General Health Questionnaire | data.frame | 22 | 4 |
Lanza | HSAUR | Prevention of Gastointestinal Damages | data.frame | 198 | 3 |
agefat | HSAUR | Total Body Composision Data | data.frame | 25 | 3 |
aspirin | HSAUR | Aspirin Data | data.frame | 7 | 4 |
birthdeathrates | HSAUR | Birth and Death Rates Data | data.frame | 69 | 2 |
bladdercancer | HSAUR | Bladder Cancer Data | data.frame | 31 | 3 |
clouds | HSAUR | Cloud Seeding Data | data.frame | 24 | 7 |
epilepsy | HSAUR | Epilepsy Data | data.frame | 236 | 6 |
foster | HSAUR | Foster Feeding Experiment | data.frame | 61 | 3 |
gardenflowers | HSAUR | Garden Flowers | dist | | |
heptathlon | HSAUR | Olympic Heptathlon Seoul 1988 | data.frame | 25 | 8 |
mastectomy | HSAUR | Survival Times after Mastectomy of Breast Cancer Patients | data.frame | 44 | 3 |
meteo | HSAUR | Meteorological Measurements for 11 Years | data.frame | 11 | 6 |
orallesions | HSAUR | Oral Lesions in Rural India | table | 8 | 3 |
phosphate | HSAUR | Phosphate Level Data | data.frame | 33 | 9 |
pistonrings | HSAUR | Piston Rings Failures | table | 4 | 3 |
planets | HSAUR | Exoplanets Data | data.frame | 101 | 3 |
plasma | HSAUR | Blood Screening Data | data.frame | 32 | 3 |
polyps | HSAUR | Familial Andenomatous Polyposis | data.frame | 20 | 3 |
polyps3 | HSAUR | Familial Andenomatous Polyposis | data.frame | 22 | 5 |
pottery | HSAUR | Romano-British Pottery Data | data.frame | 45 | 9 |
rearrests | HSAUR | Rearrests of Juvenile Felons | table | 2 | 2 |
respiratory | HSAUR | Respiratory Illness Data | data.frame | 555 | 7 |
roomwidth | HSAUR | Students Estimates of Lecture Room Width | data.frame | 113 | 2 |
schizophrenia | HSAUR | Age of Onset of Schizophrenia Data | data.frame | 251 | 2 |
schizophrenia2 | HSAUR | Schizophrenia Data | data.frame | 220 | 4 |
schooldays | HSAUR | Days not Spent at School | data.frame | 154 | 5 |
skulls | HSAUR | Egyptian Skulls | data.frame | 150 | 5 |
smoking | HSAUR | Nicotine Gum and Smoking Cessation | data.frame | 26 | 4 |
students | HSAUR | Student Risk Taking | data.frame | 35 | 3 |
suicides | HSAUR | Crowd Baiting Behaviour and Suicides | table | 2 | 2 |
toothpaste | HSAUR | Toothpaste Data | data.frame | 9 | 7 |
voting | HSAUR | House of Representatives Voting Data | matrix | 15 | 15 |
water | HSAUR | Mortality and Water Hardness | data.frame | 61 | 4 |
watervoles | HSAUR | Water Voles Data | matrix | 14 | 14 |
waves | HSAUR | Electricity from Wave Power at Sea | data.frame | 18 | 2 |
weightgain | HSAUR | Gain in Weight of Rats | data.frame | 40 | 3 |
womensrole | HSAUR | Womens Role in Society | data.frame | 42 | 4 |
gpdd_biotope | rgpdd | The biotype table | data.frame | 194 | 3 |
gpdd_data | rgpdd | The data table | data.frame | 179859 | 10 |
gpdd_datasource | rgpdd | The data source table | data.frame | 362 | 9 |
gpdd_location | rgpdd | Table of locations information for each timeseries | data.frame | 1005 | 24 |
gpdd_main | rgpdd | main table: metadata for each time series | data.frame | 5156 | 25 |
gpdd_restricted | rgpdd | Restricted data sets table | data.frame | 685 | 3 |
gpdd_taxon | rgpdd | The taxon table | data.frame | 1896 | 12 |
gpdd_timeperiod | rgpdd | The time period table | data.frame | 408 | 6 |
gpdd_version | rgpdd | GPDD version information | data.frame | 5 | 2 |
bargaining | npExact | Amount sent in the Ultimatum Game | data.frame | 30 | 2 |
french | npExact | Indices of minority shareholder protection of countries with civil law with and without french origin. | list | | |
mshscores | npExact | Indices of minority shareholder protection of countries with common and with civil law. | list | | |
pain | npExact | Pain experienced before and after a knie operation | data.frame | 50 | 3 |
uncertainty | npExact | Uncertainty in a game theoretical experiment. | data.frame | 25 | 2 |
sp_data | MultipleBubbles | S&P 500 data. | numeric | | |
FungalGrowthDataset | beast | Fungal Growth Dataset | list | | |
TNM | metapack | Triglycerides Network Meta (TNM) data | data.frame | 73 | 15 |
cholesterol | metapack | 26 double-blind, randomized, active, or placebo-controlled clinical trials on patients with primary hypercholesterolemia sponsored by Merck & Co., Inc., Kenilworth, NJ, USA. | data.frame | 52 | 19 |
AA_PROP | immunarch | Tables with amino acid properties | data.frame | 20 | 17 |
ATCHLEY | immunarch | Tables with amino acid properties | data.frame | 20 | 6 |
GENE_SEGMENTS | immunarch | Gene segments table | tbl_df | 2290 | 8 |
KIDERA | immunarch | Tables with amino acid properties | data.frame | 20 | 11 |
bcrdata | immunarch | BCR dataset | list | | |
immdata | immunarch | Single chain immune repertoire dataset | list | | |
scdata | immunarch | Paired chain immune repertoire dataset | list | | |
MRPCtruth | MRPC | Graphs used as truth in simulation | list | | |
data_GEUVADIS | MRPC | GEUVADIS data with 62 eQTL-gene sets | list | | |
data_GEUVADIS_combined | MRPC | Combined genotype and gene expression data from 62 eQTL-gene sets in 373 Europeans from GEUVADIS | matrix | 373 | 194 |
data_examples | MRPC | Example data under simple and complex models | list | | |
data_with_outliers | MRPC | Example data with outliers | data.frame | 1000 | 5 |
data_without_outliers | MRPC | Example data without outliers | data.frame | 1000 | 5 |
simu_data_M0 | MRPC | Data for Model 0 | data.frame | 1000 | 3 |
simu_data_M1 | MRPC | Data for Model 1 | data.frame | 1000 | 3 |
simu_data_M2 | MRPC | Data for Model 2 | data.frame | 1000 | 3 |
simu_data_M3 | MRPC | Data for Model 3 | data.frame | 1000 | 3 |
simu_data_M4 | MRPC | Data for Model 4 | data.frame | 1000 | 3 |
simu_data_layered | MRPC | Data for the layered model | data.frame | 1000 | 8 |
simu_data_multiparent | MRPC | Data for the multiple-parent model | data.frame | 1000 | 4 |
simu_data_starshaped | MRPC | Data for the star model | data.frame | 1000 | 6 |
Brazil_rainforest_abun_data | iNEXT.3D | Abundance data (datatype = "abundance") | data.frame | 425 | 2 |
Brazil_rainforest_distance_matrix | iNEXT.3D | Species pairwise distance matrix for species in the dataset 'Brazil_rainforest_abun_data' | data.frame | 425 | 425 |
Brazil_rainforest_phylo_tree | iNEXT.3D | Phylogenetic tree for 'Brazil_rainforest_abun_data' | phylo | | |
Fish_distance_matrix | iNEXT.3D | Species pairwise distance matrix for species in the dataset 'Fish_incidence_data' | data.frame | 88 | 88 |
Fish_incidence_data | iNEXT.3D | Incidence data (datatype = "incidence_raw") | list | | |
Fish_phylo_tree | iNEXT.3D | Phylogenetic tree for 'Fish_incidence_data' | phylo | | |
comets | sphunif | Comet orbits | data.frame | 3798 | 16 |
craters | sphunif | Craters named by the IUA | data.frame | 5235 | 7 |
planets | sphunif | Planet orbits | data.frame | 9 | 3 |
rhea | sphunif | Rhea craters from Hirata (2016) | data.frame | 3596 | 4 |
venus | sphunif | Venus craters | data.frame | 967 | 4 |
pbmcs_exprs_small | symphony | Log(CP10k+1) normalized counts matrix (genes by cells) for 10x PBMCs dataset for vignette. | dgCMatrix | | |
pbmcs_meta_small | symphony | Metadata for 10x PBMCs dataset for vignette. | data.frame | 1200 | 7 |
pGRNDB | pGRN | pGRN example data | list | | |
agri_trade | multiness | Agricultural trade multiplex network | array | | |
sample_data | pomodoro | Sample data for analysis. A dataset containing information of access to credit. | data.frame | 32765 | 26 |
common_words | proceduralnames | 999 of the most common words in the English language | character | | |
docker_adjectives | proceduralnames | Adjectives used in the procedural naming of Docker containers. | character | | |
docker_names | proceduralnames | Surnames used in the procedural naming of Docker containers. | character | | |
spanish_words | proceduralnames | 820 of the most common words in the Spanish language | character | | |
ContigCluster1 | demic | Contig Cluster 1 | data.frame | 120897 | 5 |
ContigCluster2 | demic | Contig Cluster 2 | data.frame | 66735 | 5 |
max_bin_001 | demic | MaxBin2 Cluster 001 | data.frame | 79740 | 5 |
max_bin_002 | demic | MaxBin2 Cluster 002 | data.frame | 148638 | 5 |
max_bin_003 | demic | MaxBin2 Cluster 003 | data.frame | 124578 | 5 |
ccped | Mangrove | Example data for the Mangrove vignette | MangrovePed | 20000 | 14 |
contped | Mangrove | Example data for the Mangrove vignette | MangrovePed | 1000 | 364 |
exampleBetas | Mangrove | Example data for the Mangrove vignette | MangroveBetas | 179 | 5 |
exampleORs | Mangrove | Example data for the Mangrove vignette | MangroveORs | 4 | 6 |
famped | Mangrove | Example data for the Mangrove vignette | MangrovePed | 19 | 14 |
mendT | Mangrove | Internal Mangrove objects | array | | |
warwick_palettes | warwickplots | University of Warwick brand palettes | palettes_palette | | |
warwick_palettes_discrete | warwickplots | University of Warwick brand palettes | palettes_palette | | |
warwick_palettes_divergent | warwickplots | University of Warwick brand palettes | palettes_palette | | |
warwick_palettes_sequential | warwickplots | University of Warwick brand palettes | palettes_palette | | |
diceNGrams | DICEM | Pre-trained advice concreteness features | matrix | 4916 | 2017 |
phone_offers | DICEM | Purchase offers for phone | data.frame | 355 | 2 |
polymodel | DICEM | Polynomial pre-trained fit | poly | 4916 | 9 |
uk2us | DICEM | UK to US Conversion dictionary | dictionary2 | | |
PBMC_metaData | SoupX | PBMC 4K meta data | data.frame | 4340 | 4 |
PBMC_sc | SoupX | SoupChannel from PBMC data | list | | |
scToy | SoupX | Toy SoupChanel object | list | | |
gene_edges | node2vec | 6 edges information between two genes of human | data.frame | 6 | 2 |
simulmode | segclust2d | Simulations of behavioural mode | data.frame | 302 | 12 |
simulshift | segclust2d | Simulations of home-range shift | data.frame | 30001 | 4 |
planets | planets | "Planets" | data.frame | 3915 | 12 |
heatmap | wesanderson | heatmap | data.frame | 144 | 5 |
corn | pcv | Corn data | list | | |
ISO_15924 | ISOcodes | ISO 15924 Script Codes | data.frame | 223 | 5 |
ISO_3166_1 | ISOcodes | ISO 3166 Country Codes | data.frame | 249 | 6 |
ISO_3166_2 | ISOcodes | ISO 3166 Country Codes | data.frame | 5046 | 4 |
ISO_3166_3 | ISOcodes | ISO 3166 Country Codes | data.frame | 31 | 6 |
ISO_4217 | ISOcodes | ISO 4217 Currency Codes | data.frame | 181 | 3 |
ISO_4217_Historic | ISOcodes | ISO 4217 Currency Codes | data.frame | 105 | 4 |
ISO_639_2 | ISOcodes | ISO 639 Language Codes | data.frame | 486 | 4 |
ISO_639_3 | ISOcodes | ISO 639 Language Codes | data.frame | 7919 | 15 |
ISO_639_3_Retirements | ISOcodes | ISO 639 Language Codes | data.frame | 376 | 6 |
ISO_639_5 | ISOcodes | ISO 639 Language Codes | data.frame | 114 | 5 |
ISO_8859 | ISOcodes | ISO 8859 Character Codes | array | | |
UN_M.49_Countries | ISOcodes | UN M.49 Area Codes | data.frame | 248 | 3 |
UN_M.49_Regions | ISOcodes | UN M.49 Area Codes | data.frame | 33 | 5 |
roaches | countSTAR | Data on the efficacy of a pest management system at reducing the number of roaches in urban apartments. | data.frame | 262 | 5 |
atac_small | Signac | A small example scATAC-seq dataset | Seurat | | |
blacklist_ce10 | Signac | Genomic blacklist regions for C. elegans ce10 (0-based) | GRanges | | |
blacklist_ce11 | Signac | Genomic blacklist regions for C. elegans ce11 (0-based) | GRanges | | |
blacklist_dm3 | Signac | Genomic blacklist regions for Drosophila dm3 (0-based) | GRanges | | |
blacklist_dm6 | Signac | Genomic blacklist regions for Drosophila dm6 (0-based) | GRanges | | |
blacklist_hg19 | Signac | Genomic blacklist regions for Human hg19 (0-based) | GRanges | | |
blacklist_hg38 | Signac | Genomic blacklist regions for Human GRCh38 | GRanges | | |
blacklist_hg38_unified | Signac | Unified genomic blacklist regions for Human GRCh38 | GRanges | | |
blacklist_mm10 | Signac | Genomic blacklist regions for Mouse mm10 (0-based) | GRanges | | |
simdata | SAMGEP | Simulated Dataset | list | | |
fungus | rwty | MrBayes output from analysis of Hibbett et al. data | list | | |
salamanders | rwty | MrBayes output from analysis of Williams et al. data | list | | |
actg | multipleOutcomes | ACTG 320 Clinical Trial Dataset | data.frame | 1151 | 16 |
adis_wv | wv | Wavelet variance of IMU Data from an ADIS 16405 sensor | imu_wvar | | |
imar_wv | wv | Wavelet variance of IMU Data from IMAR Gyroscopes | imu_wvar | | |
kvh1750_wv | wv | Wavelet variance of IMU Data from a KVH1750 IMU sensor | imu_wvar | | |
ln200_wv | wv | Wavelet variance of IMU Data from a LN200 sensor | imu_wvar | | |
navchip_wv | wv | Wavelet variance of IMU Data from a navchip sensor | imu_wvar | | |
news_data | TextForecast | News Data | data.frame | 323 | 1631 |
optimal_factors | TextForecast | Optimal Factors | matrix | 323 | |
optimal_x | TextForecast | Optimal x | matrix | 323 | 39 |
stock_data | TextForecast | Stock Data | tbl_df | 323 | 3 |
excel_functions | tidyxl | Names of all Excel functions | character | | |
xlsx_color_standard | tidyxl | Names and RGB values of Excel standard colours | tbl_df | 10 | 2 |
xlsx_colour_standard | tidyxl | Names and RGB values of Excel standard colours | tbl_df | 10 | 2 |
Chow_GeneExpData | CoNI | Chow gene expression data | data.frame | 10 | 10159 |
Chow_MetaboliteData | CoNI | Chow metabolite data | data.frame | 10 | 174 |
CoNIResultsHFDToy | CoNI | Toy data HFD results | data.frame | 1120 | 11 |
CoNIResults_Chow | CoNI | CoNI Results Chow | data.frame | 486 | 15 |
CoNIResults_HFD | CoNI | CoNI Results HFD | data.frame | 1065 | 15 |
GeneExpToy | CoNI | Toy data gene expression | data.frame | 40 | 18 |
HFD_GeneExpData | CoNI | HFD gene expression data | data.frame | 8 | 10159 |
HFD_MetaboliteData | CoNI | HFD metabolite data | data.frame | 8 | 174 |
MetColorTable | CoNI | Toy data annotation | data.frame | 187 | 7 |
MetaboExpToy | CoNI | Toy data metabolite expression | data.frame | 18 | 20 |
MetaboliteAnnotation | CoNI | Metabolite Annotation | data.frame | 187 | 5 |
VertexClassesSharedGenes_HFDvsChow | CoNI | Toy data comparison treatments | data.frame | 600 | 4 |
Age_Pyramids_2014 | HistDAWass | Age pyramids of all the countries of the World in 2014 | MatH | | |
Agronomique | HistDAWass | Agronomique data | MatH | | |
BLOOD | HistDAWass | Blood dataset for Histogram data analysis | MatH | | |
BloodBRITO | HistDAWass | Blood dataset from Brito P. for Histogram data analysis | MatH | | |
China_Month | HistDAWass | A monthly climatic dataset of China | MatH | | |
China_Seas | HistDAWass | A seasonal climatic dataset of China | MatH | | |
OzoneFull | HistDAWass | Full Ozone dataset for Histogram data analysis | MatH | | |
OzoneH | HistDAWass | Complete Ozone dataset for Histogram data analysis | MatH | | |
RetHTS | HistDAWass | A histogram-valued dataset of returns | HTS | | |
stations_coordinates | HistDAWass | Stations coordinates of China_Month and China_Seas datasets | data.frame | 60 | 9 |
cancers_drug_groups | EnrichIntersect | Data set 'cancers_drug_groups' | list | | |
cancers_genes_drugs | EnrichIntersect | Data set 'cancers_genes_drugs' | array | | |
quasar.agg | RCTrep | Aggregated data derived from paper of QUASAR trial | list | | |
quasar.obj | RCTrep | An object of class TEstimator_Synthetic using quasar.synthetic | TEstimator_Synthetic | | |
quasar.synthetic | RCTrep | A synthetic QUASAR trial dataset, where outcome is a binary variable, treatment is a binary variable. | data.frame | 3232 | 3 |
source.binary.data | RCTrep | A dataset of simulated observational data, where outcome is binary variable. The data is filtered after compared to target.binary.data | data.frame | 2624 | 9 |
source.data | RCTrep | A data set of simulated observational data, where outcome is continuous variable, treatment is a binary variable. | data.frame | 2622 | 9 |
target.binary.data | RCTrep | A dataset of simulated RCT data, where outcome is binary variable. The data is filtered after compared to source.binary.data | data.frame | 3194 | 9 |
target.data | RCTrep | A data set of simulated RCT data, where outcome is continuous variable, treatment is a binary variable. | data.frame | 2718 | 9 |
wq_algorithms | waterquality | wq_algorithms database | tbl_df | 161 | 5 |
dict | pseudobibeR | Dictionaries defining text features | list | | |
spacy_samples | pseudobibeR | Samples of parsed text | spacyr_parsed | 1346 | 9 |
udpipe_samples | pseudobibeR | Samples of parsed text | udpipe_connlu | | |
word_lists | pseudobibeR | Lists of words defining text features | list | | |
hfmock | Wcompo | A dataset from the HF-ACTION trial | data.frame | 1315 | 5 |
negative | pltesim | Simulated data set from Williams (2016) to illustrate negative duration dependence | data.frame | 1000 | 6 |
negative_year | pltesim | Simulated data set based on Williams (2016) to illustrate negative duration dependence in examples | data.frame | 1000 | 4 |
Asaphidae | strap | Phylogeny and age data for the Asaphidae | list | | |
Dipnoi | strap | Phylogeny and age data for dipnoans (lungfish) | list | | |
UKzones | strap | British regional stages for the Ordovician | data.frame | 5 | 3 |
Hald | lmridge | Portland Cement benchmark of Hald(1952) | matrix | 13 | 5 |
Argentina_STRs | fbnet | STRs allelic frequencies from Argentina. | list | | |
bnet | fbnet | Initialized bayesian network. | list | | |
pbn | fbnet | Prepared pedigree for bayesian network trimming. | list | | |
toybase | fbnet | Toy allele frequency database. | list | | |
toyped | fbnet | STRs allelic frequencies from specified country. | linkdat | | |
myEnvModules | RenvModule | Global instance of RenvModule | EnvModules | | |
YX_ecological_data | RolWinMulCor | Ecological data set to test the functions of _RolWinMulCor_ | matrix | 237 | 4 |
syntDATA | RolWinMulCor | Synthetic data set to test the functions of _RolWinMulCor_ | matrix | 500 | 3 |
state_2020 | apportion | state_2020 (2020 State Data) | tbl_df | 50 | 4 |
hospitalizations | gscounts | Hospitalizations | data.frame | 2323 | 4 |
SD2011 | synthpop | Social Diagnosis 2011 - Objective and Subjective Quality of Life in Poland | data.frame | 5000 | 35 |
corral_augmented | sbfc | Augmented corral data set: synthetic data with correlated attributes augmented with noise features | list | | |
heart | sbfc | Heart disease data set: disease outcomes given health attributes | list | | |
madelon | sbfc | Madelon data set: synthetic data from NIPS 2003 feature selection challenge | list | | |
demographicData_cereal | BLPestimatoR | Draws for observed heterogeneity in Nevo's cereal example. | list | | |
dummies_cars | BLPestimatoR | Ownership matrix in BLP's car example. | matrix | 2217 | |
originalDraws_cereal | BLPestimatoR | Draws for unobserved heterogeneity in Nevo's cereal example. | list | | |
productData_cars | BLPestimatoR | Product data of BLP's car example. | data.frame | 2217 | 10 |
productData_cereal | BLPestimatoR | Product data of Nevo's cereal example. | data.frame | 2256 | 28 |
theta_guesses_cereal | BLPestimatoR | Parameter starting guesses for Nevo's cereal example. | matrix | 4 | |
w_guesses_cereal | BLPestimatoR | Mean utility starting guesses for Nevo's cereal example. | matrix | 2256 | |
data_provided | quadkeyr | QuadKey-identified Dataset | data.frame | 360 | 2 |
result_read_fb_mobility_data | quadkeyr | Dataset with (fake) Facebook mobility data | data.frame | 134492 | 9 |
novels | stylest2 | Excerpts from English novels | data.frame | 21 | 3 |
novels_dfm | stylest2 | Novel excerpts in quanteda dfm object | dfm | | |
f109 | smam | GPS data of f109 | data.frame | 3919 | 4 |
f109raw | smam | GPS data of f109 (raw format) | data.frame | 3917 | 3 |
neotropical_comm | FishPhyloMaker | Abundance of stream fish species in Parana and Paraguay streams | data.frame | 20 | 61 |
spp_afrotropic | FishPhyloMaker | List of fish species with occurrence in Afrotropical ecoregion | character | | |
taxon_data_PhyloMaker | FishPhyloMaker | Data frame with species names needed to assemble the phylogenetic tree | data.frame | 45 | 3 |
ccdata | penfa | Data set for cross-cultural analysis | data.frame | 767 | 13 |
SampleMarkedPattern | DRHotNet | Marked point pattern on a road network simulating traffic accident locations | lpp | | |
soilspec_yamsys | simplerspec | Soil spectra and laboratory reference data from Baumann et al. (2021) | tbl_df | 284 | 40 |
Mex_PM10 | SpatFD | Air quality data of Mexico | matrix | 4344 | 13 |
NO2 | SpatFD | Air quality data of Mexico | matrix | 4292 | 18 |
PM10 | SpatFD | PM10 of Bogota, Colombia | data.frame | 8761 | 10 |
coord | SpatFD | Coordinates of measurement stations Bogota, Colombia | data.frame | 10 | 3 |
coord_NO2 | SpatFD | Coordinates of air quality data of Mexico | data.frame | 18 | 2 |
coord_PM10 | SpatFD | Coordinates of air quality data of Mexico | data.frame | 13 | 2 |
map | SpatFD | map of Bogota, Colombia | sf | 1 | 6 |
map_mex | SpatFD | map of Mexico | sf | 54 | 8 |
vowels | SpatFD | Imaginary thinking of the five Spanish vowels | matrix | 17100 | 22 |
vowels_coords | SpatFD | Coordinates of electrodes from the vowels data set | data.frame | 21 | 2 |
cardata | qacBase | Automobile characteristics | data.frame | 11914 | 16 |
cars74 | qacBase | Motor Trend car road tests | data.frame | 32 | 12 |
tv | qacBase | Time spent watching television - 2017 | data.frame | 10223 | 21 |
ica | ggsegIca | ICA atlas | brain_atlas | | |
ica_3d | ggsegIca | ICA atlas | ggseg3d_atlas | 4 | 4 |
GarciaF200 | SOMnmR | GarciaF200 sub data set from Garcia-Franco et al. (2021) | list | | |
Hall300 | SOMnmR | Hall sub data set from Hall et al. (2020) | list | | |
Smernik200 | SOMnmR | Smernik200 data set from Smernik et al. (2008) | list | | |
Smernik400 | SOMnmR | Smernik400 data set from Smernik et al. (2008) | list | | |
ncHall300 | SOMnmR | Hall sub data set from Hall et al. (2020) | list | | |
de_county | divseg | de_county | sf | 3 | 21 |
de_tract | divseg | de_tract | sf | 218 | 21 |
abml | forecastLSW | Gross Value Added (GVA, Average) at basis prices: CP SA time series / second differenced series | ts | | |
abmld2 | forecastLSW | Gross Value Added (GVA, Average) at basis prices: CP SA time series / second differenced series | ts | | |
windanomaly | forecastLSW | Eq. Pacific meridional wind anomaly index, Jan 1900 - June 2005 | numeric | | |
DistPar | PowerSDI | Parameters for Calculating the SDIs Provided by the ScientSDI Function | data.frame | 48 | 13 |
ObsEst | PowerSDI | Example Data of the Input Required by the Accuracy Function | data.frame | 1434 | 2 |
refHS | PowerSDI | Example of the Input Required by the Reference Function | data.frame | 10950 | 8 |
refPM | PowerSDI | Example of the Input Required by the Reference Function | data.frame | 10958 | 11 |
SimData_BPREM | BEND | Simulated data for a BPREM | data.frame | 210 | 4 |
SimData_PCREM | BEND | Simulated data for a PCREM | data.frame | 210 | 4 |
SimData_PREM | BEND | Simulated data for a PREM + Extensions | data.frame | 540 | 6 |
results_bprem | BEND | Fitted results for a BPREM | BPREM | | |
results_pcrem | BEND | Fitted results for a PCREM | CREM | | |
results_prem | BEND | Fitted results for a PREM | PREM | | |
recdata | rct3 | Recruitment and survey index data | data.frame | 28 | 14 |
Acetogens | microPop | Acetogens dataframe | data.frame | 20 | 9 |
Bacteroides | microPop | Bacteroides dataframe | data.frame | 14 | 10 |
ButyrateProducers1 | microPop | ButyrateProducers1 dataframe | data.frame | 9 | 9 |
ButyrateProducers2 | microPop | ButyrateProducers2 dataframe | data.frame | 9 | 10 |
ButyrateProducers3 | microPop | ButyrateProducers3 dataframe | data.frame | 14 | 11 |
LactateProducers | microPop | LactateProducers dataframe | data.frame | 8 | 9 |
MFG | microPop | Microbial Functional Group (MFG) dataframes | data.frame | 8 | 10 |
Methanogens | microPop | Methanogens dataframe | data.frame | 14 | 6 |
NoButyFibreDeg | microPop | NoButyFibreDeg dataframe | data.frame | 8 | 6 |
NoButyStarchDeg | microPop | NoButyStarchDeg dataframe | data.frame | 8 | 7 |
PropionateProducers | microPop | PropionateProducers dataframe | data.frame | 14 | 9 |
Xaa | microPop | Xaa dataframe | data.frame | 6 | 9 |
Xh2 | microPop | Xh2 dataframe | data.frame | 6 | 6 |
Xsu | microPop | Xsu dataframe | data.frame | 6 | 9 |
microbeSysInfo | microPop | microbeSysInfo | data.frame | 3 | 11 |
microbeSysInfoHuman | microPop | microbeSysInfoHuman dataframe | data.frame | 3 | 11 |
microbeSysInfoRumen | microPop | microbeSysInfoRumen dataframe | data.frame | 3 | 4 |
resourceSysInfo | microPop | resourceSysInfo | data.frame | 4 | 18 |
resourceSysInfoHuman | microPop | resourceSysInfoHuman dataframe | data.frame | 4 | 18 |
resourceSysInfoRumen | microPop | resourceSysInfoRumen dataframe | data.frame | 4 | 16 |
strainParams | microPop | strainParams dataframe | data.frame | 3 | 6 |
systemInfoMicrobesPhyto | microPop | systemInfoMicrobesPhyto dataframe | data.frame | 3 | 4 |
systemInfoMicrobesVirus | microPop | systemInfoMicrobesVirus dataframe | data.frame | 3 | 6 |
systemInfoResourcesPhyto | microPop | systemInfoResourcesPhyto dataframe | data.frame | 4 | 3 |
systemInfoResourcesVirus | microPop | systemInfoResourcesVirus dataframe | data.frame | 4 | 5 |
coords | multiocc | Coords | data.frame | 267 | 3 |
detection | multiocc | Detection | data.frame | 8010 | 10 |
occupancy | multiocc | Occupancy | data.frame | 2670 | 4 |
Chem97 | mlmRev | Scores on A-level Chemistry in 1997 | data.frame | 31022 | 8 |
Contraception | mlmRev | Contraceptive use in Bangladesh | data.frame | 1934 | 6 |
Early | mlmRev | Early childhood intervention study | data.frame | 309 | 4 |
Exam | mlmRev | Exam scores from inner London | data.frame | 4059 | 10 |
Gcsemv | mlmRev | GCSE exam score | data.frame | 1905 | 5 |
Hsb82 | mlmRev | High School and Beyond - 1982 | data.frame | 7185 | 8 |
Mmmec | mlmRev | Malignant melanoma deaths in Europe | data.frame | 354 | 6 |
Oxboys | mlmRev | Heights of Boys in Oxford | data.frame | 234 | 4 |
ScotsSec | mlmRev | Scottish secondary school scores | data.frame | 3435 | 6 |
Socatt | mlmRev | Social Attitudes Survey | data.frame | 1056 | 9 |
bdf | mlmRev | Language Scores of 8-Graders in The Netherlands | data.frame | 2287 | 28 |
egsingle | mlmRev | US Sustaining Effects study | data.frame | 7230 | 12 |
guImmun | mlmRev | Immunization in Guatemala | data.frame | 2159 | 13 |
guPrenat | mlmRev | Prenatal care in Guatemala | data.frame | 2449 | 15 |
s3bbx | mlmRev | Covariates in the Rodriguez and Goldman simulation | data.frame | 2449 | 6 |
s3bby | mlmRev | Responses simulated by Rodriguez and Goldman | matrix | 2449 | |
star | mlmRev | Student Teacher Achievement Ratio (STAR) project data | data.frame | 26796 | 18 |
binarydata | blockcluster | Simulated Binary Data-set | matrix | 1000 | 100 |
categoricaldata | blockcluster | Simulated categorical Data-set | matrix | 200 | 60 |
contingencydatalist | blockcluster | Simulated Contingency Data-set | list | | |
contingencydataunknown | blockcluster | Simulated Contingency Data-set | matrix | 1000 | 100 |
gaussiandata | blockcluster | Simulated Gaussian Data-set | matrix | 1000 | 100 |
psc | KraljicMatrix | Product and service contracts | tbl_df | 200 | 3 |
data.xllim | xLLiM | Simulated data to run examples of usage of 'gllim' and 'sllim' functions | matrix | 52 | |
data.xllim.test | xLLiM | Testing data to run examples of usage of 'gllim_inverse_map' and 'sllim_inverse_map' functions | matrix | 50 | |
data.xllim.trueparameters | xLLiM | True parameters used to simulate the datasets 'data.xllim' and 'data.xllim.test' | list | | |
featureList | flacco | Feature List | list | | |
dists | distreg.vis | Information about supported and not yet supported distribution families | data.frame | 125 | 8 |
STAR_MHE | rddtools | Transformation of the STAR dataset as used in Angrist and Pischke (2008) | data.frame | 5743 | 6 |
house | rddtools | Dataset used in Lee (2008) | data.frame | 6558 | 2 |
indh | rddtools | INDH data set | data.frame | 720 | 2 |
blobs | Spectrum | 8 blob like structures | data.frame | 10 | 800 |
brain | Spectrum | A brain cancer dataset | list | | |
circles | Spectrum | Three concentric circles | data.frame | 2 | 540 |
missl | Spectrum | A list of the blob data as similarity matrices with a missing entry in one | list | | |
misslfilled | Spectrum | A list of the blob data as similarity matrices with a missing entry in one filled with NAs | list | | |
spirals | Spectrum | Two spirals wrapped around one another | data.frame | 2 | 180 |
comorbidity_data | PDN | Sampele comorbidity data set | data.frame | 100 | 10 |
demographic_data | PDN | Sampele demographic data set | data.frame | 100 | 5 |
survival_data | PDN | Sampele Survival data set | data.frame | 100 | 2 |
ex_qsort | qsort | Example datasets for qsort package | list | | |
qset_aqs | qsort | AQS Q-set criteria scores and derived scales | data.frame | 90 | 8 |
qset_ccq | qsort | CCQ Q-set criteria scores and derived scales | data.frame | 100 | 9 |
qset_mbqs | qsort | MBQS Q-set criteria scores and derived scales | data.frame | 90 | 3 |
qset_pq | qsort | PQ Q-set criteria scores and derived scales | data.frame | 72 | 5 |
HEXACO_60 | stenR | Sample data of HEXACO-60 questionnaire results | data.frame | 204 | 9 |
IPIP_NEO_300 | stenR | Sample data of IPIP-NEO-300 questionnaire results | data.frame | 13161 | 7 |
SLCS | stenR | Sample data of SLCS questionnaire results | data.frame | 103 | 19 |
first_sentences | js4shiny | First Sentences of Books | tbl_df | 40 | 3 |
us_cities_ranked | js4shiny | 125 US Cities Ranked, 2019 | tbl_df | 125 | 26 |
sst_Med | RmarineHeatWaves | Optimally Interpolated 0.25 degree SST for the Mediterranean region. | data.frame | 12053 | 2 |
sst_NW_Atl | RmarineHeatWaves | Optimally Interpolated 0.25 degree SST for the NW Atlantic region. | data.frame | 12053 | 2 |
sst_WA | RmarineHeatWaves | Optimally Interpolated 0.25 degree SST for the Western Australian region. | data.frame | 12053 | 2 |
artificial_clusters | vsclust | Synthetic/artificial data comprising 5 clusters | data.frame | 500 | 50 |
protein_expressions | vsclust | Data from a typical proteomics experiment | data.frame | 574 | 12 |
coquettes | nodiv | Distribution of coquette hummingbirds in Northern South America | nodiv_data | | |
air | ks | Air quality measurements in an underground train station | data.frame | 35039 | 8 |
cardio | ks | Foetal cardiotocograms | data.frame | 2126 | 23 |
grevillea | ks | Geographical locations of grevillea plants | matrix | 222 | 2 |
hsct | ks | Haematopoietic stem cell transplant | data.frame | 39128 | 6 |
plate | ks | Geographical locations of earthquakes and tectonic plates | data.frame | 6276 | 3 |
platesf | ks | Geographical locations of earthquakes and tectonic plates | sf | 54 | 3 |
quake | ks | Geographical locations of earthquakes and tectonic plates | data.frame | 5871 | 5 |
quakesf | ks | Geographical locations of earthquakes and tectonic plates | sf | 5871 | 4 |
tempb | ks | Daily temperature | data.frame | 21908 | 5 |
unicef | ks | Unicef child mortality - life expectancy data | data.frame | 73 | 2 |
worldbank | ks | Development indicators from the World Bank Group | data.frame | 218 | 7 |
Dolphins | collpcm | Dolphins | network | | |
Karate | collpcm | Network describing loyalty in the Karate club. | network | | |
Monks | collpcm | Monks | network | | |
LeukSurv | spBayesSurv | The Leukemia Survival Data | data.frame | 1043 | 9 |
pbc | risksetROC | Incident/Dynamic (I/D) ROC curve, AUC and integrated AUC (iAUC) estimation of censored survival data | data.frame | 418 | 20 |
bunching_data | bunching | Simulated data for bunching examples. | data.frame | 27510 | 2 |
envData | SubpathwayLNCE | The variables in the environment variable 'envData' of the system | environment | | |
immigrationdata | CRTConjoint | Immigration Choice Conjoint Experiment Data from Hainmueller et. al. (2014). | data.frame | 1000 | 23 |
adviceModel | doc2concrete | Pre-trained Concreteness Detection Model for Advice | cv.glmnet | | |
adviceNgrams | doc2concrete | Pre-trained advice concreteness features | data.frame | 5 | 654 |
bootstrap_list | doc2concrete | Concreteness mTurk Word List | data.frame | 85942 | 2 |
feedback_dat | doc2concrete | Personal Feedback Dataset | data.frame | 171 | 2 |
mturk_list | doc2concrete | Concreteness mTurk Word List | data.frame | 39954 | 2 |
planModel | doc2concrete | Pre-trained Concreteness Detection Model for Plan-Making | cv.glmnet | | |
planNgrams | doc2concrete | Pre-trained plan concreteness features | matrix | 5 | 2404 |
uk2us | doc2concrete | UK to US Conversion dictionary | dictionary2 | | |
fmridata | BHMSMAfMRI | A simulated fMRI data for 3 subjects | list | | |
regulon | epiregulon.extra | regulon created using 'epiregulon' package from reprogram-seq data | DFrame | | |
RMS_dat | nlpsem | ECLS-K (2011) Sample Dataset for Demonstration | data.frame | 500 | 49 |
productos | comidistar | Productos presentados en las Catas de Expertos de El Comidista | tbl_df | 407 | 4 |
puntuaciones | comidistar | Puntuaciones de las Catas de Expertos de El Comidista | tbl_df | 452 | 5 |
videos | comidistar | Fecha y URL de los vídeos de las Catas de Expertos de El Comidista | tbl_df | 47 | 3 |
gbhs | gbhs | Lifebrain Global Brain Health Survey | tbl_df | 27590 | 107 |
familyname | ChineseNames | 1,806 Chinese surnames and nationwide frequency. | data.frame | 1806 | 7 |
givenname | ChineseNames | 2,614 Chinese characters used in given names and nationwide frequency. | data.frame | 2614 | 25 |
population | ChineseNames | Population statistics for the Chinese name database. | data.frame | 40 | 3 |
top1000name.prov | ChineseNames | Top 1,000 given names in 31 Chinese mainland provinces. | data.frame | 999 | 35 |
top100name.year | ChineseNames | Top 100 given names in 6 birth cohorts. | data.frame | 100 | 37 |
top50char.year | ChineseNames | Top 50 given-name characters in 6 birth cohorts. | data.frame | 50 | 37 |
geneCounts | MIRit | Count matrix for gene expression in thyroid cancer | matrix | 23710 | 16 |
mirnaCounts | MIRit | Count matrix for microRNA expression in thyroid cancer | matrix | 2576 | 16 |
fournival | stemmatology | Fournival Data Set | matrix | 292 | 10 |
heinrichi | stemmatology | Heinrichi data set | matrix | 1208 | 37 |
notreBesoin | stemmatology | Notre Besoin data set | matrix | 42 | 13 |
otinel | stemmatology | Otinel data set | matrix | 36 | 8 |
parzival | stemmatology | Parzival data set | matrix | 139 | 16 |
toydata | localIV | A Hypothetical Dataset for Illustrative Purpose | data.frame | 10000 | 4 |
bikeMi | conformalInference.multi | Log of all bike rentals in Milan in 2016 form January to March | data.frame | 41 | 6 |
ens | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | matrix | 27 | 24 |
ens.bin | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | matrix | 27 | 24 |
ens.cat | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | matrix | 27 | 24 |
obs | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | numeric | | |
obs.bin | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | numeric | | |
obs.cat | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | numeric | | |
obs.lag | SpecsVerification | Seasonal ensemble forecast of European average summer temperature | numeric | | |
forestData | forestat | Mixed birch-broadleaf forest data | data.frame | 320 | 16 |
plot_1 | forestat | 1st period sample plot survey data | data.frame | 62 | 23 |
plot_2 | forestat | 2nd period sample plot survey data | data.frame | 100 | 23 |
plot_3 | forestat | 3rd period sample plot survey data | data.frame | 100 | 23 |
tree_1 | forestat | 1st period trees survey data | data.frame | 1634 | 5 |
tree_2 | forestat | 2nd period trees survey data | data.frame | 4778 | 5 |
tree_3 | forestat | 3rd period trees survey data | data.frame | 4528 | 5 |
cosponsor_senate_15 | levelnet | Bill cosponsorship data for the 115th Senate | data.frame | 26392 | 3 |
Azov | FuzzyQ | Helminth communities of so-iuy mullets from the Sea of Azov | data.frame | 378 | 26 |
Japan | FuzzyQ | Helminth communities of so-iuy mullets from the Japan Sea | data.frame | 192 | 22 |
antsA | FuzzyQ | Ant species abundance from Arnan et el. (2011) | data.frame | 99 | 46 |
Countries | jsTreeR | Countries | data.frame | 250 | 6 |
col_graph | miaViz | miaViz example data | tbl_graph | | |
row_graph | miaViz | miaViz example data | tbl_graph | | |
row_graph_order | miaViz | miaViz example data | tbl_graph | | |
ebicat_2020_04_30 | gwascat | serialized gwaswloc instance from april 30 2020, sample of 50000 records | gwaswloc | | |
efo.obo.g | gwascat | convert a typical OBO text file to a graphNEL instance (using Term elements) | graphNEL | | |
g17SM | gwascat | SnpMatrix instance from chr17 | SnpMatrix | 90 | 89701 |
gg17N | gwascat | genotype matrix from chr17 1000 genomes | matrix | 90 | 87957 |
gr6.0_hg38 | gwascat | image of locon6 in GRanges, lifted over to hg38 | GRanges | | |
gw6.rs_17 | gwascat | character vector of rs numbers for SNP on chr17 | character | | |
gwastagger | gwascat | GRanges with LD information on 9998 SNP | GRanges | | |
locon6 | gwascat | location data for 10000 SNP | data.frame | 10000 | 3 |
low17 | gwascat | SnpMatrix instance from chr17 | SnpMatrix | 60 | 196327 |
si.hs.37 | gwascat | Seqinfo for GRCh37 | Seqinfo | | |
si.hs.38 | gwascat | Seqinfo for GRCh38 | Seqinfo | | |
feature_counts_list | CleanUpRNAseq | GC content and lengths of 2000 intergenic regions | list | | |
gene_GC | CleanUpRNAseq | GC content and lengths of 2000 human genes | data.frame | 62754 | 2 |
intergenic_GC | CleanUpRNAseq | GC content and lengths of 2000 intergenic regions | data.frame | 2000 | 2 |
salmon_quant | CleanUpRNAseq | GC content and lengths of 2000 intergenic regions | list | | |
gordon | ggsegGordon | gordon atlas | brain_atlas | | |
gordon_3d | ggsegGordon | gordon atlas | ggseg3d_atlas | 4 | 4 |
aseg | ggseg | Freesurfer automatic subcortical segmentation of a brain volume | brain_atlas | | |
dk | ggseg | Desikan-Killiany Cortical Atlas | brain_atlas | | |
Laboratory_Data | BoostMLR | Laboratory Data | data.frame | 18285 | 45 |
IMCAAttrs | graph | KEGG Integrin Mediated Cell Adhesion graph | list | | |
IMCAGraph | graph | KEGG Integrin Mediated Cell Adhesion graph | graphNEL | | |
MAPKsig | graph | A graph encoding parts of the MAPK signaling pathway | graphNEL | | |
MAPKsig | graph | A graph encoding parts of the MAPK signaling pathway | graphNEL | | |
apopGraph | graph | KEGG apoptosis pathway graph | graphNEL | | |
biocRepos | graph | A graph representing the Bioconductor package repository | graphNEL | | |
esetsFemale | graph | MultiGraph edgeSet data | list | | |
esetsMale | graph | MultiGraph edgeSet data | list | | |
graphExamples | graph | A List Of Example Graphs | list | | |
pancrCaIni | graph | A graph encoding parts of the pancreatic cancer initiation pathway | graphNEL | | |
hts_example_model | yahtsee | Example model for use in testing and examples | hts_inla | | |
malaria_africa_ts | yahtsee | Prevalence Rate data of Malaria in Africa | tbl_ts | 1046 | 15 |
who_regions | yahtsee | Who Regions | tbl_df | 110 | 4 |
italy_province | covid19italy | The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Provinces Outbreak Dataset | tbl_df | 165957 | 14 |
italy_region | covid19italy | The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Regions Outbreak Dataset | data.frame | 23751 | 26 |
italy_total | covid19italy | The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Outbreak Dataset | tbl_df | 1131 | 19 |
entsoe | mrf | Entsoe DataFrame containing Time Series | data.frame | 3652 | 2 |
RNAseq | lpda | Simulated RNA-Seq dataset example | data.frame | 60 | 600 |
palmdates | lpda | Spectrometry and composition chemical of Spanish and Arabian palm dates | data.frame | 21 | 2 |
hbbrPilotResp | hbbr | A list consisting of pilot data and associated discrete choice design information for the HBBR model framework. | list | | |
simAugData | hbbr | A list consisting of simulated data, design, baseline profiles, and true part-worth matrix for the Augmented HBBR model framework. | list | | |
exp.win.lengths | coarseDataTools | Exposure window lengths from an influenza outbreak at a NYC school | numeric | | |
fluA.inc.per | coarseDataTools | Coarse incubation period data for influenza A | data.frame | 151 | 7 |
nycH1N1 | coarseDataTools | Incubation period data from New York City Public Schools, 2009 H1N1 influenza outbreak | data.frame | 134 | 5 |
simulated.outbreak.deaths | coarseDataTools | Simulated case and death reports from a fictional outbreak | matrix | 120 | 5 |
Data.Election1 | StatRank | A1 Election Data | data.frame | 380 | 10 |
Data.Election6 | StatRank | A6 Election Data | data.frame | 279 | 9 |
Data.Election9 | StatRank | A9 Election Data | data.frame | 3419 | 12 |
Data.Nascar | StatRank | Nascar Data | data.frame | 36 | 83 |
Data.NascarTrimmed | StatRank | Trimmed Nascar Data | matrix | 36 | |
Data.Test | StatRank | Tiny test dataset | matrix | 5 | |
karate | orca | Karate Club network | data.frame | 78 | 2 |
petersen | orca | Petersen graph | data.frame | 15 | 2 |
usastates | orca | Contiguous USA Graph | data.frame | 107 | 2 |
yeast | orca | Yeast protein-protein interaction network | data.frame | 6646 | 2 |
lung | nphPower | Lung cancer data set | data.frame | 137 | 10 |
nasa | cubelyr | NASA spatio-temporal data | tbl_cube | | |
ex_flow | halfcircle | Traded volume of land between countries | data.frame | 10866 | 6 |
ex_node | halfcircle | country attributes | data.frame | 154 | 8 |
simulated_data | miscIC | Simulated example dataset of misclassified interval censored time-to-event data | data.frame | 908 | 3 |
mentari | kindisperse | Position & kinship information of _Aedes aegypti_ from Mentari Court, Malaysia | spec_tbl_df | 98 | 10 |
hydroquinone | gMCP | Hydroquinone Mutagenicity Assay | data.frame | 31 | 2 |
simvastatin | gMCP | Simvastatin and Colesevelam Treatment in Patients with Primary Hypercholesterolemia | data.frame | 4 | 5 |
CLbyDisease | retriever | CLbyDisease | list | | |
precip_Niamey_2016 | reliabilitydiag | Precipitation forecasts and observations at Niamey, Niger in July to September 2016 | tbl_df | 92 | 6 |
cpdata0 | eoa3 | A template for carcass persistence data with interval-censored carcass persistence times | data.frame | 42 | 2 |
days0 | eoa3 | A template for search schedule data | numeric | | |
pkdata0 | eoa3 | A template for summarized searcher efficiency data with the number of carcasses available and the number discovered for N = 12 search occasions | list | | |
simdata | BayesGWQS | Simulated data of chemical concentrations and one binary outcome variable | data.frame | 1000 | 15 |
nbc4vaData | nbc4va | Example of clean data in nbc4va | data.frame | 100 | 102 |
nbc4vaDataRaw | nbc4va | Example of unclean data in nbc4va | data.frame | 100 | 102 |
galicia | jsonstat | Galicia data | data.frame | 3960 | 7 |
A | LS2W | Examples of textured images | matrix | 1024 | |
B | LS2W | Examples of textured images | imagematrix | 512 | |
C | LS2W | Examples of textured images | matrix | 459 | |
retinal | simplexreg | Data on recorded decay of intraocular gas in complex retinal surgeries | data.frame | 181 | 6 |
sdac | simplexreg | Data on Autologous Peripheral Blood Stem Cell Transplants in Alberta Health Service | data.frame | 239 | 5 |
energy | MSwM | Price of energy in Spain | data.frame | 1784 | 7 |
example | MSwM | Example data generated | data.frame | 300 | 2 |
traffic | MSwM | Traffic Deads in Spain | data.frame | 365 | 4 |
zachary | GoodFitSBM | Zachary Karate Club Data | data.frame | 68 | 34 |
nwts2ph | addhazard | An hypothetical two-phase sampling dataset based on nwtsco dataset from the National Wilms Tumor Study (NWTS) | data.frame | 3915 | 16 |
nwtsco | addhazard | Dataset from the National Wilms Tumor Study (NWTS) | data.frame | 3915 | 12 |
indsim | BrainCon | Simulation time series data for individual | matrix | 50 | |
popsimA | BrainCon | Simulation time series data for population A | array | | |
popsimB | BrainCon | Simulation time series data for population B | array | | |
ml2 | hsem | simulated urge to smoke data | data.frame | 200 | 6 |
hotspots | spotoroo | 1070 observations of satellite hot spots | data.frame | 1070 | 3 |
vic_map | spotoroo | simple features map of Victoria | sfc_MULTIPOLYGON | | |
MEPS | assessor | Healthcare expenditure data | data.frame | 29784 | 29 |
my_twitterads_data | twitteradsR | Sample of digital marketing data from Twitter Ads downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
PakPC2017Balochistan | PakPC2017 | Balochistan Province data from Pakistan Population Census 2017 | data.table | 64 | 12 |
PakPC2017City10 | PakPC2017 | Top 10 Cities data from Pakistan Population Census 2017 | data.table | 10 | 3 |
PakPC2017FATA | PakPC2017 | FATA Province data from Pakistan Population Census 2017 | data.table | 26 | 12 |
PakPC2017Islamabad | PakPC2017 | Islamabad data from Pakistan Population Census 2017 | data.table | 2 | 12 |
PakPC2017KPK | PakPC2017 | KPK Province data from Pakistan Population Census 2017 | data.table | 50 | 12 |
PakPC2017Pak | PakPC2017 | Pakistan data from Pakistan Population Census 2017 | data.table | 12 | 10 |
PakPC2017Pakistan | PakPC2017 | Pakistan data from Pakistan Population Census 2017 | data.table | 272 | 12 |
PakPC2017Punjab | PakPC2017 | Punjab Province data from Pakistan Population Census 2017 | data.table | 72 | 12 |
PakPC2017Sindh | PakPC2017 | Sindh Province data from Pakistan Population Census 2017 | data.table | 58 | 12 |
PakPC2017Tehsil | PakPC2017 | Pakistan Tehsil data from Pakistan Population Census 2017 | tbl_df | 543 | 6 |
PakPop2017 | PakPC2017 | Pakistan Population data from Pakistan Population Census 2017 | tbl_df | 168808 | 10 |
larynx | SurvRegCensCov | Survival Times of Larynx Cancer Patients | data.frame | 90 | 5 |
defaultDispFunc | simphony | Default function for mapping expected counts to dispersion. | function | | |
GSE19188 | deltaccd | Gene expression data for GSE19188. | list | | |
hybrid_phe | predhy | Phenotypic data of hybrids | data.frame | 410 | 3 |
input_geno | predhy | Genotype in Hapmap Format | data.frame | 4979 | 359 |
input_geno1 | predhy | Genotype in Numeric Format | data.frame | 1000 | 50 |
estat_census_2020 | japanstat | Population of the 2020 census | estat | | |
word_classification_data | AcousticNDLCodeR | Data of PLoS ONE paper | data.frame | 20000 | 19 |
ElecData | KPC | 2017 Korea presidential election data | data.frame | 1250 | 9 |
med | KPC | Medical data from 35 patients | data.frame | 35 | 3 |
mcmcListExample | mcmcOutput | An object of class 'mcmc.list' produced by 'rjags::coda.samples' | mcmc.list | | |
jreast_jt | ssrn | East Japan Railway's Tokaido Line Data | tbl_df | 20 | 2 |
jreast_jt_od | ssrn | JR-East Tokaido Line OD Data | tbl_df | 197 | 4 |
ANZ0001 | ordinalCont | ANZ0001 trial | data.frame | 2473 | 11 |
ANZ0001.sub | ordinalCont | ANZ0001 trial subset | data.frame | 188 | 11 |
neck_pain | ordinalCont | Neck pain data set | data.frame | 264 | 5 |
kerenKontextual | Statial | Kontextual results from kerenSCE | data.frame | 4000 | 6 |
kerenSCE | Statial | MIBI-TOF Breast cancer intensities | SingleCellExperiment | | |
OmopReference | MHTrajectoryR | The OMOP reference set | data.frame | 399 | 3 |
exampleAE | MHTrajectoryR | A simulated data | matrix | 100 | 5 |
exampleDrugs | MHTrajectoryR | A simulated data | matrix | 100 | 146 |
factorialdata | doebioresearch | Data of Factorial Experiment | data.frame | 24 | 6 |
lsddata | doebioresearch | Data for Latin Square Design | data.frame | 25 | 5 |
splitdata | doebioresearch | Data for Split plot Design | data.frame | 36 | 5 |
exBoot | mrgsolve | Example input data sets | data.frame | 100 | 13 |
exTheoph | mrgsolve | Example input data sets | data.frame | 132 | 8 |
exidata | mrgsolve | Example input data sets | data.frame | 10 | 7 |
extran1 | mrgsolve | Example input data sets | data.frame | 9 | 8 |
extran2 | mrgsolve | Example input data sets | data.frame | 714 | 8 |
extran3 | mrgsolve | Example input data sets | data.frame | 714 | 11 |
chicago | GeneralOaxaca | Labor market and demographic data for employed Hispanic workers in metropolitan Chicago | data.frame | 712 | 9 |
UMI_duplication | scPipe | UMI duplication statistics for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe | data.frame | 1001 | 2 |
cell_barcode_matching | scPipe | cell barcode demultiplex statistics for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe | data.frame | 6 | 2 |
sc_sample_data | scPipe | a small sample scRNA-seq counts dataset to demonstrate capabilities of scPipe | matrix | 1000 | 383 |
sc_sample_qc | scPipe | quality control information for a small sample scRNA-seq dataset to demonstrate capabilities of scPipe. | data.frame | 383 | 12 |
data | rPowerSampleSize | Simulated data | data.frame | 100 | 5 |
DHdata | QTL.gCIMapping | DH example data | data.frame | 7 | 12 |
F2data | QTL.gCIMapping | F2 example data from 2 environments | data.table | 105 | 52 |
encodemotif | motifbreakR | MotifDb object containing motif information from the known and discovered motifs for the ENCODE TF ChIP-seq datasets. | MotifList | | |
example.results | motifbreakR | Example Results from motifbreakR | GRanges | | |
factorbook | motifbreakR | MotifDb object containing motif information from around the genomic regions bound by 119 human transcription factors in Factorbook. | MotifList | | |
hocomoco | motifbreakR | MotifDb object containing motif information from Homo Sapiens Comprehensive Model Collection (HOCOMOCO) of transcription factor (TF) binding models | MotifList | | |
homer | motifbreakR | MotifDb object containing motif information from motif databases included in HOMER. | MotifList | | |
motifbreakR_motif | motifbreakR | MotifDb object containing motif information from the motif databases of HOCOMOCO, Homer, FactorBook and ENCODE | MotifList | | |
cpt_names | cptcity | Names of the 7140 color gradients of cptcity R Package | list | | |
pTableYeastRokas | MSCquartets | pTable for Yeast dataset | matrix | 70 | 15 |
tableHeleconiusMartin | MSCquartets | Quartet table for Heliconius gene tree dataset | matrix | 1820 | 20 |
tableLeopardusLescroart | MSCquartets | Quartet table for Leopardus dataset | matrix | 1820 | 20 |
ussher | ussherR | Cleaned and tidied data drawn from Archbishop James Ussher's chronology of ancient history known popularly as The Annals of the World (1658). | rowwise_df | 6998 | 8 |
usshfull | ussherR | Expanded data drawn from "ussher" Archbishop James Ussher's chronology of ancient history known popularly as The Annals of the World (1658). | rowwise_df | 6998 | 12 |
usshraw | ussherR | Raw original data directly drawn with fundamental cleaning from "Annals_djvu.txt" Archbishop James Ussher's chronology of ancient history known popularly as The Annals of the World (1658). | tbl_df | 6998 | 3 |
Ancestry.list | EpiTest | Allele ancestries for the American maize NAM dataset | list | | |
Parents | EpiTest | Phenotypes for parental lines of the American maize NAM dataset | data.frame | 4 | 8 |
Pheno.list | EpiTest | Phenotypes for the American maize NAM dataset | list | | |
Zambia | cAIC4 | Subset of the Zambia data set on childhood malnutrition | data.frame | 659 | 7 |
guWahbaData | cAIC4 | Data from Gu and Wahba (1991) | data.frame | 400 | 10 |
sig | hht | Transitory Seismic Event at Deception Island Volcano | numeric | | |
tt | hht | Ocean Bottom Seismometer Sample Rate | numeric | | |
simul.data | DIFlasso | Simulated Data Set | data.frame | 100 | 13 |
UNlocations | wpp2017 | United Nations Table of Locations | data.frame | 274 | 19 |
e0F | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 241 | 15 |
e0F_supplemental | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Fproj | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 241 | 19 |
e0Fproj80l | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Fproj80u | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Fproj95l | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Fproj95u | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0M | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 241 | 15 |
e0M_supplemental | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Mproj | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 241 | 19 |
e0Mproj80l | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Mproj80u | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Mproj95l | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Mproj95u | wpp2017 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
migration | wpp2017 | Dataset on Migration | data.frame | 241 | 32 |
mxF | wpp2017 | Age-specific Mortality Data | data.frame | 5302 | 33 |
mxM | wpp2017 | Age-specific Mortality Data | data.frame | 5302 | 33 |
percentASFR | wpp2017 | Datasets on Age-specific Distribution of Fertility Rates | data.frame | 1687 | 33 |
pop | wpp2017 | Estimates and Projections of Population Counts | data.frame | 241 | 16 |
popF | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 17 |
popFT | wpp2017 | Estimates and Projections of Population Counts | data.frame | 241 | 16 |
popFTproj | wpp2017 | Estimates and Projections of Population Counts | data.frame | 241 | 19 |
popFprojHigh | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popFprojLow | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popFprojMed | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popM | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 17 |
popMT | wpp2017 | Estimates and Projections of Population Counts | data.frame | 241 | 16 |
popMTproj | wpp2017 | Estimates and Projections of Population Counts | data.frame | 241 | 19 |
popMprojHigh | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popMprojLow | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popMprojMed | wpp2017 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popproj | wpp2017 | Estimates and Projections of Population Counts | data.frame | 241 | 19 |
popproj80l | wpp2017 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popproj80u | wpp2017 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popproj95l | wpp2017 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popproj95u | wpp2017 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popprojHigh | wpp2017 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popprojLow | wpp2017 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
sexRatio | wpp2017 | Sex Ratio at Birth | data.frame | 241 | 32 |
tfr | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 15 |
tfr_supplemental | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 103 | 45 |
tfrproj80l | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrproj80u | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrproj95l | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrproj95u | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrprojHigh | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 19 |
tfrprojLow | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 19 |
tfrprojMed | wpp2017 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 19 |
D1.toy | calibrator | Toy datasets | matrix | 8 | 5 |
D2.toy | calibrator | Toy datasets | matrix | 5 | 2 |
V.toy | calibrator | Toy datasets | matrix | 5 | 5 |
X.dist.toy | calibrator | Toy datasets | list | | |
d.toy | calibrator | Toy datasets | numeric | | |
phi.toy | calibrator | Toy datasets | list | | |
t.vec.toy | calibrator | Toy datasets | matrix | 8 | 3 |
theta.toy | calibrator | Toy datasets | integer | | |
x.toy | calibrator | Toy datasets | numeric | | |
x.toy2 | calibrator | Toy datasets | numeric | | |
x.vec | calibrator | Toy datasets | matrix | 3 | 2 |
y.toy | calibrator | Toy datasets | numeric | | |
z.toy | calibrator | Toy datasets | numeric | | |
getting_started | smer | Simulated Dataset for Genome-Wide Interaction Analysis | list | | |
conductivity_2014 | GeoFIS | Soil conductivity 2014 dataset | SpatialPointsDataFrame | | |
conductivity_border | GeoFIS | Border dataset | SpatialPolygonsDataFrame | | |
fusion_cars | GeoFIS | Fusion Cars dataset | data.frame | 4 | 4 |
tolima | GeoFIS | Tolima dataset | data.frame | 30 | 8 |
GARS_Fitness_score | GARS | RNA-seq dataset for testing GARS | numeric | | |
GARS_classes | GARS | RNA-seq dataset for testing GARS | factor | | |
GARS_data_norm | GARS | RNA-seq dataset for testing GARS | matrix | 58 | 157 |
GARS_fit_list | GARS | RNA-seq dataset for testing GARS | numeric | | |
GARS_pop_list | GARS | RNA-seq dataset for testing GARS | list | | |
GARS_popul | GARS | RNA-seq dataset for testing GARS | matrix | 20 | |
GARS_res_GA | GARS | A GarsSelectedFeatures object for testing GARS | GarsSelectedFeatures | | |
BCIspeed | activity | Animal speed data | data.frame | 2204 | 3 |
BCItime | activity | Animal record time of day data | data.frame | 17820 | 3 |
Ki67 | Qindex.data | Ki67 Data | data.frame | 366812 | 18 |
AAMatrix | MSA2dist | AAMatrix-data | matrix | 27 | 27 |
hiv | MSA2dist | hiv-data | DNAStringSet | | |
iupac | MSA2dist | iupac-data | DNAStringSet | | |
returns | mtarm | Returns of the closing prices of three financial indexes | tbl_df | 1505 | 4 |
riverflows | mtarm | Rainfall and two river flows in Colombia | data.frame | 1200 | 4 |
DiUbi | DEP | DiUbi - Ubiquitin interactors for different diubiquitin-linkages (UbIA-MS dataset) | data.frame | 4071 | 72 |
DiUbi_ExpDesign | DEP | Experimental design of the DiUbi dataset | data.frame | 30 | 3 |
UbiLength | DEP | UbiLength - Ubiquitin interactors of different linear ubiquitin lengths (UbIA-MS dataset) | data.frame | 3006 | 23 |
UbiLength_ExpDesign | DEP | Experimental design of the UbiLength dataset | data.frame | 12 | 3 |
FraserSediment | CaDENCE | Sediment and stream discharge data for Fraser River at Hope | list | | |
Celegance | IMMAN | Celegance | data.frame | 49 | 1 |
FruitFly | IMMAN | Fruit Fly | data.frame | 56 | 1 |
H.sapiens | IMMAN | Homo sapiens (Human) | data.frame | 76 | 1 |
R.norvegicus | IMMAN | Rattus norvegicus (Rat) | data.frame | 70 | 1 |
acacia_pinus | hypervolume | Data for Acacia and Pinus tree distributions | data.frame | 37845 | 3 |
circles | hypervolume | Circles simulated dataset | list | | |
morphSnodgrassHeller | hypervolume | Morphological data for Darwin's finches | data.frame | 549 | 20 |
quercus | hypervolume | Data and demo for Quercus (oak) tree distributions | data.frame | 3779 | 3 |
small_vdj | Platypus | Small VDJ dataframe for function testing purposes | data.frame | 3671 | 70 |
small_vgm | Platypus | Small VDJ GEX matrix (VGM) for function testing purposes | list | | |
model | wavClusteR | Components of the non-parametric mixture moodel fitted on Ago2 PAR-CLIP data | list | | |
claims_transactional | lossrx | claims_transactional | tbl_df | 80278 | 12 |
exposures | lossrx | exposures | tbl_df | 855 | 5 |
losses | lossrx | losses | tbl_df | 79748 | 30 |
SARS_CoV2_3CL_Protease | DeepPINCS | Amino Acid Sequence for the SARS coronavirus 3C-like Protease | character | | |
antiviral_drug | DeepPINCS | List of antiviral drugs with SMILES strings | data.frame | 81 | 2 |
example_bioassay | DeepPINCS | Example Data for PubChem AID1706 bioassay | data.frame | 5000 | 4 |
example_cci | DeepPINCS | Example Data for Chemical-Chemical Interactions | data.frame | 1000 | 3 |
example_chem | DeepPINCS | Example Data for Compounds | data.frame | 200 | 2 |
example_cpi | DeepPINCS | Example Data for Compound-Protein Interactions | data.frame | 6728 | 3 |
example_pd | DeepPINCS | Example Data for Primer-Dimer | data.frame | 319 | 3 |
example_ppi | DeepPINCS | Example Data for Protein-Protein Interactions | data.frame | 5000 | 3 |
example_prot | DeepPINCS | Example Data for Proteins | data.frame | 9498 | 2 |
AutoClaim | cplm | Data sets in the cplm pakcage | data.frame | 10296 | 29 |
ClaimTriangle | cplm | Data sets in the cplm pakcage | data.frame | 55 | 3 |
FineRoot | cplm | Data sets in the cplm pakcage | data.frame | 511 | 5 |
drasdolut | visualFields | Precomputed X and Y displacement of ganglion cell bodies for any given X and Y location on the retina | list | | |
gpars | visualFields | List of graphical parameters | list | | |
locmaps | visualFields | Location maps | list | | |
normvals | visualFields | List of normative values that can be used for statistical analysis and visualization | list | | |
vfctrIowaPC26 | visualFields | Central visual field | data.frame | 196 | 81 |
vfctrIowaPeri | visualFields | Peripheral visual field | data.frame | 196 | 80 |
vfctrSunyiu10d2 | visualFields | SUNY-IU dataset of healthy eyes for 10-2 static automated perimetry | data.frame | 55 | 78 |
vfctrSunyiu24d2 | visualFields | SUNY-IU dataset of healthy eyes for 24-2 static automated perimetry | data.frame | 263 | 64 |
vfpwgRetest24d2 | visualFields | Short-term retest static automated perimetry data | data.frame | 360 | 64 |
vfpwgSunyiu24d2 | visualFields | Series of 24-2 static automated perimetry data for a patient with glaucoma | data.frame | 42 | 64 |
Moore | RcmdrPlugin.aRnova | Status, Authoritarianism, and Conformity | data.frame | 45 | 4 |
OBrienKaiser | RcmdrPlugin.aRnova | O'Brien and Kaiser's Repeated-Measures Data | data.frame | 16 | 17 |
Pottery | RcmdrPlugin.aRnova | Chemical Composition of Pottery | data.frame | 26 | 6 |
Titanic | cdparcoord | Titanic passengers | data.frame | 891 | 14 |
categoricalexample | cdparcoord | A small dataset for showing how tupleFreqs works in cdparcoord | data.frame | 5 | 4 |
demog | cdparcoord | Demographic statistics by ZIP Code. | data.frame | 236 | 1 |
hrdata | cdparcoord | A human resources simulated dataset. | data.frame | 14999 | 10 |
smallexample | cdparcoord | A small dataset for showing how tupleFreqs works in cdparcoord | data.frame | 16 | 2 |
kenyaforex | rKenyaForex | KSHS Exchange Rate Prices | data.frame | 9746 | 5 |
candidate.rxlr | effectR | An example of 'effector.summary' output | list | | |
yields | manymodelr | Plant yields | data.frame | 1000 | 4 |
MNImap_hip | VertexWiseR | Hippocampal surface in MNI space | MNIsurface | | |
ROImap_fs5 | VertexWiseR | Atlas parcellations of fsaverage5 | ROImap | | |
ROImap_fs6 | VertexWiseR | Atlas parcellations of fsaverage6 | ROImap | | |
ROImap_fslr32k | VertexWiseR | Atlas parcellations of FS_LR32k | ROImap | | |
ROImap_hip | VertexWiseR | Atlas parcellations of the hippocampus (CITI168) | ROImap | | |
edgelist_hip | VertexWiseR | List of edges for the hippocampal template | edgelist | | |
fs6_to_fs5_map | VertexWiseR | fsaverage6 template object for nearest neighbor conversion in fs6_to_fs5() | numeric | | |
hip_points_cells | VertexWiseR | points and cells data required to build the hippocampus surface template | list | | |
equipment | RelDists | Electronic equipment data | numeric | | |
mice | RelDists | Mice mortality data | numeric | | |
geopotential | metR | Geopotential height | data.table | 290304 | 5 |
surface | metR | Surface height | data.table | 2891 | 5 |
temperature | metR | Air temperature | data.table | 178704 | 4 |
burk | sparr | Burkitt's lymphoma in Uganda | list | | |
fmd | sparr | Veterinary foot-and-mouth disease outbreak data | list | | |
pbc | sparr | Primary biliary cirrhosis data | ppp | | |
counts_example | BREADR | counts_example | tbl_df | 15 | 4 |
relatedness_example | BREADR | relatedness_example | tbl_df | 15 | 12 |
Albacore | MSEtool | Stock class objects | Stock | | |
Albacore_TwoFleet | MSEtool | MOM class objects | MOM | | |
Atlantic_mackerel | MSEtool | Data class objects | Data | | |
Blue_shark | MSEtool | Stock class objects | Stock | | |
Bluefin_tuna | MSEtool | Stock class objects | Stock | | |
Bluefin_tuna_WAtl | MSEtool | Stock class objects | Stock | | |
Butterfish | MSEtool | Stock class objects | Stock | | |
China_rockfish | MSEtool | Data class objects | Data | | |
Cobia | MSEtool | Data class objects | Data | | |
DataDescription | MSEtool | DataDescription | data.frame | 94 | 2 |
DataSlots | MSEtool | DataSlots | tbl_df | 101 | 4 |
DecE_Dom | MSEtool | Fleet class objects | Fleet | | |
DecE_HDom | MSEtool | Fleet class objects | Fleet | | |
DecE_NDom | MSEtool | Fleet class objects | Fleet | | |
Example_datafile | MSEtool | Data class objects | Data | | |
FlatE_Dom | MSEtool | Fleet class objects | Fleet | | |
FlatE_HDom | MSEtool | Fleet class objects | Fleet | | |
FlatE_NDom | MSEtool | Fleet class objects | Fleet | | |
FleetDescription | MSEtool | FleetDescription | data.frame | 20 | 2 |
Generic_DecE | MSEtool | Fleet class objects | Fleet | | |
Generic_FlatE | MSEtool | Fleet class objects | Fleet | | |
Generic_Fleet | MSEtool | Fleet class objects | Fleet | | |
Generic_IncE | MSEtool | Fleet class objects | Fleet | | |
Generic_Obs | MSEtool | Obs class objects | Obs | | |
Gulf_blue_tilefish | MSEtool | Data class objects | Data | | |
Herring | MSEtool | Stock class objects | Stock | | |
HistDescription | MSEtool | HistDescription | data.frame | 76 | 2 |
ImpDescription | MSEtool | ImpDescription | data.frame | 7 | 2 |
Imprecise_Biased | MSEtool | Obs class objects | Obs | | |
Imprecise_Unbiased | MSEtool | Obs class objects | Obs | | |
IncE_HDom | MSEtool | Fleet class objects | Fleet | | |
IncE_NDom | MSEtool | Fleet class objects | Fleet | | |
LHdatabase | MSEtool | LHdatabase | list | | |
Low_Effort_Non_Target | MSEtool | Fleet class objects | Fleet | | |
MSEDescription | MSEtool | MSEDescription | data.frame | 29 | 2 |
Mackerel | MSEtool | Stock class objects | Stock | | |
OMDescription | MSEtool | OMDescription | data.frame | 15 | 2 |
ObsDescription | MSEtool | ObsDescription | data.frame | 30 | 2 |
Overages | MSEtool | Imp class objects | Imp | | |
Perfect_Imp | MSEtool | Imp class objects | Imp | | |
Perfect_Info | MSEtool | Obs class objects | Obs | | |
Porgy | MSEtool | Stock class objects | Stock | | |
Precise_Biased | MSEtool | Obs class objects | Obs | | |
Precise_Unbiased | MSEtool | Obs class objects | Obs | | |
Red_snapper | MSEtool | Data class objects | Data | | |
ReqData | MSEtool | ReqData | data.frame | 123 | 2 |
Rockfish | MSEtool | Stock class objects | Stock | | |
SimulatedData | MSEtool | SimulatedData Data | Data | | |
Simulation_1 | MSEtool | Data class objects | Data | | |
Snapper | MSEtool | Stock class objects | Stock | | |
Sole | MSEtool | Stock class objects | Stock | | |
StockDescription | MSEtool | StockDescription | data.frame | 27 | 2 |
Target_All_Fish | MSEtool | Fleet class objects | Fleet | | |
Targeting_Small_Fish | MSEtool | Fleet class objects | Fleet | | |
Taxa_Table | MSEtool | Taxa_Table | tbl_df | 34721 | 8 |
Toothfish | MSEtool | Stock class objects | Stock | | |
ourReefFish | MSEtool | Data class objects | Data | | |
testOM | MSEtool | OM class objects | OM | | |
bcr_codes | auk | BCR Codes | tbl_df | 66 | 2 |
ebird_states | auk | eBird States | tbl_df | 3145 | 4 |
ebird_taxonomy | auk | eBird Taxonomy | data.frame | 17415 | 10 |
valid_protocols | auk | Valid Protocols | character | | |
NSpec.DF | visa | Example data in the Spectra.Dataframe format | data.frame | 19 | 2 |
NSpec.Lib | visa | Example data in the Spectra/SpectraLibrary format. | Spectra | | |
agriculture | MDPIexploreR | Article data extracted from MDPI journal Agriculture | data.frame | 7160 | 7 |
horticulturae | MDPIexploreR | Article data extracted from MDPI journal Horticulturae | data.frame | 3434 | 7 |
MesoCarnivores | unmarked | Occupancy data for coyote, red fox, and bobcat | list | | |
Switzerland | unmarked | Swiss landscape data | data.frame | 42275 | 5 |
catbird | unmarked | BBS Point Count and Occurrence Data from 2 Bird Species | data.frame | 50 | 11 |
catbird.bin | unmarked | BBS Point Count and Occurrence Data from 2 Bird Species | matrix | 50 | 11 |
crossbill | unmarked | Detection/non-detection data on the European crossbill (_Loxia curvirostra_) | data.frame | 267 | 58 |
cruz | unmarked | Landscape data for Santa Cruz Island | data.frame | 2787 | 5 |
gf.data | unmarked | Green frog count index data | matrix | 220 | 3 |
gf.obs | unmarked | Green frog count index data | list | | |
issj | unmarked | Distance-sampling data for the Island Scrub Jay (_Aphelocoma insularis_) | data.frame | 307 | 8 |
jay | unmarked | European Jay data from the Swiss Breeding Bird Survey 2002 | list | | |
linetran | unmarked | Simulated line transect data | data.frame | 12 | 7 |
mallard.obs | unmarked | Mallard count data | list | | |
mallard.site | unmarked | Mallard count data | data.frame | 239 | 3 |
mallard.y | unmarked | Mallard count data | matrix | 239 | 3 |
masspcru | unmarked | Massachusetts North American Amphibian Monitoring Program Data | data.frame | 3216 | 6 |
ovendata.list | unmarked | Removal data for the Ovenbird | list | | |
pcru.bin | unmarked | 2001 Delaware North American Amphibian Monitoring Program Data | matrix | 130 | |
pcru.data | unmarked | 2001 Delaware North American Amphibian Monitoring Program Data | array | | |
pcru.y | unmarked | 2001 Delaware North American Amphibian Monitoring Program Data | cast_matrix | 130 | |
pfer.bin | unmarked | 2001 Delaware North American Amphibian Monitoring Program Data | matrix | 130 | |
pfer.data | unmarked | 2001 Delaware North American Amphibian Monitoring Program Data | array | | |
pfer.y | unmarked | 2001 Delaware North American Amphibian Monitoring Program Data | cast_matrix | 130 | |
pointtran | unmarked | Simulated point-transect data | data.frame | 30 | 7 |
woodthrush | unmarked | BBS Point Count and Occurrence Data from 2 Bird Species | data.frame | 50 | 11 |
woodthrush.bin | unmarked | BBS Point Count and Occurrence Data from 2 Bird Species | matrix | 50 | 11 |
mobius_clust_data | cardinalR | Mobius clust dataset with noise dimensions | tbl_df | 500 | 5 |
mobius_clust_tsne_param1 | cardinalR | tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 15. | tbl_df | 500 | 2 |
mobius_clust_tsne_param2 | cardinalR | tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 30. | tbl_df | 500 | 2 |
mobius_clust_tsne_param3 | cardinalR | tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 5. | tbl_df | 500 | 2 |
mobius_clust_umap_param1 | cardinalR | UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 15 and min-dist: 0.1. | tbl_df | 500 | 2 |
mobius_clust_umap_param2 | cardinalR | UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 30 and min-dist: 0.08. | tbl_df | 500 | 2 |
mobius_clust_umap_param3 | cardinalR | UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 5 and min-dist: 0.9. | tbl_df | 500 | 2 |
bearingcage | fwb | Bearing Cage field failure data | data.frame | 1703 | 2 |
example_clean | ARUtools | Example cleaned recording meta data | tbl_df | 42 | 13 |
example_files | ARUtools | Example recording files | character | | |
example_files_long | ARUtools | Example long-term deployment recording files | character | | |
example_sites | ARUtools | Example site-level meta data | data.frame | 10 | 8 |
example_sites_clean | ARUtools | Example cleaned site-level meta data | tbl_df | 10 | 8 |
task_template | ARUtools | Example template of tasks for WildTrax | tbl_df | 42 | 13 |
template_observers | ARUtools | Example template of tasks for WildTrax | spec_tbl_df | 4 | 2 |
Skabbholmen | gllvm | Skabbholmen island data | list | | |
beetle | gllvm | ground beetle assemblages | list | | |
eSpider | gllvm | Hunting spider data | list | | |
fungi | gllvm | Wood-decaying fungi data | list | | |
kelpforest | gllvm | Kelp Forest community Dynamics: Cover of sessile organisms, Uniform Point Contact | list | | |
microbialdata | gllvm | Microbial community data | list | | |
invitrodb_dd | tcpl | Short descriptions of fields for different tables are stored in a data dictionary. | data.table | 44 | 3 |
load_data_columns | tcpl | Lists of column names returned from tcplLoadData invitrodb v4.1 (same as CCTE Bioactivity API version). | list | | |
mc_test | tcpl | List of lists containing queries sent to tcplQuery associated with each test case. Each list also contains the associated ids with each case. Only meant to be used with automated testing with mocking for mc data. | list | | |
mc_vignette | tcpl | List with multi-concentration data for the vignette | list | | |
mcdat | tcpl | A subset of ToxCast data showing changes in the activity of the intracellular estrogen receptor. | data.frame | 14183 | 10 |
mthd_list_defaults | tcpl | Lists of data frames returned from tcplMthdList invitrodb v4.2 | list | | |
sc_test | tcpl | List of lists containing queries sent to tcplQuery associated with each test case. Each list also contains the associated ids with each case. Only meant to be used with automated testing with mocking for sc data. | list | | |
sc_vignette | tcpl | List with single-concentration data for the vignette | list | | |
scdat | tcpl | A subset of ToxCast data showing changes in transcription factor activity for multiple targets. | data.frame | 320 | 10 |
test_api | tcpl | List containing ids used for different automated tests of tcpl integration with the CTX APIs, randomly selected from what is available via API. | list | | |
alevchem | R2MLwiN | Chemistry A-level results from one exam board | data.frame | 2166 | 8 |
bang | R2MLwiN | Sub-sample from the 1989 Bangladesh Fertility Survey (see Huq & Cleveland, 1990) | data.frame | 2867 | 12 |
bang1 | R2MLwiN | Sub-sample from the 1989 Bangladesh Fertility Survey | data.frame | 1934 | 11 |
bes83 | R2MLwiN | Subsample from British Election Study, '83. | data.frame | 800 | 9 |
cvd | R2MLwiN | Data from the 1998 Scottish Health Survey on cardiovascular disease status of 8804 respondents | data.frame | 8804 | 8 |
diag1 | R2MLwiN | Examination dataset | data.frame | 907 | 9 |
fysio | R2MLwiN | Data on physiotherapy referrals from 100 general practices in the Netherlands, collected in 1987 | data.frame | 16700 | 14 |
gcsecomp1 | R2MLwiN | Pupils' marks from GCSE exams (UK, 1989); complete cases only. | data.frame | 1523 | 6 |
gcsemv1 | R2MLwiN | Pupils' marks from GCSE exams (UK, 1989). | data.frame | 1905 | 7 |
height | R2MLwiN | Height data. | data.frame | 100 | 1 |
hungary1 | R2MLwiN | Hungarian component of 2nd International Science Survey, '84; see Goldstein 2003 | data.frame | 2439 | 10 |
jspmix1 | R2MLwiN | Dataset of pupils' test scores, a subset of the Junior School Project. | data.frame | 1119 | 8 |
lips1 | R2MLwiN | Lips data | data.frame | 56 | 41 |
lmdp | R2MLwiN | Details of deaths from all causes in England and Wales 1972-1992, taken from the local mortality datapack | data.frame | 5639 | 8 |
mmmec | R2MLwiN | EC data on UV radiation exposure & malignant melanoma. | data.frame | 354 | 7 |
rats | R2MLwiN | Weights of 30 rats, measured weekly over 5 weeks. | data.frame | 30 | 7 |
reading1 | R2MLwiN | Students' reading attainment in inner London infant schools. | data.frame | 407 | 13 |
tutorial | R2MLwiN | Exam results for six inner London Education Authorities | data.frame | 4059 | 10 |
wage1 | R2MLwiN | Simulated dataset of office workers' salary and other employment details. | data.frame | 3022 | 21 |
xc | R2MLwiN | Examination scores of 16-year olds in Fife, Scotland. | data.frame | 3435 | 11 |
xc1 | R2MLwiN | Examination scores of 16-year olds in Fife, Scotland. | data.frame | 3435 | 11 |
BRCA1_splice_variants | geneviewer | BRCA1 Splice Variants | data.frame | 119 | 6 |
erythromycin_BlastP | geneviewer | Erythromycin BlastP results | data.frame | 148 | 16 |
erythromycin_cluster | geneviewer | Erythromycin Gene Cluster Data | data.frame | 23 | 6 |
hs_dystrophin_transcripts | geneviewer | Human Dystrophin Transcripts Data | data.frame | 202 | 5 |
human_hox_genes | geneviewer | Human HOX Gene Cluster Data | data.frame | 39 | 7 |
ophA_clusters | geneviewer | ophA Gene Cluster from Omphalotus olearius | data.frame | 17 | 5 |
ZambiaAdm1 | surveyPrev | Admin 1 Polygon Map for Zambia. | SpatialPolygonsDataFrame | | |
ZambiaAdm2 | surveyPrev | Admin 2 Polygon Map for Zambia. | SpatialPolygonsDataFrame | | |
ZambiaPopWomen | surveyPrev | Population estimates for Women of age 15 to 49 in Zambia in 2018. | list | | |
match_all_result | surveyPrev | | data.frame | 132 | 20 |
surveyPrevIndicators | surveyPrev | Table of built-in indicators. | data.frame | 22 | 4 |
sw_data_example | steppedwedge | Example stepped wedge data | data.frame | 2011 | 6 |
data_dictionary | r2dii.plot | Data Dictionary | tbl_df | 32 | 4 |
market_share | r2dii.plot | An example of a 'market_share'-like dataset | spec_tbl_df | 802 | 10 |
palette_colours | r2dii.plot | Colour datasets | tbl_df | 9 | 2 |
scenario_colours | r2dii.plot | Colour datasets | tbl_df | 5 | 2 |
sda | r2dii.plot | An example of an 'sda'-like dataset | spec_tbl_df | 110 | 6 |
sector_colours | r2dii.plot | Colour datasets | tbl_df | 8 | 2 |
technology_colours | r2dii.plot | Colour datasets | tbl_df | 18 | 3 |
congress116.regions | choroplethr | A data.frame containing geographic metadata about the Congressional Districts of the 116th US Congress | data.frame | 435 | 4 |
continental_us_states | choroplethr | A vector of the names of US Continental US States. | character | | |
df_congress116_demographics | choroplethr | A data.frame containing demographic statistics about the 116th Congressional Districts | data.frame | 435 | 9 |
df_congress116_party | choroplethr | A data.frame containing party affiliation data about the Congressional Districts of 116th US Congress | data.frame | 435 | 5 |
df_county_demographics | choroplethr | A data.frame containing demographic statistics for each county in the United States. | data.frame | 3143 | 9 |
df_japan_census | choroplethr | A data.frame containing basic demographic information about Japan. | data.frame | 47 | 4 |
df_ny_tract_demographics | choroplethr | A data.frame containing demographic statistics for each Census Tract in New York State. | data.frame | 4918 | 9 |
df_pop_country | choroplethr | A data.frame containing population estimates for Countries in 2012. | data.frame | 166 | 2 |
df_pop_county | choroplethr | A data.frame containing population estimates for US Counties in 2012. | data.frame | 3143 | 2 |
df_pop_ny_tract | choroplethr | A data.frame containing population estimates for all Census Tracts in New York State in 2012. | data.frame | 4918 | 2 |
df_pop_state | choroplethr | A data.frame containing population estimates for US States in 2012. | data.frame | 51 | 2 |
df_president | choroplethr | A data.frame containing election results from the 2012 US Presidential election. | data.frame | 51 | 2 |
df_president_ts | choroplethr | A data.frame containing all US presidential election results from 1789 to 2012 | data.frame | 51 | 58 |
df_state_age_2010 | choroplethr | A data.frame containing median age estimates for US states in 2010 | data.frame | 51 | 2 |
df_state_age_2015 | choroplethr | A data.frame containing median age estimates for US states in 2015 | data.frame | 51 | 2 |
df_state_demographics | choroplethr | A data.frame containing demographic statistics for each state plus the District of Columbia. | data.frame | 51 | 9 |
mun_reg_saude | brpop | Municipality and health region code table | tbl_df | 5591 | 2 |
mun_reg_saude_449 | brpop | Municipality and health region (449) code table | tbl_df | 5570 | 2 |
dat | SSVS | Example dataset for 'ssvs' function @format A data frame with 74 records and 76 variables | data.frame | 74 | 76 |
imputed_mtcars | SSVS | Imputed mtcars Dataset | data.frame | 160 | 13 |
emoji_samples | textrecipes | Sample sentences with emojis | tbl_df | 4 | 1 |
data_all | ClimInd | | list | | |
CN_input_amendments_LUT | SoilManageR | Look-up-table with default values to calculate C and N inputs by organic amendments | tbl_df | 23 | 8 |
C_input_crops_LUT | SoilManageR | Look-up-table with default values to calculate carbon (C) inputs by crops | tbl_df | 27 | 19 |
EXAMPLE_data | SoilManageR | Example of a management_df | management_df | 130 | 19 |
STIR_values_LUT | SoilManageR | Look-up-table with default values for tillage operations | tbl_df | 50 | 15 |
plant_cover_LUT | SoilManageR | Look-up-table with default values to estimate soil cover by plants | tbl_df | 27 | 7 |
mydata | openair | Example air quality monitoring data for openair | tbl_df | 65533 | 10 |
CPFs | moocore | Conditional Pareto fronts obtained from Gaussian processes simulations. | data.frame | 2967 | 3 |
HybridGA | moocore | Results of Hybrid GA on Vanzyl and Richmond water networks | list | | |
SPEA2minstoptimeRichmond | moocore | Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network. | data.frame | 166 | 3 |
SPEA2relativeRichmond | moocore | Results of SPEA2 with relative time-controlled triggers on Richmond water network. | data.frame | 91 | 3 |
SPEA2relativeVanzyl | moocore | Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network. | data.frame | 107 | 3 |
tpls50x20_1_MWT | moocore | Various strategies of Two-Phase Local Search applied to the Permutation Flowshop Problem with Makespan and Weighted Tardiness objectives. | data.frame | 1511 | 4 |
QIDS | MDMA | QIDS depression data | data.frame | 100 | 2 |
gnomes | MDMA | Gnomes mushroom insulation cost-effectiveness data | data.frame | 300 | 3 |
ex_binary_df | beastt | External Binary Control Data for Propensity Score Balancing | tbl_df | 150 | 6 |
ex_norm_df | beastt | External Normal Control Data for Propensity Score Balancing | tbl_df | 150 | 6 |
ex_tte_df | beastt | External Time-to-Event Control Data for Propensity Score Balancing | data.frame | 150 | 9 |
int_binary_df | beastt | Internal Binary Data for Propensity Score Balancing | tbl_df | 160 | 7 |
int_norm_df | beastt | Internal Normal Data for Propensity Score Balancing | tbl_df | 120 | 7 |
int_tte_df | beastt | Internal Time-to-Event Control Data for Propensity Score Balancing | data.frame | 160 | 10 |
obsWheat | apsimx | Observed wheat phenology, LAI and biomass | data.frame | 10 | 4 |
wop | apsimx | Wheat example optimization results | optim_apsim | | |
wop.h | apsimx | Wheat example optimization results plus Hessian | optim_apsim | | |
mobydick | tall | Lemmatized Text of Moby-Dick (Chapters 1-10) | data.frame | 23548 | 27 |
bibtag | bibliometrix | Tag list and bibtex fields. | data.frame | 47 | 6 |
countries | bibliometrix | Index of Countries. | data.frame | 199 | 4 |
customTheme | bibliometrix | Custom Theme variables for Biblioshiny. | shiny.tag | | |
logo | bibliometrix | Bibliometrix logo. | array | | |
stopwords | bibliometrix | List of English stopwords. | list | | |
nor_covid19_cases_by_time_location | plnr | Covid-19 data for PCR-confirmed cases in Norway (nation and county) | data.table | 11028 | 18 |
bball1970 | rstanarm | Datasets for rstanarm examples | data.frame | 18 | 5 |
bball2006 | rstanarm | Datasets for rstanarm examples | data.frame | 308 | 2 |
bcancer | rstanarm | Datasets for rstanarm examples | data.frame | 686 | 4 |
frail | rstanarm | Datasets for rstanarm examples | data.frame | 200 | 6 |
kidiq | rstanarm | Datasets for rstanarm examples | data.frame | 434 | 4 |
mice | rstanarm | Datasets for rstanarm examples | data.frame | 144 | 3 |
mortality | rstanarm | Datasets for rstanarm examples | data.frame | 12 | 2 |
pbcLong | rstanarm | Datasets for rstanarm examples | data.frame | 304 | 8 |
pbcSurv | rstanarm | Datasets for rstanarm examples | data.frame | 40 | 7 |
radon | rstanarm | Datasets for rstanarm examples | data.frame | 919 | 4 |
roaches | rstanarm | Datasets for rstanarm examples | data.frame | 262 | 5 |
tumors | rstanarm | Datasets for rstanarm examples | data.frame | 71 | 2 |
wells | rstanarm | Datasets for rstanarm examples | data.frame | 3020 | 5 |
acen_hg19 | numbat | centromere regions (hg19) | tbl_df | 22 | 3 |
acen_hg38 | numbat | centromere regions (hg38) | tbl_df | 22 | 3 |
annot_ref | numbat | example reference cell annotation | data.frame | 50 | 2 |
bulk_example | numbat | example pseudobulk dataframe | tbl_df | 3935 | 83 |
chrom_sizes_hg19 | numbat | chromosome sizes (hg19) | data.table | 22 | 2 |
chrom_sizes_hg38 | numbat | chromosome sizes (hg38) | data.table | 22 | 2 |
count_mat_example | numbat | example gene expression count matrix | dgCMatrix | | |
count_mat_ref | numbat | example reference count matrix | dgCMatrix | | |
df_allele_example | numbat | example allele count dataframe | data.frame | 41167 | 11 |
gaps_hg19 | numbat | genome gap regions (hg19) | data.table | 28 | 3 |
gaps_hg38 | numbat | genome gap regions (hg38) | data.table | 30 | 3 |
gexp_roll_example | numbat | example smoothed gene expression dataframe | data.frame | 10 | 2000 |
gtf_hg19 | numbat | gene model (hg19) | data.table | 26841 | 5 |
gtf_hg38 | numbat | gene model (hg38) | data.table | 26807 | 5 |
gtf_mm10 | numbat | gene model (mm10) | data.table | 30336 | 5 |
hc_example | numbat | example hclust tree | hclust | | |
joint_post_example | numbat | example joint single-cell cnv posterior dataframe | data.table | 3806 | 71 |
mut_graph_example | numbat | example mutation graph | igraph | | |
phylogeny_example | numbat | example single-cell phylogeny | tbl_graph | | |
pre_likelihood_hmm | numbat | HMM object for unit tests | list | | |
ref_hca | numbat | reference expression magnitudes from HCA | matrix | 24756 | 12 |
ref_hca_counts | numbat | reference expression counts from HCA | matrix | 24857 | 12 |
segs_example | numbat | example CNV segments dataframe | data.table | 27 | 30 |
vcf_meta | numbat | example VCF header | character | | |
DataSpecies | biomod2 | Presence-Absence data to build test SDM | data.frame | 2488 | 8 |
ModelsTable | biomod2 | Single models package and functions | data.frame | 25 | 5 |
ODMAP | biomod2 | ODMAP empty table | data.frame | 84 | 4 |
OptionsBigboss | biomod2 | Bigboss pre-defined parameter values for single models | BIOMOD.models.options | | |
bioclim_current | biomod2 | Bioclimatic variables for SDM based on current condition | PackedSpatRaster | | |
bioclim_future | biomod2 | Bioclimatic variables for SDM based on future condition | PackedSpatRaster | | |
Rsubcapitata | cvasi | An algae scenario | AlgaeTKTD | | |
Schmitt2013 | cvasi | A Lemna data set with multiple treatment levels | data.frame | 56 | 4 |
americamysis | cvasi | A DEB abj scenario of Americamysis bahia | DebAbj | | |
dmagna | cvasi | A DEBtox scenario of Daphnia magna | DebTox | | |
focusd1 | cvasi | A Lemna_SETAC scenario with variable environment | LemnaSetac | | |
metsulfuron | cvasi | Lemna data published by Schmitt (2013) | LemnaSchmittScenario | | |
minnow_it | cvasi | A fitted GUTS-RED-IT scenario of the fathead minnow | GutsRedIt | | |
minnow_sd | cvasi | A fitted GUTS-RED-SD scenario of the fathead minnow | GutsRedSd | | |
quitte_example_data | quitte | quitte example data | quitte | 19152 | 7 |
quitte_example_dataAR6 | quitte | quitte example data with three models (REMIND, GCAM, MESSAGEix) and two scenarios (Current Policies, Delayed transition) | quitte | 5040 | 7 |
remind_timesteps | quitte | REMIND time steps | tbl_df | 172 | 3 |
deer | wildlifeDI | GPS tracking data of two male deer | move2 | 1118 | 3 |
does | wildlifeDI | GPS tracking data of female white-tailed deer | move2 | 10364 | 8 |
AIBS | ShinyItemAnalysis | AIBS grant peer review scoring dataset | data.frame | 216 | 25 |
Anxiety | ShinyItemAnalysis | PROMIS Anxiety Scale Dataset | data.frame | 766 | 34 |
AttitudesExpulsion | ShinyItemAnalysis | Attitudes towards the Expulsion of the Sudeten Germans (dataset) | data.frame | 145 | 239 |
BFI2 | ShinyItemAnalysis | BFI2 Dataset | data.frame | 1733 | 63 |
CLoSEread6 | ShinyItemAnalysis | Czech Longitudinal Study in Education (CLoSE) - reading in 6th grade | data.frame | 2634 | 20 |
CZmatura | ShinyItemAnalysis | CZmatura dataset | data.frame | 15702 | 73 |
CZmaturaS | ShinyItemAnalysis | CZmatura dataset - sample | data.frame | 2000 | 73 |
EPIA | ShinyItemAnalysis | The Eysenck Personality Inventory Impulsivity Subscale | data.frame | 1033 | 6 |
GMAT | ShinyItemAnalysis | Dichotomous dataset based on GMAT with the same total score distribution for groups. | data.frame | 2000 | 22 |
HCI | ShinyItemAnalysis | Homeostasis Concept Inventory dichotomous dataset | data.frame | 651 | 23 |
HCIdata | ShinyItemAnalysis | Homeostasis concept inventory full dataset | data.frame | 669 | 49 |
HCIgrads | ShinyItemAnalysis | Homeostasis concept inventory dataset of graduate students | data.frame | 10 | 41 |
HCIkey | ShinyItemAnalysis | Key of correct answers for homeostasis concept inventory dataset | data.frame | 20 | 1 |
HCIlong | ShinyItemAnalysis | Homeostasis Concept Inventory in a long format | data.frame | 13020 | 7 |
HCIprepost | ShinyItemAnalysis | Homeostasis concept inventory pretest and posttest scores | data.frame | 16 | 3 |
HCItest | ShinyItemAnalysis | Homeostasis concept inventory multiple-choice dataset | data.frame | 651 | 23 |
HCItestretest | ShinyItemAnalysis | Homeostasis concept inventory test-retest dataset | data.frame | 90 | 44 |
HeightInventory | ShinyItemAnalysis | Height inventory dataset | data.frame | 4885 | 29 |
LearningToLearn | ShinyItemAnalysis | Dichotomous dataset of learning to learn test | data.frame | 782 | 141 |
MSATB | ShinyItemAnalysis | Dichotomous dataset of Medical School Admission Test in Biology. | data.frame | 1407 | 21 |
MSclinical | ShinyItemAnalysis | Clinical outcomes in multiple sclerosis patients dataset | data.frame | 17 | 13 |
NIH | ShinyItemAnalysis | NIH grant peer review scoring dataset | data.frame | 5802 | 27 |
TestAnxietyCor | ShinyItemAnalysis | Correlation matrix for the test anxiety dataset | matrix | 20 | 20 |
dataMedical | ShinyItemAnalysis | Dichotomous dataset of admission test to medical school | data.frame | 2392 | 102 |
dataMedicalgraded | ShinyItemAnalysis | Graded dataset of admission test to medical school | data.frame | 2392 | 102 |
dataMedicalkey | ShinyItemAnalysis | Key of correct answers for dataset of admission test to medical school | data.frame | 100 | 1 |
dataMedicaltest | ShinyItemAnalysis | Dataset of admission test to medical school | data.frame | 2392 | 102 |
mrp_list | mregions2 | Available data products at Marine Regions | tbl_df | 21 | 8 |
mrp_ontology | mregions2 | Marine Regions Data Products Ontology | tbl_df | 374 | 4 |
game_revenue | pointblank | A table with game revenue data | tbl_df | 2000 | 11 |
game_revenue_info | pointblank | A table with metadata for the 'game_revenue' dataset | tbl_df | 11 | 2 |
small_table | pointblank | A small table that is useful for testing | spec_tbl_df | 13 | 8 |
specifications | pointblank | A table containing data pertaining to various specifications | tbl_df | 8 | 12 |
PSG | MVT | Transient sleep disorder | data.frame | 82 | 3 |
WindSpeed | MVT | Wind speed data | data.frame | 278 | 3 |
companies | MVT | Financial data | data.frame | 26 | 3 |
cork | MVT | Cork borings | data.frame | 28 | 4 |
examScor | MVT | Open/Closed book data | data.frame | 88 | 5 |
Tax4Fun2_KEGG | microeco | The KEGG data files used in the trans_func class | list | | |
dataset | microeco | The dataset structured with microtable class for the demonstration of examples | microtable | | |
env_data_16S | microeco | The environmental factors for the 16S example data | data.frame | 200 | 11 |
fungi_func_FUNGuild | microeco | The FUNGuild database for fungi trait prediction | data.frame | 13017 | 9 |
fungi_func_FungalTraits | microeco | The FungalTraits database for fungi trait prediction | data.frame | 10771 | 24 |
otu_table_16S | microeco | The OTU table of the 16S example data | data.frame | 13628 | 90 |
otu_table_ITS | microeco | The OTU table of the ITS example data | data.frame | 323 | 309 |
phylo_tree_16S | microeco | The phylogenetic tree of 16S example data | phylo | | |
prok_func_FAPROTAX | microeco | The modified FAPROTAX trait database | list | | |
prok_func_NJC19_list | microeco | The modified NJC19 database | list | | |
sample_info_16S | microeco | The sample information of 16S example data | data.frame | 90 | 4 |
sample_info_ITS | microeco | The sample information of ITS example data | data.frame | 309 | 33 |
taxonomy_table_16S | microeco | The taxonomic information of 16S example data | data.frame | 13628 | 7 |
taxonomy_table_ITS | microeco | The taxonomic information of ITS example data | data.frame | 323 | 7 |
chickenPox | kDGLM | Hospital admissions by chicken pox in Brazil | data.frame | 120 | 6 |
cornWheat | kDGLM | Corn and wheat prices from 1986 to 2014 | data.frame | 7252 | 5 |
gastroBR | kDGLM | Hospital admissions from gastroenteritis in Brazil | tbl_df | 4212 | 4 |
noticeSARI | kDGLM | SARI data from Belo Horizonte | data.frame | 118 | 8 |
ereturns | L1pack | Excess returns for Martin Marietta and American Can companies | data.frame | 60 | 4 |
SSB_ewe | EcoEnsemble | Ecopath with EcoSim SSB | data.frame | 60 | 4 |
SSB_fs | EcoEnsemble | FishSUMS SSB | data.frame | 67 | 3 |
SSB_lm | EcoEnsemble | LeMans SSB | data.frame | 65 | 4 |
SSB_miz | EcoEnsemble | mizer SSB | data.frame | 67 | 4 |
SSB_obs | EcoEnsemble | Stock assessment SSB | data.frame | 34 | 4 |
Sigma_ewe | EcoEnsemble | Ecopath with EcoSim Sigma | matrix | 4 | 4 |
Sigma_fs | EcoEnsemble | FishSUMS Sigma | matrix | 3 | 3 |
Sigma_lm | EcoEnsemble | LeMans Sigma | matrix | 4 | 4 |
Sigma_miz | EcoEnsemble | mizer Sigma | matrix | 4 | 4 |
Sigma_obs | EcoEnsemble | Stock assessment Sigma | matrix | 4 | 4 |
arc_count | MetaNet | Edgelist | data.frame | 200 | 3 |
arc_taxonomy | MetaNet | Edgelist | data.frame | 161 | 8 |
co_net | MetaNet | MetaNet networks | metanet | | |
co_net2 | MetaNet | MetaNet networks | metanet | | |
co_net_rmt | MetaNet | MetaNet networks | metanet | | |
metab | MetaNet | MetaNet networks abundance | data.frame | 18 | 50 |
metab_g | MetaNet | MetaNet networks metadata | data.frame | 50 | 2 |
micro | MetaNet | MetaNet networks abundance | data.frame | 18 | 50 |
micro_g | MetaNet | MetaNet networks metadata | data.frame | 50 | 7 |
multi1 | MetaNet | MetaNet networks | metanet | | |
transc | MetaNet | MetaNet networks abundance | data.frame | 18 | 50 |
transc_g | MetaNet | MetaNet networks metadata | data.frame | 50 | 2 |
injury | ZIM | Example: Injury Series from Occupational Health | ts | | |
syph | ZIM | Example: Syphilis Series | data.frame | 209 | 69 |
Rioja.data | FORTLS | Inventoried Plots Data for a Stand Case Study in La Rioja | list | | |
Rioja.simulations | FORTLS | Simulated Metrics and Variables for a Stand Case Study in La Rioja | list | | |
SimData | rtmpt | Data simulated from the restricted 2HTM | data.frame | 2400 | 5 |
TIGER_expr | netZooR | TIGER example expression matrix | matrix | 1780 | 16 |
TIGER_prior | netZooR | TIGER example prior network | matrix | 14 | 1772 |
bladder | netZooR | Bladder RNA-seq data from the GTEx consortium | ExpressionSet | | |
exon.size | netZooR | Gene length | integer | | |
genes | netZooR | Example of a gene list | character | | |
monsterRes | netZooR | MONSTER results from example cell-cycle yeast transition | monsterAnalysis | | |
mut.ucec | netZooR | Example of mutation data | table | 248 | 19754 |
skin | netZooR | Skin RNA-seq data from the GTEx consortium | ExpressionSet | | |
small1976 | netZooR | Pollinator-plant interactions | data.frame | 442 | 3 |
yeast | netZooR | Toy data derived from three gene expression datasets and a mapping from transcription factors to genes. | list | | |
escape.gene.sets | escape | Built-In Gene Sets for escape | list | | |
dat.anderson2010 | publipha | Studies on Effect of Violent Video Games on Negative Outcomes | tbl_df | 477 | 9 |
dat.baskerville2012 | publipha | Studies on Practice Facilitation | tbl_df | 23 | 7 |
dat.cuddy2018 | publipha | Studies on the Effect of Power Posing | tbl_df | 28 | 5 |
dat.dang2018 | publipha | Meta-analysis on Ego Depletion | tbl_df | 150 | 10 |
dat.motyl2017 | publipha | Effect Sizes from 875 Studies in Psychology. | tbl_df | 874 | 9 |
Beretvas2008 | scan | Single-case example data | scdf | | |
Borckardt2014 | scan | Single-case example data | scdf | | |
Grosche2011 | scan | Single-case example data | scdf | | |
Grosche2014 | scan | Single-case example data | list | | |
GruenkeWilbert2014 | scan | Single-case example data | scdf | | |
Huber2014 | scan | Single-case example data | scdf | | |
Huitema2000 | scan | Single-case example data | scdf | | |
Leidig2018 | scan | Single-case example data | scdf | | |
Leidig2018_l2 | scan | Single-case example data | data.frame | 35 | 11 |
Lenz2013 | scan | Single-case example data | scdf | | |
Parker2007 | scan | Single-case example data | scdf | | |
Parker2009 | scan | Single-case example data | scdf | | |
Parker2009b | scan | Single-case example data | scdf | | |
Parker2011 | scan | Single-case example data | scdf | | |
Parker2011b | scan | Single-case example data | scdf | | |
SSDforR2017 | scan | Single-case example data | scdf | | |
Tarlow2017 | scan | Single-case example data | scdf | | |
Waddell2011 | scan | Single-case example data | scdf | | |
byHeart2011 | scan | Single-case example data | scdf | | |
exampleA1B1A2B2 | scan | Single-case example data | scdf | | |
exampleA1B1A2B2_zvt | scan | Single-case example data | scdf | | |
exampleAB | scan | Single-case example data | scdf | | |
exampleABAB | scan | Single-case example data | scdf | | |
exampleABC | scan | Single-case example data | scdf | | |
exampleABC_150 | scan | Single-case example data | scdf | | |
exampleABC_outlier | scan | Single-case example data | scdf | | |
exampleAB_50 | scan | Single-case example data | scdf | | |
exampleAB_50.l2 | scan | Single-case example data | data.frame | 50 | 3 |
exampleAB_add | scan | Single-case example data | scdf | | |
exampleAB_decreasing | scan | Single-case example data | scdf | | |
exampleAB_mpd | scan | Single-case example data | scdf | | |
exampleAB_score | scan | Single-case example data | scdf | | |
exampleAB_simple | scan | Single-case example data | scdf | | |
example_A24 | scan | Single-case example data | scdf | | |
allContTabs | goSorensen | Example of the output produced by the function 'allBuildEnrichTable'. | allTableList | | |
allDissMatrx | goSorensen | Example of the output produced by the function 'allSorenThreshold'. It contains the dissimilarity matrices for GO levels from 3 to 10 across the ontologies BP, CC and MF. | distList | | |
allEqTests | goSorensen | Example of the output produced by the function 'allEquivTestSorensen' using the normal asymptotic distribution. | AllEquivSDhtest | | |
allEqTests_boot | goSorensen | Example of the output produced by the function 'allEquivTestSorensen' using the approximated bootstrap distribution. | AllEquivSDhtest | | |
allOncoGeneLists | goSorensen | 7 gene lists possibly related with cancer | list | | |
cont_all_BP4 | goSorensen | Example of the output produced by the function 'buildEnrichTable'. It contains the enrichment contingency tables for all the lists from 'allOncoGeneLists' at level 4 of ontology BP. | tableList | | |
cont_atlas.sanger_BP4 | goSorensen | Example of the output produced by the function 'buildEnrichTable'. It contains the enrichment contingency table for two lists at level 4 of ontology BP. | table | 2 | 2 |
dissMatrx_BP4 | goSorensen | Example of the output produced by the function 'sorenThreshold'. It contains the dissimilarity matrix at GO level 4, for the ontology BP. | dist | | |
enrichedInBP4 | goSorensen | Example of the output produced by the function 'enrichedIn'. It contains exclusively GO terms enriched in at least one list of 'allOncoGeneLists', ontology BP, GO-Level 4. | matrix | 489 | 7 |
eqTest_all_BP4 | goSorensen | Example of the output produced by the function 'equivTestSorensen'. It contains all the possible equivalence tests for the lists from 'allOncoGeneLists' at level 4 of ontology BP. | equivSDhtestList | | |
eqTest_atlas.sanger_BP4 | goSorensen | Example of the output produced by the function 'equivTestSorensen'. It contains the equivalence test for comparing two lists at level 4 of ontology BP. | equivSDhtest | | |
fullEnrichedInBP4 | goSorensen | Example of the output produced by the function 'enrichedIn'. It contains all the GO terms enriched or not-enriched in the lists of 'allOncoGeneLists', ontology BP, GO-Level 4. | matrix | 3907 | 7 |
pbtGeneLists | goSorensen | 14 gene lists possibly related with kidney transplant rejection | list | | |
centros_yeast | HiCExperiment | Example datasets provided in 'HiCExperiment' & 'HiContactsData' | GRanges | | |
HLA.hg18 | GWASTools | HLA region base positions | data.frame | 1 | 3 |
HLA.hg19 | GWASTools | HLA region base positions | data.frame | 1 | 3 |
HLA.hg38 | GWASTools | HLA region base positions | data.frame | 1 | 3 |
centromeres.hg18 | GWASTools | Centromere base positions | data.frame | 24 | 3 |
centromeres.hg19 | GWASTools | Centromere base positions | data.frame | 24 | 3 |
centromeres.hg38 | GWASTools | Centromere base positions | data.frame | 24 | 3 |
pcaSnpFilters.hg18 | GWASTools | Regions of SNP-PC correlation to filter for Principal Component Analysis | data.frame | 4 | 4 |
pcaSnpFilters.hg19 | GWASTools | Regions of SNP-PC correlation to filter for Principal Component Analysis | data.frame | 4 | 4 |
pcaSnpFilters.hg38 | GWASTools | Regions of SNP-PC correlation to filter for Principal Component Analysis | data.frame | 4 | 4 |
pseudoautosomal.hg18 | GWASTools | Pseudoautosomal region base positions | data.frame | 6 | 4 |
pseudoautosomal.hg19 | GWASTools | Pseudoautosomal region base positions | data.frame | 6 | 4 |
pseudoautosomal.hg38 | GWASTools | Pseudoautosomal region base positions | data.frame | 6 | 4 |
relationsMeanVar | GWASTools | Mean and Variance information for full-sibs, half-sibs, first-cousins | list | | |
AP.RNAseq6l3c3t | projectR | CoGAPS patterns and genes weights for p.RNAseq6l3c3t | list | | |
CR.RNAseq6l3c3t | projectR | CogapsResult object for p.RNAseq6l3c3t | CogapsResult | | |
cr_microglial | projectR | CogapsResult object for microglial_counts | CogapsResult | | |
glial_counts | projectR | log-normalized count data from astrocytes and oligodendrocytes in the p6 mouse cortex. | dgTMatrix | | |
map.ESepiGen4c1l | projectR | RNAseqing and ChIPSeq of matched genes in differentiated human iPS cells | data.frame | 93 | 9 |
map.RNAseq6l3c3t | projectR | RNAseqing from human 3 iPS & 3 ES cell lines in 3 experimental condition at 3 time points | data.frame | 108 | 6 |
microglial_counts | projectR | log-normalized count data from microglial cells in the p6 mouse cortex. | dgTMatrix | | |
multivariateAnalysisR_seurat_test | projectR | Truncated Seurat Object with latent space projection done to unspecified cells in different stages for multivariateAnalysisR analysis | Seurat | | |
p.ESepiGen4c1l | projectR | RNAseqing and ChIPSeq of matched genes in differentiated human iPS cells | list | | |
p.RNAseq6l3c3t | projectR | RNAseqing from human 3 iPS & 3 ES cell lines in 3 experimental condition at 3 time points | data.frame | 108 | 54 |
pd.ESepiGen4c1l | projectR | RNAseqing and ChIPSeq of matched genes in differentiated human iPS cells | data.frame | 9 | 2 |
pd.RNAseq6l3c3t | projectR | RNAseqing from human 3 iPS & 3 ES cell lines in 3 experimental condition at 3 time points | data.frame | 54 | 38 |
retinal_patterns | projectR | CoGAPS patterns learned from the developing mouse retina. | data.frame | 3290 | 80 |
BLOSUM100 | pwalign | Predefined scoring matrices | matrix | 24 | 24 |
BLOSUM45 | pwalign | Predefined scoring matrices | matrix | 25 | 25 |
BLOSUM50 | pwalign | Predefined scoring matrices | matrix | 24 | 24 |
BLOSUM62 | pwalign | Predefined scoring matrices | matrix | 25 | 25 |
BLOSUM80 | pwalign | Predefined scoring matrices | matrix | 25 | 25 |
PAM120 | pwalign | Predefined scoring matrices | matrix | 24 | 24 |
PAM250 | pwalign | Predefined scoring matrices | matrix | 24 | 24 |
PAM30 | pwalign | Predefined scoring matrices | matrix | 25 | 25 |
PAM40 | pwalign | Predefined scoring matrices | matrix | 24 | 24 |
PAM70 | pwalign | Predefined scoring matrices | matrix | 25 | 25 |
phiX174Phage | pwalign | Versions of bacteriophage phiX174 complete genome and sample short reads | DNAStringSet | | |
quPhiX174 | pwalign | Versions of bacteriophage phiX174 complete genome and sample short reads | BStringSet | | |
srPhiX174 | pwalign | Versions of bacteriophage phiX174 complete genome and sample short reads | DNAStringSet | | |
wtPhiX174 | pwalign | Versions of bacteriophage phiX174 complete genome and sample short reads | integer | | |
IMexpression | MiDA | Infectious mononucleosis transcriptome | matrix | 100 | 89 |
IMspecimen | MiDA | Specimen features | data.frame | 89 | 2 |
crash2 | discSurv | Crash 2 competing risk data | data.frame | 20207 | 12 |
unempMultiSpell | discSurv | Multiple Spell employment data | data.frame | 5614 | 8 |
AngerManagement | restriktor | Reduction of aggression levels Dataset (4 treatment groups) | data.frame | 40 | 3 |
Burns | restriktor | Relation between the response variable PTSS and gender, age, TBSA, guilt and anger. | data.frame | 278 | 6 |
Exam | restriktor | Relation between exam scores and study hours, anxiety scores and average point scores. | data.frame | 20 | 4 |
FacialBurns | restriktor | Dataset for illustrating the conTest_conLavaan function. | data.frame | 77 | 6 |
Hurricanes | restriktor | The Hurricanes Dataset | data.frame | 46 | 3 |
ZelazoKolb1972 | restriktor | "Walking" in the newborn (4 treatment groups) | data.frame | 24 | 2 |
myGORICs | restriktor | An example of IC values | matrix | 4 | 3 |
myLLs | restriktor | An example of log-likelihood (LL) values | matrix | 4 | 3 |
myPTs | restriktor | An example of penalty (PT) values | matrix | 4 | 3 |
kgss_sample | simqi | A Sample of Korean General Social Survey Data, 2023 | tbl_df | 1123 | 13 |
som_sample | simqi | A Sample of SOM Institute Data, 2019-2020 | tbl_df | 2841 | 14 |
lfp | UPG | Female labor force participation data. | data.frame | 753 | 9 |
program | UPG | Students program choices. | data.frame | 200 | 5 |
titanic | UPG | Grouped Titanic survival data. | data.frame | 78 | 6 |
Anorexia | PairedData | Anorexia data from Pruzek & Helmreich (2009) | data.frame | 17 | 2 |
Barley | PairedData | Barley data from Preece (1982, Table 1) | data.frame | 12 | 3 |
Blink | PairedData | Blink data from Preece (1982, Table 2) | data.frame | 12 | 3 |
Blink2 | PairedData | Blink data (2nd example) from Preece (1982, Table 3) | data.frame | 12 | 4 |
BloodLead | PairedData | Blood lead levels data from Pruzek & Helmreich (2009) | data.frame | 33 | 3 |
ChickWeight | PairedData | Chick weight data from Preece (1982, Table 11) | data.frame | 10 | 3 |
Corn | PairedData | Corn data (Darwin) | data.frame | 15 | 4 |
Datalcoholic | PairedData | Datalcoholic: a dataset of paired datasets | data.frame | 50 | 4 |
GDO | PairedData | Agreement study | data.frame | 18 | 4 |
Grain | PairedData | Grain data from Preece (1982, Table 5) | data.frame | 9 | 3 |
Grain2 | PairedData | Wheat grain data from Preece (1982, Table 12) | data.frame | 6 | 3 |
GrapeFruit | PairedData | Grape Fruit data from Preece (1982, Table 6) | data.frame | 25 | 3 |
HorseBeginners | PairedData | Actual and imaginary performances in equitation | data.frame | 8 | 3 |
IceSkating | PairedData | Ice skating speed study | data.frame | 7 | 3 |
Iron | PairedData | Iron data from Preece (1982, Table 10) | data.frame | 10 | 3 |
Meat | PairedData | Meat data from Preece (1982, Table 4) | data.frame | 20 | 3 |
PrisonStress | PairedData | Stress in prison | data.frame | 26 | 4 |
Rugby | PairedData | Agreement study in rugby expert ratings | data.frame | 93 | 3 |
Sewage | PairedData | Chlorinating sewage data from Preece (1982, Table 9) | data.frame | 8 | 3 |
Shoulder | PairedData | Shoulder flexibility in swimmers | data.frame | 30 | 4 |
SkiExperts | PairedData | Actual and imaginary performances in ski | data.frame | 12 | 3 |
Sleep | PairedData | Sleep hours data from Preece (1982, Table 16) | data.frame | 10 | 2 |
Tobacco | PairedData | Tobacco data from Snedecor and Cochran (1967) | data.frame | 8 | 3 |
anscombe2 | PairedData | Teaching the paired t test | data.frame | 15 | 9 |
lambda.table | PairedData | Parameters for Generalised Lambda Distributions | data.frame | 8 | 4 |
NVDI | geotoolsR | Normalized Difference Vegetation Index experiment | data.frame | 88 | 3 |
soilmoisture | geotoolsR | Soil moisture experiment | data.frame | 355 | 3 |
CanWeather | gamair | Canadian Weather data | data.frame | 12775 | 5 |
Larynx | gamair | Cancer of the larynx in Germany | data.frame | 544 | 4 |
aral | gamair | Aral sea remote sensed chlorophyll data | data.frame | 488 | 3 |
aral.bnd | gamair | Aral sea remote sensed chlorophyll data | list | | |
bird | gamair | Bird distribution data from Portugal | data.frame | 25100 | 6 |
blowfly | gamair | Nicholson's Blowfly data | data.frame | 180 | 2 |
bone | gamair | Bone marrow treatemtn survival data | data.frame | 23 | 3 |
brain | gamair | Brain scan data | data.frame | 1567 | 5 |
cairo | gamair | Daily temperature data for Cairo | data.frame | 3780 | 6 |
chicago | gamair | Chicago air pollution and death rate data | data.frame | 5114 | 7 |
chl | gamair | Chlorophyll data | data.frame | 13840 | 6 |
co2s | gamair | Atmospheric CO2 at South Pole | data.frame | 507 | 3 |
coast | gamair | European coastline from -11 to 0 East and from 43 to 59 North | data.frame | 2091 | 2 |
engine | gamair | Engine wear versus size data | data.frame | 19 | 2 |
gas | gamair | Octane rating data | data.frame | 60 | 3 |
german.polys | gamair | Cancer of the larynx in Germany | list | | |
harrier | gamair | Hen Harriers Eating Grouse | data.frame | 37 | 2 |
hubble | gamair | Hubble Space Telescope Data | data.frame | 24 | 3 |
ipo | gamair | Initial Public Offering Data | data.frame | 156 | 6 |
mack | gamair | Egg data from 1992 mackerel survey | data.frame | 634 | 16 |
mackp | gamair | Prediction grid data for 1992 mackerel egg model | data.frame | 1162 | 8 |
med | gamair | Data from 2010 horse mackerel and mackerel egg survey | data.frame | 1476 | 24 |
meh | gamair | Data from 2010 horse mackerel and mackerel egg survey | data.frame | 1476 | 23 |
mpg | gamair | Data on automobile efficiency on town streets and highway. | data.frame | 205 | 26 |
prostate | gamair | Prostate cancer screening data | list | | |
sitka | gamair | Sitka spruce growth data. | data.frame | 1027 | 5 |
sole | gamair | Sole Eggs in the Bristol Channel | data.frame | 1575 | 7 |
sperm.comp1 | gamair | Sperm competition data I | data.frame | 15 | 4 |
sperm.comp2 | gamair | Sperm competition data II | data.frame | 24 | 10 |
stomata | gamair | Stomatal area and CO2 | data.frame | 24 | 3 |
swer | gamair | Swiss 12 hour extreme rainfall | data.frame | 2196 | 9 |
wesdr | gamair | Diabetic retinopathy in Wisconsin | data.frame | 669 | 4 |
wine | gamair | Bordeaux Wines | data.frame | 47 | 7 |
risks | monoreg | Absolute risks from 7 survival models | data.frame | 12609 | 10 |
alzheimers | credsubs | Clinical trial data from 369 Alzheimer's disease patients. | data.frame | 369 | 6 |
mydata | oreo | Data from the Giesikus model | data.frame | 1024 | 4 |
AlphaPart.ped | AlphaPart | Sample pedigree for partition. | data.frame | 8 | 8 |
COMBO17_lowz | astrodatR | Galaxy color-magnitude diagram | data.frame | 572 | 2 |
COUP_var | astrodatR | COUP: X-ray source variability | data.frame | 15145 | 3 |
GlobClus_mag | astrodatR | Globular cluster magnitudes | data.frame | 441 | 3 |
GlobClus_prop | astrodatR | Galactic globular cluster properties | data.frame | 147 | 20 |
HIP | astrodatR | Hipparchos stars | data.frame | 2719 | 9 |
LMC_dist | astrodatR | Distance to the Large Magellanic Cloud | data.frame | 25 | 3 |
SDSS_QSO | astrodatR | Sloan Digital Sky Survey quasars | data.frame | 77429 | 15 |
SDSS_ptsrc_test | astrodatR | Sloan Digital Sky Survey point source photometry: Test sample | data.frame | 12884 | 4 |
SDSS_ptsrc_train | astrodatR | Sloan Digital Sky Survey point source photometry: Training sample | data.frame | 9000 | 5 |
Shapley_galaxy | astrodatR | Shapley Concentration of galaxies redshift survey | data.frame | 4215 | 5 |
Sun_spot_num | astrodatR | Sunspot numbers | data.frame | 3185 | 4 |
asteroid_dens | astrodatR | Densities of asteroids | data.frame | 26 | 3 |
censor_Be | astrodatR | Stellar abundances and planets | data.frame | 68 | 8 |
ell_gal_profile | astrodatR | Elliptical galaxy radial profiles | data.frame | 150 | 3 |
exoplanet_RV | astrodatR | Exoplanet radial velocities | data.frame | 207 | 4 |
plan_neb_LF | astrodatR | Planetary nebula luminosity function | data.frame | 532 | 2 |
protostellar_disks | astrodatR | Protostellar disks | data.frame | 4 | 6 |
protostellar_jets | astrodatR | Protostellar jets | data.frame | 2 | 3 |
grouping | intervcomp | Grouping of Subjects for the Implicit Association Test | data.frame | 83 | 2 |
reactiontimes | intervcomp | Reaction Time (RT) Data for the Implicit Association Test | data.frame | 16600 | 6 |
alexithymia | PCovR | Effect of alexithymia on depression and self-esteem | list | | |
psychiatrists | PCovR | Effect of psychiatric symptoms on toxicomania, schizophrenia, depression and anxiety disorder | list | | |
erpcp | sensitivitymv | DNA Damage Among Welders | data.frame | 39 | 2 |
lead150 | sensitivitymv | Smoking and lead in 150 matched 1-5 sets. | matrix | 150 | |
lead250 | sensitivitymv | Smoking and lead in 250 matched pairs. | matrix | 250 | |
mercury | sensitivitymv | NHANES Mercury/Fish Data | data.frame | 397 | 3 |
mtm | sensitivitymv | DNA damage from exposure to chromium | matrix | 30 | 3 |
tbmetaphase | sensitivitymv | Genetic damage from drugs used to treat TB | data.frame | 15 | 3 |
normals1 | clusterability | Data generated from a single multivariate Normal distribution, 2 dimensions. | data.frame | 150 | 3 |
normals2 | clusterability | Data generated from a mixture of two multivariate Normal distributions, 2 dimensions. A dataset containing 150 observations generated from a mixture of two multivariate Normal distributions. 75 observations come from a distribution with mean vector (-3, -2) with each variable having unit variance and uncorrelated with each other. 75 observations come from a distribution with mean vector (1, 1) with each variable having unit variance and uncorrelated with each other. The dataset is clusterable. | data.frame | 150 | 3 |
normals3 | clusterability | Data generated from a mixture of three multivariate Normal distributions, 2 dimensions. A dataset containing 150 observations generated from a mixture of three multivariate Normal distributions. 50 observations are from a distribution with mean vector (3, 0), 50 observations from a distribution with mean vector (0, 3), and 50 observations from a distribution with mean vector (3, 6). For each of these three distributions, the x and y variables have unit variance and are uncorrelated. The dataset is clusterable. | data.frame | 150 | 3 |
normals4 | clusterability | Data generated from a mixture of two multivariate Normal distributions, 3 dimensions. A dataset containing 150 observations generated from a mixture of two multivariate Normal distributions. 75 observations come from a distribution with mean vector (1, 3, 2) and 75 observations come from a distribution with mean vector (4, 6, 0). For each distribution, the variables each have unit variance and are uncorrelated. The dataset is clusterable. | data.frame | 150 | 4 |
normals5 | clusterability | Data generated from a mixture of three multivariate Normal distributions, 3 dimensions. A dataset containing 150 observations generated from a mixture of three multivariate Normal distributions. 50 observations come from a distribution with mean vector (1, 3, 3), 50 observations come from a distribution with mean vector (4, 6, 0), and 50 observations come from a distribution with mean vector (2, 8, -3). For each distribution, the variables each have unit variance and are uncorrelated. The dataset is clusterable. | data.frame | 150 | 4 |
sample_labels | KRIS | Synthetic dataset containing population labels for the dataset simsnp. | character | | |
simsnp | KRIS | Synthetic dataset containing single nucleotide polymorphisms (SNP) | list | | |
service_urls | wdnr.gis | Various example data and lookup tables | data.frame | 1962 | 5 |
watershed_lookup | wdnr.gis | Various example sf polygons | data.frame | 2232 | 3 |
wi_counties | wdnr.gis | Various example sf polygons | sf | 72 | 3 |
wi_poly | wdnr.gis | Various example sf polygons | sf | 1 | 2 |
AssociatedPress | topicmodels | Associated Press data | DocumentTermMatrix | | |
JSS_papers | topicmodels | JSS Papers Dublin Core Metadata | matrix | 361 | 15 |
aid_st2 | pwrRasch | Sample of test data from subtest 2 of the Adaptive Intelligence Diagnosticum (AID3; Kubinger & Holocher-Ertl, 2014) | data.frame | 300 | 26 |
AmsterdamChess | LNIRT | Amsterdam Chess Test (ACT) data | data.frame | 259 | 81 |
CredentialForm1 | LNIRT | Credential Form data | data.frame | 1636 | 610 |
daphnia | vitality | Sample Daphnia Data | data.frame | 28 | 2 |
rainbow_trout_for_k | vitality | Sample Rainbow Trout Data | data.frame | 26 | 2 |
swedish_females | vitality | Swedish Female Mortality Data | data.frame | 111 | 10 |
RN_BarresLab_FPKM | RNentropy | RN_BarresLab_FPKM | data.frame | 12978 | 7 |
RN_BarresLab_design | RNentropy | RN_BarresLab_design | matrix | 7 | 7 |
RN_Brain_Example_design | RNentropy | RN_Brain_Example_design | matrix | 9 | 3 |
RN_Brain_Example_tpm | RNentropy | RN_Brain_Example_tpm | data.frame | 78699 | 10 |
bcnt.OR | bio.infer | Benthic count data for western Oregon | data.frame | 10581 | 3 |
bcnt.emapw | bio.infer | Benthic count data for the western United States | data.frame | 50930 | 3 |
bcnt.otu.OR | bio.infer | Benthic count data with OTU | data.frame | 10579 | 5 |
bcnt.tax.OR | bio.infer | Benthic count with taxonomic hierarchy | data.frame | 10581 | 18 |
coef.east.sed | bio.infer | Regression coefficients for eastern U.S. sediment | list | | |
coef.west.wt | bio.infer | Weighted regression coefficients for western U.S. | list | | |
envdata.OR | bio.infer | Environmental data from western OR | data.frame | 245 | 13 |
envdata.emapw | bio.infer | Environmental data from the western United States | data.frame | 1674 | 8 |
itis.ttable | bio.infer | ITIS taxonomic hierarchy table | data.frame | 51096 | 17 |
itis.ttable | bio.infer | ITIS taxonomic hierarchy table | data.frame | 58940 | 21 |
ss.OR | bio.infer | site-OTU matrix for western Oregon | data.frame | 271 | 123 |
trait.feeding | bio.infer | Feeding traits for benthic invertebrates | data.frame | 1919 | 2 |
trait.habit | bio.infer | Habit traits for benthic invertebrates | data.frame | 1581 | 2 |
wpnotsupported | wildpoker | Poker Games not supported by the wildpoker package | data.frame | 17 | 2 |
wpsupportedgames | wildpoker | Poker Games Supported by the wildpoker package | data.frame | 64 | 9 |
Brastings | SAPP | The Occurrence Times Data | numeric | | |
PProcess | SAPP | The Point Process Data | numeric | | |
PoissonData | SAPP | Poisson Data | numeric | | |
SelfExcit | SAPP | Self-Exciting Point Process Data | numeric | | |
main2003JUL26 | SAPP | The Aftershock Data | data.frame | 2305 | 9 |
res2003JUL26 | SAPP | The Residual Point Process Data | data.frame | 553 | 7 |
AphisRumicisDerrisMalaccensis | ecotoxicology | data on the toxicity to Aphis rumicis of an ether extract of Derris malaccensis | matrix | 6 | 3 |
Dunnett.t.Statistic | ecotoxicology | Critical Values of Dunnett's t Statistic | data.frame | 44 | 11 |
SheepsheadMinnow40SK | ecotoxicology | Mortality data from a fathead minnow larval survival and growth test (40 organisms per concentration) | data.frame | 6 | 2 |
Table1Finney1964 | ecotoxicology | Transformation of Percentages to Probits, table I of Finney, 1964 | matrix | 118 | 11 |
Table2Finney1964 | ecotoxicology | The Weighting Coefficient and Q/Z, table II of Finney, 1964 | matrix | 80 | 93 |
Table3Finney1964 | ecotoxicology | Maximum and Minimum working probits and Range, table III of Finney, 1964 | matrix | 55 | 5 |
Table4Finney1964 | ecotoxicology | Working probits, table IV of Finney, 1964 | matrix | 101 | 61 |
Table5Finney1964 | ecotoxicology | The Probability, P, the Ordinate, Z, and Z^2, table V of Finney, 1964 | matrix | 41 | 4 |
Table8Finney1964 | ecotoxicology | The Weighting Coefficient in Wadley's Problem, table VIII of Finney, 1964 | matrix | 80 | 2 |
Table9Finney1964 | ecotoxicology | Minimum Working Probit, Range, and Weighting Coefficient for Inverse Sampling, table IX of Finney, 1964 | matrix | 41 | 4 |
AirQual | SwissAir | Air Quality Data of Switzerland | data.frame | 17568 | 22 |
Mushroom | cba | Mushroom Data Set | data.frame | 8124 | 23 |
Votes | cba | Congressional Votes 1984 Data Set | data.frame | 435 | 17 |
townships | cba | Bertin's Characteristics and Townships Data Set | data.frame | 16 | 10 |
pvalues | fdrtool | Example p-Values | numeric | | |
bvariegatus | ENMeval | Example occurrence dataset. | data.frame | 476 | 2 |
enmeval_results | ENMeval | Example ENMevaluation object. | ENMevaluation | | |
PET_Vils | TUWmodel | Data-sample | matrix | 12053 | |
P_Vils | TUWmodel | Data-sample | matrix | 12053 | |
Q_Vils | TUWmodel | Data-sample | numeric | | |
SWE_Vils | TUWmodel | Data-sample | matrix | 12053 | |
T_Vils | TUWmodel | Data-sample | matrix | 12053 | |
areas_Vils | TUWmodel | Data-sample | numeric | | |
SIM3DATA | PresenceAbsence | Simulated Presence-Absence Data | data.frame | 1000 | 5 |
SPDATA | PresenceAbsence | Species Presence/Absence Data | data.frame | 5018 | 5 |
SPPREV | PresenceAbsence | Overall Preavalences for Species Presence/Absence Data | data.frame | 13 | 3 |
wgscan.cgu | rehh.data | Whole genome scan results for the CGU (Creole from Guadeloupe island) | data.frame | 44057 | 7 |
wgscan.eut | rehh.data | Whole genome scan results for a pool of European taurine cattle | data.frame | 44057 | 7 |
fuzzy_docs | sets | Documents on Fuzzy Theory | list | | |
Broome | TideHarmonics | Sea-Level Data At Broome | data.frame | 26304 | 2 |
CapeFerguson | TideHarmonics | Sea-Level Data At Cape Ferguson | data.frame | 26304 | 2 |
Darwin | TideHarmonics | Sea-Level Data At Darwin | data.frame | 26304 | 2 |
Esperance | TideHarmonics | Sea-Level Data At Esperance | data.frame | 26304 | 2 |
Hillarys | TideHarmonics | Sea-Level Data At Hillarys | data.frame | 26304 | 2 |
PortKembla | TideHarmonics | Sea-Level Data At Port Kembla | data.frame | 26304 | 2 |
Portland | TideHarmonics | Sea-Level Data At Portland | data.frame | 26304 | 2 |
Thevenard | TideHarmonics | Sea-Level Data At Thevenard | data.frame | 26304 | 2 |
harmonics | TideHarmonics | Table Of All 409 Harmonic Constituents | data.frame | 409 | 12 |
hc114 | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
hc37 | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
hc4 | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
hc60 | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
hc7 | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
noaa | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
spdlunar | TideHarmonics | Speeds For Basic Astronomical Periods | numeric | | |
spdsolar | TideHarmonics | Speeds For Basic Astronomical Periods | numeric | | |
task | TideHarmonics | Names Of Commonly Used Harmonic Constituents | character | | |
legfig | interactionTest | Replication data for Clark and Golder (2006) | data.frame | 754 | 33 |
climate_data | phiDelta | Meteorological data for feature selection analysis | data.frame | 29 | 7 |
BM86.data | OBsMD | Data sets in Box and Meyer (1986) | data.frame | 16 | 19 |
BM93.e1.data | OBsMD | Example 1 data in Box and Meyer (1993) | data.frame | 12 | 7 |
BM93.e2.data | OBsMD | Example 2 data in Box and Meyer (1993) | data.frame | 12 | 8 |
BM93.e3.data | OBsMD | Example 3 data in Box and Meyer (1993) | data.frame | 20 | 10 |
MetalCutting | OBsMD | Data sets in Edwards, Weese and Palmer (2014) | matrix | 64 | 8 |
OBsMD.es5 | OBsMD | OBsMD.es5 | data.frame | 8 | 6 |
PB12Des | OBsMD | 12-run Plackett-Burman Design Matrix | data.frame | 12 | 11 |
Reactor.data | OBsMD | Reactor Experiment Data | data.frame | 32 | 6 |
REcoData | EwR | REcoData | data.frame | 112 | 6 |
REcoData_DCM | EwR | REcoData_DCM | data.frame | 100 | 3 |
REcoData_Panel | EwR | REcoData_Panel | data.frame | 176 | 8 |
REcoData_Panel_UR | EwR | REcoData_Panel_UR | data.frame | 570 | 3 |
REcoData_SEM | EwR | REcoData_SEM | data.frame | 13 | 5 |
REcoData_Tourism | EwR | REcoData_Tourism | data.frame | 66 | 1 |
RATAchoc | ClustBlock | RATA data on chocolates | data.frame | 324 | 16 |
cheese | ClustBlock | cheese Just About Right data | data.frame | 576 | 11 |
choc | ClustBlock | chocolates data | data.frame | 14 | 25 |
fish | ClustBlock | fish data | data.frame | 204 | 30 |
smoo | ClustBlock | smoothies data | data.frame | 8 | 48 |
straw | ClustBlock | strawberries data | matrix | 6 | 1824 |
Beetles | KnowBR | Individual counts of species of beetles | data.frame | 15142 | 4 |
Estimators | KnowBR | Estimators obtained with the function KnowBPolygon | data.frame | 213 | 9 |
FishIrelandUK | KnowBR | Estimators obtained with the function KnowB | data.frame | 4977 | 8 |
RFishes | KnowBR | Species richness of freshwater fishes | data.frame | 181 | 361 |
States | KnowBR | States of USA | SpatialPolygonsDataFrame | | |
adworld | KnowBR | Geographical coordinates | data.frame | 329072 | 3 |
beta.limit | NST | Upper limit of different beta diversity (dissimilarity) indexes | data.frame | 20 | 2 |
beta.obs.rand | NST | Test data B observed and null beta diversity | list | | |
null.models | NST | Options of null model algorithms | data.frame | 13 | 3 |
tda | NST | Test dataset A | list | | |
ETHBE | GLMMRR | Online Survey on "Exams and Written Papers" | data.frame | 21405 | 29 |
MTURK | GLMMRR | MTurk Survey on "Mood and Personality" | data.frame | 24594 | 26 |
Plagiarism | GLMMRR | An Experimental Survey Measuring Plagiarism Using the Crosswise Model | data.frame | 812 | 24 |
DataExp | PooledMeanGroup | DataExp | data.frame | 315 | 16 |
GSEs.test | COCONUT | COCONUT test data | list | | |
DIY1 | MCI | Distance matrix for DIY stores | data.frame | 114 | 3 |
DIY2 | MCI | DIY store information | data.frame | 6 | 3 |
DIY3 | MCI | Data for origins (DIY store customers' places of residence) | data.frame | 19 | 2 |
Freiburg1 | MCI | Distance matrix for grocery stores in Freiburg | data.frame | 2646 | 4 |
Freiburg2 | MCI | Statistical districts of Freiburg | data.frame | 42 | 2 |
Freiburg3 | MCI | Grocery stores in Freiburg | data.frame | 63 | 2 |
grocery1 | MCI | Grocery store choices in Goettingen | data.frame | 179 | 5 |
grocery2 | MCI | Grocery store market areas in Goettingen | data.frame | 224 | 8 |
shopping1 | MCI | Point-of-sale survey in Karlsruhe | data.frame | 434 | 29 |
shopping2 | MCI | Distance matrix for the point-of-sale survey in Karlsruhe | data.frame | 3723 | 5 |
shopping3 | MCI | Market area data for the point-of-sale survey in Karlsruhe | data.frame | 70 | 5 |
shopping4 | MCI | Grocery store data for the point-of-sale survey in Karlsruhe | data.frame | 11 | 4 |
asap | PRISMA | The ASAP Data Set | prisma | | |
thesis | PRISMA | The Thesis Data Set | VCorpus | | |
scDatEx | scDD | Data: Toy example data | SingleCellExperiment | | |
scDatExList | scDD | Data: Toy example data list | list | | |
scDatExSim | scDD | Data: Toy example of simulated data | SingleCellExperiment | | |
HMB_data | HMB | Sample Data for HMB package | data.frame | 100000 | 8 |
demo_md | CoreMicrobiomeR | Arabidopsis thaliana - Metadata dataset | tbl_df | 103 | 6 |
demo_otu | CoreMicrobiomeR | Arabidopsis thaliana - OTU dataset | tbl_df | 188 | 1440 |
demo_tax | CoreMicrobiomeR | Arabidopsis thaliana - Taxonomy dataset | tbl_df | 188 | 5 |
countryinfo | gie | Mapping of country name and two-character country code used by GIE | tbl_df | 17 | 2 |
noweb.sty | noweb | Style file for noweb documents | character | | |
Y | GlarmaVarSel | Observation matrix Y | numeric | | |
lifeData | cdlei | HIV-related deaths from Colorado, USA, between 2000-2012. | data.frame | 104 | 3 |
auto | HDtweedie | A motor insurance dataset | list | | |
eegcoord | eegkit | EEG Cap Coordinates | data.frame | 87 | 5 |
eegdense | eegkit | Dense EEG Cap Coordinates | data.frame | 977 | 5 |
eeghead | eegkit | Dummy Head for 3d EEG Plots | mesh3d | | |
eegmesh | eegkit | EEG Cap for Dense Coordinates | mesh3d | | |
rdrobust_RDsenate | rdrobust | RD Senate Data | data.frame | 1390 | 2 |
srx | Boruta | Small redundant XOR data | data.frame | 32 | 9 |
Brachycera | BSagri | Eklektor counts of Brachycera | data.frame | 192 | 15 |
Cica1 | BSagri | Catches of Planthoppers and Leafhoppers | data.frame | 24 | 6 |
Cica2 | BSagri | Catches of Planthoppers and Leafhoppers | data.frame | 24 | 8 |
CountRep | BSagri | Simulated count data incl. repeated measurements | data.frame | 160 | 4 |
Decomp | BSagri | A simulated data set | data.frame | 1152 | 5 |
Diptera | BSagri | Soil eklektor data for some families of Diptera | data.frame | 32 | 7 |
ExNBCov | BSagri | Simulated example data, drawn from a Negative Binomial Distribution | data.frame | 32 | 12 |
ExPCov | BSagri | Simulated example data following a Poisson distribution | data.frame | 32 | 12 |
Feeding | BSagri | Pupation and Hatching rate in a feeding experiment with four varieties | data.frame | 32 | 5 |
Lepi | BSagri | Insect counts of 12 Species | data.frame | 144 | 17 |
MM1 | BSagri | Simulated data set for a simple mixed model | data.frame | 160 | 3 |
MMPois | BSagri | Simulated data for a simple mixed model with Poisson response | data.frame | 160 | 3 |
MMPoisRep | BSagri | Simulated data for a simple mixed model with Poisson response | data.frame | 192 | 4 |
Nematocera | BSagri | Trap counts of Nematocera | data.frame | 192 | 14 |
fakeln | BSagri | A simulated data set of lognormal data | data.frame | 32 | 2 |
EXP_raw | SegCorr | Simulated Gene Expression | data.frame | 500 | 30 |
SNP | SegCorr | Simulated SNP signal | data.frame | 500 | 30 |
PRIME | survivalMPLdc | PRIME data set | data.frame | 583 | 18 |
List.DCC | BondValuation | List of the day count conventions implemented. | data.frame | 16 | 3 |
NonBusDays.Brazil | BondValuation | Non-business days in Brazil from 1946-01-01 to 2299-12-31. | data.frame | 40378 | 3 |
PanelSomeBonds2016 | BondValuation | A panel of of 100 plain vanilla fixed coupon corporate bonds. | data.frame | 12718 | 16 |
SomeBonds2016 | BondValuation | Properties of 100 plain vanilla fixed coupon corporate bonds. | data.frame | 100 | 12 |
Aromatics | BivRegBLS | Aromatics petroleum data | data.frame | 35 | 8 |
SBP | BivRegBLS | Systolic blood pressure data | data.frame | 85 | 10 |
KH2017 | mousetrap | Mouse-tracking dataset from Kieslich & Henninger (2017) | mousetrap | | |
KH2017_raw | mousetrap | Raw mouse-tracking dataset from Kieslich & Henninger (2017) | data.frame | 1140 | 13 |
mt_example | mousetrap | A mousetrap data object. | mousetrap | | |
mt_example_raw | mousetrap | Raw mouse-tracking dataset for demonstrations of the mousetrap package | data.frame | 38 | 19 |
mt_prototypes | mousetrap | Mouse trajectory prototypes. | array | | |
Eng | smoppix | Spatial transcriptomics data of mouse fibroblast cells | data.frame | 100000 | 5 |
EngRois | smoppix | Spatial transcriptomics data of mouse fibroblast cells | list | | |
Yang | smoppix | Spatial transcriptomics data of Selaginella moellendorffii roots | data.frame | 150432 | 6 |
eeg | LCFdata | ERP amplitudes at electrodes Fz, Cz, Pz, and Oz from 0 to 300 milliseconds. | data.frame | 161880 | 10 |
erpFz | LCFdata | ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window. | data.frame | 120 | 3 |
z | LCFdata | Plotting data generated from a linear mixed-effects model from Tremblay & Newman (In Preparation). | matrix | 30 | 10 |
pitprops | epca | Pitprops correlation data | matrix | 13 | 13 |
exposure.response.sample | rstanemax | Sample simulated data for exposure-response. | tbl_df | 60 | 3 |
exposure.response.sample.binary | rstanemax | Sample simulated data for exposure-response for binary endpoint | tbl_df | 101 | 9 |
exposure.response.sample.with.cov | rstanemax | Sample simulated data for exposure-response with covariates | tbl_df | 60 | 7 |
morrowplots | morrowplots | Morrow Plots Yield and Treatment Data | spec_tbl_df | 3216 | 26 |
beta | fdasrvf | MPEG7 Curve Dataset | array | | |
growth_vel | fdasrvf | Berkeley Growth Velocity Dataset | list | | |
im | fdasrvf | Example Image Data set | list | | |
simu_data | fdasrvf | Simulated two Gaussian Dataset | list | | |
simu_warp | fdasrvf | Aligned Simulated two Gaussian Dataset | fdawarp | | |
simu_warp_median | fdasrvf | Aligned Simulated two Gaussian Dataset using Median | fdawarp | | |
toy_data | fdasrvf | Distributed Gaussian Peak Dataset | list | | |
toy_warp | fdasrvf | Aligned Distributed Gaussian Peak Dataset | list | | |
ed_metrics | multilandr | 'MultiLandMetrics' object | MultiLandMetrics | | |
otf_metrics | multilandr | 'MultiLandMetrics' object | MultiLandMetrics | | |
aemet_munic | climaemet | Data set with all the municipalities of Spain | tbl_df | 8132 | 6 |
climaemet_9434_climatogram | climaemet | Climatogram data for Zaragoza Airport ("9434") period 1981-2010 | data.frame | 4 | 12 |
climaemet_9434_temp | climaemet | Average annual temperatures for Zaragoza Airport ("9434") period 1950-2020 | tbl_df | 70 | 3 |
climaemet_9434_wind | climaemet | Wind conditions for Zaragoza Airport ("9434") period 2000-2020 | tbl_df | 7270 | 3 |
parameter_draws_pf | malariasimulation | Parameter draws (P. falciparum) | list | | |
parameter_draws_pv | malariasimulation | Parameter draws (P. vivax) | list | | |
parasite_parameters | malariasimulation | Parasite parameters | list | | |
J | Blend | simulated data for demonstrating the features of Blend | numeric | | |
degree | Blend | simulated data for demonstrating the features of Blend | numeric | | |
kn | Blend | simulated data for demonstrating the features of Blend | numeric | | |
t | Blend | simulated data for demonstrating the features of Blend | numeric | | |
x | Blend | simulated data for demonstrating the features of Blend | matrix | 80 | |
y | Blend | simulated data for demonstrating the features of Blend | matrix | 80 | |
command_block_classes | datadaptor | 'command_block' overview | tbl_df | 28 | 3 |
fruit_survey | datadaptor | Toy data of a fictional survey about fruits | tbl_df | 100 | 12 |
mtcars_labelled | datadaptor | Labelled mtcars version | tbl_df | 32 | 13 |
CAFE_data | CAFE | CAFE data set | list | | |
concept_abbrev | openalexR | Concepts and abbreviations. | tbl_df | 20 | 3 |
countrycode | openalexR | Index of Countries and their alpha-2 and aplha-3 codes. | data.frame | 250 | 3 |
oa2df_coverage | openalexR | Coverage of OpenAlex entity fields after converting to data frame. | list | | |
ChromHMMiterations | animint2 | ChromHMM iterations | list | | |
FluView | animint2 | FluView | list | | |
FunctionalPruning | animint2 | Functional Pruning Algorithm | list | | |
PeakConsistency | animint2 | Consistency of segmentation models in simulated Poisson peaks | list | | |
TestROC | animint2 | Test ROC curves | list | | |
UStornadoes | animint2 | Tornadoes in the United States from 1950 to 2012 | data.frame | 41620 | 32 |
VariantModels | animint2 | Error rates of supervised learning methods for variant calling | list | | |
WorldBank | animint2 | Demographics by country from 1960 to 2012 | data.frame | 11342 | 15 |
breakpoints | animint2 | The breakpointError of simulated signals | list | | |
change | animint2 | Graphical model structure change | list | | |
climate | animint2 | Climate data in central America from 1995 to 2000 | data.frame | 41472 | 16 |
compare | animint2 | Testing rank and compare SVM on simulated patterns | list | | |
diamonds | animint2 | Prices of 50,000 round cut diamonds | tbl_df | 53940 | 10 |
economics | animint2 | US economic time series. | tbl_df | 574 | 6 |
economics_long | animint2 | US economic time series. | data.frame | 2870 | 4 |
faithfuld | animint2 | 2d density estimate of Old Faithful data | tbl_df | 5625 | 3 |
generation.loci | animint2 | Evolution simulation | data.frame | 120000 | 6 |
intreg | animint2 | Interval regression | list | | |
luv_colours | animint2 | 'colors()' in Luv space. | data.frame | 657 | 4 |
malaria | animint2 | Malaria parasite genome variants | list | | |
midwest | animint2 | Midwest demographics. | tbl_df | 437 | 28 |
mixtureKNN | animint2 | K-Nearest-Neighbors model of the mixture example data | list | | |
montreal.bikes | animint2 | Montreal bikes | list | | |
mpg | animint2 | Fuel economy data from 1999 and 2008 for 38 popular models of car | tbl_df | 234 | 11 |
msleep | animint2 | An updated and expanded version of the mammals sleep dataset. | tbl_df | 83 | 11 |
pirates | animint2 | Worldwide pirate attacks from 1978 to 2013 | data.frame | 6636 | 14 |
presidential | animint2 | Terms of 11 presidents from Eisenhower to Obama. | tbl_df | 11 | 4 |
prior | animint2 | Class prior change | list | | |
prostateLasso | animint2 | Lasso model of the prostate cancer data set | list | | |
seals | animint2 | Vector field of seal movements. | tbl_df | 1155 | 4 |
txhousing | animint2 | Housing sales in TX. | tbl_df | 8602 | 9 |
vervet | animint2 | Vervet monkey intestinal microbiome | list | | |
worldPop | animint2 | World population by subcontinent | data.frame | 294 | 4 |
X | mixedBayes | simulated data for demonstrating the features of mixedBayes | matrix | 60 | |
coeff | mixedBayes | simulated data for demonstrating the features of mixedBayes | numeric | | |
e | mixedBayes | simulated data for demonstrating the features of mixedBayes | matrix | 60 | 3 |
g | mixedBayes | simulated data for demonstrating the features of mixedBayes | matrix | 60 | |
k | mixedBayes | simulated data for demonstrating the features of mixedBayes | numeric | | |
w | mixedBayes | simulated data for demonstrating the features of mixedBayes | matrix | 60 | 15 |
y | mixedBayes | simulated data for demonstrating the features of mixedBayes | numeric | | |
DB | metaMS | Information on three chemical standards measured in GC-MS (liquid injection) | list | | |
GCresults | metaMS | Results of metaMS for a small GC-MS data set | list | | |
LCDBtest | metaMS | Sample DB for LC-MS annotation | list | | |
LCresults | metaMS | Result metaMS for a small LC-MS data set | list | | |
Orbitrap.RP | metaMS | Example settings for 'metaMS' | metaMSsettings | | |
Synapt.NP | metaMS | Example settings for 'metaMS' | metaMSsettings | | |
Synapt.RP | metaMS | Example settings for 'metaMS' | metaMSsettings | | |
TSQXLS.GC | metaMS | Example settings for 'metaMS' | metaMSsettings | | |
errf | metaMS | Mass error surface for Waters Synapt Q-TOF spectrometers | lm | | |
exptable | metaMS | Sample table for the generation of a database of standards (LCMS) | data.frame | 4 | 7 |
smallDB | metaMS | Information on three chemical standards measured in GC-MS (liquid injection) | list | | |
stdInfo | metaMS | Information on three chemical standards measured in GC-MS (liquid injection) | data.frame | 3 | 9 |
basicdata | gfoRmula | Example Dataset for a Survival Outcome with Censoring | data.table | 11332 | 8 |
basicdata_nocomp | gfoRmula | Example Dataset for a Survival Outcome without Censoring | data.table | 13170 | 7 |
binary_eofdata | gfoRmula | Example Dataset for a Binary Outcome at End of Follow-Up | data.table | 17500 | 7 |
censor_data | gfoRmula | Example Dataset for a Survival Outcome with an Indicator of Censoring Variable | data.table | 118725 | 6 |
continuous_eofdata | gfoRmula | Example Dataset for a Continuous Outcome at End of Follow-Up | data.table | 17500 | 7 |
continuous_eofdata_pb | gfoRmula | Example Dataset for a Continuous Outcome at End of Follow-Up with Pre-Baseline Times | data.table | 22500 | 7 |
anole.data | phytools | Phylogenetic datasets | data.frame | 82 | 6 |
anoletree | phytools | Phylogenetic datasets | simmap | | |
ant.geog | phytools | Phylogenetic datasets | factor | | |
ant.tree | phytools | Phylogenetic datasets | phylo | | |
bat.tree | phytools | Phylogenetic datasets | phylo | | |
bat_virus.data | phytools | Phylogenetic datasets | data.frame | 17 | 2 |
betaCoV.tree | phytools | Phylogenetic datasets | phylo | | |
bonyfish.data | phytools | Phylogenetic datasets | data.frame | 90 | 2 |
bonyfish.tree | phytools | Phylogenetic datasets | phylo | | |
butterfly.data | phytools | Phylogenetic datasets | data.frame | 287 | 1 |
butterfly.tree | phytools | Phylogenetic datasets | phylo | | |
cordylid.data | phytools | Phylogenetic datasets | data.frame | 28 | 3 |
cordylid.tree | phytools | Phylogenetic datasets | phylo | | |
darter.tree | phytools | Phylogenetic datasets | phylo | | |
eel.data | phytools | Phylogenetic datasets | data.frame | 61 | 2 |
eel.tree | phytools | Phylogenetic datasets | phylo | | |
elapidae.tree | phytools | Phylogenetic datasets | phylo | | |
flatworm.data | phytools | Phylogenetic datasets | data.frame | 28 | 1 |
flatworm.tree | phytools | Phylogenetic datasets | phylo | | |
liolaemid.data | phytools | Phylogenetic datasets | data.frame | 258 | 3 |
liolaemid.tree | phytools | Phylogenetic datasets | phylo | | |
mammal.data | phytools | Phylogenetic datasets | data.frame | 49 | 2 |
mammal.geog | phytools | Phylogenetic datasets | matrix | 72 | 2 |
mammal.tree | phytools | Phylogenetic datasets | phylo | | |
primate.data | phytools | Phylogenetic datasets | data.frame | 90 | 6 |
primate.tree | phytools | Phylogenetic datasets | phylo | | |
salamanders | phytools | Phylogenetic datasets | phylo | | |
sunfish.data | phytools | Phylogenetic datasets | data.frame | 28 | 3 |
sunfish.tree | phytools | Phylogenetic datasets | simmap | | |
tortoise.geog | phytools | Phylogenetic datasets | data.frame | 15 | 2 |
tortoise.tree | phytools | Phylogenetic datasets | phylo | | |
tropidurid.data | phytools | Phylogenetic datasets | data.frame | 76 | 2 |
tropidurid.tree | phytools | Phylogenetic datasets | simmap | | |
vertebrate.data | phytools | Phylogenetic datasets | data.frame | 11 | 3 |
vertebrate.tree | phytools | Phylogenetic datasets | phylo | | |
wasp.data | phytools | Phylogenetic datasets | data.frame | 15 | 2 |
wasp.trees | phytools | Phylogenetic datasets | multiPhylo | | |
whale.tree | phytools | Phylogenetic datasets | phylo | | |
primates | corHMM | Example datasets | list | | |
primates.paint | corHMM | Example datasets | list | | |
rayDISC.example | corHMM | Example datasets | list | | |
crop2globiom | mapspam2globiom | | spec_tbl_df | 40 | 2 |
esacci2globiom | mapspam2globiom | | spec_tbl_df | 37 | 8 |
db1 | UAHDataScienceUC | Test Database 1 | data.frame | 500 | 2 |
db2 | UAHDataScienceUC | Test Database 2 | data.frame | 500 | 2 |
db3 | UAHDataScienceUC | Test Database 3 | data.frame | 500 | 2 |
db4 | UAHDataScienceUC | Test Database 4 | data.frame | 500 | 2 |
db5 | UAHDataScienceUC | Test Database 5 | data.frame | 500 | 2 |
db6 | UAHDataScienceUC | Test Database 6 | data.frame | 500 | 2 |
countries | dfeR | Lookup for valid country names and codes | data.frame | 10 | 2 |
geog_time_identifiers | dfeR | Potential names for geography and time columns | character | | |
ons_geog_shorthands | dfeR | Lookup for ONS geography columns shorthands | data.frame | 7 | 3 |
regions | dfeR | Lookup for valid region names and codes | data.frame | 16 | 2 |
wd_pcon_lad_la_rgn_ctry | dfeR | Ward to Constituency to LAD to LA to Region to Country lookup | data.frame | 24629 | 14 |
AEJapp | sreg | Replication data for: Iron Deficiency and Schooling Attainment in Peru (Chong et al, 2016) | tbl_df | 215 | 62 |
exampleGraph | leidenAlg | Conos graph | igraph | | |
data.bfi | EFAfactors | 25 Personality Items Representing 5 Factors | data.frame | 2800 | 28 |
data.datasets | EFAfactors | Subset Dataset for Training the Pre-Trained Deep Neural Network (DNN) | matrix | 1000 | 55 |
data.scaler | EFAfactors | the Scaler for the Pre-Trained Deep Neural Network (DNN) | list | | |
model.xgb | EFAfactors | the Tuned XGBoost Model for Determining the Number of Facotrs | TuneModel | | |
ASpliSE | saseR | ASpliSE | SummarizedExperiment | | |
SEbins | saseR | SEbins | SummarizedExperiment | | |
SEgenes | saseR | SEgenes | SummarizedExperiment | | |
SEjunctions | saseR | SEjunctions | SummarizedExperiment | | |
features | saseR | features | ASpliFeatures | | |
EUCO2residential | getspanel | CO2 Data for the EU Residential Sector | tbl_df | 1550 | 9 |
EU_emissions_road | getspanel | CO2 Data for EU Road Emissions | data.frame | 1550 | 13 |
pandata_simulated | getspanel | Simulated Panel Data | data.frame | 400 | 9 |
electricity | nixtlar | Electricity dataset | data.frame | 8400 | 3 |
electricity_exo_vars | nixtlar | Electricity dataset with exogenous variables | data.frame | 8400 | 12 |
electricity_future_exo_vars | nixtlar | Future values for the electricity dataset with exogenous variables | data.frame | 120 | 11 |
ChickpeaEnd.dat | asremlPlus | A large data set comprising the end of imaging data from a chick pea experiment conducted in high-throughput greenhouses | data.frame | 1056 | 20 |
Ladybird.dat | asremlPlus | Data for an experiment to investigate whether ladybirds transfer aphids | data.frame | 72 | 10 |
Oats.dat | asremlPlus | Data for an experiment to investigate nitrogen response of 3 oats varieties | data.frame | 72 | 7 |
WaterRunoff.dat | asremlPlus | Data for an experiment to investigate the quality of water runoff over time | data.frame | 440 | 13 |
Wheat.dat | asremlPlus | Data for a 1976 experiment to investigate 25 varieties of wheat | data.frame | 150 | 6 |
ea_countries | eurostat | Countries and Country Codes | tbl_df | 19 | 3 |
efta_countries | eurostat | Countries and Country Codes | tbl_df | 4 | 3 |
eu_candidate_countries | eurostat | Countries and Country Codes | tbl_df | 7 | 3 |
eu_countries | eurostat | Countries and Country Codes | tbl_df | 27 | 3 |
eurostat_geodata_60_2016 | eurostat | Geospatial data of Europe from GISCO in 1:60 million scale from year 2016 | sf | 2016 | 12 |
tgs00026 | eurostat | Auxiliary Data | tbl_df | 2723 | 6 |
STP | sonicscrewdriver | STP: Standard Temperature and Pressure | list | | |
sheepFrequencyStats | sonicscrewdriver | Sheep frequencyStats | list | | |
animal_positions | camtraptor | Sample of animal position digitization data | tbl_df | 42 | 6 |
dep_calib_models | camtraptor | Sample of deployment calibration models | calibs | | |
mica | camtraptor | Sample of Camtrap DP formatted data | datapackage | | |
penguins | ggResidpanel | Penguins Dataset | data.frame | 125 | 4 |
poissontestdata | CalibrationCurves | Simulated data sets to illustrate the package functionality | data.frame | 1000 | 6 |
poissontraindata | CalibrationCurves | Simulated data sets to illustrate the package functionality | data.frame | 5000 | 6 |
testdata | CalibrationCurves | Simulated data sets to illustrate the package functionality | data.frame | 500 | 5 |
traindata | CalibrationCurves | Simulated data sets to illustrate the package functionality | data.frame | 1000 | 5 |
cwna_data | ggsmc | Data generated from a constant velocity (or continuous white noise acceleration, CWNA) model for 20 time steps. | data.frame | 20 | 4 |
lv_output | ggsmc | 10000 simulations from a stochastic Lotka-Volterra model, assigned weights according to a Gaussian approximate Bayesian computation kernel with tolerance equal to 50. | data.frame | 320000 | 15 |
mixture_25_particles | ggsmc | The output of an SMC sampler where the initial distribution is a Gaussian and the final target is a mixture of Gaussians. 25 particles were used, with an adaptive method to determine the sequence of targets, and a Metropolis-Hastings move to move the particles at each step. | data.frame | 175 | 13 |
sir_cwna_model | ggsmc | The output of a bootstrap particle filter on the 'cwna_data'. The output consists of 100 particles over 20 time steps. | data.frame | 4000 | 13 |
minarets | RRreg | Minaret Data | data.frame | 1621 | 7 |
M_P1 | mitoClone2 | Mitochondrial exclusionlist | data.frame | 1430 | 16 |
M_P2 | mitoClone2 | Mitochondrial exclusionlist | data.frame | 1066 | 22 |
N_P1 | mitoClone2 | Mitochondrial exclusionlist | data.frame | 1430 | 16 |
N_P2 | mitoClone2 | Mitochondrial exclusionlist | data.frame | 1066 | 22 |
data_dictionary | r2dii.match | Data Dictionary | tbl_df | 33 | 4 |
BUSseqfits_example | BUSseq | An external example of the output of the 'BUSseq_MCMC' | SingleCellExperiment | | |
fake_outbreak | outbreaker2 | Small simulated outbreak | list | | |
umax | muscle | Unaligned MAX sequences | DNAStringSet | | |
doschedaData | Doscheda | Peptide Intensity data set for Doscheda | data.frame | 21140 | 15 |
processedExample | Doscheda | Processed Peptide Intensity data set for Doscheda | ChemoProtSet | | |
ensembl_hemo_id | scCustomize | Ensembl Hemo IDs | list | | |
ensembl_ieg_list | scCustomize | Immediate Early Gene (IEG) gene lists | list | | |
ensembl_mito_id | scCustomize | Ensembl Mito IDs | list | | |
ensembl_ribo_id | scCustomize | Ensembl Ribo IDs | list | | |
ieg_gene_list | scCustomize | Immediate Early Gene (IEG) gene lists | list | | |
msigdb_qc_ensembl_list | scCustomize | QC Gene Lists | list | | |
msigdb_qc_gene_list | scCustomize | QC Gene Lists | list | | |
asset_audit | robotoolbox | Examples of KoboToolbox assets and list of assets | kobo_asset | | |
asset_list | robotoolbox | Examples of KoboToolbox assets and list of assets | tbl_df | 28 | 7 |
asset_ml | robotoolbox | Examples of KoboToolbox assets and list of assets | kobo_asset | | |
asset_rg | robotoolbox | Examples of KoboToolbox assets and list of assets | kobo_asset | | |
asset_sm_label | robotoolbox | Examples of KoboToolbox assets and list of assets | kobo_asset | | |
asset_spatial | robotoolbox | Examples of KoboToolbox assets and list of assets | kobo_asset | | |
data_audit | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 29 | 15 |
data_ml_ar | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 16 |
data_ml_default | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 16 |
data_ml_en | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 16 |
data_ml_fr | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 16 |
data_ml_vlabel | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 16 |
data_rg | robotoolbox | Examples of KoboToolbox submissions data | dm | | |
data_sm | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 22 |
data_sm_label | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 5 | 22 |
data_spatial | robotoolbox | Examples of KoboToolbox submissions data | tbl_df | 1 | 24 |
baskets_fruits_weights | mFD | Dataset: Baskets Composition in Fruits Species | matrix | 10 | 25 |
fruits_traits | mFD | Dataset: Traits Values of Fruits Species | data.frame | 25 | 8 |
fruits_traits_cat | mFD | Dataset: Fruits Traits Informations | data.frame | 8 | 3 |
UPBdata | medflex | UPB data | data.frame | 385 | 9 |
baseballData | causact | Dataframe of 12,145 observations of baseball games in 2010 - 2014 | data.frame | 12145 | 5 |
beachLocDF | causact | Dataframe where each row represents data about one of the 26 mile markers (fake) from mile 0 to mile 2.5 along the Ocean City, MD beach/boardwalk. | tbl_df | 26 | 3 |
carModelDF | causact | Dataframe of 1000 (fake) observations of whether certain car buyers were willing to get information on a credit card speciailizing in rewards for adventure travellers. | rowwise_df | 1000 | 3 |
chimpanzeesDF | causact | Data from behavior trials in a captive group of chimpanzees, housed in Lousiana. From Silk et al. 2005. Nature 437:1357-1359 and further popularized in McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press, 2020. Experiment | tbl_df | 504 | 9 |
corruptDF | causact | Dataframe of 174 observations where information on the human developmet index (HDI) and the corruption perceptions index (CPI) both exist. Each observation is a country. | tbl_df | 174 | 7 |
delivDF | causact | 117,790 line items associated with 23,339 shipments. | tbl_df | 117790 | 5 |
gymDF | causact | Dataframe of 44 observations of free crossfit classes data Each observation indicates how many students that participated in the free month of crossfit signed up for the monthly membership afterwards | tbl_df | 44 | 5 |
houseDF | causact | Dataframe of 1,460 observations of home sales in Ames, Iowa. Known as The Ames Housing dataset, it was compiled by Dean De Cock for use in data science education. Each observation is a home sale. See 'houseDFDescr' for more info. | tbl_df | 1460 | 37 |
houseDFDescr | causact | Dataframe of 523 descriptions of data values from "The Ames Housing dataset", compiled by Dean De Cock for use in data science education. Each observation is a possible value from a variable in the 'houseDF' dataset. | tbl_df | 260 | 2 |
prodLineDF | causact | Product line and product category assignments for 12,026 partID's. | tbl_df | 12026 | 3 |
schoolsDF | causact | This example, often referred to as 8-schools, was popularized by its inclusion in Bayesian Data Analysis (Gelman, Carlin, & Rubin 1997). | tbl_df | 8 | 3 |
ticketsDF | causact | Dataframe of 55,167 observations of the number of tickets written by NYC precincts each day Data modified from https://github.com/stan-dev/stancon_talks/tree/master/2018/Contributed-Talks/01_auerbach which originally sourced data from https://opendata.cityofnewyork.us/ | tbl_df | 55167 | 4 |
totalBeachgoersRepSample | causact | A representative sample from a random variable that represents the annual number of beach goers to Ocean City, MD beaches on hot days. Think of this representative sample as coming from either a prior or posterior distribution. An example using this sample is can be found in The Business Analyst's Guide To Business Analytics at https://www.causact.com/. | numeric | | |
shifted_peaks | veesa | "Shifted Peaks" Simulated Dataset | list | | |
banknote | SLmetrics | Banknote Authentication Dataset | list | | |
obesity | SLmetrics | Obesity Levels Dataset | list | | |
wine_quality | SLmetrics | Wine Quality Dataset | list | | |
emotion | PCMRS | Emotional reactivity data from the Freiburg Complaint Checklist (emotion) | data.frame | 2032 | 8 |
tenseness | PCMRS | Tenseness data from the Freiburg Complaint Checklist (tenseness) | data.frame | 2042 | 8 |
hg19.refseq.transcripts | GenomicInteractions | Human Refseq transcripts from chr 17-18 | CompressedGRangesList | | |
hic_example_data | GenomicInteractions | Example HiC dataset | GenomicInteractions | | |
mm9_refseq_promoters | GenomicInteractions | Mouse Refseq promoters from chr 14-15 | GRanges | | |
thymus_enh | GenomicInteractions | Putative enhancers from mouse thymus data | GRanges | | |
cycHumanBloodData | MetaCycle | cycHumanBloodData | data.frame | 10 | 400 |
cycHumanBloodDesign | MetaCycle | cycHumanBloodDesign | data.frame | 399 | 4 |
cycMouseLiverProtein | MetaCycle | cycMouseLiverProtein | data.frame | 5 | 49 |
cycMouseLiverRNA | MetaCycle | cycMouseLiverRNA | data.frame | 10 | 49 |
cycSimu4h2d | MetaCycle | cycSimu4h2d | data.frame | 20 | 13 |
cycVignettesAMP | MetaCycle | cycVignettesAMP | data.frame | 3 | 71 |
cycYeastCycle | MetaCycle | cycYeastCycle | data.frame | 10 | 12 |
antibiotic | lucid | Effectiveness of 3 antibiotics against 16 bacterial species. | data.frame | 16 | 5 |
convergeParams | TitanCNA | TITAN EM trained results for an example dataset | list | | |
data | TitanCNA | TITAN EM trained results for an example dataset | data.table | 15623 | 7 |
GE2014 | nzelect | General Election Results 2014 | data.frame | 136195 | 7 |
Locations2014 | nzelect | General Election Voting Places 2014 | data.frame | 2568 | 13 |
nzge | nzelect | General Election Results 2002 and onwards | data.frame | 728602 | 9 |
parties_df | nzelect | New Zealand political parties | data.frame | 23 | 4 |
parties_v | nzelect | New Zealand political party colours | character | | |
polls | nzelect | New Zealand Opinion Polls | data.frame | 4336 | 9 |
voting_places | nzelect | General Election Voting Places 2008 and onwards | data.frame | 7909 | 15 |
hplt_occurrence_matrix | MHCtools | Data hplt_occurrence_matrix | data.frame | 40 | 100 |
k_summary_table | MHCtools | k_summary_table.rda | data.frame | 10 | 11 |
nest_table | MHCtools | Data nest_table | data.frame | 71 | 2 |
parents_table | MHCtools | Data parents_table | data.frame | 57 | 2 |
replicates_table | MHCtools | Data replicates_table | data.frame | 111 | 2 |
sequence_table | MHCtools | Data sequence_table | data.frame | 334 | 329 |
sequence_table_fas | MHCtools | Data sequence_table_fas | data.frame | 100 | 166 |
sequence_table_hplt | MHCtools | Data sequence_table_hplt | data.frame | 334 | 100 |
sequence_table_repl | MHCtools | Data sequence_table_repl | data.frame | 412 | 511 |
z1_matrix | MHCtools | z1_matrix.rda | data.frame | 70 | 8 |
z2_matrix | MHCtools | z2_matrix.rda | data.frame | 70 | 8 |
z3_matrix | MHCtools | z3_matrix.rda | data.frame | 70 | 8 |
z4_matrix | MHCtools | z4_matrix.rda | data.frame | 70 | 8 |
z5_matrix | MHCtools | z5_matrix.rda | data.frame | 70 | 8 |
CBRTagg | CBRT | Aggregation methods | data.table | 6 | 2 |
CBRTfreq | CBRT | Frequenciess | data.table | 8 | 4 |
allCBRTCategories | CBRT | Data categories | data.table | 44 | 2 |
allCBRTGroups | CBRT | Data groups | data.table | 497 | 10 |
allCBRTSeries | CBRT | Data series | data.table | 40826 | 12 |
adjustments | NAEPirtparams | NAEP IRT item adjustments. | data.frame | 966 | 10 |
parameters | NAEPirtparams | NAEP IRT parameters. | data.frame | 19734 | 17 |
transformations | NAEPirtparams | NAEP transformation constants. | data.frame | 502 | 10 |
udderquarterinfection | UdderQuarterInfectionData | Udder Quarter Infection Data | data.frame | 4784 | 6 |
aml | survival | Acute Myelogenous Leukemia survival data | data.frame | 23 | 3 |
bladder | survival | Bladder Cancer Recurrences | data.frame | 340 | 7 |
bladder1 | survival | Bladder Cancer Recurrences | data.frame | 294 | 11 |
bladder2 | survival | Bladder Cancer Recurrences | data.frame | 178 | 8 |
braking | survival | Reliability data sets | tmerge | 83 | 5 |
cancer | survival | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
capacitor | survival | Reliability data sets | data.frame | 64 | 5 |
cgd | survival | Chronic Granulotamous Disease data | data.frame | 203 | 16 |
cgd0 | survival | Chronic Granulotomous Disease data | data.frame | 128 | 20 |
colon | survival | Chemotherapy for Stage B/C colon cancer | data.frame | 1858 | 16 |
cracks | survival | Reliability data sets | data.frame | 8 | 2 |
diabetic | survival | Ddiabetic retinopathy | data.frame | 394 | 8 |
flchain | survival | Assay of serum free light chain for 7874 subjects. | data.frame | 7874 | 11 |
gbsg | survival | Breast cancer data sets used in Royston and Altman (2013) | data.frame | 686 | 11 |
genfan | survival | Reliability data sets | data.frame | 70 | 2 |
heart | survival | Stanford Heart Transplant data | data.frame | 172 | 8 |
hoel | survival | Mouse cancer data | data.frame | 181 | 4 |
ifluid | survival | Reliability data sets | data.frame | 41 | 2 |
imotor | survival | Reliability data sets | data.frame | 40 | 3 |
jasa | survival | Stanford Heart Transplant data | data.frame | 103 | 14 |
jasa1 | survival | Stanford Heart Transplant data | data.frame | 170 | 8 |
kidney | survival | Kidney catheter data | data.frame | 76 | 7 |
leukemia | survival | Acute Myelogenous Leukemia survival data | data.frame | 23 | 3 |
logan | survival | Data from the 1972-78 GSS data used by Logan | data.frame | 838 | 4 |
lung | survival | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
mgus | survival | Monoclonal gammopathy data | data.frame | 241 | 12 |
mgus1 | survival | Monoclonal gammopathy data | data.frame | 305 | 14 |
mgus2 | survival | Monoclonal gammopathy data | data.frame | 1384 | 11 |
myeloid | survival | Acute myeloid leukemia | data.frame | 646 | 9 |
myeloma | survival | Survival times of patients with multiple myeloma | data.frame | 3882 | 5 |
nafld1 | survival | Non-alcoholic fatty liver disease | data.frame | 17549 | 9 |
nafld2 | survival | Non-alcoholic fatty liver disease | data.frame | 400123 | 4 |
nafld3 | survival | Non-alcoholic fatty liver disease | data.frame | 34327 | 3 |
nwtco | survival | Data from the National Wilm's Tumor Study | data.frame | 4028 | 9 |
ovarian | survival | Ovarian Cancer Survival Data | data.frame | 26 | 6 |
pbc | survival | Mayo Clinic Primary Biliary Cholangitis Data | data.frame | 418 | 20 |
pbcseq | survival | Mayo Clinic Primary Biliary Cirrhosis, sequential data | data.frame | 1945 | 19 |
rats | survival | Rat treatment data from Mantel et al | data.frame | 300 | 5 |
rats2 | survival | Rat data from Gail et al. | data.frame | 253 | 6 |
retinopathy | survival | Diabetic Retinopathy | data.frame | 394 | 9 |
rhDNase | survival | rhDNASE data set | data.frame | 767 | 8 |
rotterdam | survival | Breast cancer data set used in Royston and Altman (2013) | data.frame | 2982 | 15 |
solder | survival | Data from a soldering experiment | data.frame | 900 | 6 |
stanford2 | survival | More Stanford Heart Transplant data | data.frame | 184 | 5 |
survexp.mn | survival | Census Data Sets for the Expected Survival and Person Years Functions | ratetable | | |
survexp.us | survival | Census Data Sets for the Expected Survival and Person Years Functions | ratetable | | |
survexp.usr | survival | Census Data Sets for the Expected Survival and Person Years Functions | ratetable | | |
tobin | survival | Tobin's Tobit data | data.frame | 20 | 3 |
transplant | survival | Liver transplant waiting list | data.frame | 815 | 6 |
turbine | survival | Reliability data sets | data.frame | 11 | 3 |
udca | survival | Data from a trial of usrodeoxycholic acid | data.frame | 170 | 15 |
udca1 | survival | Data from a trial of usrodeoxycholic acid | data.frame | 170 | 7 |
udca2 | survival | Data from a trial of usrodeoxycholic acid | data.frame | 1360 | 8 |
uspop2 | survival | Projected US Population | array | | |
valveSeat | survival | Reliability data sets | data.frame | 89 | 3 |
veteran | survival | Veterans' Administration Lung Cancer study | data.frame | 137 | 8 |
HCT116_r1 | multiHiCcompare | A 4 column sparse matrix for a Hi-C matrix. | data.frame | 56603 | 4 |
HCT116_r2 | multiHiCcompare | A 4 column sparse matrix for a Hi-C matrix. | data.frame | 57010 | 4 |
HCT116_r3 | multiHiCcompare | A 4 column sparse matrix for a Hi-C matrix. | data.frame | 56744 | 4 |
HCT116_r4 | multiHiCcompare | A 4 column sparse matrix for a Hi-C matrix. | data.frame | 54307 | 4 |
HCT116_r5 | multiHiCcompare | A 4 column sparse matrix for a Hi-C matrix. | data.frame | 55092 | 4 |
HCT116_r6 | multiHiCcompare | A 4 column sparse matrix for a Hi-C matrix. | data.frame | 55581 | 4 |
hg19_cyto | multiHiCcompare | A GenomicRanges object containing centromeric, gvar, and stalk regions. | GRanges | | |
hg38_cyto | multiHiCcompare | A GenomicRanges object containing centromeric, gvar, and stalk regions. | GRanges | | |
hicexp2 | multiHiCcompare | hicexp object with 4 samples from two groups. | Hicexp | | |
hicexp_diff | multiHiCcompare | hicexp object with 7 samples from two groups. | Hicexp | | |
exampleTCGA | seAMLess | TCGA-LAML bulk RNA-seq data downloaded from GDC | data.frame | 60483 | 21 |
exampleTCGAmeta | seAMLess | TCGA-LAML example data meta file downloaded from GDC | data.frame | 20 | 34 |
grch38 | seAMLess | Grch38 | tbl_df | 67495 | 3 |
minRes | seAMLess | A minimal seAMLess result list object | list | | |
venoModel | seAMLess | Trained RF model on Venetoclax Resistance | randomForest | | |
currencies | DiagrammeR | ISO-4217 currency data. | data.frame | 171 | 4 |
edge_list_1 | DiagrammeR | Edge list - Version 1. | data.frame | 19 | 2 |
edge_list_2 | DiagrammeR | Edge list - Version 2. | data.frame | 19 | 5 |
node_list_1 | DiagrammeR | Node list - Version 1. | data.frame | 10 | 2 |
node_list_2 | DiagrammeR | Node list - Version 2. | data.frame | 10 | 5 |
usd_exchange_rates | DiagrammeR | US Dollar exchange rates. | data.frame | 196 | 3 |
hal_quotes | hal9001 | HAL9000 Quotes from "2001: A Space Odyssey" | character | | |
dv_data | wpa | Sample Standard Person Query dataset for Data Validation | data.frame | 877 | 69 |
em_data | wpa | Sample Hourly Collaboration data | tbl_df | 2000 | 105 |
g2g_data | wpa | Sample Group-to-Group dataset | spec_tbl_df | 1417 | 7 |
mt_data | wpa | Sample Meeting Query dataset | tbl_df | 2001 | 37 |
sq_data | wpa | Sample Standard Person Query dataset | spec_tbl_df | 4403 | 66 |
PhenoarchDat1 | statgenHTP | Greenhouse data for a maize experiment in the PhenoArch platform. | data.frame | 40573 | 12 |
PhenovatorDat1 | statgenHTP | Growth chamber data for an Arabidopsis experiment in the Phenovator platform. | data.frame | 103839 | 10 |
RootDat1 | statgenHTP | Greenhouse data for an experiment in the RootPhAir platform. | data.frame | 16275 | 10 |
noCorrectedRoot | statgenHTP | Root data corrected for outliers for single observations. | data.frame | 15934 | 10 |
spatCorrectedArch | statgenHTP | Maize data corrected for spatial trends. | data.frame | 40573 | 10 |
spatCorrectedVator | statgenHTP | Arabidopsis data corrected for spatial trends. | data.frame | 103801 | 13 |
spatPredArch | statgenHTP | Maize data, genotypic predictions. | data.frame | 5940 | 6 |
monitoring | squeacr | Routine CMAM monitoring data from Sudan | tbl_df | 8234 | 16 |
muac_admission | squeacr | MUAC at admission | list | | |
muac_admission_tidy | squeacr | MUAC at admission in tidy format | tbl_df | 506 | 3 |
otp_beneficiaries | squeacr | Outpatient Therapeutic Care Programme (OTP) beneficiaries data | tbl_df | 405 | 13 |
seasonal_calendar | squeacr | Seasonal calendar data for Sudan | tbl_df | 28 | 4 |
time_to_travel | squeacr | Time-to-travel to health facilities for beneficiaries and volunteers | tbl_df | 165 | 9 |
hostline | SMITIDvisu | A host infomation over time | data.frame | 8 | 5 |
st.dist113_2 | SMITIDvisu | Distance matrix of observed variants sequences of a host 113 at time 2 from simulation. | matrix | 23 | 23 |
st.dist113_all | SMITIDvisu | Distance matrix of observed variants sequences of a host 113 at time 2, 3 and 4 from simulation. | matrix | 51 | 51 |
st.listTimeProp113 | SMITIDvisu | List of variants ID with subvector for time and value. | list | | |
st.prop113_2 | SMITIDvisu | Variants proportions and count for host 113 at time 2 from simulation. | data.frame | 23 | 3 |
st.prop113_all | SMITIDvisu | Variants proportions and count for an host 113 at time 2, 3 and 4 from simulation. | data.frame | 51 | 3 |
tt.edges | SMITIDvisu | Pathogen link over the time | data.frame | 13 | 5 |
tt.events | SMITIDvisu | Data.frame of hosts events information by time. Fake data. | data.frame | 63 | 7 |
tt.nodes | SMITIDvisu | Host list with there status over the time. | data.frame | 29 | 3 |
Obox | BasketballAnalyzeR | Opponents box scores dataset - NBA 2017-2018 | data.frame | 30 | 23 |
PbP.BDB | BasketballAnalyzeR | Play-by-play dataset - NBA 2017-2018 | data.frame | 37430 | 44 |
Pbox | BasketballAnalyzeR | Players box scores dataset - NBA 2017-2018 | data.frame | 605 | 22 |
Tadd | BasketballAnalyzeR | Tadd dataset - NBA 2017-2018 | data.frame | 30 | 6 |
Tbox | BasketballAnalyzeR | Teams box scores dataset - NBA 2017-2018 | data.frame | 30 | 23 |
HCL | eyetools | Example dataset from that contains binocular eye data from two participants from a simple contingency learning task (the data are from Beesley, Nguyen, Pearson, & Le Pelley, 2015). In this task there are two stimuli that appear simultaneously on each trial (to the left and right of the screen). Participants look at these cues and then make a decision by selecting an "outcome response" button. | tbl_df | 31041 | 7 |
HCL_AOIs | eyetools | Example AOIs for use with HCL | data.frame | 3 | 4 |
HCL_behavioural | eyetools | Example dataset of behavioural data to complement dataset HCL. | tbl_df | 12 | 8 |
T.Test.tumor.vs.normal | mutoss | Notterman data set | numeric | | |
notterman | mutoss | Notterman data set | data.frame | 7457 | 36 |
notterman.grpLabel | mutoss | Notterman data set | factor | | |
cascade | glitr | Cascade Indicators | tbl_df | 742 | 6 |
hfr_mmd | glitr | HFR Multi-Month Dispensing (MMD) data | tbl_df | 243 | 12 |
hts | glitr | Testing (HTS_TST & HTS_TST_POS) Indicators | tbl_df | 1695 | 7 |
hts_geo | glitr | Spatial data for mapping testing indicators | sf | 13 | 4 |
Jurkat293T | jackstraw | A Jurkat:293T equal mixture dataset from Zheng et al. (2017) | matrix | 3381 | 10 |
turtles | phylosignalDB | Ecological Traits and Phylogeny of Turtles | list | | |
GO | minimalistGODB | minimalistGODB data set | list | | |
GOA | minimalistGODB | minimalistGODB data set | matrix | 313043 | 3 |
GOGOAsmall | minimalistGODB | minimalistGODB data set | matrix | 1000 | 6 |
liver | texmex | Liver related laboratory data | data.frame | 606 | 9 |
nidd | texmex | Rain, wavesurge, portpirie and nidd datasets. | numeric | | |
portpirie | texmex | Rain, wavesurge, portpirie and nidd datasets. | data.frame | 65 | 2 |
rain | texmex | Rain, wavesurge, portpirie and nidd datasets. | numeric | | |
summer | texmex | Air pollution data, separately for summer and winter months | data.frame | 578 | 5 |
wavesurge | texmex | Rain, wavesurge, portpirie and nidd datasets. | data.frame | 2894 | 2 |
winter | texmex | Air pollution data, separately for summer and winter months | data.frame | 532 | 5 |
PTMB_matrix | pathwayTMB | PTMB_matrix, the Pathway-based Tumor Mutational Burden matrix | data.frame | 27 | 35 |
final_character | pathwayTMB | final_character, the example's final signature | character | | |
gene_path | pathwayTMB | gene_path, the pathways geneset | list | | |
genesmbol | pathwayTMB | genesmbol, the coding genes' length | list | | |
mut_matrix | pathwayTMB | mut_matrix, mutations matrix | matrix | 673 | 35 |
sur | pathwayTMB | sur, the samples' survival data | data.frame | 110 | 2 |
entity_patterns | econid | Entity Patterns | tbl_df | 249 | 6 |
df_test_proj | PIUMA | A dataset to test the 'dfToProjection' and 'dfToDistance' funtions of 'PIUMA' package. | data.frame | 30 | 2 |
tda_test_data | PIUMA | A TDAobj to test the 'PIUMA' package. | TDAobj | | |
vascEC_meta | PIUMA | Example datasets for PIUMA package | data.frame | 1180 | 2 |
vascEC_norm | PIUMA | We tested PIUMA on a subset of the single-cell RNA Sequencing dataset (GSE:GSE193346 generated and published by Feng et al. (2022) on Nature Communication to demonstrate that distinct transcriptional profiles are present in specific cell types of each heart chambers, which were attributed to have roles in cardiac development. In this tutorial, our aim will be to exploit PIUMA for identifying sub-population of vascular endothelial cells, which can be associated with specific heart developmental stages. The original dataset consisted of three layers of heterogeneity: cell type, stage and zone (i.e., heart chamber). Our testing dataset was obtained by subsetting vascular endothelial cells (cell type) by Seurat object, extracting raw counts and metadata. Thus, we filtered low expressed genes and normalized data by DaMiRseq | matrix | 1180 | 838 |
DEanalysis | dispositionEffect | Real sample data for Disposition Effect analysis | list | | |
investor | dispositionEffect | Sample investor financial transactions | tbl_df | 19 | 6 |
marketprices | dispositionEffect | Market prices of assets traded by the sample investor | data.frame | 95 | 3 |
portfolio_results | dispositionEffect | Realized and paper results | data.frame | 5 | 21 |
portfolio_results_ts | dispositionEffect | Realized and paper results | data.frame | 19 | 6 |
ant_data | Ostats | Sample Circular Data | data.frame | 4837 | 3 |
pitcher_traits | Ostats | Sample Multivariate Trait Data (Pitcher Plants) | data.frame | 120 | 9 |
reg_pool | Ostats | Sample Data for Ostats_regional | data.frame | 7905 | 6 |
small_mammal_Ostats | Ostats | Pre-calculated Overlap Statistics for NEON Small Mammals 2015 | list | | |
small_mammal_data | Ostats | Sample Continuous Trait Data (NEON Small Mammals 2015) | data.frame | 7692 | 7 |
qfStrands | lakhesis | Quattro Fontanili - Strands | strands | | |
quattrofontanili | lakhesis | Quattro Fontanili | incidence_matrix | 81 | 82 |
ciselnik_CSU | CzechData | data.frame linking codes of CSU to RUIAN codes | tbl_df | 296 | 3 |
katastralni_uzemi | CzechData | data.frame of all cadastral territories in Czech Republic | data.frame | 13078 | 9 |
kraje | CzechData | data.frame of all regions(NUTS3) in Czech Republic | data.frame | 14 | 4 |
obce | CzechData | data.frame of all settlements in Czech Republic | data.frame | 6258 | 7 |
okresy | CzechData | data.frame of all districts (LAU1) in Czech Republic | data.frame | 77 | 5 |
orp | CzechData | data.frame of all settlements of type III (orp) in Czech Republic | data.frame | 206 | 4 |
pou | CzechData | data.frame of all settlements of type II (pou) in Czech Republic | data.frame | 393 | 5 |
oil_price | tdata | Data for Vignette | data.frame | 3466 | 2 |
df_adults | whomds | Example of WHO Model Disability Survey data for adults | tbl_df | 2500 | 90 |
df_children | whomds | Example of WHO Model Disability Survey data for children | tbl_df | 2500 | 42 |
henon_x | sr | Henon Map | numeric | | |
mgls | sr | Mackey-Glass time delayed differential equation | numeric | | |
X_cibersort | tidybulk | Cibersort reference | data.frame | 547 | 22 |
breast_tcga_mini_SE | tidybulk | Needed for vignette breast_tcga_mini_SE | SummarizedExperiment | | |
counts_ensembl | tidybulk | Counts with ensembl annotation | tbl_df | 119 | 6 |
ensembl_symbol_mapping | tidybulk | Data set | spec_tbl_df | 291249 | 3 |
flybaseIDs | tidybulk | flybaseIDs | character | | |
se | tidybulk | SummarizedExperiment | RangedSummarizedExperiment | | |
se_mini | tidybulk | SummarizedExperiment mini for vignette | SummarizedExperiment | | |
tximeta_summarizeToGene_object | tidybulk | Needed for tests tximeta_summarizeToGene_object, It is SummarizedExperiment from tximeta | RangedSummarizedExperiment | | |
vignette_manuscript_signature_boxplot | tidybulk | Needed for vignette vignette_manuscript_signature_boxplot | tbl_df | 899 | 12 |
vignette_manuscript_signature_tsne | tidybulk | Needed for vignette vignette_manuscript_signature_tsne | spec_tbl_df | 283 | 10 |
vignette_manuscript_signature_tsne2 | tidybulk | Needed for vignette vignette_manuscript_signature_tsne2 | tbl_df | 283 | 9 |
adls_timevarying_region_data | EpiForsk | Simulated Time-Varying Residence Data | tbl_df | 546 | 7 |
andh_forest_data | EpiForsk | Example Data for Husby's Forest Plot Vignette | spec_tbl_df | 18 | 12 |
LLL.SETTINGS | ASSISTant | Design and trial settings used in the Lai, Lavori, Liao paper simulations | list | | |
cortex | CINNA | Macaque Visual Cortex Network | igraph | | |
drugTarget | CINNA | Drug Target Network | igraph | | |
kangaroo | CINNA | Kangaroo Network | igraph | | |
rhesus | CINNA | Moreno Rhesus Network | igraph | | |
zachary | CINNA | Zachary Karate Club Network | igraph | | |
ccd_experiment_yield | adas.utils | Central Composite Design Experiment Yields | list | | |
filtration | adas.utils | Filtration data | data.frame | 16 | 5 |
rotterdam | drsurv | Rotterdam dataset from survival | data.frame | 2982 | 15 |
area_lookup | phsmethods | Codes and names of Scottish geographical and administrative areas. | tbl_df | 17278 | 2 |
referrals | tsutils | NHS A&E Referrals | ts | | |
beetGrowth | statforbiology | Growth of sugarbeet in weed-infested and weed-free conditions | data.frame | 18 | 3 |
degradation | statforbiology | Soil degradation kinetic for a herbicide | data.frame | 24 | 2 |
metamitron | statforbiology | Degradation of metamitron in soil with co-applied herbicides | data.frame | 96 | 3 |
mixture | statforbiology | Efficacy of the mixture of two herbicides | data.frame | 16 | 2 |
Melano | timeROC | Malignant melanoma data | data.frame | 205 | 4 |
Paquid | timeROC | Paquid cohort data | data.frame | 2561 | 4 |
igo_year_format3 | igoR | Intergovernmental Organizations (IGO) by year | data.frame | 19335 | 235 |
state_year_format3 | igoR | Country membership to IGO by year | data.frame | 15557 | 537 |
states2016 | igoR | State System Membership (v2016) | data.frame | 243 | 11 |
unclean | cleaner | Example data that is not clean | data.frame | 500 | 2 |
cov | lncDIFF | Batch information for samples in hnsc.edata. | matrix | 80 | 1 |
design | lncDIFF | Design matrix for samples in hnsc.edata. | matrix | 80 | 3 |
hnsc.edata | lncDIFF | lncRNA Fragments Per Killobase per Million (FPKM) in a head and neck squamous cell carcinomas (hnsc) study. | data.frame | 1000 | 80 |
tissue | lncDIFF | Tissue type for samples in hnsc.edata. | character | | |
radf_crit | exuber | Stored Monte Carlo Critical Values | crit | | |
sim_data | exuber | Simulated dataset | tbl_df | 100 | 5 |
sim_data_wdate | exuber | Simulated dataset | tbl_df | 100 | 6 |
climatedata | ChillModels | Climate data | data.frame | 5003 | 3 |
ecog1684 | blapsr | Phase III Melanoma clinical trial. | data.frame | 284 | 5 |
kidneytran | blapsr | Survival data of kidney transplant patients. | data.frame | 863 | 6 |
laryngeal | blapsr | Survival data of male laryngeal cancer patients. | data.frame | 90 | 5 |
medicaid | blapsr | Data from the 1986 Medicaid Consumer Survey. | data.frame | 485 | 10 |
melanoma | blapsr | Melanoma survival data. | data.frame | 205 | 7 |
blist | starma | Neighbourhood weight matrices for France's 94 departments | list | | |
dlist | starma | Neighbourhood weight matrices for France's 94 departments | list | | |
klist | starma | Neighbourhood weight matrices for France's 94 departments | list | | |
AFMImageCollagenNetwork | AFM | AFM image sample | AFMImage | | |
AFMImageOfAluminiumInterface | AFM | AFM image sample | AFMImage | | |
AFMImageOfNormallyDistributedHeights | AFM | AFM image sample | AFMImage | | |
AFMImageOfOnePeak | AFM | AFM image sample | AFMImage | | |
AFMImageOfRegularPeaks | AFM | AFM image sample | AFMImage | | |
testDataMFA | autoMFA | Test dataset for the MFA model | matrix | 720 | |
CviCol | netgwas | Arabidopsis thaliana genotype data | matrix | 367 | 90 |
tetraPotato | netgwas | tetraploid potato genotype data | matrix | 156 | 1972 |
thaliana | netgwas | Arabidopsis thaliana phenotype and genotype data | matrix | 197 | 189 |
Kmat_y2h_sc | xnet | Protein interaction for yeast | matrix | 150 | 150 |
drugSim | xnet | drug target interactions for neural receptors | matrix | 54 | 54 |
drugTargetInteraction | xnet | drug target interactions for neural receptors | matrix | 26 | 54 |
proteinInteraction | xnet | Protein interaction for yeast | matrix | 150 | 150 |
targetSim | xnet | drug target interactions for neural receptors | matrix | 26 | 26 |
Choptank_CIAnnualResults | EGRETci | Example eBoot | data.frame | 32 | 5 |
Choptank_caseSetUp | EGRETci | Example eBoot | data.frame | 1 | 11 |
Choptank_dailyBootOut | EGRETci | Example eBoot | matrix | 11688 | |
Choptank_eBoot | EGRETci | Example eBoot | list | | |
Choptank_repAnnual | EGRETci | Example eBoot | list | | |
income_emilia | Spbsampling | The income of municipalities of "Emilia Romagna". | data.frame | 334 | 7 |
lucas_abruzzo | Spbsampling | LUCAS data for the region "Abruzzo", Italy. | data.frame | 2699 | 7 |
simul1 | Spbsampling | Simulated Population 1. | data.frame | 1000 | 11 |
simul2 | Spbsampling | Simulated Population 2. | data.frame | 1000 | 11 |
simul3 | Spbsampling | Simulated Population 3. | data.frame | 1000 | 11 |
ac | tidyCoverage | Example 'CoverageExperiment' and 'AggregatedCoverage' objects | AggregatedCoverage | | |
ce | tidyCoverage | Example 'CoverageExperiment' and 'AggregatedCoverage' objects | CoverageExperiment | | |
df_cpi_combined | tatooheene | Consumer Price Index (CPI) data from CBS | data.frame | 11 | 8 |
df_fp | tatooheene | Job vacancy data from CBS | tbl_df | 27 | 7 |
df_ppp | tatooheene | Purchasing Power Parity (PPP) data from OECD | data.frame | 64 | 2 |
df_rp_medical | tatooheene | Medical unit cost data from the Costing manual: Methods and Reference Prices for Economic Evaluations in Healthcare | data.frame | 116 | 4 |
df_rp_patient | tatooheene | Patient & family unit cost data from the Costing manual: Methods and Reference Prices for Economic Evaluations in Healthcare | data.frame | 6 | 4 |
df_rp_prod | tatooheene | Productivity & other unit cost data from the Costing manual: Methods and Reference Prices for Economic Evaluations in Healthcare | data.frame | 38 | 4 |
bibles | qlcMatrix | A selection of bible-texts | list | | |
huber | qlcMatrix | Comparative vocabulary for indigenous languages of Colombia (Huber & Reed 1992) | data.frame | 27521 | 4 |
wals | qlcMatrix | The World Atlas of Language Structures (WALS) | list | | |
earthquake | feature | Mt St Helens earthquake data | data.frame | 510 | 3 |
BMTplat | timeEL | Bone Marrow Transplant Registry | data.frame | 408 | 3 |
BMTtcell | timeEL | Bone Marrow Transplant Registry | data.frame | 408 | 3 |
Freireich | timeEL | Acute Leukemia data from Freireich et al (1963) | data.frame | 42 | 3 |
SimA100 | timeEL | Simulated competing risks data | data.frame | 100 | 3 |
melanoma5 | timeEL | Melanoma competing risks data | data.frame | 173 | 2 |
Init | BAREB | The initial value of patient- and site-level covariates for simulation | list | | |
obs | BAREB | The simulation observation | list | | |
truth | BAREB | The simulation truth | list | | |
Exampleindividual | PPMR | Individual level dataset | list | | |
Examplesummary | PPMR | Summary level dataset | list | | |
asylum_applications | refugees | Asylum Applications | tbl_df | 113379 | 12 |
asylum_decisions | refugees | Asylum Decisions | tbl_df | 107396 | 15 |
countries | refugees | Countries | tbl_df | 232 | 7 |
demographics | refugees | Demographics | tbl_df | 226063 | 26 |
flows | refugees | Forced displacement flow dataset | tbl_df | 100993 | 11 |
idmc | refugees | IDMC Data | tbl_df | 907 | 8 |
population | refugees | Population figures | tbl_df | 132491 | 16 |
solutions | refugees | Solutions | tbl_df | 15481 | 11 |
unrwa | refugees | UNRWA data | tbl_df | 291 | 8 |
duke | uncmbb | Duke Men's Basketball Match Results From 1949 - 1950 Season. | data.frame | 2258 | 10 |
unc | uncmbb | UNC Men's Basketball Match Results From 1949 - 1950 Season. | data.frame | 2261 | 10 |
data_pop | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 6000 | 2 |
data_pop_clust | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 10048 | 3 |
data_pop_st | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 17800 | 3 |
data_pop_stclust | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 14511 | 4 |
data_samp_clust | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 281 | 3 |
data_samp_srs | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 1850 | 2 |
data_samp_stclust | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 409 | 4 |
data_samp_stsrs | bootsurv | Populations and samples gerenated in the 'bootsurv' package | data.frame | 5350 | 3 |
X.kc | QuantileGradeR | Example Inspection Scores Matrix. | matrix | 1557 | 4 |
zips.kc | QuantileGradeR | Example ZIP Code Vector. | character | | |
guideSetExample_basic | crisprShiny | Example of a GuideSet object storing gRNA sequences targeting the CDS of the human gene KRAS | GuideSet | | |
guideSetExample_kras | crisprShiny | Example of a GuideSet object storing gRNA sequences targeting the CDS of the human gene KRAS | GuideSet | | |
guideSetExample_kras_be | crisprShiny | Example of a GuideSet object storing gRNA sequences targeting the CDS of the human gene KRAS | GuideSet | | |
guideSetExample_ntcs | crisprShiny | Example of a GuideSet object storing gRNA sequences targeting the CDS of the human gene KRAS and NTCs | GuideSet | | |
tooltipAnnotation | crisprShiny | List of tooltip annotations | list | | |
tss_kras | crisprShiny | Example of a 'GenomicRanges' object storing annotated TSS ranges for the human gene KRAS | GRanges | | |
txdb_kras | crisprShiny | Example of a 'CompressedGenomicRangesList' object storing annotated ranges for the human gene KRAS | CompressedGRangesList | | |
cdsPosTransc | RiboProfiling | Per transcript relative position of start and end codons for dataset ctrlGAlignments | list | | |
codonDataCtrl | RiboProfiling | Codon frequency and coverage in ORFs on chromosome 1, for dataset ctrlGAlignments | list | | |
codonIndexCovCtrl | RiboProfiling | The read coverage for each codon in ORFs on chromosome 1, for dataset ctrlGAlignments | list | | |
ctrlGAlignments | RiboProfiling | Ribosome profiling data on chr1 in human primary BJ fibroblasts control data: PMID: 23594524. | GAlignments | | |
demoKnownSuperPop1KG | RAIDS | The known super population ancestry of the demo 1KG reference profiles. | character | | |
demoPCA1KG | RAIDS | The PCA results of the demo 1KG reference dataset for demonstration purpose. Beware that the PCA has been run on a very small subset of the 1KG reference dataset and should not be used to call ancestry inference on a real profile. | list | | |
demoPCASyntheticProfiles | RAIDS | The PCA result of demo synthetic profiles projected on the demo subset 1KG reference PCA. | list | | |
demoPedigreeEx1 | RAIDS | The pedigree information about a demo profile called 'ex1'. | data.frame | 1 | 5 |
matKNNSynthetic | RAIDS | A small 'data.frame' containing the inferred ancestry on the synthetic profiles. | data.frame | 10192 | 4 |
pedSynthetic | RAIDS | A small 'data.frame' containing the information related to synthetic profiles. The ancestry of the profiles used to generate the synthetic profiles must be present. | data.frame | 52 | 7 |
snpPositionDemo | RAIDS | A small 'data.frame' containing the SNV information. | data.frame | 200 | 17 |
special_characters | exams.mylearn | List of Special Characters that Users Have to Deal with Manually | data.frame | 3 | 2 |
state_codes | USAboundaries | State codes and abbreviations for U.S. states and territories | tbl_df | 69 | 4 |
state_proj | USAboundaries | Data for projections from the State Plane Coordinate System | tbl_df | 123 | 5 |
states_contemporary_lores | USAboundaries | U.S. state boundaries | sf | 52 | 13 |
foxp2 | BMRMM | Simulated FoxP2 Data Set. | data.frame | 17391 | 6 |
foxp2sm | BMRMM | Shortened Simulated FoxP2 Data Set. | data.frame | 69 | 6 |
sd | name | South Dakota Election and Demographic Data | tbl_df | 1 | 38 |
Titanic.cramer | plot.matrix | Survival of passengers on the Titanic | matrix | 4 | 4 |
air.pvalue | plot.matrix | New York Air Quality Measurements | matrix | 4 | 4 |
bfi.2 | plot.matrix | 25 Personality items representing 5 factors | data.frame | 2436 | 25 |
samplefa | ProbeDeveloper | sample data for target sequence region with class 'DNAStringSet' | DNAStringSet | | |
SiChildren | quantregGrowth | Age, height and weight in a sample of Italian children | data.frame | 1424 | 3 |
growthData | quantregGrowth | Simulated data to illustrate capabilities of the package | data.frame | 200 | 3 |
names_gender_es | genero | Names with gender in Spanish | data.frame | 9810 | 2 |
names_gender_pt | genero | Names with gender in Portuguese | data.frame | 52715 | 2 |
BirthWeights | NPIstats | BirthWeights data set | data.frame | 24 | 2 |
BreakdownTimes | NPIstats | Breakdown times of units from two groups | data.frame | 20 | 2 |
ChemicalReaction | NPIstats | Chemical reaction of two methods | data.frame | 20 | 2 |
FourSources | NPIstats | Four sources | data.frame | 56 | 2 |
match_adresses | volleystat | Match adresses data | data.frame | 1718 | 6 |
matches | volleystat | Matches data | tbl_df | 3392 | 12 |
matchstats | volleystat | Matchstats data | tbl_df | 32755 | 23 |
players | volleystat | Players data | tbl_df | 1853 | 13 |
sets | volleystat | Sets data | tbl_df | 12660 | 9 |
staff | volleystat | Team staff data | tbl_df | 1214 | 10 |
team_adresses | volleystat | Team adresses data | tbl_df | 137 | 8 |
fish | nicheROVER | Fish stable isotope dataset. | data.frame | 277 | 4 |
elwha | linbin | Elwha River Survey | data.frame | 249 | 33 |
fishmotion | linbin | Fish Movements | list | | |
netmap | linbin | Dungeness River (NetMap) | data.frame | 16616 | 47 |
quinault | linbin | Quinault River Survey | data.frame | 363 | 31 |
simple | linbin | Simple Event Table | data.frame | 11 | 6 |
data_syllables_en | nsyllable | Syllable counts of English words | integer | | |
MktDATA | UBStats | Data: MktDATA | data.frame | 2224 | 26 |
MktDATA.Orig | UBStats | Data: MktDATA.Orig | data.frame | 2224 | 19 |
GarwayHeath | womblR | Garway-Heath angles for the HFA-II | numeric | | |
HFAII_Queen | womblR | HFAII Queen Adjacency Matrix | matrix | 54 | |
HFAII_QueenHF | womblR | HFAII Queen Hemisphere Adjacency Matrix | matrix | 54 | |
HFAII_Rook | womblR | HFAII Rook Adjacency Matrix | matrix | 54 | |
VFSeries | womblR | Visual field series for one patient. | data.frame | 486 | 4 |
example_fpkm | OncoSubtype | example FPKM data | RangedSummarizedExperiment | | |
hnsc_centroids | OncoSubtype | HNSC predictor centroids | data.frame | 728 | 4 |
luad_centroids | OncoSubtype | LUAD predictor centroids | data.frame | 506 | 3 |
lusc_centroids | OncoSubtype | LUSC predictor centroids | data.frame | 208 | 4 |
d.blast | relevance | Blasting for a tunnel | data.frame | 388 | 6 |
d.everest | relevance | Data of an 'anchoring' experiment in psychology | data.frame | 20 | 2 |
d.negposChoice | relevance | Data of an 'anchoring' experiment in psychology | data.frame | 4 | 4 |
d.osc15 | relevance | Data from the OSC15 replication study | data.frame | 100 | 149 |
d.osc15Onesample | relevance | Data from the OSC15 replication study, one sample tests | data.frame | 10 | 7 |
ovarian | relevance | ovarian | data.frame | 26 | 6 |
n.times.eBias.of.mad | rQCC | Empirical biases (times n) | numeric | | |
n.times.eBias.of.shamos | rQCC | Empirical biases (times n) | numeric | | |
n.times.eVar.of.HL1 | rQCC | Empirical variances (times n) | numeric | | |
n.times.eVar.of.HL2 | rQCC | Empirical variances (times n) | numeric | | |
n.times.eVar.of.HL3 | rQCC | Empirical variances (times n) | numeric | | |
n.times.eVar.of.mad | rQCC | Empirical variances (times n) | numeric | | |
n.times.eVar.of.median | rQCC | Empirical variances (times n) | numeric | | |
n.times.eVar.of.shamos | rQCC | Empirical variances (times n) | numeric | | |
adu340_4small | DynClust | Calcium-imaging dataset using Fura-2 | array | | |
basketball | Clustering | This data set contains a series of statistics (5 attributes) about 96 basketball players: | data.frame | 96 | 5 |
bolts | Clustering | Data from an experiment on the affects of machine adjustments on the time to count bolts. | data.frame | 40 | 8 |
stock | Clustering | The data provided are daily stock prices from January 1988 through October 1991, for ten aerospace companies. | data.frame | 950 | 10 |
stulong | Clustering | The study was performed at the 2nd Department of Medicine, 1st Faculty of Medicine of Charles University and Charles University Hospital. The data were transferred to electronic form by the European Centre of Medical Informatics, Statisticsand Epidemiology of Charles University and Academy of Sciences. | data.frame | 1417 | 5 |
weather | Clustering | One of the most known testing data sets in machine learning. This data sets describes several situations where the weather is suitable or not to play sports, depending on the current outlook, temperature, humidity and wind. | data.frame | 14 | 5 |
vector_playing_cards | mmcards | Vector Playing Cards Image Names | character | | |
bacteria | ProcMod | DNA metabarcoding Australia South-North Gradient | data.frame | 21 | 2150 |
climat | ProcMod | DNA metabarcoding Australia South-North Gradient | data.frame | 21 | 6 |
eukaryotes | ProcMod | DNA metabarcoding Australia South-North Gradient | data.frame | 21 | 1393 |
geography | ProcMod | DNA metabarcoding Australia South-North Gradient | data.frame | 21 | 2 |
soil | ProcMod | DNA metabarcoding Australia South-North Gradient | data.frame | 21 | 12 |
ReturnSeries_data | MSGARCHelm | Return Series Data | data.frame | 86 | 1 |
X_globaltas | QUALYPSO | Annual warming levels simulated by different CMIP5 GCMs | matrix | 20 | |
X_time_mat | QUALYPSO | Years 1971-2099 repeated for the 20 scenarios | matrix | 20 | |
X_time_vec | QUALYPSO | X_time_vec gives the years corr. to Y, i.e. from 1971 to 2099 | integer | | |
Xfut_globaltas | QUALYPSO | Vector of of future warming levels | numeric | | |
Xfut_time | QUALYPSO | Xfut_time is a vector of 11 years equally spaced from 1999 to 2099 | numeric | | |
Y | QUALYPSO | Mean winter temperature over CEU with 20 GCM/RCM combinations for 1971-2099 | matrix | 20 | |
scenAvail | QUALYPSO | List of GCM and RCM which have been used for the 20 climate projections | data.frame | 20 | 2 |
haldport | RXshrink | Portland Cement data of Hald(1952) | data.frame | 13 | 6 |
longley2 | RXshrink | Art Hoerl's update of the infamous Longley(1967) benchmark dataset | data.frame | 29 | 7 |
mpg | RXshrink | Hocking(1976) Miles Per Gallon data: a Multiple Regression Benchmark | data.frame | 32 | 11 |
tycobb | RXshrink | Ty Cobb batting statistics for 1905-1928 with Carl Morris' 2-piece Spline term. | data.frame | 24 | 6 |
test_cnv | SARC | test cnv | data.frame | 15 | 7 |
test_cnv2 | SARC | test cnv 2 | data.frame | 15 | 9 |
test_cov | SARC | test coverage file | data.frame | 34987 | 24 |
RevelioGeneList | tricycle | 5 stage cell cycle gene marker list from Revelio | list | | |
neuroRef | tricycle | Pre-learned reference projection matrix from the Neurosphere dataset | data.frame | 500 | 5 |
neurosphere_example | tricycle | Example SingleCellExperiment dataset | SingleCellExperiment | | |
RobinsonDelhomme2014 | easyRNASeq | Dataset included in the package | data.frame | 17 | 7 |
GDPIP | ASSA | A Real-time Vintage of GDP and IP for the US Economy | mts | 268 | 2 |
brexit | ASSA | Brexit Poll Tracker | data.frame | 272 | 6 |
merval | ASSA | MERVAL interval data | data.frame | 353 | 5 |
metadata_example | ata | ATA Package Example Item Metadata | data.frame | 10 | 10 |
metadata_large_example | ata | ATA Package Large Example Item Metadata | data.frame | 1096 | 44 |
metadata_withreplic_example | ata | ATA Package Example Item Metadata with Item Replications | data.frame | 14 | 10 |
DATASET.1 | represent | A 50 x 5 data set | matrix | 50 | |
DATASET.2 | represent | A 50 x 5 data set | matrix | 50 | |
DATASET.3 | represent | A 50 x 10 data set | matrix | 50 | |
DATASET.4 | represent | A 50 x 10 data set | matrix | 50 | |
ricaCarrots | SEPaLS | The RICA dataset describing the production of carrots (open field) (in quintals) from 2000 to 2015. | list | | |
Ishikawa | TFHAZ | Contains genomic regions of transcription factors at the ranges side and the name of the transcription factors at the metadata side. | GRanges | | |
TF_acc_w_0 | TFHAZ | Contains an output of the accumulation function. | list | | |
TF_dense_w_0 | TFHAZ | Contains an output of the dense_zones function. | list | | |
TF_dense_w_10 | TFHAZ | Contains an output of the dense_zones function. | list | | |
TF_dense_w_100 | TFHAZ | Contains an output of the dense_zones function. | list | | |
TF_dense_w_1000 | TFHAZ | Contains an output of the dense_zones function. | list | | |
TF_dense_w_10000 | TFHAZ | Contains an output of the dense_zones function. | list | | |
base_dense_w_10 | TFHAZ | Contains an output of the dense_zones function. | list | | |
reg_dense_w_10 | TFHAZ | Contains an output of the dense_zones function. | list | | |
dedup | spiky | spike-in counts for two samples, as a wide data.frame | data.frame | 26 | 3 |
genbank_mito | spiky | various mitochondrial genomes sometimes used as endogenous spike-ins | DFrame | | |
genomic_res | spiky | A Granges object with genomic coverage from chr21q22, binned every 300bp for the genomic contigs then averaged across the bin. (In other words, the default output of scan_genomic_contigs or scan_genomic_bedpe, restricted to a small enough set of genomic regions to be practical for examples.) This represents what most users will want to generate from their own genomic BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package. | GRanges | | |
phage | spiky | lambda and phiX phage sequences, sometimes used as spike-ins | DFrame | | |
spike | spiky | spike-in contig properties for Sam's cfMeDIP spikes | DFrame | | |
spike_cram_counts | spiky | spike-in counts, as a long data.frame | data.frame | 312 | 3 |
spike_read_counts | spiky | spike-in counts, as a long data.frame | data.frame | 52 | 3 |
spike_res | spiky | A Granges object with spike-in sequence coverage, and summarized for each spike contig as (the default) 'max' coverage. (In other words, the default output of scan_spike_contigs or scan_spike_bedpe) This represents what most users will want to generate from their own spike-in BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package. | GRanges | | |
ssb_res | spiky | scan_spiked_bam results from a merged cfMeDIP CRAM file (chr22 and spikes) | CompressedGRangesList | | |
testGR | spiky | a test GRanges with UMI'ed genomic sequences used as controls | GRanges | | |
annot | transcriptR | Reference annotation (knownGene from UCSC) | GRanges | | |
cds | transcriptR | Example of 'ChipDataSet' object. | ChipDataSet | | |
tds | transcriptR | Example of 'TranscriptionDataSet' object. | TranscriptionDataSet | | |
hgsc | vissE | The Hallmark collection from the MSigDB | GeneSetCollection | | |
GDPmix | gfer | Table about GDP mix of China provinces in 2015 | data.table | 11 | 11 |
cm | gfer | Matrix showing complicated management of China's Water Resource | data.frame | 11 | 13 |
clinical | MethReg | TCGA-COAD clinical matrix for 38 samples retrieved from GDC using TCGAbiolinks | data.frame | 38 | 4 |
dna.met.chr21 | MethReg | TCGA-COAD DNA methylation matrix (beta-values) for 38 samples (only chr21) retrieved from GDC using TCGAbiolinks | matrix | 3012 | 38 |
gene.exp.chr21.log2 | MethReg | TCGA-COAD gene expression matrix (log2 (FPKM-UQ + 1)) for 38 samples (only chromosome 21) retrieved from GDC using TCGAbiolinks | matrix | 752 | 38 |
testmat | fastLiquidAssociation | Example results from fastMLA requiring all significance methods | data.frame | 9 | 5 |
fs_prices | piar | Price data | data.frame | 40 | 5 |
fs_weights | piar | Price data | data.frame | 5 | 2 |
ms_prices | piar | Price data | data.frame | 40 | 4 |
ms_weights | piar | Price data | data.frame | 5 | 3 |
Basal | TRESS | Bin-level and region-level data from basal mouse brain samples | list | | |
DMR_M3vsWT | TRESS | Transcriptome location and read counts of 200 candidate DMRs, and size factors. | list | | |
DMR_SixWeekvsTwoWeek | TRESS | Transcriptome location and read counts of 200 candidate DMRs. | list | | |
catr_srs_values | CatastRo | Reference SRS codes for 'CatastRo' APIs | tbl_df | 16 | 4 |
AmpGram_predictions | AmpGram | Prediction of antimicrobial peptides | list | | |
hoCort | ggsegHO | Harvard-Oxford Cortical atlas | brain_atlas | | |
data.anscombe | desk | Anscombe's Quartet | data.frame | 11 | 8 |
data.auto | desk | Prices and Qualitative Characteristics of US-Cars | data.frame | 52 | 10 |
data.ballb | desk | Defective Ball Bearings | data.frame | 6 | 2 |
data.burglary | desk | Burglaries and Power Blackouts | data.frame | 12 | 3 |
data.cars | desk | Speed and Stopping Distances of Cars | data.frame | 50 | 2 |
data.cobbdoug | desk | Cobb-Douglas Production Function | data.frame | 100 | 3 |
data.comp | desk | Monthly Rentals and Qualitative Characteristics of Computers | data.frame | 34 | 4 |
data.eu | desk | Expenditures of the EU-25 | data.frame | 25 | 7 |
data.fertilizer | desk | Fertilizer in the Cultivation of Barley | data.frame | 30 | 3 |
data.filter | desk | Water Filter Sales | data.frame | 24 | 2 |
data.govexpend | desk | Government Expenditures of US-States | data.frame | 50 | 5 |
data.icecream | desk | Sales of Ice Cream | data.frame | 35 | 2 |
data.income | desk | Income Per Capita | data.frame | 75 | 3 |
data.insurance | desk | Sales of Insurance Contracts | data.frame | 30 | 4 |
data.iv | desk | Instrumental Variables | data.frame | 8 | 5 |
data.lifesat | desk | Life Satisfaction | data.frame | 40 | 3 |
data.macro | desk | Macroeconomic Data from Germany | data.frame | 129 | 7 |
data.milk | desk | Milk Production | data.frame | 12 | 2 |
data.pharma | desk | Pharmaceutical Advertisements | data.frame | 24 | 4 |
data.printer | desk | Prices and Qualitative Characteristics of Laser Printers | data.frame | 44 | 5 |
data.regional | desk | Regional Cost of Living in Germany | data.frame | 401 | 7 |
data.rent | desk | Average Basic Rent in City Districts | data.frame | 12 | 4 |
data.savings | desk | International Life-Cycle Savings and Disposable Income | data.frame | 50 | 5 |
data.sick | desk | Sick Leave and Unemployment | data.frame | 23 | 3 |
data.software | desk | Employment Data of a Software Company | data.frame | 36 | 3 |
data.spurious | desk | Non-Stationary Time Series Data | data.frame | 143 | 5 |
data.tip | desk | Tip Data in a Restaurant | data.frame | 3 | 2 |
data.tip.all | desk | Tip Data in a Restaurant with all 20 observations. Only used in textbook. | tbl_df | 20 | 2 |
data.trade | desk | Gravity Model Applied to Germany | data.frame | 27 | 5 |
data.unempl | desk | German Economic Growth and Unemployment Rates | data.frame | 30 | 3 |
data.wage | desk | Wage Data in a Company | data.frame | 20 | 7 |
data.windscreen | desk | Efficiency of a Car Glass Service Company | data.frame | 248 | 8 |
corrdat | robumeta | Data for Fitting Correlated Effects Model | data.frame | 172 | 8 |
corrdat.sm | robumeta | Data for Fitting Correlated Effects Model With Small-Sample Corrections | data.frame | 300 | 6 |
hedgesdat | robumeta | hedgesdat | data.frame | 179 | 4 |
hierdat | robumeta | Data for Fitting Hierarchical Effects Model | data.frame | 68 | 9 |
oswald2013 | robumeta | IAT Criterion-Related Correlations | data.frame | 308 | 12 |
oswald2013.ex1 | robumeta | IAT Criterion-Related Correlations | data.frame | 32 | 14 |
RoeDeerMassData | OnAge | Data on 454 roe deer | data.frame | 1428 | 16 |
titanic_gender_class_model | titanic | Titanic gender class model data. | data.frame | 418 | 2 |
titanic_gender_model | titanic | Titanic gender model data. | data.frame | 418 | 2 |
titanic_test | titanic | Titanic test data. | data.frame | 418 | 11 |
titanic_train | titanic | Titanic train data. | data.frame | 891 | 12 |
ngrams | ptwikiwords | | tbl_df | 1293779 | 2 |
ptwikiwords | ptwikiwords | ptwikiwords | tbl_df | 157860 | 3 |
img | plotfunctions | Image object. | list | | |
ProteoDiscographyExample.hg19 | ProteoDisco | Example ProteoDiscography. | ProteoDiscography | | |
MCMCtest | rvalues | MCMC sample output | matrix | 1500 | |
NBA1314 | rvalues | National Basketball Association, free throw data, 2013-2014 season | data.frame | 482 | 10 |
batavgs | rvalues | Batting Averages Data | data.frame | 929 | 7 |
bcwest | rvalues | Breast Cancer Gene Expression Data | data.frame | 7129 | 2 |
fluEnrich | rvalues | Flu Enrichment Data | data.frame | 5719 | 3 |
hiv | rvalues | HIV Data Set | data.frame | 7680 | 2 |
metal | AsthmaNHANES | metal | data.frame | 30442 | 27 |
percent | AsthmaNHANES | percent | data.frame | 40979 | 6 |
education | selectMeta | Dataset open vs. traditional education on creativity | data.frame | 10 | 5 |
passive_smoking | selectMeta | Dataset on the effect of environmental tobacco smoke | data.frame | 37 | 2 |
gastro_label_binary | EMMIXSSL | Gastrointestinal binary labels | spec_tbl_df | 76 | 9 |
gastro_label_trinary | EMMIXSSL | Gastrointestinal trinary labels | spec_tbl_df | 76 | 9 |
gastrodata | EMMIXSSL | Gastrointestinal dataset | spec_tbl_df | 701 | 152 |
LRdb | SingleCellSignalR | Ligand/Receptor interactions data table | data.frame | 3251 | 4 |
PwC_ReactomeKEGG | SingleCellSignalR | Pathway Commons Reactome KEGG 2019-05-08 | data.frame | 343230 | 4 |
example_dataset | SingleCellSignalR | Example dataset | data.frame | 1629 | 401 |
markers_default | SingleCellSignalR | A list of cell types markers | data.frame | 95 | 15 |
mm2Hs | SingleCellSignalR | Mus Musculus (mm) to Homo Sapiense (Hs) Orthology table | data.frame | 16519 | 2 |
medfly25 | fdapace | Number of eggs laid daily from medflies | data.frame | 19725 | 4 |
GM12878.40kb.raw.chr2 | TADCompare | A subset of chomosome 2 contact matrix, GM12878 cell line. | matrix | 1001 | 1001 |
IMR90.40kb.raw.chr2 | TADCompare | A subset of chomosome 2 contact matrix, IMR90 cell line. | matrix | 1001 | 1001 |
rao_chr22_prim | TADCompare | Chromosome 22 combined intrachromosomal primary contact matrix from Rao et al. 2014. | matrix | 704 | 704 |
rao_chr22_rep | TADCompare | Chromosome 22 combined intrachromosomal replicate contact matrix from Rao et al. 2014. | matrix | 704 | 704 |
time_mats | TADCompare | Chromosome 22 time-varying contact matrices from Rao et al. 2017. | list | | |
rao_chr20_25_rep | SpectralTAD | Contact matrix from Rao 2014, chromosome 20, 25kb resolution | data.frame | 2125980 | 3 |
celdaCGGridSearchRes | celda | celdaCGGridSearchRes | celdaList | | |
celdaCGMod | celda | celdaCGmod | celda_CG | | |
celdaCGSim | celda | celdaCGSim | list | | |
celdaCMod | celda | celdaCMod | celda_C | | |
celdaCSim | celda | celdaCSim | list | | |
celdaGMod | celda | celdaGMod | celda_G | | |
celdaGSim | celda | celdaGSim | list | | |
contaminationSim | celda | contaminationSim | list | | |
sampleCells | celda | sampleCells | matrix | 10 | |
sceCeldaC | celda | sceCeldaC | SingleCellExperiment | | |
sceCeldaCG | celda | sceCeldaCG | SingleCellExperiment | | |
sceCeldaCGGridSearch | celda | sceCeldaCGGridSearch | SingleCellExperiment | | |
sceCeldaG | celda | sceCeldaG | SingleCellExperiment | | |
pedigree | SeqVarTools | Pedigree for example data | data.frame | 90 | 6 |
chr_length | cageminer | Pepper chromosome lengths | GRanges | | |
gcn | cageminer | Simulation of the output list from BioNERO::exp2gcn() with pepper data | list | | |
gene_ranges | cageminer | Genomic coordinates of pepper genes | GRanges | | |
guides | cageminer | Guide genes associated with defense and resistance to oomycetes | data.frame | 23699 | 2 |
hubs | cageminer | Example hub genes for the network stored in the gcn object | data.frame | 352 | 3 |
mine2 | cageminer | Example output from mine_step2() | list | | |
mined_candidates | cageminer | Example output from mined_candidates() | data.frame | 5 | 4 |
pepper_se | cageminer | Gene expression data from Kim et al., 2018. | SummarizedExperiment | | |
snp_pos | cageminer | Capsicum annuum SNPs associated with resistance to Phytophthora root rot. | GRanges | | |
tfs | cageminer | Pepper transcription factors | data.frame | 1665 | 2 |
fhch2010 | afex | Data from Freeman, Heathcote, Chalmers, & Hockley (2010) | data.frame | 13222 | 10 |
ks2013.3 | afex | Data from Klauer & Singmann (2013, Experiment 3) | data.frame | 1440 | 6 |
laptop_urry | afex | Replication of Laptop Note Taking Study (Urry et al. 2021, Psych. Science) | data.frame | 142 | 6 |
md_12.1 | afex | Data 12.1 from Maxwell & Delaney | data.frame | 60 | 4 |
md_15.1 | afex | Data 15.1 / 11.5 from Maxwell & Delaney | data.frame | 48 | 4 |
md_16.1 | afex | Data 16.1 / 10.9 from Maxwell & Delaney | data.frame | 24 | 3 |
md_16.4 | afex | Data 16.4 from Maxwell & Delaney | data.frame | 29 | 6 |
obk.long | afex | O'Brien Kaiser's Repeated-Measures Dataset with Covariate | data.frame | 240 | 7 |
sk2011.1 | afex | Data from Singmann & Klauer (2011, Experiment 1) | data.frame | 640 | 9 |
sk2011.2 | afex | Data from Singmann & Klauer (2011, Experiment 2) | data.frame | 2268 | 8 |
stroop | afex | Stroop data from Lin et al. (2020, Psych. Science) | data.frame | 246600 | 7 |
Chipseq_Peak_demo | seq2pathway | chip seq loci data example | data.frame | 5 | 5 |
GRanges_demo | seq2pathway | loci information with GRanges format | GRanges | | |
dat_RNA | seq2pathway | RNA sequence data example | data.frame | 5000 | 5 |
dat_chip | seq2pathway | chip seq data example | data.frame | 639 | 1 |
acgh.data | SIM | Array Comparative Genomic Hybridization data | data.frame | 99 | 46 |
chrom.table | SIM | Table with chromosome information | data.frame | 862 | 6 |
expr.data | SIM | Expression data example | data.frame | 99 | 46 |
samples | SIM | Samples for example data | character | | |
finnish_stoplist | unine | Finnish stop list | character | | |
french_stoplist | unine | French stop list | character | | |
german_stoplist | unine | German stop list | character | | |
italian_stoplist | unine | Italian stop list | character | | |
portuguese_stoplist | unine | Portuguese stop list | character | | |
spanish_stoplist | unine | Spanish stop list | character | | |
swedish_stoplist | unine | Swedish stop list | character | | |
GDS1615 | TULIP | GDS1615 data introduced in Burczynski et al. (2012). | list | | |
csa | TULIP | Colorimetric sensor array (CSA) data | list | | |
KRAScounts | twoddpcr | KRAS mutant and wild type droplet counts and Poisson estimates. | data.frame | 12 | 17 |
KRAScountsQS | twoddpcr | KRAS mutant and wild type droplet counts and Poisson estimates. | data.frame | 24 | 63 |
KRAScountsWellCol | twoddpcr | KRAS mutant and wild type droplet counts and Poisson estimates. | data.frame | 12 | 18 |
KRASdata | twoddpcr | Droplet amplitude data for KRAS mutant and wild type molecules. | list | | |
exampleSingleCell | SDAMS | Two example datasets for SDAMS package | SummarizedExperiment | | |
exampleSumExp | SDAMS | Two example datasets for SDAMS package | SummarizedExperiment | | |
counts | ssviz | counts data | numeric | | |
ctrlbam | ssviz | ctrlbam data | DFrame | | |
pctrlbam | ssviz | pctrlbam data | DFrame | | |
ptreatbam | ssviz | ptreatbam data | DFrame | | |
treatbam | ssviz | treatbam data | DFrame | | |
MPINetData | famat | The variables in the environment variable 'MPINetData' of the system | environment | | |
compl_data_result | famat | Output of 'compl_data' function | list | | |
genes | famat | List of genes. | character | | |
interactions_result | famat | Output of 'interactions' function | list | | |
listk | famat | Pathway enrichment analysis results for KEGG pathways. | list | | |
listr | famat | Pathway enrichment analysis results for Reactome pathways. | list | | |
listw | famat | Pathway enrichment analysis results for Wikipathways pathways. | list | | |
meta | famat | List of metabolites. | character | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
IAccr | SemDist | Information Accretion Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
parentcnt | SemDist | Parent Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
termcnt | SemDist | Term Count Data | numeric | | |
MUD1 | ChemoSpec2D | Made Up 2D NMR-Like Data Sets | Spectra2D | | |
MUD2 | ChemoSpec2D | Made Up 2D NMR-Like Data Sets | Spectra2D | | |
fishSize | rmetalog | Fish size measurements from the Pacific Northwest. | data.frame | 3474 | 1 |
merged_data | elaborator | Demo data documentation for elaborator app | data.frame | 12000 | 9 |
wash | EScvtmle | WASH Benefits Bangladesh Dataset | data.frame | 900 | 8 |
labs | constellation | Synthesized lab results for cohort of 100 synthetic patients | data.table | 3150 | 4 |
orders | constellation | Synthesized blood culture orders for cohort of 100 synthetic patients | data.table | 59 | 3 |
vitals | constellation | Synthesized vital sign measurements for cohort of 100 synthetic patients | data.table | 35146 | 4 |
commonvariants_1kgenomes_subset | demuxSNP | Sample vcf file | CollapsedVCF | | |
multiplexed_scrnaseq_sce | demuxSNP | SingleCellExperiment object containing multiplexed RNA and HTO data from six biological smamples | SingleCellExperiment | | |
vartrix_consensus_snps | demuxSNP | Sample VarTrix output | matrix | 2542 | |
HCC1395_Germline | GenVisR | Germline Calls | data.frame | 9200 | 5 |
HCC1395_N | GenVisR | Normal BAM | data.frame | 59 | 6 |
HCC1395_T | GenVisR | Tumor BAM | data.frame | 52 | 6 |
LucCNseg | GenVisR | Truncated CN segments | data.frame | 3336 | 6 |
PIK3CA | GenVisR | Subset MAF file for PIK3CA gene | data.frame | 361 | 19 |
SNPloci | GenVisR | Identity snps | data.frame | 24 | 5 |
brcaMAF | GenVisR | Truncated BRCA MAF file | data.frame | 2773 | 55 |
cytoGeno | GenVisR | Cytogenetic banding dataset | data.frame | 2777 | 6 |
hg19chr | GenVisR | hg19 chromosome boundaries | data.frame | 24 | 3 |
TCData | TableToLongForm | Example hierarchical Tables | list | | |
ensembles | text2sdgData | A list of trained 'ranger::ranger()' random forest models that are used by the text2sdg 'detect_sdg()' function. | list | | |
AngloSaxonBurials | ArchaeoPhases.dataset | Anglo-Saxon Female Burials with Beads | archaeophases_mcmc | 5000 | 79 |
Fishpond | ArchaeoPhases.dataset | Calibration of a fishpond chronology | data.frame | 55965 | 12 |
KADatesChronoModel | ArchaeoPhases.dataset | Ksar Akil dates calibrated by ChronoModel | data.frame | 30000 | 17 |
KADatesOxcal | ArchaeoPhases.dataset | Ksar Akil dates calibrated by OxCal | data.frame | 10000 | 27 |
KAPhasesChronoModel | ArchaeoPhases.dataset | Ksar Akil phases calibrated by ChronoModel | data.frame | 30000 | 9 |
spe | scider | Description of the scider example datasets | SpatialExperiment | | |
Fattorini | MLpreemption | Australian bird abudances. | data.frame | 31 | 2 |
Ganeshaiah | MLpreemption | Indian dung beetles from Ganeshaiah et al. (1997) | data.frame | 16 | 2 |
Mehrabi | MLpreemption | Costa Rica dung beetle counts from Mehrabi et al. (2014) | data.frame | 31 | 16 |
SBGNhub.id.mapping.tables | SBGNview | Mapping tables available in SBGNhub | matrix | 822 | 1 |
mapped.ids | SBGNview | IDs mappable by SBGNview | list | | |
pathways.info | SBGNview | Information of collected pathways | data.frame | 5200 | 8 |
pathways.stats | SBGNview | Number of pathways collected | data.frame | 5 | 3 |
ara_test_dat | CheckSumStats | A example dataset of genetic summary data for arachidonic acid | data.frame | 436 | 9 |
charge_top_hits | CheckSumStats | GWAS top hits for arachidonic acid in the CHARGE consortium | character | | |
charge_top_hits_cleaned | CheckSumStats | GWAS top hits for arachidonic acid in the CHARGE consortium after post-GWAS cleaning | character | | |
glioma_test_dat | CheckSumStats | A example dataset of genetic summary data | data.frame | 98 | 15 |
refdat_1000G_superpops | CheckSumStats | A dataset of reference allele frequencies from 1000 genomes superpopulations | data.frame | 13782 | 7 |
ex_imp | rexposome | 'imExposomeSet' for testing purpouses | imExposomeSet | | |
expo | rexposome | 'ExposomeSet' for testing purpouses | ExposomeSet | | |
expo_c | rexposome | 'ExposomeClust' for testing purpouses | ExposomeClust | | |
me | rexposome | 'data.frame' for testing purpouses | data.frame | 654 | 57 |
BTM.geneSets | qusage | Example Gene Sets | list | | |
ISG.geneSet | qusage | Example Gene Sets | character | | |
MSIG.geneSets | qusage | Example Gene Sets | list | | |
eset.full | qusage | Example gene expression set | matrix | 4147 | 252 |
flu.meta | qusage | Example gene expression set | data.frame | 252 | 7 |
fluVaccine | qusage | Gene expression sets from Flu Vaccine trials | list | | |
example_clusters | XINA | Randomly generated example datasets for XINA users. A dataset containing the XINA clustering results. | list | | |
gn | XINA | A character vector containing 19,396 human genes This is for the randome data generation of XINA | character | | |
gn_desc | XINA | A character vector containing 19,396 human gene descriptions This is for the randome data generation of XINA | character | | |
hprd_ppi | XINA | Protein-protein interaction resource downloaded from HPRD DB A data frame containing HRPD protein-protein interaction data | data.frame | 39240 | 3 |
string_example | XINA | Protein-protein interaction resource downloaded from STRING DB for XINA's example dataset A data frame containing protein-protein interactions | data.frame | 7984 | 3 |
xina_result_example | XINA | Previously processed xina analysis using XINA's random example data A list containing 'xina_analysis' results | list | | |
choice_matrix | cIRT | Choice Matrix Data | data.frame | 3780 | 7 |
payout_matrix | cIRT | Payout Matrix Data | data.frame | 252 | 4 |
survey_data | cIRT | Survey Data | data.frame | 252 | 2 |
trial_matrix | cIRT | Trial Matrix Data | data.frame | 252 | 30 |
diffCorDat | statVisual | A Dataset for Differential Correlation Analysis | data.frame | 100 | 3 |
esSim | statVisual | A Simulated Gene Expression Dataset | ExpressionSet | | |
genoSim | statVisual | An ExpressionSet Object Storing Simulated Genotype Data | ExpressionSet | | |
longDat | statVisual | A Simulated Dataset for Longitudinal Data Analysis | data.frame | 540 | 4 |
cellmarker_tissue | EasyCellType | Tissues in CellMarker database. | list | | |
clustermole_tissue | EasyCellType | Tissues in Clustermole database. | list | | |
gene_pbmc | EasyCellType | Differential expressed marker genes in 9 clusters. | data.frame | 2701 | 3 |
panglao_tissue | EasyCellType | Tissues in Panglao database. | list | | |
pbmc_data | EasyCellType | Peripheral Blood Mononuclear Cells (PBMC) data. | dgCMatrix | | |
chr7Profiles | similaRpeak | ChIP-Seq profiles of region chr7:61968807-61969730 related to enhancers H3K27ac and H3K4me1 (for demonstration purpose) | list | | |
demoProfiles | similaRpeak | ChIP-Seq profiles of region chr7:61968807-61969730 related to enhancers H3K27ac and H3K4me1 (for demonstration purpose) | list | | |
bandprec | sparsenetgls | bandprec data for vignette | data.frame | 50 | 50 |
Diabetes | cblttr | Diabetes | spec_tbl_df | 130 | 11 |
rset | MultiDataSet | Example 'ResultSet' | ResultSet | | |
ce10.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
ce11.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
dm3.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
dm6.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
grch37.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
grch38.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
greyList | GreyListChIP | A sample 'GreyList' object for use in examples. | GreyList | | |
hg19.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
hg38.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
mm10.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
mm9.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
jaguar | pegas | Jaguar Micro-Satellites | loci | 59 | 14 |
exon.data | xmapbridge | Sample exon array dataset | data.frame | 8888 | 18 |
pbmc3k_500 | ILoReg | A toy dataset with 500 cells downsampled from the pbmc3k dataset. | dgCMatrix | | |
Ova | icpack | Ovarian cancer data | data.frame | 358 | 5 |
drugusers | icpack | Interval-censored drug users data | data.frame | 940 | 5 |
daily | precintcon | Daily precipitation between 1976 and 2010 | data.frame | 420 | 33 |
monthly | precintcon | Monthly precipitation between 1950 and 1992. | data.frame | 516 | 3 |
accmini | RTCGAToolbox | A subset of the Adrenocortical Carcinoma (ACC) dataset | FirehoseData | | |
COVIDsignatures | TBSignatureProfiler | A list of published/pre-print COVID-19 signatures. | list | | |
OriginalTrainingData | TBSignatureProfiler | Discovery datasets for corresponding gene signatures. | list | | |
TB_hiv | TBSignatureProfiler | An example TB dataset with TB/HIV data. | SummarizedExperiment | | |
TB_indian | TBSignatureProfiler | An example TB dataset with Indian population data. | SummarizedExperiment | | |
TBcommon | TBSignatureProfiler | A list of published TB signatures, using author-given names. | list | | |
TBsignatures | TBSignatureProfiler | A list of published TB signatures. | list | | |
TBsignaturesSplit | TBSignatureProfiler | Up/Down-regulated genes information for selected TB signatures. | list | | |
common_sigAnnotData | TBSignatureProfiler | Annotation information for published TB signatures. | data.frame | 80 | 3 |
sigAnnotData | TBSignatureProfiler | Annotation information for published TB signatures. | data.frame | 80 | 3 |
batch_indicator | BatchQC | Batch and Condition indicator for signature data | data.frame | 89 | 2 |
protein_data | BatchQC | Protein data with 39 protein expression levels | data.frame | 39 | 24 |
protein_sample_info | BatchQC | Batch and Condition indicator for protein expression data | data.frame | 24 | 2 |
signature_data | BatchQC | Signature data with 1600 gene expression levels | data.frame | 1600 | 89 |
hospitaldata | dematel | Hospital Location Selection Data | data.frame | 10 | 10 |
medicaldevice | dematel | Medical Device Selection Data | data.frame | 5 | 5 |
nurseselection | dematel | Nurse Selection Data | data.frame | 8 | 8 |
example_motifs | motifmatchr | example_motifs | PFMatrixList | | |
hmeto | ptm | Human MetO sites oxidized by hydrogen peroxide treatment. | data.frame | 4472 | 15 |
ivs | humidity | Viability of influenza A virus for 1 hour after spraying | data.frame | 11 | 3 |
ivt | humidity | Aerosol transmission efficiency of influenza A virus from guinea pigs to guinea pigs | data.frame | 24 | 4 |
columbus | gwrr | Columbus crime | data.frame | 49 | 6 |
INDEX_2010 | sparseIndexTracking | Database of the net returns of the index S&P 500 and its underlying assets during the year 2010 | list | | |
Islam2011 | Linnorm | scRNA-seq data from Islam et al. 2011 | matrix | 14913 | 96 |
LIHC | Linnorm | Partial RNA-seq data from TCGA LIHC (Liver Hepatocellular Carcinoma) | matrix | 19442 | 10 |
SEQC | Linnorm | Partial RNA-seq data from SEQC/MAQC-III Sample A | matrix | 42639 | 10 |
angiosperm_phylogeny | syntenet | Microsynteny-based angiosperm phylogeny. | phylo | | |
annotation | syntenet | Filtered genome annotation for Ostreococcus sp. species | CompressedGRangesList | | |
blast_list | syntenet | List of data frames containing BLAST-like tabular output | list | | |
clusters | syntenet | Synteny network clusters of BUSCO genes for 25 eudicot species | data.frame | 32653 | 2 |
edges | syntenet | Synteny network of Ostreococcus genomes represented as an edge list | data.frame | 1064 | 2 |
network | syntenet | Synteny network of BUSCO genes for 25 eudicot species | data.frame | 226677 | 2 |
proteomes | syntenet | Filtered proteomes of Ostreococcus sp. species | list | | |
scerevisiae_annot | syntenet | Genome annotation of the yeast species S. cerevisiae | list | | |
scerevisiae_diamond | syntenet | Intraspecies DIAMOND output for S. cerevisiae | list | | |
dataSimExample | methInheritSim | A 'list' containing methylation information used by some internal functions (for demo purpose. | list | | |
samplesForChrSynthetic | methInheritSim | All samples information, formated by 'methylKit', in a 'methylBase' format (for demo purpose). | methylBase | 9864 | 40 |
NCI60_4array_supdata | mogsa | supp data for Microarray gene expression profiles of the NCI 60 cell lines from 4 different platforms | list | | |
NCI60_4arrays | mogsa | Microarray gene expression profiles of the NCI 60 cell lines from 4 different platforms | list | | |
dGMMirGraph | RLassoCox | The KEGG network | igraph | | |
mRNA_matrix | RLassoCox | The expression data | matrix | 314 | 670 |
survData | RLassoCox | Survival data | data.frame | 314 | 2 |
hs_peptides | ProteoMM | hs_peptides - peptide-level intensities for human | data.frame | 695 | 13 |
mm_peptides | ProteoMM | mm_peptides - peptide-level intensities for mouse | data.frame | 1102 | 13 |
one_titre_data | sclr | Simulated one-titre antibody data | tbl_df | 5000 | 2 |
two_titre_data | sclr | Simulated two-titre antibody data | tbl_df | 5000 | 3 |
LongDat_cont_feature_table | LongDat | data/LongDat_cont_feature_table.RData documentation | data.frame | 20 | 4 |
LongDat_cont_master_table | LongDat | data/LongDat_cont_master_table.RData documentation | data.frame | 20 | 9 |
LongDat_cont_metadata_table | LongDat | data/LongDat_cont_metadata_table.RData documentation | data.frame | 20 | 7 |
LongDat_disc_feature_table | LongDat | data/LongDat_disc_feature_table.RData documentation | data.frame | 30 | 4 |
LongDat_disc_master_table | LongDat | data/LongDat_disc_master_table.RData documentation | data.frame | 30 | 9 |
LongDat_disc_metadata_table | LongDat | data/LongDat_disc_metadata_table.RData documentation | data.frame | 30 | 7 |
ctpbs | DesignCTPB | The clinical trial design for strong biomarker effect | list | | |
ctpbw | DesignCTPB | The clinical trial design for weak biomarker effect | list | | |
guo | destiny | Guo at al. mouse embryonic stem cell qPCR data | ExpressionSet | | |
guo_norm | destiny | Guo at al. mouse embryonic stem cell qPCR data | ExpressionSet | | |
claimdata | cascsim | Sample Claim Data | data.frame | 10030 | 15 |
tropicbird | dominanceanalysis | Distribution of a tropical native bird species inhabiting a small oceanic island. | data.frame | 2398 | 8 |
character_table | nichevol | Example of character table for six species | matrix | 6 | 28 |
occ_list | nichevol | Example of occurrence records for six species | list | | |
par_rec_table | nichevol | Example of table with results from parsimony reconstructions | matrix | 11 | 20 |
tree | nichevol | Example of a phylogenetic tree for six species | phylo | | |
tree5 | nichevol | Example of a phylogenetic tree for five species | phylo | | |
tree_data | nichevol | Example of a list containing a tree and a table of characters for six species | list | | |
crc.gr | biovizBase | CRC | GRanges | | |
crc1.GeRL | biovizBase | crc1.GeRL | SimpleGRangesList | | |
darned_hg19_subset500 | biovizBase | Subset of RNA editing sites in hg19... | GRanges | | |
genesymbol | biovizBase | Gene symbols with position... | GRanges | | |
hg19Ideogram | biovizBase | Hg19 ideogram without cytoband information... | GRanges | | |
hg19IdeogramCyto | biovizBase | Hg19 ideogram with cytoband information... | GRanges | | |
hg19sub | biovizBase | CRC | GRanges | | |
ideo | biovizBase | ideogram without cytoband information | list | | |
ideoCyto | biovizBase | ideogram with cytoband information | list | | |
mut.gr | biovizBase | CRC | GRanges | | |
QTLDetectionExample | MM4LMM | QTL Detection Example | list | | |
VarianceComponentExample | MM4LMM | Variance Component Example | list | | |
bestgrp | DirichletMultinomial | Data objects used for examples and the vignette | DMNGroup | | |
fit | DirichletMultinomial | Data objects used for examples and the vignette | list | | |
xval | DirichletMultinomial | Data objects used for examples and the vignette | data.frame | 254 | 3 |
expSetSpeCondExample | SpeCond | An ExpressionSet example object used in the SpeCond package | ExpressionSet | | |
expressionSpeCondExample | SpeCond | The expression matrix example used in the SpeCond package | matrix | 220 | 32 |
simulatedSpeCondData | SpeCond | An example of simulated expression matrix used in the SpeCond package | matrix | 600 | 30 |
exprs.dat | attract | Gene Expression Matrix of Published Data | matrix | 11044 | 68 |
loring.eset | attract | An ExpressionSet Object containing published data from M?ller et al. | ExpressionSet | | |
samp.info | attract | samp.info Contains the Sample Information for the Mueller data set. | data.frame | 68 | 2 |
subset.loring.eset | attract | An ExpressionSet Object containing published data from M?ller et al. | ExpressionSet | | |
EM2_H1ESB1_MeDIP_sigleCpG | SIMD | A simulation dataset of MeDIP CpG sites. | data.frame | 2000 | 4 |
EM2_H1ESB2_MeDIP_sigleCpG | SIMD | A simulation dataset of MeDIP CpG sites. | data.frame | 2000 | 4 |
EM_H1ESB1_MeDIP_sigleCpG | SIMD | A simulation dataset of MeDIP CpG sites. | data.frame | 2000 | 4 |
H1ESB1_MRE_sigleCpG | SIMD | A simulation dataset of MRE CpG sites. | data.frame | 2000 | 4 |
H1ESB2_MRE_sigleCpG | SIMD | A simulation dataset of MRE CpG sites. | data.frame | 2000 | 4 |
all_CpGsite_bin_chr18 | SIMD | A simulation dataset of CpG sites. | data.frame | 2000 | 4 |
three_mre_cpg | SIMD | A simulation dataset of MRE CpG sites. | data.frame | 2000 | 4 |
carbon.fabric | cmstatr | Sample data for a generic carbon fabric | data.frame | 216 | 5 |
carbon.fabric.2 | cmstatr | Sample data for a generic carbon fabric | data.frame | 177 | 9 |
exampleEaRes | CBNplot | Example enrichment analysis result | enrichResult | | |
exampleGeneExp | CBNplot | Example gene expression data | data.frame | 7 | 100 |
GSE6136_cli | BioTIP | GSE6136 cli dataset | data.frame | 36 | 27 |
GSE6136_matrix | BioTIP | GSE6136 matrix dataset | data.frame | 22690 | 27 |
ILEF | BioTIP | Chromosome ranges of chr21 dataset | data.frame | 300 | 6 |
cod | BioTIP | cod dataset | data.frame | 19689 | 6 |
gencode | BioTIP | A chr21 data from GENCODE GRCh37 | GRanges | | |
intron | BioTIP | Coding transcriptome in chr21 dataset | GRanges | | |
membersL | BioTIP | | list | | |
subcounts | BioTIP | | list | | |
sample_annotationdata | blacksheepr | sample_annotationdata | data.frame | 76 | 6 |
sample_phosphodata | blacksheepr | sample_phosphodata | data.frame | 15532 | 76 |
sample_rnadata | blacksheepr | sample_rnadata | data.frame | 4317 | 76 |
MMD | MMDiff2 | DBAmmd Object for Cfp1 example | DBAmmd | | |
ATAC_normCount | FindIT2 | ATAC normCount of E50h-72h in Chr5 | matrix | 5902 | 16 |
RNADiff_LEC2_GR | FindIT2 | RNA diff result from LEC2_GR VS LEC2_DMSO | data.frame | 8236 | 3 |
RNA_normCount | FindIT2 | RNA normCount of E50h-72h in Chr5 | data.frame | 8236 | 16 |
TF_target_database | FindIT2 | TF-target database | data.frame | 7549 | 2 |
test_featureSet | FindIT2 | test_featureSet | character | | |
test_geneSet | FindIT2 | test_geneSet | character | | |
case | epistasisGA | Genotypes for the affected children of case-parent triads. | data.frame | 1000 | 100 |
case.gxe | epistasisGA | Genotypes for the cases of case-parent triads with a simulated gene environment interaction. | matrix | 1000 | 24 |
case.mci | epistasisGA | Genotypes for the affected cases of case-parent triads with a simulated maternal-fetal interaction. | matrix | 1000 | 24 |
dad | epistasisGA | Genotypes for the fathers of case-parent triads. | data.frame | 1000 | 100 |
dad.gxe | epistasisGA | Genotypes for the fathers of case-parent triads with a simulated gene environment interaction. | matrix | 1000 | 24 |
dad.mci | epistasisGA | Genotypes for the fathers of case-parent triads with a simulated maternal-fetal interaction. | matrix | 1000 | 24 |
exposure | epistasisGA | Exposures for the cases of case-parent triads with a simulated gene environment interaction. | data.frame | 1000 | 1 |
mom | epistasisGA | Genotypes for the mothers of case-parent triads. | data.frame | 1000 | 100 |
mom.gxe | epistasisGA | Genotypes for the mothers of case-parent triads with a simulated gene environment interaction. | matrix | 1000 | 24 |
mom.mci | epistasisGA | Genotypes for the mothers of case-parent triads with a simulated maternal-fetal interaction. | matrix | 1000 | 24 |
snp.annotations | epistasisGA | RSID, REF, and ALT annotations for example dataset SNPs | data.frame | 100 | 3 |
snp.annotations.mci | epistasisGA | RSID, REF, and ALT annotations for example dataset SNPs | data.frame | 24 | 3 |
TT | bumphunter | Example data | list | | |
endotoxin | edge | Gene expression dataset from Calvano et al. (2005) Nature | list | | |
gibson | edge | Gene expression dataset from Idaghdour et al. (2008) | list | | |
kidney | edge | Gene expression dataset from Rodwell et al. (2004) | list | | |
kirc.exprs | messina | Example TCGA KIRC RNAseq expression and survival data | matrix | 3 | 422 |
kirc.surv | messina | Example TCGA KIRC RNAseq expression and survival data | Surv | 422 | 2 |
atlas1006 | microbiome | HITChip Atlas with 1006 Western Adults | phyloseq | | |
dietswap | microbiome | Diet Swap Data | phyloseq | | |
hitchip.taxonomy | microbiome | HITChip Taxonomy | list | | |
peerj32 | microbiome | Probiotics Intervention Data | list | | |
CfData | fgga | A set of characterized protein coding genes from the Cannis familiaris organism annotated to a target GO subgraph considering both experimental and electronic evidence. | list | | |
metabBatches | metabCombiner | Three LC-MS Metabolomics Batch Datasets | list | | |
plasma20 | metabCombiner | 20 minute LC-MS Analysis of Human Plasma | data.frame | 8910 | 22 |
plasma30 | metabCombiner | 30 minute LC-MS Analysis of Human Plasma | data.frame | 8286 | 22 |
batch_summary | cogeqc | BUSCO summary output for batch mode | data.frame | 8 | 4 |
interpro_ath | cogeqc | Intepro annotation for Arabidopsis thaliana's genes | data.frame | 131661 | 2 |
interpro_bol | cogeqc | Intepro annotation for Brassica oleraceae's genes | data.frame | 212665 | 2 |
og | cogeqc | Orthogroups between Arabidopsis thaliana and Brassica oleraceae | data.frame | 88934 | 3 |
synnet | cogeqc | Synteny network for Brassica oleraceae, B. napus, and B. rapa | data.frame | 436508 | 2 |
tree | cogeqc | Species tree for model species | phylo | | |
SNP358.data | GMRP | Data of 358 SNPs | data.frame | 358 | 3 |
SNP368annot.data | GMRP | Annotation data of 368SNPs | data.frame | 1053 | 6 |
beta.data | GMRP | Beta Data Of SNP Regressed on Causal Variables and Disease | data.frame | 368 | 5 |
cad.data | GMRP | boldGWAS Meta-analyzed Data of Coronary Artery Disease | data.table | 1609 | 12 |
lpd.data | GMRP | *GWAS* Meta-analyzed Data of Lipoprotein Cholesterols | data.table | 1609 | 40 |
example | ScottKnottESD | An example dataset of Breiman's variable importance scores | data.frame | 1000 | 9 |
maven | ScottKnottESD | An example dataset of Breiman's variable importance scores | data.frame | 1000 | 27 |
Affy1_Distance_Final | IntramiRExploreR | Targets for the microRNA analyzed from Affy1 plaform using Distance. | data.frame | 53399 | 8 |
Affy1_Pearson_Final | IntramiRExploreR | Targets for the microRNA analyzed from Affy1 plaform using Pearson. | data.frame | 41845 | 8 |
Affy2_Distance_Final | IntramiRExploreR | Targets for the microRNA analyzed from Affy2 plaform using Distance. | data.frame | 73374 | 8 |
Affy2_Pearson_Final | IntramiRExploreR | Targets for the microRNA analyzed from Affy2 plaform using Pearson. | data.frame | 52913 | 8 |
miRNA_ID_to_Function | IntramiRExploreR | Contains the miRNA function information from Flybase database. | data.frame | 66 | 3 |
miRNA_summary_DB | IntramiRExploreR | Contains the summary for the intragenic miRNA. | data.frame | 257 | 6 |
hedenfalk | qvalue | P-values and test-statistics from the Hedenfalk et al. (2001) gene expression dataset | list | | |
swirl | marray | Gene expression data from Swirl zebrafish cDNA microarray experiment | marrayRaw | | |
lifetech | miRcomp | The processed data generated using the LifeTech software. | list | | |
qpcRb4 | miRcomp | The processed data generated using the 4 parameter sigmoidal method from the qpcR software. | list | | |
qpcRb5 | miRcomp | The processed data generated using the 5 parameter sigmodial method from the qpcR software. | list | | |
qpcRdefault | miRcomp | The processed data generated using the default method (4 parameter log-logistic) implemented in the qpcR software package. | list | | |
qpcRl5 | miRcomp | The processed data generated using the 5 parameter log-logistic method from the qpcR software. | list | | |
qpcRlinexp | miRcomp | The processed data generated using the linear-exponential method implemented in the qpcR software package. | list | | |
ais | dr | Australian institute of sport data | data.frame | 202 | 14 |
mussels | dr | Mussels' muscles data | data.frame | 82 | 5 |
GSPC | DMwR2 | A set of daily quotes for SP500 | xts | 11622 | 6 |
algae | DMwR2 | Training data for predicting algae blooms | tbl_df | 200 | 18 |
algae.sols | DMwR2 | The solutions for the test data set for predicting algae blooms | tbl_df | 140 | 7 |
sales | DMwR2 | A data set with sale transaction reports | tbl_df | 401146 | 5 |
sp500 | DMwR2 | A set of daily quotes for SP500 in CSV Format | data.frame | 11622 | 1 |
test.algae | DMwR2 | Testing data for predicting algae blooms | tbl_df | 140 | 11 |
bats | fuzzyreg | Temperature Data of Hibernating Bats and Climate at Site | data.frame | 528 | 2 |
fuzzydat | fuzzyreg | Data For Fuzzy Linear Regression | list | | |
SAGEartifacts | sagenhaft | Functions for SAGE library extraction | data.frame | 329 | 2 |
Bakal2007 | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | matrix | 273 | 7 |
Bakal2007Cluster | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | integer | | |
bm1 | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | BetaMixture | | |
nodeColor | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | character | | |
airfoil | RRBoost | Airfoil data | data.frame | 1503 | 6 |
hsMirrorLocs | KCsmart | Mirror locations of the human genome | list | | |
hsSampleData | KCsmart | Homo Sapiens artificial cgh data set | data.frame | 3268 | 22 |
mmMirrorLocs | KCsmart | Mirror locations of the mouse genome | list | | |
NYleukemia | RgoogleMaps | Upstate New York Leukemia Data | list | | |
bbs | RgoogleMaps | Columbus OH spatial analysis data set | matrix | 49 | |
col.gal.nb | RgoogleMaps | Columbus OH spatial analysis data set | nb | | |
columbus | RgoogleMaps | Columbus OH spatial analysis data set | data.frame | 49 | 22 |
coords | RgoogleMaps | Columbus OH spatial analysis data set | matrix | 49 | |
incidents | RgoogleMaps | San Francisco crime data | data.frame | 5000 | 16 |
pennLC | RgoogleMaps | Pennsylvania Lung Cancer | list | | |
polys | RgoogleMaps | Columbus OH spatial analysis data set | polylist | | |
items_ecpe | edmdata | Examination for the Certificate of Proficiency in English (ECPE) Item Responses | matrix | 2922 | 28 |
items_fractions | edmdata | Fraction Subtraction and Addition Assessment Item Responses | matrix | 536 | 20 |
items_hcp_penn_matrix | edmdata | Human Connectome Project's Penn Progressive Matrices Fluid Intelligence Assessment | matrix | 1201 | 24 |
items_hcp_penn_matrix_missing | edmdata | Human Connectome Project's Penn Progressive Matrices Fluid Intelligence Assessment | matrix | 1201 | 24 |
items_matrix_reasoning | edmdata | Experimental Matrix Reasoning Test Item Responses | matrix | 400 | 25 |
items_narcissistic_personality_inventory | edmdata | Narcissistic Personality Inventory Item Responses | matrix | 11243 | 40 |
items_ordered_eclsk_atl | edmdata | Subset of Early Childhood Longitudinal Study, Kindergarten (ECLS-K)'s Approaches to Learning Item Responses | matrix | 13354 | 12 |
items_ordered_pisa12_us_vignette | edmdata | Programme for International Student Assessment (PISA) 2012 U.S. Student Questionnaire Problem-Solving Vignettes | matrix | 3075 | 12 |
items_ordered_pswc_hw | edmdata | Calculus-based probability and statistics course homework problems | matrix | 288 | 29 |
items_ordered_timss15_background | edmdata | Trends in International Mathematics and Science Study 2015 (TIMSS) Grade 8 Student Background Survey Item Responses | matrix | 9672 | 16 |
items_pisa12_us_math | edmdata | Programme for International Student Assessment (PISA) 2012 US Math Assessment | matrix | 4978 | 76 |
items_probability_part_one_full | edmdata | Elementary Probability Theory Assessment Item Responses | matrix | 504 | 12 |
items_probability_part_one_reduced | edmdata | Elementary Probability Theory Assessment Item Responses | matrix | 431 | 12 |
items_revised_psvtr | edmdata | Revised PSVT:R Item Responses | matrix | 516 | 30 |
items_spm_ls | edmdata | Last Series of the Standard Progressive Matrices (SPM-LS) Item Responses | matrix | 499 | 12 |
items_taylor_manifest_anxiety_scale | edmdata | Taylor Manifest Anxiety Scale Item Responses | matrix | 4468 | 50 |
qmatrix_ecpe | edmdata | Examination for the Certificate of Proficiency in English (ECPE) Expert-Derived Q matrix | q_matrix | 28 | 3 |
qmatrix_fractions | edmdata | Fraction Subtraction and Addition Assessment Expert-Derived Q Matrix | matrix | 20 | 8 |
qmatrix_oracle_k2_j12 | edmdata | Oracle Q Matrices | matrix | 12 | |
qmatrix_oracle_k3_j20 | edmdata | Oracle Q Matrices | matrix | 20 | |
qmatrix_oracle_k4_j20 | edmdata | Oracle Q Matrices | matrix | 20 | |
qmatrix_oracle_k5_j30 | edmdata | Oracle Q Matrices | matrix | 30 | |
qmatrix_probability_part_one | edmdata | Elementary Probability Theory Assessment Expert-Derived Q Matrix | matrix | 12 | 4 |
strategy_oracle_k3_j20_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k3_j30_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k3_j40_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k3_j50_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k4_j20_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k4_j30_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k4_j40_s2 | edmdata | Strategy Oracle Sets | array | | |
strategy_oracle_k4_j50_s2 | edmdata | Strategy Oracle Sets | array | | |
heart_data | ClustAll | Heart Disease Dataset | data.frame | 918 | 12 |
obj_noNA1 | ClustAll | obj_noNA1: Processed wdbc dataset for testing purposed | ClustAllObject | | |
obj_noNA1simplify | ClustAll | obj_noNA1simplify: Processed wdbc dataset for testing purposed | ClustAllObject | | |
obj_noNAno1Validation | ClustAll | obj_noNAno1Validation: Processed wdbc dataset for testing purposed | ClustAllObject | | |
wdbc | ClustAll | wdbc: Diagnostic Wisconsin Breast Cancer Database. | data.frame | 569 | 32 |
wdbcMIDS | ClustAll | wdbcMIDS: Diagnostic Wisconsin Breast Cancer Database with imputed values | mids | | |
wdbcNA | ClustAll | wdbcNA: Diagnostic Wisconsin Breast Cancer Database with missing values | data.frame | 569 | 31 |
theoFitOde | nlmixr2extra | Example single dose Theophylline ODE model | nlmixr2FitData | 132 | 22 |
ExampleQueryGenes | CellMapper | Example Gene Lists | data.frame | 30 | 4 |
NegativeControlGenes | CellMapper | Example Gene Lists | list | | |
PositiveControlGenes | CellMapper | Example Gene Lists | list | | |
med_dec | fddm | Medicial decision data | data.frame | 11000 | 9 |
Dm.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Drosophila Melanogaster. | integer | | |
Hs.datamat | DTA | Gene expression profiles of the Homo Sapiens DTA experiment from Doelken et al. | matrix | 19791 | 9 |
Hs.enst2ensg | DTA | Mapping of Homo Sapiens gene and transcript identifiers. | character | | |
Hs.phenomat | DTA | Design of the Homo Sapiens DTA experiment from Doelken et al. | matrix | 9 | 5 |
Hs.reliable | DTA | Gene identifiers valid for parameter estimation from the Homo Sapiens Doelken et al. DTA experiment. | character | | |
Hs.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Homo Sapiens. | integer | | |
Hs.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Homo Sapiens. | integer | | |
Mm.datamat | DTA | Gene expression profiles of the Mus Musculus DTA experiment from Doelken et al. | matrix | 16747 | 9 |
Mm.enst2ensg | DTA | Mapping of Mus Musculus gene and transcript identifiers. | character | | |
Mm.phenomat | DTA | Design of the Mus Musculus DTA experiment from Doelken et al. | matrix | 9 | 5 |
Mm.reliable | DTA | Gene identifiers valid for parameter estimation from the Mus Musculus Doelken et al. DTA experiment. | character | | |
Mm.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Mus Musculus. | integer | | |
Mm.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Mus Musculus. | integer | | |
Pol.phenomat | DTA | Design of the Saccharomyces Cerevisiae rpb1-N488D (Slow Polymerase) cDTA experiment from Sun et al. | matrix | 4 | 14 |
Raw.datamat | DTA | Gene expression profiles of the Saccharomyces Cerevisiae rpb1-N488D (Slow Polymerase) and wild-type cDTA experiment from Sun et al. | matrix | 10849 | 8 |
Sc.affy2ensg | DTA | Mapping of SaccharomycesCerevisiae Affymetrix Yeast 2.0 and gene identifiers. | character | | |
Sc.datamat | DTA | Gene expression profiles of the Saccharomyces Cerevisiae wild-type DTA experiment from Miller et al. | matrix | 5976 | 12 |
Sc.datamat.dynamic | DTA | Gene expression profiles of the Saccharomyces Cerevisiae salt stress DTA experiment from Miller et al. | matrix | 5976 | 24 |
Sc.ensg.reliable | DTA | Gene identifiers valid for parameter estimation from the Saccharomyces Cerevisiae Sun et al. cDTA experiment. | character | | |
Sc.phenomat | DTA | Design of the Saccharomyces Cerevisiae wild-type DTA experiment from Miller et al. | matrix | 12 | 4 |
Sc.phenomat.dynamic | DTA | Design of the Saccharomyces Cerevisiae salt stress DTA experiment from Miller et al. | matrix | 24 | 11 |
Sc.reliable | DTA | Gene identifiers valid for parameter estimation from the Saccharomyces Cerevisiae Miller et al. wild-type DTA experiment. | character | | |
Sc.reliable.dynamic | DTA | Gene identifiers valid for parameter estimation from the Saccharomyces Cerevisiae Miller et al. salt stress DTA experiment. | character | | |
Sc.ribig.ensg | DTA | Ribosome biogenesis genes. | character | | |
Sc.rpg.ensg | DTA | Ribosomal protein genes. | character | | |
Sc.stress.ensg | DTA | ISA stress module. | character | | |
Sc.tf.ensg | DTA | Transcription factors. | character | | |
Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
Sp.affy.reliable | DTA | Gene identifiers valid for cDTA normalization from the Saccharomyces Cerevisiae Sun et al. cDTA experiment. | character | | |
Sp.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Schizosaccharomyces Pombe. | integer | | |
Wt.phenomat | DTA | Design of the Saccharomyces Cerevisiae wild-type cDTA experiment from Sun et al. | matrix | 4 | 14 |
wu_subset | barcodetrackR | Small subset of Wu barcoding dataset | SummarizedExperiment | | |
pvaluesExample | BioNet | Example p-values for aggregation statistics | matrix | 6154 | 2 |
RImatrix | TargetSearch | Example GC-MS data for TargetSearch Package | matrix | 3 | 15 |
corRI | TargetSearch | Example GC-MS data for TargetSearch Package | matrix | 12 | 15 |
metabProfile | TargetSearch | Example GC-MS data for TargetSearch Package | tsProfile | | |
peakData | TargetSearch | Example GC-MS data for TargetSearch Package | tsMSdata | | |
refLibrary | TargetSearch | Example GC-MS data for TargetSearch Package | tsLib | | |
rimLimits | TargetSearch | Class for representing retention index markers | tsRim | | |
sampleDescription | TargetSearch | Example GC-MS data for TargetSearch Package | tsSample | | |
fiftyGenes | EBcoexpress | The fiftyGenes expression matrix | matrix | 50 | 125 |
festives | EpiInvert | A dataset containing festive days in France, Germany, UK and the USA | data.frame | 63 | 4 |
incidence | EpiInvert | A dataset containing daily incidence of COVID-19 for France, Germany, UK and the USA | data.frame | 833 | 5 |
incidence_weekly_aggregated | EpiInvert | A dataset containing weekly aggregated incidence of COVID-19 for France, Germany, UK and the USA | data.frame | 119 | 5 |
owid | EpiInvert | A dataset with COVID-19 indicators | data.frame | 6007 | 13 |
restored_incidence_database | EpiInvert | A dataset of restored daily incidence trend curves | matrix | 27418 | 56 |
si_distr_data | EpiInvert | A dataset containing the values of a serial interval | numeric | | |
G.small | grmsem | Symmetric GRM data: small data set | matrix | 100 | |
fit.large | grmsem | Prefitted model: large data set | grmsem.fit | | |
ph.large | grmsem | Phenotype data: large data set | matrix | 5000 | 4 |
ph.small | grmsem | Phenotype data: small data set | matrix | 100 | 3 |
gala | CTDquerier | 'CTDdata' for ilustrative purpouses | CTDdata | | |
primary | inters | Data on the direct primary in US congressional elections | data.frame | 1164 | 7 |
remit | inters | Cross-national data on remittances and protest | data.frame | 2429 | 13 |
copasi | BUS | copasi data | data.frame | 100 | 100 |
tumors.mRNA | BUS | Gene expression data from Human brain tumors | matrix | 7089 | 12 |
tumors.miRNA | BUS | miRNA data from Human brain tumors | matrix | 93 | 12 |
datasets | DeconRNASeq | data objects for liver and kidney mixing samples | data.frame | 31979 | 7 |
datasets | DeconRNASeq | data objects for liver and kidney mixing samples | data.frame | 31979 | 7 |
fraction | DeconRNASeq | mixing fractions for multi-tissues mixing samples | data.frame | 10 | 5 |
proportions | DeconRNASeq | proportions for liver and kidney mixing samples | data.frame | 7 | 2 |
proportions | DeconRNASeq | proportions for liver and kidney mixing samples | data.frame | 7 | 2 |
signatures | DeconRNASeq | data objects for liver and kidney pure samples | data.frame | 630 | 2 |
signatures | DeconRNASeq | data objects for liver and kidney pure samples | data.frame | 630 | 2 |
x.data | DeconRNASeq | data objects for multi-tissues mixing samples | data.frame | 28745 | 11 |
x.signature | DeconRNASeq | data objects for multi-tissues pure samples | data.frame | 26385 | 7 |
x.signature.filtered | DeconRNASeq | filtered signatures for multi-tissues pure samples | data.frame | 25861 | 7 |
x.signature.filtered.optimal | DeconRNASeq | selected signatures from multi-tissues pure samples | data.frame | 1570 | 7 |
golub.pval | LBE | p-values corresponding to the gene expression data from Golub et al. (1999). | numeric | | |
hedenfalk.pval | LBE | p-values corresponding to the gene expression data from Hedenfalk et al. (2001). | numeric | | |
this_is_a_really_long_name | joholearnr | Object with a long name | numeric | | |
ACTG175 | speff2trial | AIDS Clinical Trials Group Study 175 | data.frame | 2139 | 27 |
adultsNeonates | flowPlots | The adultsNeonates example data set of "stacked" data. | data.frame | 6912 | 10 |
marginalDF | flowPlots | An example of marginalData | data.frame | 432 | 11 |
markerMatrix | flowPlots | An example of the markers data. | matrix | 16 | 4 |
pfdDF | flowPlots | An example of pfdData | data.frame | 432 | 10 |
pfdPartsList | flowPlots | An example of pfdPartsData | list | | |
profileDF | flowPlots | An example of profileData | data.frame | 432 | 22 |
italy08 | italy | Italian Survey on Household and Wealth, 2008 | data.frame | 13702 | 12 |
italy10 | italy | Italian Survey on Household and Wealth, 2010 | data.frame | 13733 | 12 |
sw | tracheideR | Monthly soil moisture data for the period 1901-2013. | data.frame | 113 | 12 |
tch | tracheideR | Raw light intensity for two years (2010 and 2013) of Pinus pinaster | list | | |
delayAth | NetOrigin | Delay propagation data examples simulated by LinTim software | data.frame | 510 | 53 |
delayGoe | NetOrigin | Delay propagation data examples simulated by LinTim software | data.frame | 2570 | 259 |
ptnAth | NetOrigin | Public transportation network datasets from LinTim software (Integrated Optimization in Public Transportation) | igraph | | |
ptnGoe | NetOrigin | Public transportation network datasets from LinTim software (Integrated Optimization in Public Transportation) | igraph | | |
BCRANKout | BCRANK | BCRANK results for USF1 ChIP-chip data | BCRANKresult | | |
miete | DMRnet | miete dataset | data.frame | 2053 | 12 |
promoter | DMRnet | promoter dataset | data.frame | 106 | 58 |
abetaL | LMMstar | Data From The Bland Altman Study (Long Format) | data.frame | 262 | 13 |
abetaW | LMMstar | Data From The abeta Study (Wide Format) | data.frame | 131 | 14 |
blandAltmanL | LMMstar | Data From The Bland Altman Study (Long Format) | data.frame | 68 | 4 |
blandAltmanW | LMMstar | Data From The Bland Altman Study (Wide Format) | data.frame | 17 | 5 |
bloodpressureL | LMMstar | Data From The Blood Pressure Study (Long Format) | data.frame | 36 | 5 |
calciumL | LMMstar | Data From The Calcium Supplements Study (Long Format) | data.frame | 560 | 7 |
calciumW | LMMstar | Data From The Calcium Supplements Study (Wide Format) | data.frame | 112 | 14 |
ckdL | LMMstar | CKD long | data.frame | 153 | 9 |
ckdW | LMMstar | CKD wide | data.frame | 51 | 11 |
gastricbypassL | LMMstar | Data From The Gastric Bypass Study (Long Format) | data.frame | 80 | 5 |
gastricbypassW | LMMstar | Data From The Gastric Bypass Study (Wide Format) | data.frame | 20 | 9 |
ncgsL | LMMstar | Data From National Cooperative Gallstone Study (Long Format) | data.frame | 515 | 5 |
ncgsW | LMMstar | Data From National Cooperative Gallstone Study (Wide Format) | data.frame | 103 | 7 |
onycholysisL | LMMstar | Data From The toenail onycholysis Study (Long Format) | data.frame | 2058 | 7 |
onycholysisW | LMMstar | Data From The toenail onycholysis Study (Wide Format) | data.frame | 294 | 16 |
potassiumRepeatedL | LMMstar | Data From The Potassium Intake Study (Long Format with intermediate measurements) | data.frame | 300 | 6 |
potassiumSingleL | LMMstar | Data From The Potassium Intake Study (Long Format) | data.frame | 50 | 7 |
potassiumSingleW | LMMstar | Data From The Potassium Intake Study (Wide Format) | data.frame | 25 | 10 |
schoolL | LMMstar | Simulated Data with 3-level struture (Long Format) | data.frame | 366 | 4 |
sleepL | LMMstar | | data.frame | 86 | 7 |
swabsL | LMMstar | Data From The SWABS Study (Long Format) | data.frame | 90 | 4 |
swabsW | LMMstar | Data From The SWABS Study (Wide Format) | data.frame | 18 | 7 |
vasscoresL | LMMstar | Data From The VAS Study (Long Format) | data.frame | 90 | 5 |
vasscoresW | LMMstar | Data From The VAS Study (Wide Format) | data.frame | 30 | 5 |
vitaminL | LMMstar | Data From The Vitamin Study (Long Format) | data.frame | 60 | 5 |
vitaminW | LMMstar | Data From The Vitamin Study (Wide Format) | data.frame | 10 | 8 |
bcellAnno | DeMAND | Annotation for the expression data | matrix | 1577 | 2 |
bcellExp | DeMAND | B cell expression data | matrix | 1577 | 12 |
bcellNetwork | DeMAND | B cell network | matrix | 682 | 4 |
caseIndex | DeMAND | Case sample index | integer | | |
controlIndex | DeMAND | Control sample index | integer | | |
datA.amp | maCorrPlot | Example data for package maCorrSample | matrix | 1000 | 30 |
datA.mas5 | maCorrPlot | Example data for package maCorrSample | matrix | 1000 | 30 |
datA.rma | maCorrPlot | Example data for package maCorrSample | matrix | 1000 | 30 |
datB.amp | maCorrPlot | Example data for package maCorrSample | matrix | 1000 | 30 |
datB.mas5 | maCorrPlot | Example data for package maCorrSample | matrix | 1000 | 30 |
datB.rma | maCorrPlot | Example data for package maCorrSample | matrix | 1000 | 30 |
rain | isodistrreg | Frankfurt airport precipitation data | data.frame | 3617 | 54 |
coxrfx_object_sample | ebmstate | Example of an empirical Bayes model fit | coxrfx | | |
msfit_object_sample | ebmstate | Estimated cumulative hazard rates under an empirical Bayes Cox model (example) | msfit | | |
mstate_data | ebmstate | An example of long-format multistate data | data.frame | 1210 | 39 |
mstate_data_sample | ebmstate | A simulated event-history data set | data.frame | 649 | 18 |
BlueMountains | ppmlasso | Blue Mountains eucalypt and environmental data. | list | | |
Chicago | modeldata | Chicago ridership data | tbl_df | 5698 | 50 |
Sacramento | modeldata | Sacramento CA home prices | tbl_df | 932 | 9 |
Smithsonian | modeldata | Smithsonian museums | tbl_df | 20 | 3 |
ad_data | modeldata | Alzheimer's disease data | tbl_df | 333 | 131 |
ames | modeldata | Ames Housing Data | tbl_df | 2930 | 74 |
attrition | modeldata | Job attrition | data.frame | 1470 | 31 |
biomass | modeldata | Biomass data | data.frame | 536 | 8 |
bivariate_test | modeldata | Example bivariate classification data | tbl_df | 710 | 3 |
bivariate_train | modeldata | Example bivariate classification data | tbl_df | 1009 | 3 |
bivariate_val | modeldata | Example bivariate classification data | tbl_df | 300 | 3 |
car_prices | modeldata | Kelly Blue Book resale data for 2005 model year GM cars | tbl_df | 804 | 18 |
cat_adoption | modeldata | Cat Adoption | tbl_df | 2257 | 20 |
cells | modeldata | Cell body segmentation | tbl_df | 2019 | 58 |
check_times | modeldata | Execution time data | tbl_df | 13626 | 25 |
chem_proc_yield | modeldata | Chemical manufacturing process data set | tbl_df | 176 | 58 |
concrete | modeldata | Compressive strength of concrete mixtures | tbl_df | 1030 | 9 |
covers | modeldata | Raw cover type data | data.frame | 40 | 1 |
credit_data | modeldata | Credit data | data.frame | 4454 | 14 |
crickets | modeldata | Rates of Cricket Chirps | tbl_df | 31 | 3 |
deliveries | modeldata | Food Delivery Time Data | tbl_df | 10012 | 31 |
drinks | modeldata | Sample time series data | tbl_df | 309 | 2 |
grants_2008 | modeldata | Grant acceptance data | integer | | |
grants_other | modeldata | Grant acceptance data | data.frame | 8190 | 1503 |
grants_test | modeldata | Grant acceptance data | data.frame | 518 | 1503 |
hepatic_injury_qsar | modeldata | Predicting hepatic injury from chemical information | tbl_df | 281 | 377 |
hotel_rates | modeldata | Daily Hotel Rate Data | tbl_df | 15402 | 28 |
hpc_cv | modeldata | Class probability predictions | data.frame | 3467 | 7 |
hpc_data | modeldata | High-performance computing system data | tbl_df | 4331 | 8 |
ischemic_stroke | modeldata | Clinical data used to predict ischemic stroke | tbl_df | 126 | 29 |
leaf_id_flavia | modeldata | Leaf identification data (Flavia) | tbl_df | 1907 | 59 |
lending_club | modeldata | Loan data | tbl_df | 9857 | 23 |
meats | modeldata | Fat, water and protein content of meat samples | tbl_df | 215 | 103 |
mlc_churn | modeldata | Customer churn data | tbl_df | 5000 | 20 |
oils | modeldata | Fatty acid composition of commercial oils | tbl_df | 96 | 8 |
parabolic | modeldata | Parabolic class boundary data | tbl_df | 500 | 3 |
pathology | modeldata | Liver pathology data | data.frame | 344 | 2 |
pd_speech | modeldata | Parkinson's disease speech classification data set | tbl_df | 252 | 752 |
penguins | modeldata | Palmer Station penguin data | tbl_df | 344 | 7 |
permeability_qsar | modeldata | Predicting permeability from chemical information | tbl_df | 165 | 1108 |
scat | modeldata | Morphometric data on scat | tbl_df | 110 | 19 |
solubility_test | modeldata | Solubility predictions from MARS model | data.frame | 316 | 2 |
stackoverflow | modeldata | Annual Stack Overflow Developer Survey Data | tbl_df | 5594 | 21 |
stations | modeldata | Chicago ridership data | character | | |
steroidogenic_toxicity | modeldata | Predicting steroidogenic toxicity with assay data | tbl_df | 162 | 13 |
tate_text | modeldata | Tate Gallery modern artwork metadata | tbl_df | 4284 | 5 |
taxi | modeldata | Chicago taxi data set | tbl_df | 10000 | 7 |
testing_data | modeldata | Fine foods example data | tbl_df | 1000 | 3 |
training_data | modeldata | Fine foods example data | tbl_df | 4000 | 3 |
two_class_dat | modeldata | Two class data | tbl_df | 791 | 3 |
two_class_example | modeldata | Two class predictions | data.frame | 500 | 4 |
wa_churn | modeldata | Watson churn data | tbl_df | 7043 | 20 |
ecc_plaque | ADAPT | Plaque samples from early childhood dental caries studies | phyloseq | | |
ecc_saliva | ADAPT | Saliva samples from early childhood dental caries studies | phyloseq | | |
sample_data | rbbnp | Sample Data | data.frame | 1000 | 2 |
Cu | peramo | Biomarker Responses of the Ragworms to Copper and Warming | data.frame | 60 | 6 |
Cu | peramo | Biomarker Responses of the Ragworms to Copper and Warming | data.frame | 60 | 6 |
bmk_worm | peramo | Biomarker Responses of the Ragworms to Copper and Warming | data.frame | 210 | 22 |
ctm_Cu | peramo | Biomarker Responses of the Ragworms to Copper and Warming | data.frame | 60 | 7 |
ctm_worm | peramo | Biomarker Responses of the Ragworms to Copper and Warming | data.frame | 210 | 26 |
mussel_SoS | peramo | Biomarker Responses of the Blue Mussels to Organic UV Filters | data.frame | 15 | 7 |
mussel_digest | peramo | Biomarker Responses of the Blue Mussels to Organic UV Filters | data.frame | 120 | 24 |
mussel_gill | peramo | Biomarker Responses of the Blue Mussels to Organic UV Filters | data.frame | 120 | 24 |
worm | peramo | Biomarker Responses of the Ragworms to Copper and Warming | data.frame | 210 | 26 |
drops_GE | factReg | DROPS data sets | data.frame | 6150 | 24 |
drops_GnE | factReg | DROPS data sets | data.frame | 384 | 24 |
drops_K | factReg | DROPS data sets | matrix | 302 | 302 |
drops_nGnE | factReg | DROPS data sets | data.frame | 224 | 24 |
Lyme_GSE63085 | RegEnrich | Example RNAseq dataset [Human] | list | | |
TFs | RegEnrich | Human gene regulators | data.frame | 1712 | 2 |
kinase_domains | ActiveDriver | Example kinase domains for ActiveDriver | data.frame | 1 | 4 |
mutations | ActiveDriver | Example mutations for ActiveDriver | data.frame | 408 | 5 |
phosphosites | ActiveDriver | Example phosphosites for ActiveDriver | data.frame | 131 | 4 |
sequence_disorder | ActiveDriver | Example protein disorder for ActiveDriver | character | | |
sequences | ActiveDriver | Example protein sequences for ActiveDriver | character | | |
RGT_cycle_14 | IceSat2R | Reference Ground Tracks (RGTs) for IceSat-2 Cycle 14 | data.table | 131765 | 8 |
ne_10m_glaciated_areas | IceSat2R | Natural Earth 10m Glaciated Areas (1:10 million scale) | sf | 68 | 6 |
calendar | ecos | Calendar for the cycle argument | data.frame | 73049 | 6 |
CI | AMIM | Confidence Interval Data to compute AMIM | data.table | 3015 | 3 |
exampledata | AMIM | Example Data to compute AMIM | data.table | 144 | 3 |
skin | cTOST | Log transformed cutaneous delivery of econazole (ECZ) from bioequivalent products on porcine skin | data.frame | 17 | 2 |
df_curve_reach_freq | Robyn | Robyn Dataset: Reach & frequency simulated dataset | data.frame | 300 | 3 |
dt_prophet_holidays | Robyn | Robyn Dataset: Holidays by Country | tbl_df | 87651 | 4 |
dt_simulated_weekly | Robyn | Robyn Dataset: MMM Demo Data | tbl_df | 208 | 12 |
LSP | btergm | Longitudinal international defense alliance network, 1981-2000 | list | | |
advice | btergm | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | 1 |
alcohol | btergm | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | 3 |
allyNet | btergm | Longitudinal international defense alliance network, 1981-2000 | list | | |
ch_coaut | btergm | Swiss political science co-authorship network 2013 | matrix | 156 | 156 |
ch_dist100km | btergm | Swiss political science co-authorship network 2013 | matrix | 156 | 156 |
ch_en_article_sim | btergm | Swiss political science co-authorship network 2013 | matrix | 156 | |
ch_nodeattr | btergm | Swiss political science co-authorship network 2013 | data.frame | 156 | 35 |
ch_topicsim | btergm | Swiss political science co-authorship network 2013 | matrix | 156 | 156 |
committee | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 20 |
contigMat | btergm | Longitudinal international defense alliance network, 1981-2000 | matrix | 164 | 164 |
delinquency | btergm | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | 4 |
demographics | btergm | Longitudinal classroom friendship network and behavior (Andrea Knecht) | data.frame | 26 | 4 |
friendship | btergm | Longitudinal classroom friendship network and behavior (Andrea Knecht) | list | | |
infrep | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
intpos | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 6 |
lNet | btergm | Longitudinal international defense alliance network, 1981-2000 | list | | |
pol | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
primary | btergm | Longitudinal classroom friendship network and behavior (Andrea Knecht) | matrix | 26 | |
scifrom | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
scito | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | matrix | 30 | 30 |
types | btergm | German Toxic Chemicals Policy Network in the 1980s (Volker Schneider) | data.frame | 30 | 1 |
warNet | btergm | Longitudinal international defense alliance network, 1981-2000 | list | | |
ibd_phylo_otu | corncob | IBD data, OTU count data frame. | data.frame | 36349 | 91 |
ibd_phylo_sample | corncob | IBD data, sample data frame. | data.frame | 91 | 15 |
ibd_phylo_taxa | corncob | IBD data, taxonomy data frame. | matrix | 36349 | 7 |
soil_phylo_otu | corncob | Soil data, otu table as data frame. | data.frame | 7770 | 119 |
soil_phylo_sample | corncob | Soil data, sample data. | data.frame | 119 | 5 |
soil_phylo_taxa | corncob | Soil data, taxa table as data frame. | matrix | 7770 | 7 |
soil_phylum_contrasts_otu | corncob | Small soil phylum data for contrasts examples, otu table as data frame | data.frame | 39 | 56 |
soil_phylum_contrasts_sample | corncob | Small soil phylum data for contrasts examples, sample data as data frame | data.frame | 56 | 5 |
soil_phylum_small_otu | corncob | Small soil phylum data for examples, otu table as data frame | data.frame | 39 | 32 |
soil_phylum_small_otu1 | corncob | Small soil phylum data for examples, sample data as data frame combined with counts for OTU 1 and sequencing depth. | data.frame | 32 | 7 |
soil_phylum_small_sample | corncob | Small soil phylum data for examples, sample data as data frame | data.frame | 32 | 5 |
esp_codelist | mapSpain | Database with codes and names of spanish regions | data.frame | 59 | 44 |
esp_munic.sf | mapSpain | 'sf' object with all the municipalities of Spain (2019) | sf | 8131 | 8 |
esp_nuts.sf | mapSpain | 'sf' object with all the NUTS levels of Spain (2016) | sf | 86 | 10 |
esp_tiles_providers | mapSpain | Database of public WMS and WMTS of Spain | list | | |
leaflet.providersESP.df | mapSpain | (Superseded) Database of public WMS and WMTS of Spain | data.frame | 802 | 3 |
pobmun19 | mapSpain | Database with the population of Spain by municipality (2019) | data.frame | 8131 | 7 |
osm_amenities | nominatimlite | OpenStreetMap amenity database | tbl_df | 136 | 3 |
evas_list | restatis | List of EVAS Codes | tbl_df | 1132 | 3 |
SGPstateData | SGP | State assessment program data from large scale state assessments for use with SGP package | environment | | |
example_noise_params_pk | serocalculator | Small example of noise parameters for typhoid | noise_params | 4 | 7 |
example_noise_params_sees | serocalculator | Small example of noise parameters for typhoid | noise_params | 16 | 7 |
sees_pop_data_100 | serocalculator | Small example cross-sectional data set | pop_data | 1000 | 8 |
sees_pop_data_pk_100 | serocalculator | Small example cross-sectional data set | pop_data | 200 | 8 |
sees_pop_data_pk_100_old_names | serocalculator | Small example cross-sectional data set | pop_data | 200 | 8 |
sees_typhoid_ests_strat | serocalculator | Example '"seroincidence.by"' object | seroincidence.by | | |
typhoid_curves_nostrat_100 | serocalculator | Small example of antibody response curve parameters for typhoid | curve_params | 500 | 7 |
coffee | pgmm | Coffee | data.frame | 43 | 14 |
olive | pgmm | Italian Olive Oil | data.frame | 572 | 10 |
wine | pgmm | Italian Wine | data.frame | 178 | 28 |
safo | flowchart | Random generated dataset from the SAFO study | tbl_df | 925 | 21 |
CarTask | betareg | Partition-Primed Probability Judgement Task for Car Dealership | data.frame | 155 | 3 |
FoodExpenditure | betareg | Proportion of Household Income Spent on Food | data.frame | 38 | 3 |
GasolineYield | betareg | Estimation of Gasoline Yields from Crude Oil | data.frame | 32 | 6 |
ImpreciseTask | betareg | Imprecise Probabilities for Sunday Weather and Boeing Stock Task | data.frame | 242 | 3 |
LossAversion | betareg | (No) Myopic Loss Aversion in Adolescents | data.frame | 570 | 7 |
MockJurors | betareg | Confidence of Mock Jurors in Their Verdicts | data.frame | 104 | 3 |
ReadingSkills | betareg | Dyslexia and IQ Predicting Reading Accuracy | data.frame | 44 | 4 |
StressAnxiety | betareg | Dependency of Anxiety on Stress | data.frame | 166 | 2 |
WeatherTask | betareg | Weather Task with Priming and Precise and Imprecise Probabilities | data.frame | 345 | 3 |
RainIbk | crch | Precipitation Observations and Forecasts for Innsbruck | data.frame | 4971 | 12 |
hass_market | avocado | Hass Avocado Market Sales | tbl_df | 38522 | 13 |
hass_region | avocado | Hass Avocado Regional Sales | tbl_df | 6480 | 12 |
hass_usa | avocado | Hass Avocado Weekly US Sales | tbl_df | 810 | 11 |
producers | accumulate | | data.frame | 1734 | 7 |
behav_data | webgazeR | | data.frame | 66350 | 78 |
eyedata | webgazeR | | data.frame | 104823 | 24 |
dichotomous_response | CTTvis | dichotomous item responses | data.frame | 100 | 10 |
reliability_df | CTTvis | reliability dataframe | data.frame | 100 | 10 |
hg18 | gap | Chromosomal lengths for build 36 | numeric | | |
hg19 | gap | Chromosomal lengths for build 37 | numeric | | |
hg38 | gap | Chromosomal lengths for build 38 | numeric | | |
dat.age | metamedian | Example data set: Comparing the age between COVID-19 survivors and nonsurvivors (cleaned version) | data.frame | 51 | 13 |
dat.age_raw | metamedian | Example data set: Comparing the age between COVID-19 survivors and nonsurvivors (raw version) | data.frame | 52 | 13 |
dat.asat | metamedian | Example data set: Comparing aspartate transaminase levels between COVID-19 survivors and nonsurvivors (cleaned version) | data.frame | 26 | 13 |
dat.asat_raw | metamedian | Example data set: Comparing aspartate transaminase levels between COVID-19 survivors and nonsurvivors (raw version) | data.frame | 27 | 13 |
dat.ck | metamedian | Example data set: Comparing creatine kinase levels between COVID-19 survivors and nonsurvivors (cleaned version) | data.frame | 17 | 13 |
dat.ck_raw | metamedian | Example data set: Comparing creatine kinase transaminase levels between COVID-19 survivors and nonsurvivors (raw version) | data.frame | 18 | 13 |
dat.lung | metamedian | Example data set: Overall survival times in patients with lung cancer | data.frame | 30 | 11 |
dat.phq9 | metamedian | Example data set: Patient Health Questionnaire-9 (PHQ-9) scores (processed version) | data.frame | 58 | 9 |
dat.phq9_raw | metamedian | Example data set: Patient Health Questionnaire-9 (PHQ-9) scores (raw version) | data.frame | 58 | 9 |
Heart | ncvreg | Risk factors associated with heart disease | list | | |
Lung | ncvreg | VA lung cancer data set | list | | |
Prostate | ncvreg | Factors associated with prostate specific antigen | list | | |
prostate | ncvreg | Factors associated with prostate specific antigen | data.frame | 97 | 9 |
trait | OUwie | An example dataset | data.frame | 64 | 3 |
trait | OUwie | An example dataset | data.frame | 64 | 3 |
tree | OUwie | An example dataset | phylo | | |
tree | OUwie | An example dataset | phylo | | |
BeckLee_ages | dispRity | Beck and Lee 2014 datasets | data.frame | 14 | 2 |
BeckLee_disparity | dispRity | BeckLee_disparity | dispRity | | |
BeckLee_mat50 | dispRity | Beck and Lee 2014 datasets | matrix | 50 | |
BeckLee_mat99 | dispRity | Beck and Lee 2014 datasets | matrix | 99 | |
BeckLee_tree | dispRity | Beck and Lee 2014 datasets | phylo | | |
charadriiformes | dispRity | Charadriiformes | list | | |
demo_data | dispRity | Demo datasets | list | | |
disparity | dispRity | disparity | dispRity | | |
indpro | rumidas | Monthly U.S. Industrial Production | xts | 363 | 1 |
rv5 | rumidas | S&P 500 realized variance at 5-minutes | xts | 5079 | 1 |
sp500 | rumidas | S&P 500 daily log-returns | xts | 5079 | 1 |
vix | rumidas | VIX daily data | xts | 5093 | |
dep_wor_data | topics | Example data about mental health descirptions . | tbl_df | 500 | 12 |
adult | liver | adult data set | data.frame | 48598 | 15 |
advertising | liver | advertising data set | data.frame | 1143 | 11 |
bank | liver | Bank marketing data set | data.frame | 4521 | 17 |
cereal | liver | Cereal data set | data.frame | 77 | 16 |
churn | liver | Churn data set | data.frame | 5000 | 20 |
churnCredit | liver | Churn dataset for Credit Card Customers | data.frame | 10127 | 21 |
churnTel | liver | churnTel dataset | data.frame | 7032 | 21 |
corona | liver | Corona data set | character | | |
fertilizer | liver | Fertilizer data set | data.frame | 96 | 4 |
house | liver | house data set | data.frame | 414 | 6 |
housePrice | liver | housePrice dataset | data.frame | 1460 | 81 |
insurance | liver | insurance data set | data.frame | 1338 | 7 |
marketing | liver | marketing data set | data.frame | 40 | 8 |
redWines | liver | Red wines data set | data.frame | 1599 | 12 |
risk | liver | Risk data set | data.frame | 246 | 6 |
whiteWines | liver | White wines data set | data.frame | 4898 | 12 |
ginga | gibasa | Whole text of 'Ginga Tetsudo no Yoru' written by Miyazawa Kenji from Aozora Bunko | character | | |
hiroba | audubon | Whole tokens of 'Porano no Hiroba' written by Miyazawa Kenji from Aozora Bunko | data.frame | 26849 | 5 |
polano | audubon | Whole text of 'Porano no Hiroba' written by Miyazawa Kenji from Aozora Bunko | character | | |
engagement | tna | Example Data on Student Engagement | stslist | 200 | 20 |
engagement_mmm | tna | Example Mixed Markov Model Fitted to the 'engagement' Data | mhmm | | |
group_regulation | tna | Example Data on Group Regulation | data.frame | 2000 | 26 |
allMethods | dtComb | Includes machine learning models used for the mlComb function | data.frame | 113 | 2 |
exampleData2 | dtComb | A data set containing the carriers of a rare genetic disorder for 120 samples. | data.frame | 120 | 5 |
exampleData3 | dtComb | A simulation data containing 250 diseased and 250 healthy individuals. | data.frame | 500 | 3 |
laparoscopy | dtComb | Examples data for the dtComb package | data.frame | 225 | 3 |
sampleObj | GeneNMF | Sample dataset to test GeneNMF installation | Seurat | | |
basicRelationships | ribd | Basic relationships | data.frame | 12 | 7 |
jicaque | ribd | Jicaque pedigree | data.frame | 22 | 4 |
DataApes | RRphylo | Example dataset | list | | |
DataCetaceans | RRphylo | Example dataset | list | | |
DataFelids | RRphylo | Example dataset | list | | |
DataOrnithodirans | RRphylo | Example dataset | list | | |
DataSimians | RRphylo | Example dataset | list | | |
DataUng | RRphylo | Example dataset | list | | |
examples_results | crm12Comb | Output dataset for examples given list of inputs | tbl_df | 1296 | 15 |
Anime | cSEM | Data: Anime | data.frame | 183 | 13 |
Benitezetal2020 | cSEM | Data: Benitezetal2020 | data.frame | 300 | 22 |
BergamiBagozzi2000 | cSEM | Data: BergamiBagozzi2000 | data.frame | 305 | 22 |
ITFlex | cSEM | Data: ITFlex | data.frame | 100 | 16 |
LancelotMiltgenetal2016 | cSEM | Data: LancelotMiltgenetal2016 | data.frame | 1090 | 11 |
PoliticalDemocracy | cSEM | Data: political democracy | data.frame | 75 | 11 |
Russett | cSEM | Data: Russett | data.frame | 47 | 10 |
SQ | cSEM | Data: SQ | data.frame | 411 | 23 |
Sigma_Summers_composites | cSEM | Data: Summers | matrix | 18 | 18 |
Switching | cSEM | Data: Switching | data.frame | 767 | 26 |
Yooetal2000 | cSEM | Data: Yooetal2000 | data.frame | 569 | 34 |
dgp_2ndorder_cf_of_c | cSEM | Data: Second order common factor of composites | matrix | 500 | 20 |
satisfaction | cSEM | Data: satisfaction | data.frame | 250 | 27 |
satisfaction_gender | cSEM | Data: satisfaction including gender | data.frame | 250 | 28 |
threecommonfactors | cSEM | Data: threecommonfactors | matrix | 500 | 9 |
dataConstr | DVHmetrics | Constraint data frame | data.frame | 6 | 3 |
dataMZ | DVHmetrics | DVH data from 3 patients | DVHLstLst | | |
DP_projections_HILS_SWLS_100 | text | Data for plotting a Dot Product Projection Plot. | list | | |
Language_based_assessment_data_3_100 | text | Example text and numeric data. | tbl_df | 100 | 3 |
Language_based_assessment_data_8 | text | Text and numeric data for 10 participants. | tbl_df | 40 | 8 |
PC_projections_satisfactionwords_40 | text | Example data for plotting a Principle Component Projection Plot. | tbl_df | 292 | 4 |
centrality_data_harmony | text | Example data for plotting a Semantic Centrality Plot. | tbl_df | 2126 | 4 |
raw_embeddings_1 | text | Word embeddings from textEmbedRawLayers function | list | | |
word_embeddings_4 | text | Word embeddings for 4 text variables for 40 participants | list | | |
schaper2019 | anticlust | Ratings for 96 words | data.frame | 96 | 7 |
librarian | cohortBuilder | Sample of library database | list | | |
curated_genes | SpaceMarkers | Curated Genes for example purposes | character | | |
optParams | SpaceMarkers | Optimal paramters of 5 patterns from CoGAPS. | matrix | 2 | 2 |
GIST.data_frame | CoGAPS | GIST gene expression data from Ochs et al. (2009) | data.frame | 1363 | 9 |
GIST.matrix | CoGAPS | GIST gene expression data from Ochs et al. (2009) | matrix | 1363 | 9 |
GIST.result | CoGAPS | CoGAPS result from running on GIST dataset | CogapsResult | | |
GIST.uncertainty | CoGAPS | GIST gene expression uncertainty matrix from Ochs et al. (2009) | matrix | 1363 | 9 |
modsimdata | CoGAPS | Toy example to run CoGAPS on. | data.frame | 25 | 20 |
modsimresult | CoGAPS | Result of applying CoGAPS on the Toy example. | CogapsResult | | |
CellPhoneDB | dominoSignal | CellPhoneDB subset | list | | |
PBMC | dominoSignal | PBMC RNAseq data subset | list | | |
SCENIC | dominoSignal | SCENIC AUC subset | list | | |
se_simple | plyxp | Plyxp Simple Example Summarized Experiment | PlySummarizedExperiment | | |
beta | SISIR | Dataset "Truffles" | numeric | | |
rainfall | SISIR | Dataset "Truffles" | data.frame | 25 | 15 |
truffles | SISIR | Dataset "Truffles" | numeric | | |
hct220ALSFRS | HCT | Objects Created by hct for the ALS of Clinical Trial Results | list | | |
hct220Delta | HCT | Objects Created by hct for the ALS of Clinical Trial Results | list | | |
Example_data | rjaf | Simulated randomized experiment data | data.frame | 100 | 12 |
BMUHepta | Umatrix | Best matching units (BMU) of Hepta from FCPS (Fundamental Clustering Problem Suite) | matrix | 212 | 3 |
Hepta | Umatrix | Hepta from FCPS (Fundamental Clustering Problem Suite) | list | | |
swim | catalytic | Simulated SWIM Dataset with Binary Response | list | | |
MarelCarnot | uHMM | MarelCarnot dataset | data.frame | 131487 | 16 |
ad12.eff.acc | nmaplateplot | Network Meta-Analysis Results (Efficacy and Acceptability) on 12 Antidepressants | list | | |
ad12.pma.nma | nmaplateplot | Network Meta-Analysis Results (PMA and NMA) on 12 Antidepressants | list | | |
ad12.rr.rd | nmaplateplot | Network Meta-Analysis Results (RR and RD) on 12 Antidepressants | list | | |
ad22 | nmaplateplot | Network Meta-Analysis Results on 21 Antidepressants and Placebo | list | | |
dmrp_covars | HMP | Paper Covariate Set | data.frame | 128 | 11 |
dmrp_data | HMP | Paper Taxa Data Set | data.frame | 128 | 29 |
hmp.pkg.env | HMP | Internal Functions | environment | | |
saliva | HMP | Saliva Data Set | matrix | 24 | 21 |
throat | HMP | Throat Data Set | matrix | 23 | 21 |
tongue | HMP | Tongue Data Set | matrix | 24 | 21 |
tonsils | HMP | Palatine Tonsil Data Set | matrix | 23 | 21 |
crashDat | elrm | Crash Dataset: Calibration of Crash Dummies in Automobile Safety Tests | data.frame | 58 | 5 |
diabDat | elrm | Simulated Diabetes Dataset | data.frame | 229 | 7 |
drugDat | elrm | Drug Dataset | data.frame | 4 | 4 |
titanDat | elrm | Titanic Dataset | data.frame | 14 | 5 |
utiDat | elrm | Urinary Tract Infection and Contraceptive Use | data.frame | 55 | 11 |
Berkeley | Renvlp | Berkeley Guidance Study Data | data.frame | 93 | 32 |
NJdata | Renvlp | New Jersey Open Covid-19 Dataset | data.frame | 1281 | 9 |
amitriptyline | Renvlp | Amitriptyline Data | data.frame | 17 | 7 |
concrete | Renvlp | Concrete Slump Test Dataset | data.frame | 103 | 10 |
fiberpaper | Renvlp | Pulp and Paper Data | data.frame | 62 | 8 |
horseshoecrab | Renvlp | Horseshoe Crab Data | data.frame | 173 | 5 |
sales | Renvlp | Sales staff Data | data.frame | 50 | 7 |
vehicles | Renvlp | Automobile Dataset | data.frame | 30 | 15 |
waterstrider | Renvlp | Water strider data | data.frame | 90 | 9 |
wheatprotein | Renvlp | Wheat Protein Data | data.frame | 50 | 8 |
conditions | pathling | Synthetic conditions data | data.frame | 19 | 6 |
DOW | rpnf | This is some free available quote data for the DOW Chemical Company. | data.frame | 248 | 13 |
extax | phyreg | Example data | data.frame | 100 | 3 |
myd0 | phyreg | Example data | data.frame | 100 | 7 |
myd1 | phyreg | Example data | data.frame | 100 | 7 |
myd2 | phyreg | Example data | data.frame | 100 | 7 |
myd3 | phyreg | Example data | data.frame | 100 | 7 |
newickstr | phyreg | Example data | character | | |
DXCCSR | CCSRfind | Data to showcase CCSR-ICD-10 crosswalk | data.frame | 86855 | 7 |
Legend | CCSRfind | This dataset contains CCSR codes that are associated with over all ICD-10 codes | data.frame | 543 | 3 |
LegendExtend | CCSRfind | This dataset contains CCSR codes for which there are over 1,000 individual ICD-10 codes | data.frame | 9 | 3 |
Current | SailoR | Ocean Current Data | list | | |
Dragonera | SailoR | Wind data | list | | |
Ensembles | SailoR | Multimodel Ensembles | list | | |
EstacaDeBares | SailoR | Wave Energy Flux data | list | | |
Reanalysis | SailoR | Surface Wind from different Reanalyses | list | | |
Synthetic | SailoR | Ocean Current Data | list | | |
WRF | SailoR | Vertically Integrated Water Vapor Transport Data | list | | |
datos | RGE | Potato regional trial in Colombia | data.frame | 440 | 17 |
m1 | RGE | Samples of the posterior distribution by GIBBS sampler | matrix | 20 | |
ASBESTOS_QUEBEC | ggversa | Asbestos_Quebec | data.frame | 466 | 5 |
Anolis | ggversa | Anolis | tbl_df | 503 | 15 |
Camas_Hospital | ggversa | Camas_Hospital | data.frame | 134 | 4 |
Crecimiento_domestico_bruto | ggversa | Crecimiento_domestico_bruto | data.frame | 28 | 14 |
CypripediumA | ggversa | CypripediumA | data.frame | 82 | 9 |
Edu_Salud_Gastos_GDP | ggversa | Edu_Salud_Gastos_GDP | data.frame | 160 | 4 |
Educacion_Ninas | ggversa | Educacion_Ninas | data.frame | 24 | 13 |
ElphickBirdData | ggversa | ElphickBirdData | data.frame | 2035 | 66 |
Godwits | ggversa | Godwits | data.frame | 330 | 9 |
Internet2 | ggversa | Internet2 | data.frame | 20 | 6 |
LIKERT_DATA | ggversa | LIKERT_DATA | tbl_df | 30 | 3 |
Lelto | ggversa | Lelto | data.frame | 33 | 16 |
MORELIA.MICH.Tmin | ggversa | MORELIA.MICH.Tmin | data.frame | 23552 | 4 |
PBI | ggversa | Producto Bruto Interno | data.frame | 28 | 13 |
PIB_vs_Alfabetismo | ggversa | PIB_vs_Alfabetismo | data.frame | 20 | 3 |
PIB_vs_Salud | ggversa | PIB_vs_Salud | data.frame | 20 | 3 |
PartosInfantes | ggversa | PartosInfantes | data.frame | 179 | 5 |
Pop_PR | ggversa | Pop_PR | data.frame | 36 | 3 |
Razon_mortandad | ggversa | Razon_mortandad | data.frame | 194 | 11 |
SparrowsElphick | ggversa | SparrowsElphick | data.frame | 1295 | 16 |
Tiroide | ggversa | Tiroide | data.frame | 76 | 3 |
VegSamplesV1 | ggversa | VegSamplesV1 | data.frame | 60 | 19 |
caladeniavalida | ggversa | Data de Caladenia valida | data.frame | 164 | 15 |
dipodium | ggversa | Dipodium de la Reserva de Wombat, Victoria, Australia | tbl_df | 1363 | 21 |
nlsy | glvmfit | Subset of 221 children from the 1979 National Longitudinal Survey of Youth | data.frame | 221 | 12 |
strokeCTdensity | WRI | Stroke data: clinical, radiological scalar variables and density curves of the hematoma of 393 stroke patients | list | | |
SimX | MatSkew | Simulated Data | list | | |
DK | DOvalidation | Denmark Female Mortality Data | data.frame | 71 | 2 |
Iceland | DOvalidation | Iceland Female Mortality Data | data.frame | 71 | 2 |
UK | DOvalidation | UK Female Mortality Data | data.frame | 71 | 2 |
US | DOvalidation | US Female Mortality Data | data.frame | 71 | 2 |
data_model | spdownscale | Data-sample | data.frame | 37992 | 5 |
data_model_future | spdownscale | Data-sample | data.frame | 58440 | 5 |
data_observation | spdownscale | Data-sample | data.frame | 37264 | 5 |
test | Pstat | Example of a data frame | data.frame | 200 | 12 |
sas7bdat.sources | sas7bdat | Internet SAS Database Resources | data.frame | 232 | 16 |
Mortality | RkMetrics | Switzerland Mortality Data | list | | |
dataXXmirmeth | multiridge | Contains R-object 'dataXXmirmeth' | list | | |
nanoarchaeum | spgs | DNA sequence for the Nanoarchaeum equitans Kin4-M Chromosome | SeqFastadna | | |
pieris | spgs | DNA sequence for the Pieris Rapae Granulovirus Genome | SeqFastadna | | |
fdata.lats | fossil | A Sample Species Abundance Dataset | SpatialPoints | | |
fdata.list | fossil | A Sample Species Abundance Dataset | data.frame | 52 | 5 |
fdata.mat | fossil | A Sample Species Abundance Dataset | matrix | 12 | 12 |
int_pairs | AlteredPQR | Protein pairs | data.frame | 86505 | 2 |
quant_data_all | AlteredPQR | Proteomic measurements data matrix | data.frame | 36 | 41 |
examplePrereg_1 | preregr | An example (pre)registration specification using the Inclusive General-Purpose Registration Form | preregr | | |
form_OSFprereg_v1 | preregr | OSF Prereg form | preregr | | |
form_OSFqual1_v1 | preregr | Qualitative Preregistration Template | preregr | | |
form_almostEmptyForm | preregr | A mostly empty example form specification | preregr | | |
form_genSysRev_v1 | preregr | Generalized Systematic Review Registration Form | preregr | | |
form_generalPurpose_v1 | preregr | Inclusive General-Purpose Registration Form | preregr | | |
form_generalPurpose_v1_1 | preregr | Inclusive General-Purpose Registration Form | preregr | | |
form_inclDivAddon_v0_1 | preregr | Inclusivity & Diversity Add-on for preregistration forms | preregr | | |
form_inclSysRev_v0_92 | preregr | Inclusive Systematic Review Registration Form | preregr | | |
form_prereg2D_v1 | preregr | Preregistration Template for Secondary Data Analysis | preregr | | |
form_preregQE_v0_93 | preregr | Preregistration Template for Qualitative and Quantitative Ethnographic Studies | preregr | | |
form_preregQE_v0_94 | preregr | Preregistration Template for Qualitative and Quantitative Ethnographic Studies | preregr | | |
form_preregQE_v0_95 | preregr | Preregistration Template for Qualitative and Quantitative Ethnographic Studies | preregr | | |
form_prpQuant_v1 | preregr | Psychological Research Preregistration-Quantitative (aka PRP-QUANT) Template | preregr | | |
GSPS | BayesRGMM | The German socioeconomic panel study data | data.frame | 27326 | 25 |
Example_Spatial.plot_Alignment | sidier | example alignment #1 (fasta format) | data.frame | 226 | 1 |
alignExample | sidier | example alignment #1 ('DNAbin' class) | DNAbin | 16 | |
ex_BLAST | sidier | example BLAST output | data.frame | 22 | 5 |
ex_Coords | sidier | example coordinates | data.frame | 8 | 3 |
Enzyme2.out | BNPdensity | Fit of MixNRMI2 function to the enzyme dataset | NRMI2 | | |
Galaxy1.out | BNPdensity | Fit of MixNRMI1 function to the galaxy dataset | NRMI1 | | |
Galaxy2.out | BNPdensity | Fit of MixNRMI2 function to the galaxy dataset | NRMI2 | | |
acidity | BNPdensity | Acidity Index Dataset | numeric | | |
enzyme | BNPdensity | Enzyme Dataset | numeric | | |
galaxy | BNPdensity | Galaxy Data Set | numeric | | |
salinity | BNPdensity | Salinity tolerance | data.frame | 108 | 2 |
CO2Inhalation | kcpRS | CO2 Inhalation Data | data.frame | 239 | 10 |
MentalLoad | kcpRS | Mental Load Data | data.frame | 1393 | 4 |
smoking | CBnetworkMA | Smoking Cessation Data | data.frame | 50 | 4 |
data_US | matrisk | Historical data for the US (GDP and Financial Conditions) from 1973:Q1 to 2022:Q3 | data.frame | 200 | 3 |
data_euro | matrisk | Historical data for the eurozone (GDP and Financial Conditions) from 2008:Q4 to 2022:Q3 | data.frame | 56 | 4 |
Sample | gcerisk | Sample dataset | data.frame | 479 | 16 |
geoMite | pMEM | Borcard's Oribatid Mite Data Set - Geographic Information System Version - | list | | |
salmon | pMEM | The St. Marguerite River Altantic Salmon Parr Transect | data.frame | 76 | 5 |
Enron | Linkage | The Enron email network | data.frame | 9260 | 3 |
antineoplastic | evident | Biomonitoring of Workers Exposed to Antineoplastic Drugs | data.frame | 59 | 9 |
benzene | evident | Chromosome Damage from Exposure to Benzene | data.frame | 78 | 5 |
ck | evident | Minimum Wages and Employment | data.frame | 198 | 7 |
ckA | evident | Matching the Minimum Wage Data | data.frame | 351 | 9 |
hsmoke | evident | Smoking and Homocysteine | data.frame | 2475 | 15 |
lead | evident | Lead in Children | data.frame | 33 | 6 |
leadworker | evident | DNA Damage in Lead Workers | data.frame | 22 | 7 |
periodontal | evident | Smoking and Periodontal Disease | data.frame | 882 | 12 |
tannery | evident | DNA Damage Among Tannery Workers | data.frame | 30 | 4 |
ccmm_test_data | ccmm | Test Data | data.frame | 200 | 24 |
asbestos | CMHNPA | Asbestos data | data.frame | 1117 | 2 |
cereal | CMHNPA | Cereal data | data.frame | 30 | 3 |
corn | CMHNPA | Corn data | data.frame | 34 | 2 |
crossover | CMHNPA | Cross-over data | data.frame | 33 | 3 |
dynamite | CMHNPA | Dynamite data | data.frame | 25 | 4 |
food | CMHNPA | Food data | data.frame | 30 | 3 |
hr | CMHNPA | HR data | data.frame | 50 | 3 |
icecream | CMHNPA | Ice Cream data | data.frame | 21 | 3 |
intelligence | CMHNPA | Intelligence data | data.frame | 15 | 2 |
jam | CMHNPA | Jam data | data.frame | 24 | 4 |
job_satisfaction | CMHNPA | Job Satisfaction data | data.frame | 104 | 3 |
lemonade | CMHNPA | Lemonade data | data.frame | 20 | 3 |
lemonade_sugar | CMHNPA | Lemonade Sugar data | data.frame | 50 | 3 |
lizard | CMHNPA | Lizard data | data.frame | 24 | 3 |
marriage | CMHNPA | Marriage data | data.frame | 133 | 3 |
milk | CMHNPA | Milk data | data.frame | 16 | 4 |
peanuts | CMHNPA | Peanuts data | data.frame | 16 | 4 |
saltiness | CMHNPA | Saltiness data | data.frame | 321 | 3 |
strawberry | CMHNPA | Strawberry data | data.frame | 28 | 4 |
traffic | CMHNPA | Traffic data | data.frame | 25 | 4 |
whiskey | CMHNPA | Whiskey data | data.frame | 8 | 2 |
sst_c | vapour | SST contours | sf | 7 | 3 |
tas_wkt | vapour | Example WKT coordinate reference system | character | | |
example1 | deFit | Univariate second-order differential equation | spec_tbl_df | 30 | 3 |
example2 | deFit | Bivariate first-order differential equation | data.frame | 15 | 3 |
example3 | deFit | University of Michigan consumer sentiment index | data.frame | 540 | 6 |
example4 | deFit | Bivariate first-order differential equation | data.frame | 15 | 3 |
absorb | EngrExpt | Oil absorption of silica | data.frame | 102 | 1 |
adhesion | EngrExpt | Adhesive qualities of a lens coating | data.frame | 30 | 2 |
adhesion2 | EngrExpt | Adhesive qualities of a lens coating | data.frame | 30 | 3 |
alum | EngrExpt | Aluminum impurity amounts | data.frame | 25 | 1 |
applicat | EngrExpt | Application of powder coating | data.frame | 18 | 4 |
assay | EngrExpt | Yield from two processes | data.frame | 9 | 2 |
bacteria | EngrExpt | Bacteria count in water samples | data.frame | 50 | 1 |
bath | EngrExpt | Electrical resistance after water bath | data.frame | 4 | 3 |
battery | EngrExpt | Battery lifetime in laptop computers | data.frame | 30 | 2 |
bright | EngrExpt | Brightness of de-inked newspaper | data.frame | 16 | 6 |
calcium | EngrExpt | Calcium levels before and after taking vitamin | data.frame | 11 | 2 |
caliper | EngrExpt | Diameters of rods measured by two calipers | data.frame | 14 | 3 |
ccthickn | EngrExpt | Clear coat thickness | data.frame | 40 | 1 |
cement | EngrExpt | Drying times of concrete | data.frame | 19 | 2 |
cheese | EngrExpt | Percentage fat in cheese | data.frame | 90 | 3 |
chemreac | EngrExpt | Yield of a chemical reaction | data.frame | 16 | 4 |
computer | EngrExpt | Repair time of computers | data.frame | 36 | 3 |
cure | EngrExpt | Yield from a chemical curing process | data.frame | 18 | 3 |
curl | EngrExpt | Curl of transparencies | data.frame | 12 | 3 |
defoam | EngrExpt | Height of solution with defoamer | data.frame | 27 | 4 |
deink | EngrExpt | De-inking of newsprint | data.frame | 27 | 3 |
deink2 | EngrExpt | De-inking of newsprint | data.frame | 15 | 3 |
dhaze | EngrExpt | Haze of lenses after abrasion | data.frame | 28 | 2 |
diagnostic | EngrExpt | Consistency of diagnostic kits | data.frame | 16 | 2 |
diameter | EngrExpt | Diameter of product | data.frame | 20 | 2 |
drought | EngrExpt | Water usage in 2001 and 2002 | data.frame | 5 | 3 |
drums | EngrExpt | Weights of drums before and after filling | data.frame | 30 | 3 |
dry | EngrExpt | Moisture content after drying | data.frame | 16 | 3 |
epoxy | EngrExpt | Effect of epoxy level on appearance | data.frame | 10 | 2 |
exposure | EngrExpt | Appearance of panels after exposure to weather | data.frame | 36 | 2 |
fbuild | EngrExpt | Appearance for film builds | data.frame | 9 | 2 |
fill | EngrExpt | Fill amount in tamped cylinders | data.frame | 18 | 3 |
fillweight | EngrExpt | Fill weight by batch | data.frame | 20 | 3 |
fish | EngrExpt | Toxin concentrations in fish by site | data.frame | 16 | 2 |
fish2 | EngrExpt | Toxin concentrations in fish by site | data.frame | 17 | 2 |
fluoride | EngrExpt | Fluoride levels from water sources | data.frame | 58 | 2 |
gloss | EngrExpt | Gloss of paint on cars | data.frame | 40 | 2 |
labcomp | EngrExpt | Inter-lab study | data.frame | 28 | 3 |
lw | EngrExpt | Automotive paint formulation | data.frame | 24 | 3 |
lwsw | EngrExpt | Appearance measures of automotive paints | data.frame | 13 | 2 |
moisture | EngrExpt | Moisture content of a silica product | data.frame | 8 | 5 |
mw | EngrExpt | Appearance measure of paint | data.frame | 32 | 6 |
odor | EngrExpt | Odor, yellowing and hardness of optical coating | data.frame | 35 | 3 |
oven | EngrExpt | Drying of silica | data.frame | 30 | 3 |
ph | EngrExpt | pH measurements in a chemical process | data.frame | 35 | 2 |
phmeas | EngrExpt | Comparison of instruments to measure pH | data.frame | 11 | 2 |
pigment | EngrExpt | Yellowing of paint for different pigments | data.frame | 9 | 3 |
protein | EngrExpt | Assay of protein in blood | data.frame | 54 | 5 |
purity | EngrExpt | Purity of product from a filtration process | data.frame | 12 | 4 |
railcar | EngrExpt | Rail car hold times | data.frame | 53 | 1 |
railcar2 | EngrExpt | Rail car hold times | data.frame | 43 | 1 |
railcar3 | EngrExpt | Moisture level versus type of rail car | data.frame | 17 | 2 |
ratings | EngrExpt | Ratings of raw materials | data.frame | 26 | 1 |
ratings2 | EngrExpt | Product ratings and moisture content | data.frame | 26 | 2 |
reflect | EngrExpt | Anti-reflective coating measurements | data.frame | 8 | 5 |
safety | EngrExpt | Safety violations over time | data.frame | 30 | 4 |
sales | EngrExpt | Sales versus capital expenditure | data.frame | 48 | 3 |
sarea | EngrExpt | Surface area of silica | data.frame | 12 | 3 |
separate | EngrExpt | Electrical resistance of battery separators | data.frame | 24 | 4 |
soap | EngrExpt | Soap sales by packaging type | data.frame | 5 | 3 |
stab | EngrExpt | Stability of a chemical product | data.frame | 12 | 4 |
stretch | EngrExpt | Stretch of hot pizza cheese | data.frame | 30 | 3 |
surfarea | EngrExpt | Surface area of silica | data.frame | 32 | 1 |
tablets | EngrExpt | Lifetime of chlorine tablets | data.frame | 30 | 2 |
temprate | EngrExpt | Effect of water bath on moisture content | data.frame | 12 | 3 |
tennis | EngrExpt | Durability of tennis ball covers | data.frame | 20 | 2 |
tensile | EngrExpt | Tensile breaking strength of steel samples | data.frame | 18 | 1 |
thinfilm | EngrExpt | Strength of thin film coatings | data.frame | 30 | 3 |
timetemp | EngrExpt | Time to nominal temperature | data.frame | 24 | 3 |
tpaste | EngrExpt | Turbidity of a toothpaste formulation | data.frame | 8 | 4 |
urine | EngrExpt | Mercury level in employee urine samples | data.frame | 12 | 5 |
uvcoatin | EngrExpt | Comparison of eyeglass ultra-violet coatings | data.frame | 10 | 3 |
uvoven | EngrExpt | UV absorbance for lens cured in different ovens | data.frame | 60 | 2 |
viscosity | EngrExpt | Time to gelling of paint samples | data.frame | 17 | 1 |
vitamin | EngrExpt | Calcium levels before and after vitamin supplement | data.frame | 49 | 3 |
wash | EngrExpt | Appearance of washed and unwashed panels | data.frame | 36 | 3 |
water | EngrExpt | Bacteria concentrations in water samples | data.frame | 50 | 1 |
webtraff | EngrExpt | Web site traffic during a marketing campaign | data.frame | 10 | 2 |
webvisit | EngrExpt | Web site visits over a 3-week period | data.frame | 21 | 1 |
weight | EngrExpt | Weight plastic bags held before breaking | data.frame | 43 | 1 |
whitearea | EngrExpt | Comparison of mixing processes | data.frame | 48 | 2 |
yellow | EngrExpt | Initial and 1 month color measure of coated lens | data.frame | 23 | 2 |
yield | EngrExpt | Yield of a chemical process | data.frame | 20 | 3 |
welfare | hdpGLM | Fake data set with 2000 observations | data.frame | 2000 | 4 |
welfare2 | hdpGLM | Fake data set with 2000 observations | tbl_df | 3200 | 6 |
kew | spuRs | 303 years of monthly rainfall data from Kew Gardens, London, U.K. | data.frame | 303 | 13 |
treeg | spuRs | Grand fir tree growth data from northern and central Idaho, USA. | data.frame | 542 | 6 |
trees | spuRs | von Guttenberg Norway spruce tree measurement data | data.frame | 1200 | 3 |
ufc | spuRs | Upper Flat Creek forest cruise tree data | data.frame | 336 | 5 |
ufc.plots | spuRs | Upper Flat Creek forest cruise plot data | data.frame | 144 | 6 |
dataset | CDVineCopulaConditional | Random dataset from a given vine copula model | list | | |
quantiles | npcp | Estimated Quantiles for the Open-end Nonparametric Sequential Change-Point Detection Tests | list | | |
DemoDataC2D2a | SoftClustering | A small two-dimensional dataset with two clusters for demonstration purposes. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). | data.frame | 200 | 2 |
initMeansC2D2a | SoftClustering | Two-dimensional dataset with two initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). | data.frame | 2 | 2 |
initMeansC3D2a | SoftClustering | Two-dimensional dataset with three initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). | data.frame | 3 | 2 |
initMeansC4D2a | SoftClustering | Two-dimensional dataset with four initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). | data.frame | 4 | 2 |
initMeansC5D2a | SoftClustering | Two-dimensional dataset with five initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). | data.frame | 5 | 2 |
wOBA | ebnm | 2022 MLB wOBA Data | data.frame | 688 | 6 |
bank | L2E | Bank data | data.frame | 1949 | 14 |
Auto | HoRM | Canadian Auto Insurance Dataset | data.frame | 20 | 6 |
BAC | HoRM | Blood Alcohol Concentration Dataset | data.frame | 15 | 2 |
GRB | HoRM | Gamma-Ray Burst Dataset | data.frame | 63 | 2 |
JamesBond | HoRM | James Bond Dataset | data.frame | 24 | 18 |
amit | HoRM | Amitriptyline Dataset | data.frame | 17 | 7 |
auditory | HoRM | Auditory Discrimination Dataset | data.frame | 20 | 3 |
cheese | HoRM | Cheese-Tasting Experiment Dataset | data.frame | 36 | 3 |
chem | HoRM | Odor Dataset | coded.data | 15 | 4 |
compasst | HoRM | Computer-Assisted Learning Dataset | data.frame | 12 | 2 |
cracker | HoRM | Cracker Dataset | data.frame | 15 | 4 |
credloss | HoRM | Credit Loss Dataset | data.frame | 24 | 5 |
fiber | HoRM | Fiber Strength Dataset | data.frame | 30 | 2 |
fly | HoRM | Fruit Fly Dataset | data.frame | 23 | 8 |
gas | HoRM | Natural Gas Prices Dataset | data.frame | 46 | 2 |
light | HoRM | Light Dataset | data.frame | 12 | 2 |
repair | HoRM | Computer Repair Dataset | data.frame | 14 | 2 |
stock | HoRM | Google Stock Dataset | xts | 105 | 1 |
tortoise | HoRM | Tortoise Eggs Dataset | data.frame | 18 | 2 |
toy | HoRM | Toy Dataset | data.frame | 5 | 2 |
wood | HoRM | Pulp Property Dataset | data.frame | 7 | 2 |
yarn | HoRM | Yarn Fiber Dataset | data.frame | 15 | 4 |
aidssi | mstate | Data from the Amsterdam Cohort Studies on HIV infection and AIDS | data.frame | 329 | 5 |
aidssi2 | mstate | Data from the Amsterdam Cohort Studies on HIV infection and AIDS | data.frame | 329 | 10 |
bmt | mstate | BMT data from Klein and Moeschberger | data.frame | 137 | 22 |
ebmt1 | mstate | Data from the European Society for Blood and Marrow Transplantation (EBMT) | data.frame | 1977 | 8 |
ebmt2 | mstate | Data from the European Society for Blood and Marrow Transplantation (EBMT) | data.frame | 8966 | 9 |
ebmt3 | mstate | Data from the European Society for Blood and Marrow Transplantation (EBMT) | data.frame | 2204 | 9 |
ebmt4 | mstate | Data from the European Society for Blood and Marrow Transplantation (EBMT) | data.frame | 2279 | 15 |
prothr | mstate | Abnormal prothrombin levels in liver cirrhosis | msdata | 2152 | 8 |
metadata | glmmSeq | Minimal metadata from PEAC | data.frame | 123 | 3 |
tpm | glmmSeq | TPM count data from PEAC | matrix | 50 | 123 |
example_rep | ogrdbstats | Example repertoire data | list | | |
example.lumi | lumi | Example Illumina Expression data in LumiBatch class | LumiBatch | | |
example.lumiMethy | lumi | Example Illumina Infinium Methylation data in MethyLumiM class | MethyLumiM | | |
example.methyTitration | lumi | Example Illumina Infinium Methylation titration data in MethyLumiM class | MethyLumiM | | |
tract2221 | imputeMulti | Observational data on individuals living in census tract 2221 | data.frame | 3974 | 10 |
ginhoux | SCORPIUS | scRNA-seq data of dendritic cell progenitors. | list | | |
bioreactors_large | measure | Raman Spectra Bioreactor Data | tbl_df | 42 | 2655 |
bioreactors_small | measure | Raman Spectra Bioreactor Data | tbl_df | 210 | 2655 |
meats_long | measure | Fat, water and protein content of meat samples | tbl_df | 21500 | 6 |
aastveit.barley.covs | agridat | Barley heights and environmental covariates in Norway | data.frame | 9 | 20 |
aastveit.barley.height | agridat | Barley heights and environmental covariates in Norway | data.frame | 135 | 3 |
acorsi.grayleafspot | agridat | Multi-environment trial evaluating 36 maize genotypes in 9 locations | data.frame | 648 | 4 |
adugna.sorghum | agridat | Multi-environment trial of sorghum at 3 locations across 5 years | data.frame | 289 | 6 |
allcroft.lodging | agridat | Multi-environment trial of cereal with lodging data | data.frame | 224 | 3 |
alwan.lamb | agridat | For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. | data.frame | 340 | 11 |
ansari.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 768 | 3 |
archbold.apple | agridat | Split-split plot experiment of apple trees | data.frame | 120 | 8 |
ars.earlywhitecorn96 | agridat | Multi-environment trial of early white food corn | data.frame | 540 | 9 |
australia.soybean | agridat | Multi-environment trial of soybean in Australia | data.frame | 464 | 10 |
bachmaier.nitrogen | agridat | Trial of wheat with nitrogen fertilizer in two fertility zones | data.frame | 88 | 3 |
bailey.cotton.uniformity | agridat | Uniformity trial of cotton in Egypt | data.frame | 794 | 5 |
baker.barley.uniformity | agridat | Uniformity trials of barley, 10 years on same ground | data.frame | 570 | 4 |
baker.strawberry.uniformity | agridat | Uniformity trial of strawberry | data.frame | 700 | 4 |
baker.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 225 | 3 |
bancroft.peanut.uniformity | agridat | Uniformity trial of peanuts | data.frame | 216 | 4 |
barrero.maize | agridat | Multi-environment trial of maize in Texas. | data.frame | 14568 | 14 |
batchelor.apple.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 224 | 3 |
batchelor.lemon.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 364 | 3 |
batchelor.navel1.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 1000 | 3 |
batchelor.navel2.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 495 | 3 |
batchelor.valencia.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 240 | 3 |
batchelor.walnut.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 280 | 3 |
battese.survey | agridat | Survey and satellite data for corn and soy areas in Iowa | data.frame | 37 | 9 |
beall.webworms | agridat | Counts of webworms in a beet field, with insecticide treatments. | data.frame | 1300 | 7 |
beaven.barley | agridat | Yields of 8 barley varieties in 1913 as used by Student. | data.frame | 160 | 4 |
becker.chicken | agridat | Mating crosses of chickens | data.frame | 45 | 3 |
beckett.maize.uniformity | agridat | A uniformity trial of maize in Ghana. | data.frame | 83 | 8 |
belamkar.augmented | agridat | Multi-environment trial of wheat with Augmented design | data.frame | 2700 | 9 |
besag.bayesian | agridat | RCB experiment of spring barley in United Kingdom | data.frame | 225 | 4 |
besag.beans | agridat | Competition experiment in beans with height measurements | data.frame | 152 | 6 |
besag.checks | agridat | Check variety yields in winter wheat. | data.frame | 364 | 4 |
besag.elbatan | agridat | RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. | data.frame | 150 | 4 |
besag.endive | agridat | Presence of footroot disease in an endive field | data.frame | 2506 | 3 |
besag.met | agridat | Multi-environment trial of corn, incomplete-block design | data.frame | 1188 | 7 |
besag.triticale | agridat | Four-way factorial agronomic experiment in triticale | data.frame | 54 | 7 |
blackman.wheat | agridat | Multi-environment trial of wheat, conventional and semi-dwarf varieties | data.frame | 168 | 5 |
bliss.borers | agridat | Corn borer infestation under four treatments | data.frame | 48 | 3 |
bond.diallel | agridat | Diallel cross of winter beans | data.frame | 36 | 11 |
bose.multi.uniformity | agridat | Uniformity trials of barley, wheat, lentils | data.frame | 1170 | 5 |
box.cork | agridat | Weight of cork samples on four sides of trees | data.frame | 112 | 3 |
bradley.multi.uniformity | agridat | Uniformity trial of 4 crops on the same land | data.frame | 440 | 5 |
brandle.rape | agridat | Multi-environment trial of rape in Manitoba | data.frame | 135 | 4 |
brandt.switchback | agridat | Switchback experiment on dairy cattle, milk yield for two treatments | data.frame | 30 | 5 |
bridges.cucumber | agridat | Multi-environment trial of cucumbers in a latin square design | data.frame | 32 | 5 |
broadbalk.wheat | agridat | Long term wheat yields on Broadbalk fields at Rothamsted. | data.frame | 1258 | 4 |
bryan.corn.uniformity | agridat | Uniformity trial of corn at 3 locations in Iowa. | data.frame | 1728 | 4 |
buntaran.wheat | agridat | Multi-environment trial of wheat in Sweden in 2016. | data.frame | 1069 | 6 |
burgueno.alpha | agridat | Incomplete block alpha design | data.frame | 48 | 6 |
burgueno.rowcol | agridat | Row-column design | data.frame | 128 | 5 |
burgueno.unreplicated | agridat | Field experiment with unreplicated genotypes plus one repeated check. | data.frame | 434 | 4 |
butron.maize | agridat | Multi-environment trial of maize with pedigrees | data.frame | 245 | 5 |
byers.apple | agridat | Diameters of apples | data.frame | 480 | 6 |
caribbean.maize | agridat | Multi-environment trial of maize with fertilization | data.frame | 612 | 10 |
carlson.germination | agridat | Germination of alfalfa seeds at various salt concentrations | data.frame | 120 | 3 |
carmer.density | agridat | Nonlinear maize yield-density model | data.frame | 32 | 3 |
cate.potassium | agridat | Relative cotton yield for different soil potassium concentrations | data.frame | 24 | 2 |
chakravertti.factorial | agridat | Factorial experiment of rice, 3x5x3x3 | data.frame | 405 | 7 |
chinloy.fractionalfactorial | agridat | Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 | data.frame | 81 | 10 |
christidis.competition | agridat | Competition between varieties in cotton | data.frame | 270 | 8 |
christidis.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 1024 | 4 |
christidis.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 288 | 3 |
cleveland.soil | agridat | Soil resistivity in a field | data.frame | 8641 | 5 |
cochran.beets | agridat | Yield and number of plants in a sugarbeet fertilizer experiment | data.frame | 42 | 4 |
cochran.bib | agridat | Multi-environment trial of corn, balanced incomplete block design | data.frame | 52 | 3 |
cochran.crd | agridat | Potato scab infection with sulfur treatments | data.frame | 32 | 4 |
cochran.eelworms | agridat | Counts of eelworms before and after fumigant treatments | data.frame | 48 | 10 |
cochran.factorial | agridat | Factorial experiment of beans, 2x2x2x2 | data.frame | 32 | 8 |
cochran.latin | agridat | Latin square design in wheat | data.frame | 36 | 4 |
cochran.lattice | agridat | Balanced lattice experiment in cotton | data.frame | 80 | 5 |
cochran.wireworms | agridat | Wireworms controlled by fumigants in a latin square | data.frame | 25 | 4 |
connolly.potato | agridat | Potato yields in single-drill plots | data.frame | 80 | 6 |
coombs.rice.uniformity | agridat | Uniformity trial of rice in Malaysia | data.frame | 54 | 3 |
cornelius.maize | agridat | Multi-environment trial of maize for 9 cultivars at 20 locations. | data.frame | 180 | 3 |
corsten.interaction | agridat | Multi-environment trial of corn | data.frame | 140 | 3 |
cox.stripsplit | agridat | Strip-split-plot of barley with fertilizer, calcium, and soil factors. | data.frame | 96 | 5 |
cramer.cucumber | agridat | Cucumber yields and quantitative traits | data.frame | 24 | 9 |
crampton.pig | agridat | Weight gain in pigs for different treatments | data.frame | 50 | 5 |
crossa.wheat | agridat | Multi-environment trial of wheat for 18 genotypes at 25 locations | data.frame | 450 | 5 |
crowder.seeds | agridat | Germination of Orobanche seeds for two genotypes and two treatments. | data.frame | 21 | 5 |
cullis.earlygen | agridat | Early generation variety trial in wheat | data.frame | 670 | 6 |
damesa.maize | agridat | Incomplete-block experiment of maize in Ethiopia. | data.frame | 264 | 8 |
darwin.maize | agridat | Darwin's maize data of crossed/inbred plant heights | data.frame | 30 | 4 |
dasilva.maize | agridat | Multi-environment trial of maize | data.frame | 1485 | 4 |
dasilva.soybean.uniformity | agridat | Uniformity trial of soybean | data.frame | 1152 | 3 |
davidian.soybean | agridat | Growth of soybean varieties in 3 years | data.frame | 412 | 5 |
davies.pasture.uniformity | agridat | Uniformity trial of pasture. | data.frame | 760 | 3 |
day.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 3100 | 4 |
denis.missing | agridat | Multi-environment trial with structured missing values | data.frame | 130 | 3 |
denis.ryegrass | agridat | Multi-environment trial of perennial ryegrass in France | data.frame | 147 | 3 |
depalluel.sheep | agridat | Latin square of four breeds of sheep with four diets | data.frame | 32 | 5 |
devries.pine | agridat | Graeco-Latin Square experiment in pine | data.frame | 36 | 7 |
digby.jointregression | agridat | Multi-environment trial of wheat | data.frame | 134 | 3 |
diggle.cow | agridat | Bodyweight of cows in a 2-by-2 factorial experiment | data.frame | 598 | 5 |
draper.safflower.uniformity | agridat | Uniformity trial of safflower | data.frame | 640 | 4 |
ducker.groundnut.uniformity | agridat | Uniformity trial of groundnut | data.frame | 215 | 3 |
durban.competition | agridat | Sugar beet yields with competition effects | data.frame | 114 | 5 |
durban.rowcol | agridat | Row-column experiment of spring barley, many varieties | data.frame | 544 | 5 |
durban.splitplot | agridat | Split-plot experiment of barley with fungicide treatments | data.frame | 560 | 6 |
eden.nonnormal | agridat | Height of barley plants in a study of non-normal data | data.frame | 256 | 3 |
eden.potato | agridat | Potato yields in response to potash and nitrogen fertilizer | data.frame | 225 | 9 |
eden.tea.uniformity | agridat | Uniformity trial of tea | data.frame | 144 | 4 |
edwards.oats | agridat | Multi-environment trial of oats in United States, 5 locations, 7 years. | data.frame | 3694 | 7 |
engelstad.nitro | agridat | Multi-environment trial of corn with nitrogen fertilizer | data.frame | 60 | 4 |
evans.sugarcane.uniformity | agridat | Uniformity trial of sugarcane | data.frame | 710 | 3 |
fan.stability | agridat | Multi-environment trial of maize hybrids in China | data.frame | 260 | 5 |
federer.diagcheck | agridat | Wheat experiment with diagonal checks | data.frame | 180 | 4 |
federer.tobacco | agridat | RCB of tobacco, height plants exposed to radiation | data.frame | 56 | 4 |
fisher.barley | agridat | Multi-environment trial of 5 barley varieties, 6 locations, 2 years | data.frame | 60 | 4 |
fisher.latin | agridat | Latin square experiment on mangolds | data.frame | 25 | 4 |
forster.wheat.uniformity | agridat | Uniformity trial of wheat in Australia. | data.frame | 160 | 3 |
foulley.calving | agridat | Calving difficulty by calf sex and age of dam | data.frame | 54 | 4 |
fox.wheat | agridat | Multi-environment trial of wheat, 22 varieties at 14 sites in Australia | data.frame | 308 | 4 |
garber.multi.uniformity | agridat | Uniformity trials of oat hay and wheat grain | data.frame | 3060 | 6 |
gartner.corn | agridat | Yield monitor data from a corn field in Minnesota | data.frame | 4949 | 8 |
gathmann.bt | agridat | Impact of Bt corn on non-target species | data.frame | 16 | 3 |
gauch.soy | agridat | Multi-environment trial of soybeans in New York, 1977 to 1988 | data.frame | 1454 | 6 |
george.wheat | agridat | Multi-location/year breeding trial in California | data.frame | 13996 | 5 |
giles.wheat | agridat | Straw length and ear emergence for wheat genotypes. | data.frame | 247 | 4 |
gilmour.serpentine | agridat | Wheat yield in South Australia with serpentine row/col effects | data.frame | 330 | 5 |
gilmour.slatehall | agridat | Slate Hall Farm 1978 | data.frame | 150 | 5 |
gomez.fractionalfactorial | agridat | Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 | data.frame | 64 | 12 |
gomez.groupsplit | agridat | Group balanced split-plot design in rice | data.frame | 270 | 7 |
gomez.heterogeneity | agridat | RCB experiment of rice, heterogeneity of regressions | data.frame | 21 | 3 |
gomez.heteroskedastic | agridat | RCB experiment of rice, heteroskedastic varieties | data.frame | 105 | 4 |
gomez.multilocsplitplot | agridat | Multi-environment trial of rice, split-plot design | data.frame | 108 | 5 |
gomez.nitrogen | agridat | Soil nitrogen at three times for eight fertilizer treatments | data.frame | 96 | 4 |
gomez.nonnormal1 | agridat | Insecticide treatment effectiveness | data.frame | 36 | 3 |
gomez.nonnormal2 | agridat | RCB experiment of rice, measuring white heads | data.frame | 42 | 3 |
gomez.nonnormal3 | agridat | RCB experiment of rice, 12 varieties with leafhopper survival | data.frame | 36 | 3 |
gomez.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 648 | 3 |
gomez.seedrate | agridat | RCB experiment of rice, 6 densities | data.frame | 24 | 3 |
gomez.splitplot.subsample | agridat | Split-plot experiment of rice, with subsamples | data.frame | 192 | 5 |
gomez.splitsplit | agridat | Split-split-plot experiment of rice | data.frame | 135 | 7 |
gomez.stripplot | agridat | Strip-plot experiment of rice | data.frame | 54 | 6 |
gomez.stripsplitplot | agridat | Strip-split-plot experiment of rice | data.frame | 108 | 7 |
gomez.wetdry | agridat | Rice yield in wet & dry seasons with nitrogen fertilizer treatments | data.frame | 30 | 4 |
gorski.oats.uniformity | agridat | Uniformity trial of oats in Poland | data.frame | 500 | 4 |
gotway.hessianfly | agridat | Hessian fly damage to wheat varieties | data.frame | 64 | 6 |
goulden.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 2304 | 3 |
goulden.eggs | agridat | Sample of egg weights on 24 consecutive days | data.frame | 240 | 2 |
goulden.latin | agridat | Latin square experiment for testing fungicide | data.frame | 25 | 4 |
goulden.splitsplit | agridat | Split-split-plot experiment of wheat | data.frame | 160 | 9 |
graybill.heteroskedastic | agridat | Multi-environment trial of wheat varieties with heteroskedastic yields | data.frame | 52 | 3 |
gregory.cotton | agridat | Factorial experiment of cotton in Sudan. | data.frame | 144 | 6 |
grover.diallel | agridat | Diallel 6x6 | data.frame | 144 | 5 |
grover.rcb.subsample | agridat | Rice RCB with subsamples | data.frame | 144 | 4 |
gumpertz.pepper | agridat | Phytophtera disease incidence in a pepper field | data.frame | 800 | 6 |
hadasch.lettuce | agridat | Lettuce resistance to downy mildew resistance (with marker data) | data.frame | 703 | 4 |
hadasch.lettuce.markers | agridat | Lettuce resistance to downy mildew resistance (with marker data) | data.frame | 89 | 301 |
hanks.sprinkler | agridat | Wheat yields in a line-source sprinkler experiment | data.frame | 108 | 7 |
hanover.whitepine | agridat | Mating crosses of white pine trees | data.frame | 112 | 4 |
hansen.multi.uniformity | agridat | Multi-year uniformity trial in Denmark | data.frame | 662 | 6 |
haritonenko.sugarbeet.uniformity | agridat | Uniformity trial of sugar beet | data.frame | 416 | 3 |
harris.multi.uniformity | agridat | Uniformity trials with multiple crops, 15 years on the same land | data.frame | 1058 | 5 |
harris.wateruse | agridat | Water use by horticultural trees | data.frame | 1040 | 5 |
harrison.priors | agridat | Ranges of analytes in soybean from other authors | data.frame | 80 | 5 |
hartman.tomato.uniformity | agridat | Uniformity trial of tomato | data.frame | 384 | 3 |
harvey.lsmeans | agridat | Average daily gain of 65 steers for 3 lines, 9 sires. | data.frame | 65 | 7 |
harville.lamb | agridat | Birth weight of lambs from different lines/sires | data.frame | 62 | 4 |
hayman.tobacco | agridat | Diallel cross of Aztec tobacco | data.frame | 256 | 5 |
hazell.vegetables | agridat | Gross profit for 4 vegetable crops in 6 years | data.frame | 6 | 5 |
heady.fertilizer | agridat | Yield of corn, alfalfa, clover with two fertilizers | data.frame | 648 | 6 |
heath.cabbage.uniformity | agridat | Uniformity trial of cabbage. | data.frame | 48 | 3 |
heath.radish.uniformity | agridat | Uniformity trial of radish | data.frame | 400 | 4 |
henderson.milkfat | agridat | Milk fat yields for a single cow | data.frame | 35 | 2 |
hernandez.nitrogen | agridat | Multi-environment trial of corn with nitrogen fertilizer at 5 sites. | data.frame | 136 | 5 |
hessling.argentina | agridat | Relation between wheat yield and weather in Argentina | data.frame | 30 | 16 |
hildebrand.systems | agridat | Multi-environment trial of maize for four cropping systems | data.frame | 56 | 4 |
holland.arthropods | agridat | Counts of arthropods in a grid-sampled wheat field | data.frame | 63 | 8 |
holshouser.splitstrip | agridat | Split-strip-plot of soybeans | data.frame | 160 | 8 |
holtsmark.timothy.uniformity | agridat | Uniformity trial of timothy grass in Norway | data.frame | 240 | 3 |
huehn.wheat | agridat | Multi-environment trial of wheat to illustrate stability statistics | data.frame | 200 | 3 |
hughes.grapes | agridat | RCB experiment of grape, disease incidence | data.frame | 270 | 6 |
hunter.corn | agridat | Multi-environment trial of corn with nitrogen fertilizer | data.frame | 54 | 4 |
hutchinson.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 2000 | 3 |
igue.sugarcane.uniformity | agridat | Uniformity trial with sugarcane | data.frame | 1512 | 3 |
ilri.sheep | agridat | Birth weight and weaning weight of Dorper x Red Maasi lambs | data.frame | 882 | 12 |
immer.sugarbeet.uniformity | agridat | Uniformity trial of sugarbeets, measurements of yield, sugar, purity | data.frame | 1200 | 6 |
ivins.herbs | agridat | Percent ground cover of herbage species and nettles. | data.frame | 78 | 4 |
iyer.wheat.uniformity | agridat | Uniformity trials of wheat in India | data.frame | 2000 | 3 |
jansen.apple | agridat | Infestation of apple shoots by apple canker. | data.frame | 36 | 5 |
jansen.carrot | agridat | Infestation of carrots by fly larvae | data.frame | 96 | 5 |
jansen.strawberry | agridat | Ordered disease ratings of strawberry crosses. | data.frame | 144 | 5 |
jayaraman.bamboo | agridat | Bamboo progeny trial | data.frame | 216 | 5 |
jegorow.oats.uniformity | agridat | Uniformity trial of oats in Russia | data.frame | 240 | 3 |
jenkyn.mildew | agridat | Yields from treatment for mildew control | data.frame | 38 | 4 |
john.alpha | agridat | Alpha lattice design of spring oats | data.frame | 72 | 7 |
johnson.blight | agridat | Potato blight due to weather in Prosser, Washington | data.frame | 25 | 6 |
johnson.douglasfir | agridat | A study of small-plots of old-growth Douglas Fir in Oregon. | data.frame | 1600 | 3 |
jones.corn.uniformity | agridat | Uniformity trial of corn. | data.frame | 144 | 3 |
jurowski.wheat.uniformity | agridat | Uniformity trial of wheat in Russia | data.frame | 480 | 3 |
kadam.millet.uniformity | agridat | Uniformity trial of millet | data.frame | 240 | 4 |
kalamkar.potato.uniformity | agridat | Uniformity trial of potatoes | data.frame | 576 | 3 |
kalamkar.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 1280 | 4 |
kang.maize | agridat | Multi-environment trial of maize in Louisianna at 4 locs in 3 years | data.frame | 204 | 5 |
kang.peanut | agridat | Multi-environment trial of peanuts for 10 genotypes in 15 environments | data.frame | 590 | 4 |
karcher.turfgrass | agridat | Turfgrass ratings for different treatments | data.frame | 128 | 6 |
kayad.alfalfa | agridat | Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. | data.frame | 8628 | 4 |
keen.potatodamage | agridat | Damage to potato tubers from lifting rods. | data.frame | 1152 | 6 |
kempton.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 196 | 3 |
kempton.competition | agridat | Sugar beet trial with competition effects | data.frame | 108 | 5 |
kempton.rowcol | agridat | Row-column experiment of wheat | data.frame | 68 | 5 |
kempton.slatehall | agridat | Slate Hall Farm 1976 spring wheat | data.frame | 150 | 5 |
kenward.cattle | agridat | Repeated measurement of weights of calves with two treatments. | data.frame | 660 | 4 |
kerr.sugarcane.uniformity | agridat | Uniformity trials of sugarcane, 4 fields | data.frame | 564 | 4 |
khan.brassica.uniformity | agridat | Uniformity trial of brassica. | data.frame | 648 | 4 |
khin.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 1080 | 3 |
kiesselbach.oats.uniformity | agridat | Uniformity trial of oats | data.frame | 207 | 3 |
kirk.potato | agridat | Variety trial of potatoes, highly replicated | data.frame | 380 | 5 |
kling.augmented | agridat | Augmented design of meadowfoam | data.frame | 68 | 7 |
kotowski.potato.uniformity | agridat | Uniformity trial of potato in Poland. | data.frame | 152 | 5 |
kreusler.maize | agridat | Growth of maize plants in Germany during 1875-1878 | data.frame | 165 | 16 |
kristensen.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 242 | 3 |
kulkarni.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 480 | 4 |
lambert.soiltemp | agridat | Average monthly soil temperature near Zurich | data.frame | 84 | 3 |
lander.multi.uniformity | agridat | Uniformity trials of wheat and chari, 4 years on the same land. | data.frame | 780 | 5 |
larsen.timothy.uniformity | agridat | Uniformity trial of timothy grass in Norway | data.frame | 960 | 3 |
lasrosas.corn | agridat | Yield monitor data for a corn field in Argentina with variable nitrogen. | data.frame | 3443 | 9 |
lavoranti.eucalyptus | agridat | Height of Eucalyptus trees in southern Brazil | data.frame | 490 | 4 |
laycock.tea.uniformity | agridat | Uniformity trials of tea | data.frame | 86 | 4 |
lee.potatoblight | agridat | Repeated measurements of resistance to potato blight | data.frame | 14570 | 7 |
lehmann.millet.uniformity | agridat | Uniformity trial of millet in India | data.frame | 396 | 5 |
lehmann.rice.uniformity | agridat | Uniformity trial of rice in India | data.frame | 153 | 5 |
lehner.soybeanmold | agridat | Yield, white mold, and sclerotia for soybeans in Brazil | data.frame | 382 | 9 |
lessman.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 2640 | 3 |
li.millet.uniformity | agridat | Uniformity trial of millet | data.frame | 600 | 3 |
lillemo.wheat | agridat | Multi-environment trial of wheat susceptibile to powdery mildew | data.frame | 408 | 4 |
lin.superiority | agridat | Multi-environment trial of 33 barley genotypes in 12 locations | data.frame | 396 | 4 |
lin.unbalanced | agridat | Multi-environment trial of 33 barley genotypes in 18 locations | data.frame | 405 | 4 |
linder.wheat | agridat | Multi-environment trial of wheat in Switzerland | data.frame | 252 | 4 |
little.splitblock | agridat | Split-block experiment of sugar beets | data.frame | 80 | 6 |
loesell.bean.uniformity | agridat | Uniformity trial of white pea beans | data.frame | 1890 | 3 |
lonnquist.maize | agridat | Multi-environment trial of maize, half diallel | data.frame | 78 | 3 |
lord.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 560 | 5 |
love.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 170 | 3 |
love.sugarcane.uniformity | agridat | Uniformity Trial of Sugarcane in Puerto Rico | data.frame | 400 | 3 |
lu.stability | agridat | Multi-environment trial of maize, to illustrate stability statistics | data.frame | 120 | 4 |
lucas.switchback | agridat | Switchback experiment on dairy cattle, milk yield for 3 treatments | data.frame | 36 | 5 |
lyon.potato.uniformity | agridat | Uniformity trial of potatoes | data.frame | 204 | 3 |
lyons.wheat | agridat | Multi-environment trial of winter wheat at 12 sites in 4 years. | data.frame | 48 | 3 |
magistad.pineapple.uniformity | agridat | Uniformity trial of pineapple | data.frame | 137 | 6 |
masood.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 288 | 3 |
mcclelland.corn.uniformity | agridat | Uniformity trial of corn | data.frame | 438 | 3 |
mcconway.turnip | agridat | RCB experiment of turnips | data.frame | 64 | 5 |
mckinstry.cotton.uniformity | agridat | Uniformity trial of cotton in South Rhodesia | data.frame | 480 | 3 |
mcleod.barley | agridat | Multi-environment trial of barley in South Canterbury with yield and yield components | data.frame | 40 | 10 |
mead.cauliflower | agridat | Leaves for cauliflower plants at different times | data.frame | 14 | 3 |
mead.cowpea.maize | agridat | Intercropping experiment of maize/cowpea | data.frame | 72 | 6 |
mead.germination | agridat | Seed germination with different temperatures/concentrations | data.frame | 64 | 5 |
mead.lamb | agridat | Number of lambs born to 3 breeds on 3 farms | data.frame | 36 | 4 |
mead.strawberry | agridat | RCB experiment of strawberry | data.frame | 32 | 5 |
mead.turnip | agridat | Density/spacing experiment for turnips in 3 blocks. | data.frame | 60 | 4 |
mercer.mangold.uniformity | agridat | Uniformity trial of mangolds | data.frame | 200 | 4 |
mercer.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 500 | 4 |
miguez.biomass | agridat | Biomass of 3 crops in Greece | data.frame | 212 | 5 |
minnesota.barley.weather | agridat | Monthly weather at 6 sites in Minnesota 1927-1936. | data.frame | 719 | 8 |
minnesota.barley.yield | agridat | Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. | data.frame | 2083 | 5 |
montgomery.wheat.uniformity | agridat | Uniformity trial of wheat, 2 years on the same land | data.frame | 448 | 4 |
moore.bushbean.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 576 | 3 |
moore.carrot.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 288 | 3 |
moore.fallcauliflower.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 240 | 4 |
moore.polebean.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 144 | 3 |
moore.springcauliflower.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 240 | 4 |
moore.sweetcorn.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 288 | 4 |
nagai.strawberry.uniformity | agridat | Uniformity trial of strawberry | data.frame | 432 | 3 |
nair.turmeric.uniformity | agridat | Uniformity trial of turmeric. | data.frame | 864 | 3 |
narain.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 160 | 3 |
nass.barley | agridat | U.S. historical crop yields by state | data.frame | 4839 | 4 |
nass.corn | agridat | U.S. historical crop yields by state | data.frame | 6381 | 4 |
nass.cotton | agridat | U.S. historical crop yields by state | data.frame | 2338 | 4 |
nass.hay | agridat | U.S. historical crop yields by state | data.frame | 5044 | 4 |
nass.rice | agridat | U.S. historical crop yields by state | data.frame | 662 | 4 |
nass.sorghum | agridat | U.S. historical crop yields by state | data.frame | 1647 | 4 |
nass.soybean | agridat | U.S. historical crop yields by state | data.frame | 2528 | 4 |
nass.wheat | agridat | U.S. historical crop yields by state | data.frame | 5963 | 4 |
nebraska.farmincome | agridat | Nebraska farm income in 2007 by county | data.frame | 93 | 4 |
nonnecke.peas.uniformity | agridat | Uniformity trial of canning peas | data.frame | 540 | 5 |
nonnecke.sweetcorn.uniformity | agridat | Uniformity trial of sweet corn | data.frame | 1728 | 4 |
obsi.potato.uniformity | agridat | Uniformity trial of potato in Africa 2001 | data.frame | 2569 | 4 |
odland.soybean.uniformity | agridat | Uniformity trials of soy hay and soybeans | data.frame | 1540 | 3 |
odland.soyhay.uniformity | agridat | Uniformity trials of soy hay and soybeans | data.frame | 1008 | 3 |
omer.sorghum | agridat | Multi-environment trial of sorghum, 6 environments | data.frame | 432 | 4 |
onofri.winterwheat | agridat | Multi-environment trial of winter wheat, 7 years | data.frame | 168 | 5 |
ortiz.tomato.covs | agridat | Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates | data.frame | 18 | 19 |
ortiz.tomato.yield | agridat | Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates | data.frame | 270 | 4 |
pacheco.soybean | agridat | Multi-environment trial of soybean in Brazil. | data.frame | 198 | 3 |
paez.coffee.uniformity | agridat | Uniformity trial of coffee | data.frame | 4190 | 5 |
panse.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 1280 | 3 |
parker.orange.uniformity | agridat | Uniformity trial of oranges | data.frame | 1890 | 4 |
patterson.switchback | agridat | Switchback experiment on dairy cattle, milk yield for 4 treatments | data.frame | 36 | 4 |
payne.wheat | agridat | Long term rotation experiment at Rothamsted | data.frame | 480 | 4 |
pearce.apple | agridat | Apple tree yields for 6 treatments with covariate | data.frame | 24 | 4 |
pearl.kernels | agridat | Counts of yellow/white and sweet/starchy maize kernels by 15 observers | data.frame | 59 | 6 |
pederson.lettuce.repeated | agridat | Repeated measurements of lettuce growth | data.frame | 594 | 4 |
perry.springwheat | agridat | Multi-environment trial of wheat cultivars introduced 1860-1982. | data.frame | 560 | 6 |
petersen.sorghum.cowpea | agridat | Intercropping experiment of sorghum/cowpea | data.frame | 18 | 5 |
piepho.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 1080 | 3 |
piepho.cocksfoot | agridat | Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years | data.frame | 111 | 3 |
polson.safflower.uniformity | agridat | Uniformity trial of safflower | data.frame | 1716 | 3 |
rangaswamy.groundnut.uniformity | agridat | Uniformity trial of groundnut | data.frame | 96 | 3 |
ratkowsky.onions | agridat | Onion yields for different densities at two locations | data.frame | 84 | 3 |
reid.grasses | agridat | Yields of four grasses for a wide range of nitrogen fertilizer | data.frame | 210 | 5 |
riddle.wheat | agridat | Modified Latin Square experiments of wheat | data.frame | 650 | 7 |
ridout.appleshoots | agridat | Root counts for propagated columnar apple shoots. | data.frame | 270 | 4 |
robinson.peanut.uniformity | agridat | Uniformity trial of peanuts | data.frame | 1152 | 4 |
roemer.sugarbeet.uniformity | agridat | Uniformity trial of sugar beets | data.frame | 192 | 4 |
rothamsted.brussels | agridat | RCB experiment of brussels sprouts, 9 fertilizer treatments | data.frame | 48 | 5 |
rothamsted.oats | agridat | RCB experiment of oats, straw and grain, 9 fertilizer treatments | data.frame | 96 | 6 |
ryder.groundnut | agridat | RCB experiment of groundut, wet and dry yields | data.frame | 24 | 6 |
salmon.bunt | agridat | Fungus infection in varieties of wheat | data.frame | 400 | 4 |
saunders.maize.uniformity | agridat | Uniformity trial of maize in South Africa | data.frame | 2500 | 4 |
sawyer.multi.uniformity | agridat | Uniformity trials of wheat, swedes, oats, 3 years on the same land | data.frame | 144 | 9 |
sayer.sugarcane.uniformity | agridat | Uniformity trial of sugarcane in India, 1932, 1933 & 1934. | data.frame | 2056 | 4 |
senshu.rice | agridat | Multi-environment trial of rice, with solar radiation and temperature | data.frame | 40 | 7 |
shafi.tomato.uniformity | agridat | Uniformity trial of tomato | data.frame | 200 | 3 |
shafii.rapeseed | agridat | Multi-environment trial of rapeseed in U.S. | data.frame | 648 | 5 |
sharma.met | agridat | Multi-environment trial | data.frame | 126 | 5 |
shaw.oats | agridat | Multi-environment trial of oats in India | data.frame | 390 | 5 |
siao.cotton.uniformity | agridat | Uniformity trials of cotton in China | data.frame | 858 | 4 |
silva.cotton | agridat | Number of cotton bolls for different levels of defoliation. | data.frame | 250 | 9 |
sinclair.clover | agridat | Clover yields in a factorial fertilizer experiment | data.frame | 25 | 3 |
smith.beans.uniformity | agridat | Uniformity trials of beans, 2 species in 2 years | data.frame | 912 | 4 |
smith.corn.uniformity | agridat | Uniformity trial of corn, 3 years on same ground | data.frame | 360 | 5 |
smith.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 1080 | 4 |
snedecor.asparagus | agridat | Asparagus yields for different cutting treatments | data.frame | 80 | 4 |
snijders.fusarium | agridat | Fusarium infection in wheat varieties | data.frame | 204 | 4 |
stephens.sorghum.uniformity | agridat | Uniformity trial of sorghum silage | data.frame | 2000 | 3 |
steptoe.morex.geno | agridat | Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex | dh | | |
steptoe.morex.pheno | agridat | Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex | data.frame | 2432 | 10 |
stickler.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 1600 | 4 |
stirret.borers | agridat | Corn borer control by application of fungal spores. | data.frame | 60 | 4 |
streibig.competition | agridat | Competition experiment between barley and sinapis. | data.frame | 135 | 8 |
strickland.apple.uniformity | agridat | Uniformity trial in apple | data.frame | 198 | 3 |
strickland.grape.uniformity | agridat | Uniformity trial of grape | data.frame | 155 | 3 |
strickland.peach.uniformity | agridat | Uniformity trial of peach | data.frame | 144 | 3 |
strickland.tomato.uniformity | agridat | Uniformity trial of tomato | data.frame | 180 | 3 |
stroup.nin | agridat | RCB experiment of wheat at the Nebraska Intrastate Nursery | data.frame | 242 | 5 |
stroup.splitplot | agridat | Split-plot experiment of simulated data | data.frame | 24 | 4 |
student.barley | agridat | Multi-environment trial of barley | data.frame | 102 | 7 |
summerby.multi.uniformity | agridat | Uniformity trial of maize, oat, alfalfa, mangolds | data.frame | 2600 | 6 |
tai.potato | agridat | Multi-environment trial of potato | data.frame | 48 | 6 |
talbot.potato.traits | agridat | Multi-environment trial of potato in UK, yields and trait scores at 12 locations | data.frame | 126 | 3 |
talbot.potato.yield | agridat | Multi-environment trial of potato in UK, yields and trait scores at 12 locations | data.frame | 108 | 3 |
tesfaye.millet | agridat | Multi-environment trial of millet | data.frame | 415 | 9 |
theobald.barley | agridat | Multi-environment trial of barley, multiple years & fertilizer levels | data.frame | 105 | 5 |
theobald.covariate | agridat | Multi-environment trial of corn silage, Year * Loc * Variety with covariate | data.frame | 256 | 5 |
thompson.cornsoy | agridat | Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation | data.frame | 165 | 12 |
tulaikow.wheat.uniformity | agridat | Uniformity trial of winter/spring wheat | data.frame | 480 | 4 |
turner.herbicide | agridat | Herbicide control of larkspur | data.frame | 12 | 4 |
urquhart.feedlot | agridat | Weight gain calves in a feedlot | data.frame | 67 | 5 |
usgs.herbicides | agridat | Concentrations of herbicides in streams in the United States | data.frame | 184 | 19 |
vaneeuwijk.drymatter | agridat | Multi-environment trial of maize, dry matter content | data.frame | 168 | 4 |
vaneeuwijk.fusarium | agridat | Infection of wheat varieties by Fusarium strains from 1990 to 1993 | data.frame | 560 | 4 |
vaneeuwijk.nematodes | agridat | Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. | data.frame | 55 | 3 |
vargas.txe.covs | agridat | Treatment x environment interaction in agronomy trials | data.frame | 10 | 28 |
vargas.txe.yield | agridat | Treatment x environment interaction in agronomy trials | data.frame | 240 | 3 |
vargas.wheat1.covs | agridat | Wheat yields in 7 years with genetic and environment covariates | data.frame | 6 | 17 |
vargas.wheat1.traits | agridat | Wheat yields in 7 years with genetic and environment covariates | data.frame | 126 | 19 |
vargas.wheat2.covs | agridat | Multi-environment trial of wheat with environmental covariates | data.frame | 21 | 14 |
vargas.wheat2.yield | agridat | Multi-environment trial of wheat with environmental covariates | data.frame | 168 | 3 |
verbyla.lupin | agridat | Multi-environment trial of lupin, multiple varieties and densities | data.frame | 1420 | 13 |
vishnaadevi.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 288 | 3 |
vold.longterm | agridat | Long-term barley yields at different fertilizer levels | data.frame | 76 | 3 |
vsn.lupin3 | agridat | Multi-environment trial of lupin, early generation trial | data.frame | 1236 | 5 |
wallace.iowaland | agridat | Iowa farmland values by county in 1925 | data.frame | 99 | 10 |
walsh.cottonprice | agridat | Acres and price of cotton 1910-1943 | data.frame | 34 | 9 |
wassom.brome.uniformity | agridat | Uniformity trials of bromegrass | data.frame | 3888 | 4 |
waynick.soil | agridat | Soil nitrogen and carbon in two fields | data.frame | 200 | 6 |
wedderburn.barley | agridat | Multi-environment trial of barley, percent of leaves affected by leaf blotch | data.frame | 90 | 3 |
weiss.incblock | agridat | Soybean balanced incomplete block experiment | data.frame | 186 | 5 |
weiss.lattice | agridat | Lattice experiment in soybeans. | data.frame | 196 | 5 |
welch.bermudagrass | agridat | Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers | data.frame | 64 | 4 |
wheatley.carrot | agridat | Insecticide treatments for carrot fly larvae | data.frame | 36 | 6 |
wiebe.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 1500 | 3 |
wiedemann.safflower.uniformity | agridat | Uniformity trial of safflower | data.frame | 1782 | 3 |
williams.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 720 | 3 |
williams.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 288 | 3 |
williams.trees | agridat | Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand | data.frame | 192 | 4 |
woodman.pig | agridat | Weight gain in pigs for different treatments | data.frame | 30 | 10 |
wyatt.multi.uniformity | agridat | Uniformity trial of oats and wheat on the same ground. | data.frame | 258 | 5 |
yan.winterwheat | agridat | Multi-environment trial of winter wheat in Ontario | data.frame | 162 | 3 |
yang.barley | agridat | Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. | data.frame | 108 | 3 |
yates.missing | agridat | Factorial experiment of potato, 3x3 with missing values | data.frame | 80 | 6 |
yates.oats | agridat | Split-plot experiment of oats | data.frame | 72 | 8 |
zuidhof.broiler | agridat | Daily weight, feed, egg measurements for a broiler chicken | data.frame | 59 | 6 |
CCAMLRp | CCAMLRGIS | CCAMLRGIS Projection | character | | |
Coast | CCAMLRGIS | Simplified and subsettable coastline | sf | 22 | 2 |
Depth_cols | CCAMLRGIS | Bathymetry colors | character | | |
Depth_cols2 | CCAMLRGIS | Bathymetry colors with Fishable Depth range | character | | |
Depth_cuts | CCAMLRGIS | Bathymetry depth classes | numeric | | |
Depth_cuts2 | CCAMLRGIS | Bathymetry depth classes with Fishable Depth range | numeric | | |
GridData | CCAMLRGIS | Example dataset for create_PolyGrids | data.frame | 125 | 4 |
Labels | CCAMLRGIS | Polygon labels | data.frame | 151 | 4 |
LineData | CCAMLRGIS | Example dataset for create_Lines | data.frame | 14 | 6 |
PieData | CCAMLRGIS | Example dataset for create_Pies | data.frame | 17 | 5 |
PieData2 | CCAMLRGIS | Example dataset for create_Pies | data.frame | 200 | 6 |
PointData | CCAMLRGIS | Example dataset for create_Points | data.frame | 10 | 6 |
PolyData | CCAMLRGIS | Example dataset for create_Polys | data.frame | 14 | 6 |
COVID19_2020 | nda | Covid'19 case datesets of countries (2020), where the data frame has 138 observations of 18 variables. | data.frame | 138 | 18 |
CWTS_2020 | nda | CWTS Leiden's University Ranking 2020 for all scientific fields, within the period of 2016-2019. 1176 observations (i.e., universities), and 42 variables (i.e., indicators). | data.frame | 1176 | 42 |
CrimesUSA1990.X | nda | Crimes in USA cities in 1990. Independent variables (X) | data.frame | 1994 | 123 |
CrimesUSA1990.Y | nda | Crimes in USA cities in 1990. Dependent variable (Y) | data.frame | 1994 | 1 |
GOVDB2020 | nda | Governmental and economic data of countries (2020), where the data frame has 138 observations of 2161 variables. | data.frame | 138 | 2161 |
I40_2020 | nda | NUTS2 regional development data (2020) of I4.0 readiness, where the data frame has 414 observations of 101 variables. | data.frame | 414 | 101 |
BCI.env2 | natto | Habitat Types of Barro Colorado Island Forest Plots | data.frame | 50 | 7 |
BCI.taxon | natto | Botanical Classification of Barro Colorado Island Species | data.frame | 225 | 5 |
pond | natto | Relative Zooplankton Abundances in Ponds | data.frame | 5 | 8 |
spurn | natto | Dune Scrub Vegetation in Spurn Point, Yorkshire | data.frame | 20 | 40 |
UnitTable | caffsim | Unit data of PK parameters | data.frame | 16 | 2 |
example.agff | ACME | An example ACME data structure of class ACMESet | ACMESet | | |
elec2 | dynaTree | The ELEC2 Data Set | data.frame | 27552 | 5 |
sample.ped.geno | famSKATRC | Sample Pedigree Genotype Data | data.frame | 20 | 36 |
simple_dwc_terms | rvertnet | Darwin core terms | character | | |
datamat | paramGUI | This is an example dataset included in this package | matrix | 197 | |
times | paramGUI | This is an example dataset included in this package | numeric | | |
waves | paramGUI | This is an example dataset included in this package | numeric | | |
dsa01a | aprean3 | Dataset for Appendix A, Chapter 01 | data.frame | 25 | 10 |
dsa06b | aprean3 | Dataset for Appendix B, Chapter 06 | data.frame | 70 | 4 |
dsa15a | aprean3 | Dataset for Appendix A, Chapter 15 | data.frame | 13 | 5 |
dse03a | aprean3 | Dataset for Exercise A, Chapter 03 | data.frame | 11 | 2 |
dse03aa | aprean3 | Dataset for Exercise AA, Chapter 03 | data.frame | 7 | 2 |
dse03bb | aprean3 | Dataset for Exercise BB, Chapter 03 | data.frame | 10 | 2 |
dse03c | aprean3 | Dataset for Exercise C, Chapter 03 | data.frame | 13 | 2 |
dse03cc | aprean3 | Dataset for Exercise CC, Chapter 03 | data.frame | 10 | 2 |
dse03dd | aprean3 | Dataset for Exercise DD, Chapter 03 | data.frame | 9 | 2 |
dse03e | aprean3 | Dataset for Exercise E, Chapter 03 | data.frame | 7 | 3 |
dse03ee | aprean3 | Dataset for Exercise EE, Chapter 03 | data.frame | 23 | 2 |
dse03f | aprean3 | Dataset for Exercise F, Chapter 03 | data.frame | 12 | 2 |
dse03g | aprean3 | Dataset for Exercise G, Chapter 03 | data.frame | 17 | 2 |
dse03gg | aprean3 | Dataset for Exercise GG, Chapter 03 | data.frame | 5 | 2 |
dse03h | aprean3 | Dataset for Exercise H, Chapter 03 | data.frame | 14 | 2 |
dse03hh | aprean3 | Dataset for Exercise HH, Chapter 03 | data.frame | 29 | 2 |
dse03i | aprean3 | Dataset for Exercise I, Chapter 03 | data.frame | 13 | 2 |
dse03ii | aprean3 | Dataset for Exercise II, Chapter 03 | data.frame | 32 | 2 |
dse03j | aprean3 | Dataset for Exercise J, Chapter 03 | data.frame | 15 | 2 |
dse03jj | aprean3 | Dataset for Exercise JJ, Chapter 03 | data.frame | 48 | 2 |
dse03jj2 | aprean3 | Dataset for Exercise JJ2, Chapter 03 | data.frame | 9 | 2 |
dse03jj3 | aprean3 | Dataset for Exercise JJ3, Chapter 03 | data.frame | 48 | 2 |
dse03k | aprean3 | Dataset for Exercise K, Chapter 03 | data.frame | 34 | 2 |
dse03kk | aprean3 | Dataset for Exercise KK, Chapter 03 | data.frame | 12 | 2 |
dse03ll | aprean3 | Dataset for Exercise LL, Chapter 03 | data.frame | 13 | 2 |
dse03n | aprean3 | Dataset for Exercise N, Chapter 03 | data.frame | 12 | 2 |
dse03o | aprean3 | Dataset for Exercise O, Chapter 03 | data.frame | 7 | 2 |
dse03r | aprean3 | Dataset for Exercise R, Chapter 03 | data.frame | 43 | 2 |
dse03v | aprean3 | Dataset for Exercise V, Chapter 03 | data.frame | 50 | 2 |
dse03w | aprean3 | Dataset for Exercise W, Chapter 03 | data.frame | 14 | 2 |
dse03x | aprean3 | Dataset for Exercise X, Chapter 03 | data.frame | 11 | 6 |
dse03z | aprean3 | Dataset for Exercise Z, Chapter 03 | data.frame | 17 | 2 |
dse04d | aprean3 | Dataset for Exercise D, Chapter 04 | data.frame | 14 | 2 |
dse04e | aprean3 | Dataset for Exercise E, Chapter 04 | data.frame | 10 | 2 |
dse04f | aprean3 | Dataset for Exercise F, Chapter 04 | data.frame | 10 | 3 |
dse06a | aprean3 | Dataset for Exercise A, Chapter 06 | data.frame | 11 | 4 |
dse06b | aprean3 | Dataset for Exercise B, Chapter 06 | data.frame | 12 | 3 |
dse06c | aprean3 | Dataset for Exercise C, Chapter 06 | data.frame | 15 | 3 |
dse06d | aprean3 | Dataset for Exercise D, Chapter 06 | data.frame | 8 | 3 |
dse06e | aprean3 | Dataset for Exercise E, Chapter 06 | data.frame | 16 | 3 |
dse06f | aprean3 | Dataset for Exercise F, Chapter 06 | data.frame | 17 | 3 |
dse06g | aprean3 | Dataset for Exercise G, Chapter 06 | data.frame | 17 | 3 |
dse06h | aprean3 | Dataset for Exercise H, Chapter 06 | data.frame | 13 | 3 |
dse06i | aprean3 | Dataset for Exercise I, Chapter 06 | data.frame | 7 | 3 |
dse06j | aprean3 | Dataset for Exercise J, Chapter 06 | data.frame | 10 | 3 |
dse06k | aprean3 | Dataset for Exercise K, Chapter 06 | data.frame | 5 | 3 |
dse06l | aprean3 | Dataset for Exercise L, Chapter 06 | data.frame | 9 | 4 |
dse06z | aprean3 | Dataset for Exercise Z, Chapter 06 | data.frame | 19 | 2 |
dse07b | aprean3 | Dataset for Exercise B, Chapter 07 | data.frame | 48 | 4 |
dse07c | aprean3 | Dataset for Exercise C, Chapter 07 | data.frame | 50 | 7 |
dse08b | aprean3 | Dataset for Exercise B, Chapter 08 | data.frame | 5 | 2 |
dse09b | aprean3 | Dataset for Exercise B, Chapter 09 | data.frame | 5 | 3 |
dse12a | aprean3 | Dataset for Exercise A, Chapter 12 | data.frame | 18 | 5 |
dse12b | aprean3 | Dataset for Exercise B, Chapter 12 | data.frame | 20 | 4 |
dse12c | aprean3 | Dataset for Exercise C, Chapter 12 | data.frame | 24 | 2 |
dse12d | aprean3 | Dataset for Exercise D, Chapter 12 | data.frame | 15 | 6 |
dse12e | aprean3 | Dataset for Exercise E, Chapter 12 | data.frame | 28 | 5 |
dse12h | aprean3 | Dataset for Exercise H, Chapter 12 | data.frame | 27 | 4 |
dse13a | aprean3 | Dataset for Exercise A, Chapter 13 | data.frame | 6 | 3 |
dse13b | aprean3 | Dataset for Exercise B, Chapter 13 | data.frame | 47 | 4 |
dse13c | aprean3 | Dataset for Exercise C, Chapter 13 | data.frame | 11 | 6 |
dse13d | aprean3 | Dataset for Exercise D, Chapter 13 | data.frame | 24 | 4 |
dse13e | aprean3 | Dataset for Exercise E, Chapter 13 | data.frame | 43 | 2 |
dse13f | aprean3 | Dataset for Exercise F, Chapter 13 | data.frame | 16 | 3 |
dse13g | aprean3 | Dataset for Exercise G, Chapter 13 | data.frame | 35 | 4 |
dse13h | aprean3 | Dataset for Exercise H, Chapter 13 | data.frame | 8 | 5 |
dse14b | aprean3 | Dataset for Exercise B, Chapter 14 | data.frame | 18 | 5 |
dse14c | aprean3 | Dataset for Exercise C, Chapter 14 | data.frame | 9 | 4 |
dse14d | aprean3 | Dataset for Exercise D, Chapter 14 | data.frame | 6 | 6 |
dse14e | aprean3 | Dataset for Exercise E, Chapter 14 | data.frame | 6 | 6 |
dse14f | aprean3 | Dataset for Exercise F, Chapter 14 | data.frame | 6 | 6 |
dse14g | aprean3 | Dataset for Exercise G, Chapter 14 | data.frame | 20 | 5 |
dse14j | aprean3 | Dataset for Exercise J, Chapter 14 | data.frame | 72 | 2 |
dse14l | aprean3 | Dataset for Exercise L, Chapter 14 | data.frame | 8 | 3 |
dse14q | aprean3 | Dataset for Exercise Q, Chapter 14 | data.frame | 8 | 6 |
dse14r | aprean3 | Dataset for Exercise R, Chapter 14 | data.frame | 48 | 4 |
dse14s | aprean3 | Dataset for Exercise S, Chapter 14 | data.frame | 8 | 4 |
dse14t | aprean3 | Dataset for Exercise T, Chapter 14 | data.frame | 15 | 4 |
dse14u | aprean3 | Dataset for Exercise U, Chapter 14 | data.frame | 15 | 4 |
dse15a | aprean3 | Dataset for Exercise A, Chapter 15 | data.frame | 17 | 6 |
dse15b | aprean3 | Dataset for Exercise B, Chapter 15 | data.frame | 9 | 4 |
dse15c | aprean3 | Dataset for Exercise C, Chapter 15 | data.frame | 20 | 6 |
dse15e | aprean3 | Dataset for Exercise E, Chapter 15 | data.frame | 90 | 8 |
dse15f | aprean3 | Dataset for Exercise F, Chapter 15 | data.frame | 20 | 6 |
dse15h | aprean3 | Dataset for Exercise H, Chapter 15 | data.frame | 33 | 10 |
dse15i | aprean3 | Dataset for Exercise I, Chapter 15 | data.frame | 48 | 6 |
dse15j | aprean3 | Dataset for Exercise J, Chapter 15 | data.frame | 16 | 7 |
dse15k | aprean3 | Dataset for Exercise K, Chapter 15 | data.frame | 16 | 6 |
dse15l | aprean3 | Dataset for Exercise L, Chapter 15 | data.frame | 15 | 8 |
dse15n | aprean3 | Dataset for Exercise N, Chapter 15 | data.frame | 21 | 4 |
dse15p | aprean3 | Dataset for Exercise P, Chapter 15 | data.frame | 16 | 6 |
dse16a | aprean3 | Dataset for Exercise A, Chapter 16 | data.frame | 5 | 4 |
dse16b | aprean3 | Dataset for Exercise B, Chapter 16 | data.frame | 6 | 4 |
dse16c | aprean3 | Dataset for Exercise C, Chapter 16 | data.frame | 7 | 3 |
dse16d | aprean3 | Dataset for Exercise D, Chapter 16 | data.frame | 5 | 3 |
dse17b | aprean3 | Dataset for Exercise B, Chapter 17 | data.frame | 8 | 2 |
dse19d | aprean3 | Dataset for Exercise D, Chapter 19 | data.frame | 8 | 4 |
dse22a | aprean3 | Dataset for Exercise A, Chapter 22 | data.frame | 11 | 9 |
dse22b | aprean3 | Dataset for Exercise B, Chapter 22 | data.frame | 6 | 4 |
dse22c | aprean3 | Dataset for Exercise C, Chapter 22 | data.frame | 20 | 7 |
dse22d | aprean3 | Dataset for Exercise D, Chapter 22 | data.frame | 10 | 9 |
dse22e | aprean3 | Dataset for Exercise E, Chapter 22 | data.frame | 9 | 7 |
dse22f | aprean3 | Dataset for Exercise F, Chapter 22 | data.frame | 12 | 7 |
dse22g | aprean3 | Dataset for Exercise G, Chapter 22 | data.frame | 24 | 5 |
dse23a_a | aprean3 | Dataset for Exercise A-a, Chapter 23 | data.frame | 18 | 7 |
dse23a_b | aprean3 | Dataset for Exercise A-b, Chapter 23 | data.frame | 18 | 7 |
dse23d | aprean3 | Dataset for Exercise D, Chapter 23 | data.frame | 19 | 2 |
dse23e | aprean3 | Dataset for Exercise E, Chapter 23 | data.frame | 16 | 9 |
dse23f | aprean3 | Dataset for Exercise F, Chapter 23 | data.frame | 9 | 4 |
dse23g_1 | aprean3 | Dataset for Exercise G-1, Chapter 23 | data.frame | 15 | 9 |
dse23g_2 | aprean3 | Dataset for Exercise G-2, Chapter 23 | data.frame | 15 | 9 |
dse23h | aprean3 | Dataset for Exercise H, Chapter 23 | data.frame | 16 | 6 |
dse24a | aprean3 | Dataset for Exercise A, Chapter 24 | data.frame | 3 | 2 |
dse24b | aprean3 | Dataset for Exercise B, Chapter 24 | data.frame | 12 | 2 |
dse24c | aprean3 | Dataset for Exercise C, Chapter 24 | data.frame | 4 | 2 |
dse24d | aprean3 | Dataset for Exercise D, Chapter 24 | data.frame | 5 | 2 |
dse24e | aprean3 | Dataset for Exercise E, Chapter 24 | data.frame | 14 | 2 |
dse24g | aprean3 | Dataset for Exercise G, Chapter 24 | data.frame | 5 | 2 |
dse24h | aprean3 | Dataset for Exercise H, Chapter 24 | data.frame | 38 | 3 |
dse24i | aprean3 | Dataset for Exercise I, Chapter 24 | data.frame | 8 | 3 |
dse24j | aprean3 | Dataset for Exercise J, Chapter 24 | data.frame | 13 | 3 |
dse24k | aprean3 | Dataset for Exercise K, Chapter 24 | data.frame | 5 | 2 |
dse24l | aprean3 | Dataset for Exercise L, Chapter 24 | data.frame | 55 | 3 |
dse24m | aprean3 | Dataset for Exercise M, Chapter 24 | data.frame | 43 | 2 |
dse24n | aprean3 | Dataset for Exercise N, Chapter 24 | data.frame | 7 | 6 |
dse24o | aprean3 | Dataset for Exercise O, Chapter 24 | data.frame | 4 | 2 |
dse24p | aprean3 | Dataset for Exercise P, Chapter 24 | data.frame | 5 | 3 |
dsq23_3_3 | aprean3 | Dataset for Equation 23.3.3 | data.frame | 30 | 5 |
dsq23_3_7 | aprean3 | Dataset for Equation 23.3.7 | data.frame | 30 | 4 |
dsq23_7_5 | aprean3 | Dataset for Equation 23.7.5 | data.frame | 24 | 12 |
dss217 | aprean3 | Dataset for Section 21.7 | data.frame | 25 | 3 |
dss222 | aprean3 | Dataset for Section 22.2 | data.frame | 75 | 8 |
dss2310 | aprean3 | Dataset for Section 23.10 | data.frame | 24 | 3 |
dst021 | aprean3 | Dataset from Table 02.1 | data.frame | 23 | 3 |
dst032 | aprean3 | Dataset from Table 03.2 | data.frame | 49 | 3 |
dst033 | aprean3 | Dataset from Table 03.3 | data.frame | 20 | 2 |
dst081 | aprean3 | Dataset from Table 08.1 | data.frame | 21 | 2 |
dst121 | aprean3 | Dataset from Table 12.1 | data.frame | 20 | 5 |
dst132 | aprean3 | Dataset from Table 13.2 | data.frame | 23 | 3 |
dst134 | aprean3 | Dataset from Table 13.4 | data.frame | 23 | 3 |
dst141 | aprean3 | Dataset from Table 14.1 | data.frame | 13 | 4 |
dst144 | aprean3 | Dataset from Table 14.4 | data.frame | 9 | 4 |
dst145 | aprean3 | Dataset from Table 14.5 | data.frame | 9 | 4 |
dst146 | aprean3 | Dataset from Table 14.6 | data.frame | 9 | 5 |
dst181 | aprean3 | Dataset from Table 18.1 | data.frame | 10 | 3 |
dst191 | aprean3 | Dataset from Table 19.1 | data.frame | 10 | 4 |
dst192 | aprean3 | Dataset from Table 19.2 | data.frame | 36 | 8 |
dst232 | aprean3 | Dataset from Table 23.2 | data.frame | 30 | 2 |
dst242 | aprean3 | Dataset from Table 24.2 | data.frame | 44 | 2 |
dst252 | aprean3 | Dataset from Table 25.2 | data.frame | 20 | 3 |
dst253 | aprean3 | Dataset from Table 25.3 | data.frame | 25 | 2 |
dst261 | aprean3 | Dataset from Table 26.1 | data.frame | 10 | 2 |
dsx161 | aprean3 | Dataset for Example 1, Chapter 16 | matrix | 6 | |
fl | sitree | Plot Data | list | | |
stand.west.st | sitree | Stand and plot characteritics for stand.west.tr | data.frame | 4 | 8 |
stand.west.tr | sitree | A whole stand dataset | data.frame | 651 | 5 |
tr | sitree | Individual Tree Data | data.frame | 2265 | 5 |
patternsWithFormula | DrugUtilisation | Patterns valid to compute daily dose with the associated formula. | tbl_df | 41 | 9 |
RS.data | rENA | Coded Rescushell Chat Data | data.frame | 3824 | 20 |
TCGA_E9_A1N5_mirnanormal | ceRNAnetsim | TCGA_E9_A1N5_mirnanormal | tbl_df | 750 | 6 |
TCGA_E9_A1N5_mirnatumor | ceRNAnetsim | TCGA_E9_A1N5_mirnatumor | tbl_df | 648 | 6 |
TCGA_E9_A1N5_normal | ceRNAnetsim | TCGA_E9_A1N5_normal | tbl_df | 56830 | 7 |
TCGA_E9_A1N5_tumor | ceRNAnetsim | TCGA_E9_A1N5_tumor | tbl_df | 56830 | 7 |
huge_example | ceRNAnetsim | huge example | data.frame | 26176 | 7 |
midsamp | ceRNAnetsim | midsamp | data.frame | 26 | 7 |
midsamp_new_counts | ceRNAnetsim | midsamp_new_counts | data.frame | 26 | 4 |
minsamp | ceRNAnetsim | minsamp | data.frame | 7 | 7 |
mirtarbasegene | ceRNAnetsim | mirtarbasegene | tbl_df | 380627 | 2 |
new_counts | ceRNAnetsim | new_counts | data.frame | 7 | 4 |
fdat | FunSurv | Simulated datasets for demonstration | data.frame | 572 | 3 |
sdat | FunSurv | Simulated datasets for demonstration | data.frame | 209 | 7 |
school_ses | segregation | Student-level data including SES status | data.frame | 5153 | 3 |
schools00 | segregation | Ethnic/racial composition of schools for 2000/2001 | tbl_df | 8142 | 5 |
schools05 | segregation | Ethnic/racial composition of schools for 2005/2006 | tbl_df | 8013 | 5 |
comp_matrix | warbleR | Example matrix listing selections to be compared by 'cross_correlation' | matrix | 2 | |
lbh_selec_table | warbleR | Example data frame of selections (i.e. selection table). | data.frame | 11 | 7 |
sim_coor_sing | warbleR | Simulated coordinated singing events. | data.frame | 446 | 4 |
sth_annotations | warbleR | Example data frame of annotations from a Scale-throated hermit song (i.e. selection table). | data.frame | 46 | 9 |
car.insur | DLSSM | Dataset contains information of full comprehensive Australian automobile insurance policies between years 2004 and 2005 A dataset containing the claim and three attributes of 67,856 policies | data.frame | 67856 | 4 |
items_diao | eatATA | Small simulated item pool example. | data.frame | 165 | 5 |
items_lsa | eatATA | Simulated item pool example. | data.frame | 209 | 8 |
items_mini | eatATA | Small simulated item pool example. | data.frame | 30 | 4 |
items_pilot | eatATA | Small simulated item pool example. | data.frame | 100 | 6 |
items_vera | eatATA | Small artificial item pool example. | data.frame | 80 | 13 |
PS_SAM_data | SAMprior | Simulated Data for the Construction of Propensity Score-Integrated Informative Priors | data.frame | 600 | 7 |
netsam_output | NetSAM | An example of the list object returned by NetSAM function | list | | |
DEnma | DAAGbio | Spotted microarray M and A values; differentially expressed controls | MAList | | |
coralRG | DAAGbio | Spotted microarray red and green foreground and background values | RGList | | |
coralTargets | DAAGbio | Targets file to accompany spotted expression array data | data.frame | 6 | 4 |
plantStressCounts | DAAGbio | Matrix holding mRNA counts | matrix | 28775 | 9 |
primateDNA | DAAGbio | Mitochondrial DNA sequence data from 14 primates | matrix | 14 | |
GBSG2 | TH.data | German Breast Cancer Study Group 2 | data.frame | 686 | 10 |
GlaucomaM | TH.data | Glaucoma Database | data.frame | 196 | 63 |
Westbc | TH.data | Breast Cancer Gene Expression | list | | |
birds | TH.data | Habitat Suitability for Breeding Bird Communities | data.frame | 258 | 10 |
bodyfat | TH.data | Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths | data.frame | 71 | 10 |
geyser | TH.data | Old Faithful Geyser Data | data.frame | 299 | 2 |
mammoexp | TH.data | Mammography Experience Study | data.frame | 412 | 6 |
mn6.9 | TH.data | I.Q. and attitude towards science | data.frame | 2982 | 5 |
sphase | TH.data | S-phase Fraction of Tumor Cells | data.frame | 109 | 3 |
wpbc | TH.data | Wisconsin Prognostic Breast Cancer Data | data.frame | 198 | 34 |
sample_data_extern | RobustPrediction | Sample External Validation Data Subset | data.frame | 50 | 201 |
sample_data_train | RobustPrediction | Sample Training Data Subset | data.frame | 50 | 201 |
anger | ggm | Anger data | matrix | 4 | 4 |
derived | ggm | Data on blood pressure body mass and age | list | | |
glucose | ggm | Glucose control | data.frame | 68 | 8 |
marks | ggm | Mathematics marks | data.frame | 88 | 5 |
stress | ggm | Stress | matrix | 4 | 4 |
surdata | ggm | A simulated data set | data.frame | 600 | 4 |
NAEP | cherry | National Assessment of Educational Progress (NAEP) p-values | numeric | | |
DEGsmatrix | MoonlightR | DEG Differentially expressed genes | data.frame | 3390 | 5 |
DiseaseList | MoonlightR | Information on 101 biological processes | list | | |
EAGenes | MoonlightR | Information about genes | data.frame | 20038 | 5 |
GDCprojects | MoonlightR | Information on GDC projects | character | | |
GEO_TCGAtab | MoonlightR | Information on GEO data (and overlap with TCGA)#' A data set containing the following data: | data.frame | 18 | 12 |
dataFilt | MoonlightR | Gene Expression (Rnaseqv2) data from TCGA LUAD | matrix | 13742 | 20 |
dataGRN | MoonlightR | GRN gene regulatory network output | list | | |
dataURA | MoonlightR | Output example from function Upstram Regulator Analysis | matrix | 100 | 2 |
geneInfo | MoonlightR | Information about genes for normalization | matrix | 20531 | 3 |
knownDriverGenes | MoonlightR | Information on known cancer driver gene from COSMIC | list | | |
listMoonlight | MoonlightR | Output list from Moonlight | list | | |
tabGrowBlock | MoonlightR | Information growing/blocking characteristics for 101 selected biological processes | data.frame | 101 | 3 |
LyonIris | geocmeans | social and environmental indicators for the Iris of the metropolitan region of Lyon (France) | sf | 506 | 16 |
expr | interactiveDisplay | An Example ExpressionSet object | ExpressionSet | | |
mmgr | interactiveDisplay | An Example GRanges Object | GRanges | | |
mmgrl | interactiveDisplay | An Example GRangesList Object | CompressedGRangesList | | |
se | interactiveDisplay | An Example RangedSummarizedExperiment Object | RangedSummarizedExperiment | | |
CIACountries | mdsr | Several variables on countries from the CIA Factbook, 2014. | data.frame | 236 | 8 |
Cancer | mdsr | Gene expression in cancer | tbl_df | 1770 | 3 |
Cherry | mdsr | Cherry Blossom runs | tbl_df | 41248 | 8 |
CholeraDeaths | mdsr | Deaths and Pumps from 1854 London cholera outbreak | sf | 250 | 3 |
CholeraPumps | mdsr | Deaths and Pumps from 1854 London cholera outbreak | sf | 8 | 2 |
Cuisines | mdsr | NYC Restaurant Health Violations | tbl_df | 84 | 2 |
DataSciencePapers | mdsr | Data Science Papers from arXiv.org | tbl_df | 1089 | 15 |
Elections | mdsr | Election Statistics from the 2013 Minneapolis Mayoral Election | tbl_df | 117 | 13 |
Emails_test | mdsr | Email Train | data.frame | 1382 | 3 |
Emails_train | mdsr | Email Train | data.frame | 5526 | 3 |
Headlines_test | mdsr | Headlines_train | data.frame | 4589 | 3 |
Headlines_train | mdsr | Headlines_train | data.frame | 18360 | 3 |
MLB_teams | mdsr | Data about recent major league baseball teams | tbl_df | 210 | 11 |
Macbeth_raw | mdsr | Text of Macbeth | character | | |
MedicareCharges | mdsr | Charges to and Payments from Medicare | tbl_df | 5025 | 4 |
MedicareProviders | mdsr | Medicare Providers | data.frame | 3337 | 7 |
Minneapolis2013 | mdsr | Ballots in the 2013 Mayoral election in Minneapolis | data.frame | 80101 | 5 |
NCI60_tiny | mdsr | Gene expression in cancer | data.frame | 6 | 61 |
Parties | mdsr | Votes from Scottish Parliament | data.frame | 134 | 2 |
SAT_2010 | mdsr | State SAT scores from 2010 | data.frame | 50 | 9 |
ViolationCodes | mdsr | NYC Restaurant Health Violations | tbl_df | 97 | 3 |
Violations | mdsr | NYC Restaurant Health Violations | tbl_df | 480621 | 16 |
Votes | mdsr | Votes from Scottish Parliament | data.frame | 103582 | 3 |
ordway_birds | mdsr | Birds captured and released at Ordway, complete and uncleaned | tbl_df | 15829 | 26 |
saratoga_codes | mdsr | Saratoga Houses | spec_tbl_df | 13 | 3 |
saratoga_houses | mdsr | Saratoga Houses | spec_tbl_df | 1728 | 16 |
world_cities | mdsr | Cities and their populations | tbl_df | 4428 | 9 |
lungData | metagenomeSeq | OTU abundance matrix of samples from a smoker/non-smoker study | MRexperiment | | |
mouseData | metagenomeSeq | OTU abundance matrix of mice samples from a diet longitudinal study | MRexperiment | | |
yeastchemostat | Rnits | Yeast chemostat data from Ronen and Botstein (Proc Natl Acad Sci U S A. 2006 Jan 10;103(2):389-94. Epub 2005 Dec 28.) | ExpressionSet | | |
eg_adae | autoslider.core | Cached ADAE | tbl_df | 1934 | 93 |
eg_adeg | autoslider.core | Cached ADEG | tbl_df | 13600 | 88 |
eg_adex | autoslider.core | Cached ADEX | tbl_df | 6400 | 79 |
eg_adlb | autoslider.core | Cached ADLB | tbl_df | 8400 | 102 |
eg_adrs | autoslider.core | Cached ADRS | tbl_df | 3200 | 65 |
eg_adsl | autoslider.core | Cached ADSL | tbl_df | 400 | 55 |
eg_adtr | autoslider.core | Cached ADTR | data.frame | 2800 | 76 |
eg_adtte | autoslider.core | Cached ADTTE | tbl_df | 2000 | 67 |
eg_advs | autoslider.core | Cached ADVS | tbl_df | 16800 | 87 |
glo.parms | SongEvo | Default model parameters | list | | |
song.data | SongEvo | White Crown Sparrow Song Observations | data.frame | 89 | 3 |
ERCCDef | erccdashboard | ERCCDef dataframe | data.frame | 96 | 3 |
ERCCMix1and2 | erccdashboard | ERCCMix1and2 dataframe | data.frame | 96 | 4 |
MET.CTL.countDat | erccdashboard | Rat toxicogenomics count data | data.frame | 16590 | 7 |
MET.CTL.totalReads | erccdashboard | Rat toxicogenomics total read data | integer | | |
UHRR.HBRR.arrayDat | erccdashboard | UHRR and HBRR Illumina BeadArray data | data.frame | 17627 | 7 |
UHRR.HBRR.countDat | erccdashboard | UHRR and HBRR RNA-Seq Illumina count data | data.frame | 43919 | 9 |
UHRR.HBRR.totalReads | erccdashboard | UHRR and HBRR sample total read data | integer | | |
tern_ex_adae | tern | Simulated CDISC data for examples | tbl_df | 541 | 42 |
tern_ex_adlb | tern | Simulated CDISC data for examples | tbl_df | 4200 | 50 |
tern_ex_adpp | tern | Simulated CDISC data for examples | tbl_df | 522 | 25 |
tern_ex_adrs | tern | Simulated CDISC data for examples | tbl_df | 1600 | 29 |
tern_ex_adsl | tern | Simulated CDISC data for examples | tbl_df | 200 | 21 |
tern_ex_adtte | tern | Simulated CDISC data for examples | tbl_df | 1000 | 28 |
relative_periods | dhis2r | Relative periods in DHIS2 | tbl_df | 30 | 2 |
tte.df | WARDEN | Example TTE IPD data | data.frame | 1000 | 8 |
isotopes | CorMID | A data frame containing isotope information. | data.frame | 308 | 5 |
known_frags | CorMID | The known fragments and their relative position to '[M+H]'. | numeric | | |
precalc_idx | CorMID | Pre-calculated index matrices to speed up calculations in function 'getMID'. | list | | |
prep | CorMID | Example data as used in function 'CorMID'. | list | | |
faceratings | faux | Attractiveness ratings of faces | tbl_df | 256326 | 9 |
fr4 | faux | Attractiveness rating subset | tbl_df | 768 | 9 |
simdata | weightQuant | Simulated dataset | data.frame | 2123 | 7 |
combined_pbmc | APackOfTheClones | Example Multi-sampled T-cell seurat object with integrated TCR library | Seurat | | |
bt_list_db | babyTimeR | Sample 'BabyTime' Data List Database | Clean BT List DB | | |
nas1982 | betaSandwich | 1982 National Academy of Sciences Doctoral Programs Data | data.frame | 46 | 7 |
carbs | mdatools | Raman spectra of carbonhydrates | list | | |
pellets | mdatools | Image data | array | | |
people | mdatools | People data | matrix | 32 | 12 |
simdata | mdatools | Spectral data of polyaromatic hydrocarbons mixing | list | | |
bigdat | msmbayes | A simulated multistate dataset with lots of observations and covariates | data.frame | 36000 | 65 |
cav_misc | msmbayes | Example fitted model objects used for testing msmbayes | msmbayes | 1 | 13 |
infsim | msmbayes | Simulated infection testing data | data.frame | 3600 | 9 |
infsim2 | msmbayes | Simulated infection testing data | data.frame | 360 | 14 |
infsim_model | msmbayes | Example fitted model objects used for testing msmbayes | msmbayes | 1 | 9 |
infsim_modelc | msmbayes | Example fitted model objects used for testing msmbayes | msmbayes | 1 | 11 |
infsim_modelp | msmbayes | Example fitted model objects used for testing msmbayes | msmbayes | 1 | 8 |
infsim_modelpc | msmbayes | Example fitted model objects used for testing msmbayes | msmbayes | 1 | 9 |
flankerData | DMCfun | A summarised dataset: This is the flanker task data from Ulrich et al. (2015) | dmcob | | |
simonData | DMCfun | A summarised dataset: This is the simon task data from Ulrich et al. (2015) | dmcob | | |
ohsome_api_url | ohsome | ohsome API URL | list | | |
ohsome_endpoints | ohsome | ohsome API endpoints | list | | |
sample_sce_data | CellBench | This is data for testing functions in CellBench. | SingleCellExperiment | | |
cuts | msgbsR | A GRanges object of differentially methylated MspI cut sites on chromosome 20 in Rat from a MS-GBS experiment. | GRanges | | |
ratdata | msgbsR | Read counts of potential MspI cut sites from a MS-GBS experiment of prostates from rats | RangedSummarizedExperiment | | |
ratdata2 | msgbsR | Read counts of correct MspI cut sites from a MS-GBS experiment of prostates from rats | RangedSummarizedExperiment | | |
example_log | processanimateR | Example event log used in documentation | eventlog | 20 | 7 |
HRQoL | forestplot | Regression coefficients and confidence intervals from HRQoL study | list | | |
dfHRQoL | forestplot | Regression coefficients and confidence intervals from HRQoL study | tbl_df | 8 | 5 |
MOD_RNA_DICT_MODOMICS | Modstrings | Modstrings internals | DFrame | | |
MOD_RNA_DICT_TRNADB | Modstrings | Modstrings internals | DFrame | | |
modsDNA | Modstrings | Modstrings internals | DFrame | | |
modsRNA | Modstrings | Modstrings internals | DFrame | | |
Path_ES | ssMutPA | Path_ES Single-sample mutation-based pathway enrichment score profiles. | matrix | 215 | 882 |
Path_Name | ssMutPA | Path_Name | character | | |
RWR_res | ssMutPA | RWR_res Random walk results based on mutated genes in a single sample. | numeric | | |
Seeds_Score | ssMutPA | Seeds_Score The local weight of seed nodes. | data.frame | 35 | 2 |
cox_data | ssMutPA | cox_data Univariate cox proportional hazards regression data. | data.frame | 20 | 293 |
dot_data | ssMutPA | dot_data A data frame. | data.frame | 350 | 4 |
mut_onco | ssMutPA | mut_onco A binary mutation matrix. | matrix | 11968 | 44 |
mut_status | ssMutPA | mut_status A binary mutation matrix. | matrix | 12802 | 50 |
samp_class_onco | ssMutPA | samp_class_onco | integer | | |
sample_class | ssMutPA | sample_class The subtype labels of samples. | integer | | |
sur_onco | ssMutPA | sur_onco | data.frame | 44 | 2 |
cghs | RDHonest | Oreopoulos (2006) UK general household survey dataset | data.frame | 73954 | 2 |
headst | RDHonest | Head Start data from Ludwig and Miller (2007) | data.frame | 3127 | 18 |
lee08 | RDHonest | Lee (2008) US House elections dataset | data.frame | 6558 | 2 |
rcp | RDHonest | Battistin, Brugiavini, Rettore, and Weber (2009) retirement consumption puzzle dataset | data.frame | 30006 | 8 |
rebp | RDHonest | Austrian unemployment duration data from Lalive (2008) | data.frame | 29371 | 4 |
ADL | exhaustiveRasch | Activities of Daily Living - dichotomous example data | data.frame | 588 | 17 |
InterprofessionalCollaboration | exhaustiveRasch | InterprofessionalCollaboration - polytomous example data | data.frame | 716 | 23 |
cognition | exhaustiveRasch | cognition - polytomous example data data measured with the FACT-cog (subscale perceived cognitive functioning) sample size: N=1009 | data.frame | 1009 | 29 |
mozzies_nsw2301 | ggmapinset | Mosquito counts from NSW Arbovirus Surveillance program | tbl_df | 720 | 7 |
mpn_DE_1990 | migraph | Two-mode European Values Survey, 1990 and 2008 (EVS 2020) | tbl_graph | | |
mpn_DE_2008 | migraph | Two-mode European Values Survey, 1990 and 2008 (EVS 2020) | tbl_graph | | |
mpn_IT_1990 | migraph | Two-mode European Values Survey, 1990 and 2008 (EVS 2020) | tbl_graph | | |
mpn_IT_2008 | migraph | Two-mode European Values Survey, 1990 and 2008 (EVS 2020) | tbl_graph | | |
mpn_UK_1990 | migraph | Two-mode European Values Survey, 1990 and 2008 (EVS 2020) | tbl_graph | | |
mpn_UK_2008 | migraph | Two-mode European Values Survey, 1990 and 2008 (EVS 2020) | tbl_graph | | |
mpn_bristol | migraph | Multimodal (3) Bristol protest events, 1990-2002 (Diani and Bison 2004) | mnet | | |
mpn_cow_igo | migraph | One-mode interstate trade relations and two-mode state membership in IGOs (COW) | mnet | | |
mpn_cow_trade | migraph | One-mode interstate trade relations and two-mode state membership in IGOs (COW) | mnet | | |
mpn_elite_mex | migraph | One-mode Mexican power elite database (Knoke 1990) | mnet | | |
mpn_elite_usa_advice | migraph | Two-mode and three-mode American power elite database (Domhoff 2016) | mnet | | |
mpn_elite_usa_money | migraph | Two-mode and three-mode American power elite database (Domhoff 2016) | mnet | | |
mpn_ryanair | migraph | One-mode EU policy influence network, June 2004 (Christopoulos 2006) | mnet | | |
mpn_senate_dem | migraph | Two-mode 112th Congress Senate Voting (Knoke et al. 2021) | mnet | | |
mpn_senate_over | migraph | Two-mode 112th Congress Senate Voting (Knoke et al. 2021) | mnet | | |
mpn_senate_rep | migraph | Two-mode 112th Congress Senate Voting (Knoke et al. 2021) | mnet | | |
meta_basic_cs_2016 | rqog | Metadata for 2016 Quality of Government institute Basic Data - cross-sectional | tbl_df | 438 | 5 |
meta_basic_cs_2017 | rqog | Metadata for 2017 Quality of Government institute Basic Data - cross-sectional | tbl_df | 477 | 5 |
meta_basic_cs_2018 | rqog | Metadata for 2018 Quality of Government institute Basic Data - cross-sectional | tbl_df | 644 | 5 |
meta_basic_cs_2019 | rqog | Metadata for 2019 Quality of Government institute Basic Data - cross-sectional | tbl_df | 652 | 5 |
meta_basic_cs_2020 | rqog | Metadata for 2020 Quality of Government institute Basic Data - cross-sectional | tbl_df | 527 | 5 |
meta_basic_cs_2021 | rqog | Metadata for 2021 Quality of Government institute Basic Data - cross-sectional | tbl_df | 498 | 5 |
meta_basic_cs_2022 | rqog | Metadata for 2022 Quality of Government institute Basic Data - cross-sectional | tbl_df | 465 | 5 |
meta_basic_cs_2023 | rqog | Metadata for 2023 Quality of Government institute Basic Data - cross-sectional | data.frame | 458 | 5 |
meta_basic_ts_2016 | rqog | Metadata for 2016 Quality of Government institute Basic Data - time-series | tbl_df | 335 | 5 |
meta_basic_ts_2017 | rqog | Metadata for 2017 Quality of Government institute Basic Data - time-series | tbl_df | 330 | 5 |
meta_basic_ts_2018 | rqog | Metadata for 2018 Quality of Government institute Basic Data - time-series | tbl_df | 558 | 5 |
meta_basic_ts_2019 | rqog | Metadata for 2019 Quality of Government institute Basic Data - time-series | tbl_df | 556 | 5 |
meta_basic_ts_2020 | rqog | Metadata for 2020 Quality of Government institute Basic Data - time-series | tbl_df | 484 | 5 |
meta_basic_ts_2021 | rqog | Metadata for 2021 Quality of Government institute Basic Data - time-series | tbl_df | 447 | 5 |
meta_basic_ts_2022 | rqog | Metadata for 2022 Quality of Government institute Basic Data - time-series | tbl_df | 458 | 5 |
meta_basic_ts_2023 | rqog | Metadata for 2023 Quality of Government institute Basic Data - time-series | data.frame | 432 | 5 |
meta_oecd_cs_2016 | rqog | Metadata for 2016 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1608 | 5 |
meta_oecd_cs_2017 | rqog | Metadata for 2017 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1311 | 5 |
meta_oecd_cs_2018 | rqog | Metadata for 2018 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1319 | 5 |
meta_oecd_cs_2019 | rqog | Metadata for 2019 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1825 | 5 |
meta_oecd_cs_2020 | rqog | Metadata for 2020 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1170 | 5 |
meta_oecd_cs_2021 | rqog | Metadata for 2021 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1226 | 5 |
meta_oecd_cs_2022 | rqog | Metadata for 2022 Quality of Government institute OECD Data - cross-sectional | tbl_df | 1223 | 5 |
meta_oecd_cs_2023 | rqog | Metadata for 2023 Quality of Government institute OECD Data - cross-sectional | data.frame | 1246 | 5 |
meta_oecd_ts_2016 | rqog | Metadata for 2016 Quality of Government institute OECD Data - time-series | tbl_df | 1507 | 5 |
meta_oecd_ts_2017 | rqog | Metadata for 2017 Quality of Government institute OECD Data - time-series | tbl_df | 1269 | 5 |
meta_oecd_ts_2018 | rqog | Metadata for 2018 Quality of Government institute OECD Data - time-series | tbl_df | 1407 | 5 |
meta_oecd_ts_2019 | rqog | Metadata for 2019 Quality of Government institute OECD Data - time-series | tbl_df | 1769 | 5 |
meta_oecd_ts_2020 | rqog | Metadata for 2020 Quality of Government institute OECD Data - time-series | tbl_df | 1351 | 5 |
meta_oecd_ts_2021 | rqog | Metadata for 2021 Quality of Government institute OECD Data - time-series | tbl_df | 1374 | 5 |
meta_oecd_ts_2022 | rqog | Metadata for 2022 Quality of Government institute OECD Data - time-series | tbl_df | 1464 | 5 |
meta_oecd_ts_2023 | rqog | Metadata for 2023 Quality of Government institute OECD Data - time-series | data.frame | 1468 | 5 |
meta_std_cs_2016 | rqog | Metadata for 2016 Quality of Government institute Standard Data - cross-sectional | tbl_df | 2825 | 5 |
meta_std_cs_2017 | rqog | Metadata for 2017 Quality of Government institute Standard Data - cross-sectional | tbl_df | 2455 | 5 |
meta_std_cs_2018 | rqog | Metadata for 2018 Quality of Government institute Standard Data - cross-sectional | tbl_df | 2393 | 5 |
meta_std_cs_2019 | rqog | Metadata for 2019 Quality of Government institute Standard Data - cross-sectional | tbl_df | 2981 | 5 |
meta_std_cs_2020 | rqog | Metadata for 2020 Quality of Government institute Standard Data - cross-sectional | tbl_df | 2063 | 5 |
meta_std_cs_2021 | rqog | Metadata for 2021 Quality of Government institute Standard Data - cross-sectional | tbl_df | 1997 | 5 |
meta_std_cs_2022 | rqog | Metadata for 2022 Quality of Government institute Standard Data - cross-sectional | tbl_df | 1937 | 5 |
meta_std_cs_2023 | rqog | Metadata for 2023 Quality of Government institute Standard Data - cross-sectional | data.frame | 1920 | 5 |
meta_std_ts_2016 | rqog | Metadata for 2016 Quality of Government institute Standard Data - time-series | tbl_df | 2882 | 5 |
meta_std_ts_2017 | rqog | Metadata for 2017 Quality of Government institute Standard Data - time-series | tbl_df | 3075 | 5 |
meta_std_ts_2018 | rqog | Metadata for 2018 Quality of Government institute Standard Data - time-series | tbl_df | 3221 | 5 |
meta_std_ts_2019 | rqog | Metadata for 2019 Quality of Government institute Standard Data - time-series | tbl_df | 3254 | 5 |
meta_std_ts_2020 | rqog | Metadata for 2020 Quality of Government institute Standard Data - time-series | tbl_df | 2620 | 5 |
meta_std_ts_2021 | rqog | Metadata for 2021 Quality of Government institute Standard Data - time-series | tbl_df | 2347 | 5 |
meta_std_ts_2022 | rqog | Metadata for 2022 Quality of Government institute Standard Data - time-series | tbl_df | 2408 | 5 |
meta_std_ts_2023 | rqog | Metadata for 2023 Quality of Government institute Standard Data - time-series | data.frame | 2427 | 5 |
availableBoundaries | sgapi | List of available ONS boundaries | character | | |
lookup | sgapi | Lookup to match Office for National Statistics (ONS) and 'nomis' boundary names | data.table | 30 | 4 |
nomisTables | sgapi | #' List of tables on 'nomis' | data.frame | 1610 | 3 |
scalesForEachDataset | sgapi | #' List of tables available at each ONS resolution | data.frame | 22775 | 3 |
kits | simDNAmixtures | Properties such as loci, dye, sizes for most standard forensic kits | list | | |
liberiaStdData | anthrocheckr | Dataset from a standardisation exercise done in Liberia in preparation for a coverage survey. | data.frame | 744 | 7 |
smartStd | anthrocheckr | Standardisation test data - sample 1 | data.frame | 110 | 8 |
smartStdLong | anthrocheckr | Standardisation test data - sample 2 | data.frame | 660 | 5 |
smartWide | anthrocheckr | Standardisation test data - sample 3 | tbl_df | 10 | 67 |
stature | anthrocheckr | Example dataset from *Ulijaszek and Kerr (1999)* containing repeat measurements of stature 'm' carried out by four observers on ten subjects. | data.frame | 10 | 8 |
theta0 | first90 | | numeric | | |
IVaids | dblcens | Data: AIDS patient among IV drug user | data.frame | 232 | 7 |
gest | tidygam | Number of gestures by infants at 10, 11 and 12 months | tbl_df | 540 | 5 |
struct | tidygam | ERP to structural violation in music and language | tbl_df | 4400 | 7 |
poke_data | shinyNextUI | Pokemon API data | list | | |
bitcoin | fastcpd | Bitcoin Market Price (USD) | data.frame | 1354 | 2 |
occupancy | fastcpd | Occupancy Detection Data Set | data.frame | 9752 | 7 |
transcriptome | fastcpd | Transcription Profiling of 57 Human Bladder Carcinoma Samples | data.frame | 2215 | 43 |
uk_seatbelts | fastcpd | UK Seatbelts Data | mts | 192 | 8 |
well_log | fastcpd | Well-log Dataset from Numerical Bayesian Methods Applied to Signal Processing | ts | | |
ant | iNEXT | Ant data (datatype = "incidence_freq") | list | | |
bird | iNEXT | Bird data (datatype = "abundance") | data.frame | 41 | 2 |
ciliates | iNEXT | Ciliates data (datatype = "incidence_raw") | list | | |
spider | iNEXT | Spider data (datatype = "abundance") | list | | |
exampleMS | MassSpecWavelet | An example mass spectrum | matrix | 37656 | |
injd | injurytools | Example of an 'injd' object | injd | 108 | 19 |
raw_df_exposures | injurytools | Minimal example of exposure data | data.frame | 42 | 19 |
raw_df_injuries | injurytools | Minimal example of injury data | tbl_df | 82 | 11 |
toy_evyth | comunicacion | Muestra no representativa (de "juguete) de la Encuesta de Viajes y Turismo de los Hogares (EVyTH-DNMyE). | data.frame | 5000 | 88 |
GSE42861small | omicwas | Small Subset of GSE42861 Dataset From GEO | list | | |
GSE79262small | omicwas | Small Subset of GSE79262 Dataset From GEO | list | | |
GTExsmall | omicwas | Small Subset of GTEx Dataset | list | | |
FC | metaRNASeq | Simulated fold changes (FC) | list | | |
adjpval | metaRNASeq | Simulated adjusted p-values | list | | |
dispFuncs | metaRNASeq | Gamma regression parameters describing the mean-dispersion relationship for two real datasets. | list | | |
param | metaRNASeq | Mean simulation parameters | data.frame | 26408 | 3 |
rawpval | metaRNASeq | Simulated p-values | list | | |
wdpa_countries | worldpa | World Countries Listed in the UNEP-WCMC Regions | data.frame | 248 | 5 |
anger | plfm | Situational determinants of anger-related behavior | list | | |
anger2 | plfm | Situational determinants of anger-related behavior | list | | |
car | plfm | Ratings of associations between car models and car attributes | list | | |
car2 | plfm | Judgements on associations between car models and car attributes | list | | |
hostility | plfm | self-reported hostile behavior in frustrating situations | list | | |
bone_marrow_genex | TAPseq | Mouse bone marrow 10x data | Seurat | | |
chr11_genes | TAPseq | Chromosome 11 genes | GRanges | | |
chr11_polyA_sites | TAPseq | Chromosome 11 polyA sites | GRanges | | |
chr11_primers | TAPseq | Chromosome 11 primers | TsIOList | | |
chr11_truncated_txs | TAPseq | Chromosome 11 truncated transcripts | CompressedGRangesList | | |
chr11_truncated_txs_seq | TAPseq | Chromosome 11 truncated transcript sequences | DNAStringSet | | |
fpkm | RNAAgeCalc | An example of FPKM data | matrix | 24989 | 2 |
rawcount | RNAAgeCalc | An example of RNASeq counts data | matrix | 24989 | 2 |
deer_sprites | nara | List of deer native rasters | list | | |
vac1.day0vs31.de.genes | RITAN | This dataset is included as an example in the package: | character | | |
vac1.day0vs56.de.genes | RITAN | This dataset is included as an example in the package: | character | | |
vac2.day0vs31.de.genes | RITAN | This dataset is included as an example in the package: | character | | |
vac2.day0vs56.de.genes | RITAN | This dataset is included as an example in the package: | character | | |
Dialysis | RcmdrPlugin.survival | Hemodialysis Data from Brazil | data.frame | 6805 | 7 |
Rossi | RcmdrPlugin.survival | Rossi et al.'s Criminal Recidivism Data | data.frame | 432 | 62 |
exampleTrees | nLTT | example trees to test the functionality of the package | list | | |
stl_race_income | biscale | Race and Median Income in St. Louis by Census Tract, 2017 | sf | 106 | 4 |
stl_race_income_point | biscale | Race and Median Income in St. Louis by Census Tract, 2017 | sf | 106 | 4 |
ct5.1 | PowerTOST | Sample Size Tables for the Classical 2x2 Crossover Design | data.frame | 26 | 10 |
ct5.2 | PowerTOST | Sample Size Tables for the Classical 2x2 Crossover Design | data.frame | 26 | 12 |
ct5.3 | PowerTOST | Sample Size Tables for the Classical 2x2 Crossover Design | data.frame | 18 | 9 |
ct5.4.1 | PowerTOST | Sample Size Tables for the Classical 2x2 Crossover Design | data.frame | 32 | 6 |
ct9.6.2 | PowerTOST | Sample Size Tables for the 2x2x3 Replicate Crossover Design | data.frame | 32 | 6 |
ct9.6.4 | PowerTOST | Sample Size Tables for the 2x4x4 Replicate Crossover Design | data.frame | 32 | 6 |
ct9.6.6 | PowerTOST | Sample Size Tables for the 2x2x3 Replicate Crossover Design | data.frame | 32 | 10 |
ct9.6.8 | PowerTOST | Sample Size Tables for the 2x4x4 Replicate Crossover Design | data.frame | 32 | 10 |
ctCW.III | PowerTOST | Sample Size Tables for the Parallel Group Design | data.frame | 32 | 6 |
ctSJ.VIII.10 | PowerTOST | Sample Size Tables for the Parallel Group Design | data.frame | 12 | 5 |
ctSJ.VIII.20 | PowerTOST | Sample Size Tables for the Parallel Group Design | data.frame | 12 | 10 |
tensor | LTAR | Sea Surface Temperatures | array | | |
prefs_rstudio_v | rsprefs | prefs_rstudio_v | list | | |
NHANES | NHANES | NHANES 2009-2012 with adjusted weighting | tbl_df | 10000 | 76 |
NHANESraw | NHANES | NHANES 2009-2012 with adjusted weighting | tbl_df | 20293 | 78 |
pathway | mseapca | Example dataset for fasting and covid19 datasets | list | | |
RCircos.Gene.Label.Data | RCircos | Sample Data for Gene Labels | data.frame | 192 | 4 |
RCircos.Heatmap.Data | RCircos | Sample Data for RCircos Heatmap Plot | data.frame | 6660 | 10 |
RCircos.Histogram.Data | RCircos | Sample Data for RCircos Histogram Plot | data.frame | 324 | 4 |
RCircos.Line.Data | RCircos | Sample Data for RCircos Line Plot | data.frame | 2037 | 5 |
RCircos.Link.Data | RCircos | Sample Data for RCircos Link Plot | data.frame | 71 | 6 |
RCircos.Mouse.Expr.Data | RCircos | Sample Data of Mouse Gene Expression | data.frame | 16499 | 5 |
RCircos.Polygon.Data | RCircos | RCircos Polygon Demo Data | data.frame | 77 | 4 |
RCircos.Rat.Expr.Data | RCircos | Sample Data of Rat Gene Expression | data.frame | 11426 | 5 |
RCircos.Ribbon.Data | RCircos | Sample Data for RCircos Ribbon Plot | data.frame | 4 | 6 |
RCircos.Scatter.Data | RCircos | Sample Data for RCircos Scatter Plot | data.frame | 1757 | 5 |
RCircos.Tile.Data | RCircos | Sample Data for RCircos Tile Plot | data.frame | 152 | 3 |
UCSC.Baylor.3.4.Rat.cytoBandIdeogram | RCircos | Cytoband Information for Rat Chromosome Ideogram | data.frame | 246 | 5 |
UCSC.HG19.Human.CytoBandIdeogram | RCircos | Cytoband Information for Human Chromosome Ideogram | data.frame | 862 | 5 |
UCSC.HG38.Human.CytoBandIdeogram | RCircos | Human Chromosome Ideogram Version 38 | data.frame | 862 | 5 |
UCSC.Mouse.GRCm38.CytoBandIdeogram | RCircos | Cytoband Inforamtion for Mouse Chromosome Ideogram | data.frame | 403 | 5 |
banana_colors | bananas | Data Set of Ripening Bananas | character | | |
bananas | bananas | Data Set of Ripening Bananas | data.frame | 220 | 8 |
CpGs | methylumi | Data frame describing loci on the 27 and 450k arrays. | data.frame | 487177 | 5 |
mldat | methylumi | Example SAM format Illumina methylation dataset | MethyLumiSet | | |
france.data | dynamac | Data on French Energy Consumption and GDP | data.frame | 53 | 4 |
ineq | dynamac | Data on public concern about economic inequality | data.frame | 49 | 8 |
supreme.sup | dynamac | Data on US Supreme Court Approval | data.frame | 42 | 9 |
bostonhouseprice | smoothic | Boston House Price Data (Original) | data.frame | 506 | 9 |
bostonhouseprice2 | smoothic | Boston House Price Data (Corrected Version) | data.frame | 506 | 13 |
citycrime | smoothic | City Crime Data | data.frame | 50 | 7 |
diabetes | smoothic | Diabetes Data | data.frame | 442 | 11 |
pcancer | smoothic | Prostate Cancer Data | data.frame | 97 | 9 |
sniffer | smoothic | Sniffer Data | data.frame | 125 | 5 |
atc | decoder | Anatomical Therapeutic Chemical (ATC) Classification System codes | keyvalue | 14526 | 2 |
avgm | decoder | Help tables from Rockan | keyvalue | 9 | 2 |
ben | decoder | Help tables from Rockan | keyvalue | 2 | 2 |
digr | decoder | Help tables from Rockan | keyvalue | 8 | 2 |
distrikt | decoder | Swedish district codes | keyvalue | 2523 | 2 |
dodca | decoder | Help tables from Rockan | keyvalue | 3 | 2 |
figo | decoder | FIGO-stadium | keyvalue | 20 | 2 |
forsamling | decoder | Swedish parish codes | keyvalue | 1377 | 2 |
hemort | decoder | hemort and hemort2 codes (geographical codes) | keyvalue | 5893 | 2 |
hemort2 | decoder | hemort and hemort2 codes (geographical codes) | keyvalue | 1688 | 2 |
hsn | decoder | HSN code (Hälso- och sjukvårdsnamnd) | keyvalue | 49 | 2 |
icd10cm | decoder | ICD-10-CM code | keyvalue | 72184 | 2 |
icd10se | decoder | ICD-10-SE code | keyvalue | 33547 | 2 |
icd7 | decoder | ICD-7 | keyvalue | 196 | 2 |
icd7_grov | decoder | ICD-7 Grov | keyvalue | 54 | 2 |
icd9 | decoder | ICD-9 | keyvalue | 311 | 2 |
icd9cmd | decoder | ICD-9-CM diagnosis and procedure codes | keyvalue | 14567 | 2 |
icd9cmp | decoder | ICD-9-CM diagnosis and procedure codes | keyvalue | 3882 | 2 |
icdo | decoder | ICD-O | keyvalue | 657 | 2 |
icdo3 | decoder | ICD-O3 | keyvalue | 325 | 2 |
icdo3_grov | decoder | ICD-O3 Grov | keyvalue | 70 | 2 |
klinik | decoder | Clinic codes | keyvalue | 144 | 2 |
kommun | decoder | Swedish municipality codes | keyvalue | 290 | 2 |
kon | decoder | Gender code (kon) | keyvalue | 2 | 2 |
kva | decoder | Klassifikation av vardatgarder (KVA) | keyvalue | 10552 | 2 |
lan | decoder | Swedish county codes | keyvalue | 21 | 2 |
m_rtr | decoder | M-stadium | keyvalue | 6 | 2 |
manuell | decoder | Help tables from Rockan | keyvalue | 1 | 2 |
n_rtr | decoder | N-stadium | keyvalue | 15 | 2 |
obd | decoder | Help tables from Rockan | keyvalue | 2 | 2 |
pad | decoder | PAD (C24) code | keyvalue | 159 | 2 |
patologiavdelning | decoder | Pathology department codes | keyvalue | 119 | 2 |
region | decoder | Swedish health care regional codes | keyvalue | 6 | 2 |
sida | decoder | Sida | keyvalue | 3 | 2 |
sjukhus | decoder | Hospital codes | keyvalue | 690 | 2 |
sjukhus_inca | decoder | Hospital codes used by INCA | keyvalue | 4467 | 2 |
sjukhus_par | decoder | Hospital codes used by Socialstyrelsen and the National Patient Register | keyvalue | 452 | 2 |
sjukvardsomrade | decoder | Geographical health care areas | keyvalue | 52 | 2 |
snomed | decoder | Snomed code | keyvalue | 831 | 2 |
snomed3 | decoder | Snomed 3 | keyvalue | 912 | 2 |
status | decoder | Help tables from Rockan | keyvalue | 3 | 2 |
t_rtr | decoder | T-stadium | keyvalue | 22 | 2 |
tnmgrund | decoder | Grund till TNM (patologisk/klinisk) | keyvalue | 2 | 2 |
dupRadar_examples | dupRadar | Example data containing precomputed matrices for two RNASeq experiments | list | | |
PCGroups | PCLassoReg | Protein complexes. | data.frame | 3512 | 6 |
classData | PCLassoReg | A dataset for classification | list | | |
survivalData | PCLassoReg | A dataset for prognostic model | list | | |
redsb | mgwrhw | Data to show stunting prevalence in every district from an island | sf | 33 | 15 |
ames_HCD | predint | Historical numbers of revertant colonies in the Ames test (OECD 471) | data.frame | 28 | 2 |
bb_dat1 | predint | Beta-binomial data (example 1) | data.frame | 10 | 2 |
bb_dat2 | predint | Beta-binomial data (example 2) | data.frame | 3 | 2 |
c2_dat1 | predint | Cross-classified data (example 1) | data.frame | 27 | 3 |
c2_dat2 | predint | Cross-classified data (example 2) | data.frame | 21 | 3 |
c2_dat3 | predint | Cross-classified data (example 3) | data.frame | 8 | 3 |
c2_dat4 | predint | Cross-classified data (example 4) | data.frame | 6 | 3 |
mortality_HCD | predint | Historical mortality of male B6C3F1-mice | data.frame | 10 | 2 |
qb_dat1 | predint | Quasi-binomial data (example 1) | data.frame | 10 | 2 |
qb_dat2 | predint | Quasi-binomial data (example 2) | data.frame | 3 | 2 |
qp_dat1 | predint | Quasi-Poisson data (example 1) | data.frame | 10 | 2 |
qp_dat2 | predint | Quasi-Poisson data (example 2) | data.frame | 3 | 2 |
GM12878_HiCCUPS | plyinteractions | Loops identified in GM12878 with HiCCUPS | GInteractions | | |
ce10_ARCC | plyinteractions | Interactions identified in L3 C. elegans by ARC-C | GInteractions | | |
ce10_REs | plyinteractions | Annotated regulatory elements in C. elegans | GRanges | | |
RNA_halflife_comparison | bridger2 | test BRIC-seq dataset for RNA half-life comparison | data.table | 200 | 20 |
RNA_halflife_comparison_HK | bridger2 | test BRIC-seq dataset for RNA half-life comparison using House-keeping genes. | data.table | 206 | 20 |
RNA_halflife_grubbs_test | bridger2 | test BRIC-seq dataset for p-value estimation using grubbs test | data.table | 200 | 40 |
halflife_table | bridger2 | BRIC-seq result dataset for p-value estimation using grubbs test | data.table | 200 | 52 |
bardWordCount | deconvolveR | Shakespeare word counts in the entire canon: 14,376 distinct words appeared exactly once, 4343 words appeared twice etc. | numeric | | |
disjointTheta | deconvolveR | A set of Theta values that have a bimodal distribution for testing | numeric | | |
surg | deconvolveR | Intestinal surgery data involving 844 cancer patients. The data consists of pairs (n_i, s_i) where n_i is the number of satellites removed and s_i is the number of satellites found to be malignant. | data.frame | 844 | 2 |
train | Information | Training dataset | data.frame | 10000 | 70 |
valid | Information | Validation dataset | data.frame | 10000 | 70 |
example | MCTrend | example | data.frame | 30 | 34 |
x | divo | Example dataset for divo package | matrix | 6208 | 8 |
importdemo_data | redcaptools | Example import data | data.frame | 17 | 30 |
importdemo_dict | redcaptools | Example data dictionary | data.frame | 38 | 18 |
chicagoNMMAPS | dlnm | Daily Mortality Weather and Pollution Data for Chicago | data.frame | 5114 | 14 |
drug | dlnm | A Trial on the Effect of Time-Varying Doses of a Drug | data.frame | 200 | 7 |
nested | dlnm | Nested Case-Control Study with a Time-Varying Exposure and a Cancer Outcome | data.frame | 600 | 14 |
mediationVC | pathmodelfit | Williams and Anderson (1994) Mediated Multifoci Model Dataset | matrix | 12 | 12 |
birds | VLF | Bird Nucleotide Sequences | matrix | 11333 | 650 |
birds_aminoAcids | VLF | Bird Amino Acid Sequences | matrix | 11333 | 218 |
peptide_data | MixTwice | Peptide array data example | data.frame | 152603 | 16 |
pac.clas | vortexRdata | Collated results from Vortex scenarios - Pacioni et al. (2017) | data.frame | 2904 | 68 |
pac.clas.Nadults | vortexRdata | Harmonic mean of adults and population sizes | data.table | 24 | 4 |
pac.clas.Ne | vortexRdata | Effective population size | data.table | 24 | 2 |
pac.clas.lookup | vortexRdata | Look-up table | data.table | 24 | 8 |
pac.clas.pairw | vortexRdata | Results of pairwise comparisons of simulation scenarios | list | | |
pac.lhs | vortexRdata | Collated results from Vortex scenarios - Pacioni et al. (2017) | data.frame | 6171 | 68 |
pac.run.lhs | vortexRdata | Collated results from Vortex scenarios - Pacioni et al. (2017) | list | | |
pac.yr | vortexRdata | Collated results from Vortex scenarios - Pacioni et al. (2017) | list | | |
sta.evy5 | vortexRdata | Collated results from Vortex scenarios - Campbell et al (2016) | data.frame | 1020 | 47 |
sta.evy5.b11 | vortexRdata | Collated results from Vortex scenarios - Campbell et al (2016) | data.frame | 1020 | 47 |
sta.main | vortexRdata | Collated results from Vortex scenarios - Campbell et al (2016) | data.frame | 1632 | 44 |
unemployrate | BAYSTAR | U.S. monthly civilian unemployment rate | numeric | | |
input_data | netie | input_data | data.frame | 297 | 7 |
reg.bfa_sp | spBFA | Pre-computed regression results from 'bfa_sp' | spBFA | | |
census2010FIPS | cdlTools | U.S. Census 2010 FIPS Data | data.frame | 3235 | 5 |
corn | cdlTools | CDL corn classes | numeric | | |
cotton | cdlTools | CDL cotton classes | numeric | | |
cultivated | cdlTools | CDL cultivated classes | numeric | | |
durumWheat | cdlTools | CDL durum wheat classes | numeric | | |
nothing | cdlTools | CDL nothing class | numeric | | |
pasture | cdlTools | CDL pasture classes | numeric | | |
projCDL | cdlTools | The default projection of CDL data | character | | |
soybeans | cdlTools | CDL soybeans classes | numeric | | |
springWheat | cdlTools | CDL spring wheat classes | numeric | | |
stateNames | cdlTools | U.S. Census 2010 State FIPS Data | data.frame | 55 | 3 |
varNamesCDL | cdlTools | Enumerated CDL classes | character | | |
water | cdlTools | CDL water classes | numeric | | |
winterWheat | cdlTools | CDL winter wheat classes | numeric | | |
icecream | echoice2 | icecream | tbl_df | 39600 | 8 |
icecream_discrete | echoice2 | icecream_discrete | tbl_df | 43200 | 8 |
pizza | echoice2 | pizza | tbl_df | 12240 | 11 |
daynight_temperature | ggdaynight | Sensor Temperature Data | data.frame | 1833 | 3 |
exports.m | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
exports.m | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
exports.q | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
exports.q | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
gdp.q | tempdisagg | Gross Domestic Product | data.frame | 59 | 2 |
imports.q | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
imports.q | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
sales.a | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
sales.a | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
sales.q | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
sales.q | tempdisagg | Trade and Sales of Chemical and Pharmaceutical Industry | ts | | |
spi.d | tempdisagg | SPI Swiss Performance Index | data.frame | 5493 | 2 |
AK | sketching | AK | data.frame | 247199 | 42 |
ref_sf | segmetric | LEM+ dataset | sf | 195 | 2 |
sample_ref_sf | segmetric | LEM+ dataset | sf | 5 | 2 |
sample_seg_sf | segmetric | Segmentation dataset | sf | 6 | 2 |
seg1000_sf | segmetric | Segmentation dataset | sf | 158 | 2 |
seg200_sf | segmetric | Segmentation dataset | sf | 547 | 2 |
seg500_sf | segmetric | Segmentation dataset | sf | 215 | 2 |
seg800_sf | segmetric | Segmentation dataset | sf | 169 | 2 |
polymod | socialmixr | Social contact data from 8 European countries | survey | | |
allcounts | INSPEcT | A list containing mature and nascent counts for exons and introns, three replicates and 11 time points: 0,1/6,1/3,1/2,1,1.5,2,4,8,12,16 hours. | list | | |
BRCA1 | riskyr | Cumulative risk of breast cancer in women with the BRCA1 mutation. | data.frame | 11 | 2 |
BRCA1_mam | riskyr | Cumulative risk of breast cancer in women with the BRCA1 mutation. | data.frame | 63 | 2 |
BRCA1_ova | riskyr | Cumulative risk of ovarian cancer in women with the BRCA1 mutation. | data.frame | 63 | 2 |
BRCA2 | riskyr | Cumulative risk of breast cancer in women with the BRCA2 mutation. | data.frame | 11 | 2 |
BRCA2_mam | riskyr | Cumulative risk of breast cancer in women with the BRCA2 mutation. | data.frame | 63 | 2 |
BRCA2_ova | riskyr | Cumulative risk of ovarian cancer in women with the BRCA2 mutation. | data.frame | 63 | 2 |
df_scenarios | riskyr | A collection of riskyr scenarios from various sources (as df). | data.frame | 25 | 21 |
t_A | riskyr | Cumulative risk curve A (main/transfer task A). | data.frame | 17 | 2 |
t_B | riskyr | Cumulative risk curve B (main/transfer task B). | data.frame | 17 | 2 |
t_I | riskyr | Cumulative risk curve I (introductory task). | data.frame | 17 | 2 |
ca125 | xmeta | Recurrent ovarian carcinoma study | data.frame | 15 | 6 |
data | xmeta | | data.frame | 30 | 5 |
nat2 | xmeta | A meta-analysis of the association between N-acetyltransterase 2 acetylation status and colorectal cancer | data.frame | 20 | 4 |
prostate | xmeta | Comparison between overall survival and disease-free survival for prostate cancer | data.frame | 5 | 4 |
sim_dat | xmeta | Simulated data | data.frame | 12 | 7 |
markov_mix_ex | markovmix | Mixture of Markov chain example | MarkovMix | | |
edits | editrules | | data.frame | 9 | 3 |
currency_info | priceR | Information for each of 191 currencies | data.frame | 191 | 15 |
DATA | SizeEstimation | The number of people who inject drugs in Bangladesh in 2004. | data.frame | 64 | 9 |
SandP500 | HyperbolicDist | S&P 500 | numeric | | |
hyperbWSqTable | HyperbolicDist | Percentage Points for the Cramer-von Mises Test of the Hyperbolic Distribution | matrix | 55 | 5 |
mamquam | HyperbolicDist | Size of Gravels from Mamquam River | data.frame | 16 | 2 |
resistors | HyperbolicDist | Resistance of One-half-ohm Resistors | data.frame | 28 | 2 |
exdata_continuous | tipr | Example Data (Continuous Outcome) | tbl_df | 2000 | 4 |
exdata_rr | tipr | Example Data (Risk Ratio) | tbl_df | 2000 | 4 |
lip | gamlss.cens | Data for lip | data.frame | 120 | 14 |
popPER | popPyramid | Peru population (1995-2030) | tbl_df | 5832 | 5 |
airfoil | LIC | Airfoil self-noise | data.frame | 1503 | 6 |
estate | LIC | Real estate valuation | data.frame | 414 | 8 |
gt2015 | LIC | Gas turbine NOx emission | data.frame | 7384 | 11 |
Qaqish | mipfp | Qaqish | list | | |
spnamur | mipfp | Synthetic population of Namur (Belgium) | data.frame | 105248 | 6 |
bins_CadeMenun2015 | nmrrr | NMR grouping bins from Cade-Menun (2015). | data.frame | 5 | 5 |
bins_Clemente2012 | nmrrr | NMR grouping bins from Clemente et al. (2012). | tbl_df | 6 | 5 |
bins_Hertkorn2013 | nmrrr | NMR grouping bins from Hertkorn et al. (2013). | data.frame | 5 | 5 |
bins_Lynch2019 | nmrrr | NMR grouping bins from Lynch et al. (2019). | tbl_df | 4 | 5 |
bins_Mitchell2018 | nmrrr | NMR grouping bins from Mitchell et al. (2018). | tbl_df | 7 | 5 |
bins_ss_Baldock2004 | nmrrr | NMR grouping bins from Baldock et al. (2004). | data.frame | 7 | 5 |
bins_ss_Clemente2012 | nmrrr | NMR grouping bins from Clemente et al. (2012) - ss. | data.frame | 5 | 5 |
bins_ss_Preston2009 | nmrrr | NMR grouping bins from Preston et al. (2009). | data.frame | 7 | 5 |
MU284 | sampling | The MU284 population | data.frame | 284 | 11 |
belgianmunicipalities | sampling | The Belgian municipalities population | data.frame | 589 | 17 |
rec99 | sampling | The 1999 census data | data.frame | 554 | 10 |
swissmunicipalities | sampling | The Swiss municipalities population | data.frame | 2896 | 22 |
egg_price_index | EXPAR | Price Index of Eggs in Urban Ares of India | data.frame | 86 | 3 |
aml | Pigengene | AML gene expression profile | matrix | 202 | 1000 |
eigengenes33 | Pigengene | Eigengenes of 33 modules | list | | |
mds | Pigengene | MDS gene expression profile | matrix | 164 | 1000 |
pigengene | Pigengene | An object of class 'Pigengene' | pigengene | | |
dti | mlr3fda | The dti dataset | data.frame | 382 | 5 |
fuel | mlr3fda | The fuel dataset | data.frame | 129 | 4 |
phoneme | mlr3fda | The phoneme dataset | data.frame | 250 | 151 |
allometry | lgrdata | Allometry | data.frame | 63 | 5 |
anthropometry | lgrdata | Child anthropometry | data.frame | 3898 | 4 |
automobiles | lgrdata | Cars data | data.frame | 398 | 9 |
berkeley | lgrdata | Berkeley admissions data, 1973 | data.frame | 6 | 5 |
brunhild | lgrdata | A Baboon Named Brunhilda | data.frame | 21 | 2 |
callitrishydraulic | lgrdata | Cavitation resistance for Callitris branches | data.frame | 31 | 3 |
cereal1 | lgrdata | Cereal nutrition data - small subset nr1 | data.frame | 10 | 2 |
cereal2 | lgrdata | Cereal nutrition data - small subset nr2 | data.frame | 8 | 2 |
cereal3 | lgrdata | Cereal nutrition data - small subset nr3 | data.frame | 6 | 2 |
cereals | lgrdata | Cereal nutrition data | data.frame | 77 | 13 |
choat_precipp50 | lgrdata | Choat's Plant Drought Tolerance | data.frame | 115 | 2 |
coweeta | lgrdata | Coweeta tree data | data.frame | 87 | 9 |
dutchelection | lgrdata | Dutch election data | data.frame | 22 | 12 |
eucface_gasexchange | lgrdata | Leaf gas exchange at the EucFACE | data.frame | 84 | 7 |
eucfacegc | lgrdata | EucFACE ground cover data | data.frame | 192 | 8 |
fluxtower | lgrdata | Fluxtower data | data.frame | 244 | 8 |
germination_fire | lgrdata | Seed germination as affected by fire | data.frame | 576 | 7 |
germination_water | lgrdata | Seed germination as affected by water | data.frame | 352 | 5 |
hfeifbytree | lgrdata | I x F at the HFE - tree observations | data.frame | 9592 | 6 |
hfeifplotmeans | lgrdata | I x F at the HFE - plot-level observations | data.frame | 320 | 5 |
hfemet2008 | lgrdata | Weather data at the Hawkesbury Forest Experiment | data.frame | 17568 | 9 |
howell | lgrdata | Howell height, age and weight data | data.frame | 783 | 4 |
hydro | lgrdata | Hydro dam storage data | data.frame | 314 | 2 |
icecream | lgrdata | Icecream sales and temperature | data.frame | 40 | 3 |
masslost | lgrdata | Genetically modified soybean litter decomposition | data.frame | 246 | 8 |
memory | lgrdata | Memory of words dataset | data.frame | 100 | 3 |
oil | lgrdata | Crude oil production | data.frame | 376 | 3 |
pulse | lgrdata | Pulse Rates before and after Exercise | data.frame | 110 | 11 |
pupae | lgrdata | Pupae data | data.frame | 84 | 5 |
rain | lgrdata | Rain data | data.frame | 3653 | 3 |
sydney_hobart_times | lgrdata | Sydney to Hobart winning times | data.frame | 72 | 5 |
titanic | lgrdata | Passengers on the Titanic | data.frame | 1313 | 5 |
treecanopy | lgrdata | Tree canopy gradients in the Priest River Experimental Forest (PREF) | data.frame | 249 | 7 |
vessel | lgrdata | Xylem vessel diameters | data.frame | 550 | 3 |
weightloss | lgrdata | Weight loss data | data.frame | 67 | 2 |
wildmousemetabolism | lgrdata | Mouse metabolism | data.frame | 864 | 9 |
housing | A3 | Boston Housing Prices | data.frame | 506 | 14 |
multifunctionality | A3 | Ecosystem Multifunctionality | data.frame | 224 | 11 |
occ.example | occ | Total volumes of distribution (VT) from a simulated PET study | matrix | 5 | 3 |
cheng | SGCP | Normalized gene expression data from Cheng et al.'s publication on ischemic cardiomyopathy (ICM). | SummarizedExperiment | | |
resClus | SGCP | An example of the output from 'clustering' function in the SGCP pipeline | list | | |
resFinalGO | SGCP | An example of the output from 'geneOntololgy' function in the SGCP pipeline | list | | |
resInitialGO | SGCP | An example of the output from the 'geneOntololgy' function in the SGCP pipeline | list | | |
resSemiLabel | SGCP | An example of the output from 'semiLabeling' function in the SGCP pipeline | list | | |
resSemiSupervised | SGCP | An example of the output from 'semiSupervised' function in the SGCP pipeline | list | | |
sgcp | SGCP | An example of the output of 'ezSGCP' function in the SGCP pipeline | list | | |
RainFallExample | RainfallErosivityFactor | The Rainfall Example Data Set-Runoff Erosivity Factor | data.frame | 105120 | 3 |
scClassify_example | scClassify | Example data used in scClassify package | list | | |
trainClassExample_wang | scClassify | Subset of pretrained model of Wang et al. | scClassifyTrainModel | | |
trainClassExample_xin | scClassify | Subset of pretrained model of Xin et al. | scClassifyTrainModel | | |
example_dt | supersigs | Example dataset of mutations | data.frame | 25 | 6 |
supersig_ls | supersigs | Trained SuperSigs from TCGA | list | | |
feat.crc.zeller | SIAMCAT | Example feature matrix | data.frame | 1754 | 141 |
meta.crc.zeller | SIAMCAT | Example metadata matrix | data.frame | 141 | 6 |
siamcat_example | SIAMCAT | SIAMCAT example | siamcat | | |
E14 | HiTC | HiTC - 5C data | HTClist | | |
MEF | HiTC | HiTC - 5C data | HTClist | | |
HCC_sig | ToxicoGx | HCC_sig dataset | data.frame | 7158 | 2 |
TGGATESsmall | ToxicoGx | TGGATESsmall dataset | ToxicoSet | | |
toydata | zinbwave | Toy dataset to check the model | matrix | 96 | 500 |
CellTypes | tenXplore | cellTypes: data.frame with ids and terms | TermSet | | |
tenx500 | tenXplore | tenx500: serialized full SummarizedExperiment for demonstration | SummarizedExperiment | | |
cell_type_df | tidySingleCellExperiment | Cell types of 80 PBMC single cells | tbl_df | 80 | 2 |
pbmc_small | tidySingleCellExperiment | pbmc_small | SingleCellExperiment | | |
pbmc_small_nested_interactions | tidySingleCellExperiment | Intercellular ligand-receptor interactions for 38 ligands from a single cell RNA-seq cluster. | tbl_df | 100 | 9 |
geneInfo | DEGreport | data.frame with chromose information for each gene | data.frame | 49 | 2 |
humanGender | DEGreport | DGEList object for DE genes betwen Male and Females | SummarizedExperiment | | |
test.data.2comp | DeMixT | Simulated two-component test data | list | | |
test.data.3comp | DeMixT | Simulated three-component mixed cell line test data | list | | |
CheA3_TF_nTargets | ChromSCape | A data.frame with the number of targets of each TF in ChEA3 | data.frame | 1632 | 2 |
hg38.GeneTSS | ChromSCape | Data.frame of gene TSS - hg38 | data.frame | 32937 | 5 |
hg38.chromosomes | ChromSCape | Data.frame of chromosome length - hg38 | data.frame | 24 | 3 |
hg38.cytoBand | ChromSCape | Data.frame of cytoBandlocation - hg38 | data.frame | 862 | 4 |
mm10.GeneTSS | ChromSCape | Data.frame of gene TSS - mm10 | data.frame | 27916 | 5 |
mm10.chromosomes | ChromSCape | Data.frame of chromosome length - mm10 | data.frame | 21 | 3 |
mm10.cytoBand | ChromSCape | Data.frame of cytoBandlocation - mm10 | data.frame | 403 | 4 |
scExp | ChromSCape | A SingleCellExperiment outputed by ChromSCape | SingleCellExperiment | | |
exampleBidirectional | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
exampleCTSSs | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
exampleDesign | CAGEfightR | Example CAGE Data | DFrame | | |
exampleGenes | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
exampleUnidirectional | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
kerinci | overlap | Times of 'capture' of large mammals | data.frame | 1098 | 3 |
pigObs | overlap | Simulated data for diel activity patterns | numeric | | |
pigTrue | overlap | Simulated data for diel activity patterns | numeric | | |
simCalls | overlap | Simulated data for bird calls influenced by sunrise | data.frame | 100 | 2 |
tigerObs | overlap | Simulated data for diel activity patterns | numeric | | |
tigerTrue | overlap | Simulated data for diel activity patterns | numeric | | |
create_synthetic_example | SynDI | Example data for Create.Synthetic() | list | | |
initial_estimates_example | SynDI | Example data for Initial.estimates() | list | | |
sweetpotato | randtests | Sweet potato production | list | | |
seating_plan | swissparl | Seating plan of the National Council | tbl_df | 800 | 6 |
geneData | colorhcplot | Sample Gene Expression Dataset | list | | |
skittles | skittles | Dataset of Skittles pack colour counts, with a pair of identical packs | data.frame | 468 | 7 |
skittles_raw | skittles | Dataset of Skittles pack colour counts, with a pair of identical packs | data.frame | 468 | 6 |
leukstats | sgpv | Test Statistics from Gloub (1999) Leukemia data set | data.frame | 7128 | 7 |
TCGA.BRCA.ExpressionData | NGCHMDemoData | A subset of the breast cancer (BRCA) expression data from TCGA | matrix | 3437 | 200 |
TCGA.BRCA.TP53MutationData | NGCHMDemoData | TP53 mutation data for TCGA breast cancer (BRCA) samples | character | | |
TCGA.GBM.EXPR | NGCHMDemoData | Glioblastoma Multiforme (GBM) microarray expression data from TCGA | matrix | 2000 | 547 |
TCGA.GBM.ExpressionData | NGCHMDemoData | Glioblastoma Multiforme (GBM) RNASeq expression data from TCGA | matrix | 3540 | 169 |
TCGA.GBM.TP53MutationData | NGCHMDemoData | TP53 mutation data for TCGA glioblastoma multiforme (GBM) samples | character | | |
bh08 | glba | Example data from Brown and Heathcote (2008). | data.frame | 3000 | 3 |
ilpp2 | glba | Implicit learning data from Visser et al (2007). | data.frame | 4740 | 10 |
numpp1 | glba | Example data from a numerosity task. | data.frame | 186 | 10 |
GRN_params_100 | scMultiSim | 100_gene_GRN is a matrix of GRN params consisting of 100 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID | data.frame | 130 | 3 |
GRN_params_1139 | scMultiSim | GRN_params_1139 is a matrix of GRN params consisting of 1139 genes where: # - column 1 is the target gene ID, # - column 2 is the gene ID which acts as a transcription factor for the target (regulated) gene # - column 3 is the effect of the column 2 gene ID on the column 1 gene ID | data.frame | 1432 | 3 |
dens_nonzero | scMultiSim | this is the density function of log(x+1), where x is the non-zero values for ATAC-SEQ data | density | | |
gene_len_pool | scMultiSim | a pool of gene lengths to sample from | integer | | |
len2nfrag | scMultiSim | from transcript length to number of fragments (for the nonUMI protocol) | matrix | 8118 | |
match_params | scMultiSim | distribution of kinetic parameters learned from the Zeisel UMI cortex datasets | matrix | 78120 | 3 |
biofam3c | seqHMM | Three-channel biofam data | list | | |
colorpalette | seqHMM | Color palettes | list | | |
hmm_biofam | seqHMM | Hidden Markov model for the biofam data | hmm | | |
hmm_mvad | seqHMM | Hidden Markov model for the mvad data | hmm | | |
mhmm_biofam | seqHMM | Mixture hidden Markov model for the biofam data | mhmm | | |
mhmm_mvad | seqHMM | Mixture hidden Markov model for the mvad data | mhmm | | |
pksData | RTN | Pre-processed datasets for the RTN package. | list | | |
stni | RTN | A pre-processed TNI for demonstration purposes only. | TNI | | |
tfsData | RTN | Pre-processed datasets for the RTN package. | list | | |
tnaData | RTN | Pre-processed datasets for the RTN package. | list | | |
tniData | RTN | Pre-processed datasets for the RTN package. | list | | |
Zeller | RCM | Microbiomes of colorectal cancer patients and healthy controls | phyloseq | | |
CBS_PBMC_array | TOAST | An example dataset for partial reference-free cell composition estimation from tissue gene expression | list | | |
RA_100samples | TOAST | An example dataset for cellular proportion estimation and multiple factor design | list | | |
beta_emp | TOAST | Simulated methylation 450K array data with related | list | | |
scData | scShapes | Sample data for analysis | list | | |
pasilla | tidySummarizedExperiment | Read counts of RNA-seq samples of Pasilla knock-down by Brooks et al. | SummarizedExperiment | | |
se | tidySummarizedExperiment | Read counts of RNA-seq samples derived from Pasilla knock-down by Brooks et al. | RangedSummarizedExperiment | | |
wm | scSpatialSIM | Round spatstat window | owin | | |
GOdata | topGO | Sample topGOdata and topGOresult objects | topGOdata | | |
affyLib | topGO | A toy example of a list of gene identifiers and the respective p-values | character | | |
geneList | topGO | A toy example of a list of gene identifiers and the respective p-values | numeric | | |
resultFisher | topGO | Sample topGOdata and topGOresult objects | topGOresult | | |
resultKS | topGO | Sample topGOdata and topGOresult objects | topGOresult | | |
topDiffGenes | topGO | A toy example of a list of gene identifiers and the respective p-values | function | | |
ex_pseudotime | switchde | Synthetic gene pseudotimes | array | | |
synth_gex | switchde | Synthetic gene expression matrix | matrix | 12 | 100 |
mask | tomoseqr | A matrix containing mask data. | array | | |
testx | tomoseqr | A data.frame object containing a simulated Tomo-seq data for x-axis sections. | tbl_df | 4 | 51 |
testy | tomoseqr | A data.frame object containing a simulated Tomo-seq data for y-axis sections. | tbl_df | 4 | 51 |
testz | tomoseqr | A data.frame object containing a simulated Tomo-seq data for z-axis sections. | tbl_df | 4 | 51 |
tomoObj | tomoseqr | A tomoSeq object. | tomoSeq | | |
count_table | tempted | OTU read count table from the ECAM data | data.frame | 852 | 2425 |
meta_table | tempted | Meta data table from the ECAM data | data.frame | 852 | 3 |
processed_table | tempted | Central-log-ratio (clr) transformed OTU table from the ECAM data | matrix | 852 | 795 |
networkSample | hybridModels | Daily record of animal's movement (from 2012 to 2013). | data.frame | 78 | 4 |
nodesCensus | hybridModels | Information about animal premises (from 2012 to 2013). | data.frame | 43 | 2 |
formats | calendar | Convenient datetime formats | list | | |
holidays | calendar | Example ics file on English and Welsh holidays | character | | |
ical_example | calendar | Minimal example of raw ical data | character | | |
ical_outlook | calendar | Example of event data with multi-line description from Outlook | list | | |
properties | calendar | The key 'properties' that are allowed in ical files | character | | |
properties_core | calendar | The key 'properties' that are allowed in ical files | list | | |
properties_ical | calendar | ical default VCALENDAR properties in one line vectors. | character | | |
sampleMetadata | MicrobiomeBenchmarkData | sampleMetadata | spec_tbl_df | 1125 | 35 |
Baayen2001 | zipfR | Frequency Spectra from Baayen (2001) (zipfR) | list | | |
Brown.emp.vgc | zipfR | Brown Corpus Frequency Data (zipfR) | vgc | 1007 | 3 |
Brown.spc | zipfR | Brown Corpus Frequency Data (zipfR) | spc | 541 | 2 |
Brown.tfl | zipfR | Brown Corpus Frequency Data (zipfR) | tfl | 45215 | 3 |
Brown100k.spc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | spc | 177 | 2 |
BrownAdj.emp.vgc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | vgc | 81 | 3 |
BrownAdj.spc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | spc | 191 | 2 |
BrownImag.emp.vgc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | vgc | 259 | 3 |
BrownImag.spc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | spc | 290 | 2 |
BrownInform.emp.vgc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | vgc | 749 | 3 |
BrownInform.spc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | spc | 465 | 2 |
BrownNoun.emp.vgc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | vgc | 217 | 3 |
BrownNoun.spc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | spc | 286 | 2 |
BrownVer.emp.vgc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | vgc | 167 | 3 |
BrownVer.spc | zipfR | Brown Corpus Subset Frequency Data (zipfR) | spc | 243 | 2 |
Dickens.emp.vgc | zipfR | Dickens' Frequency Data (zipfR) | vgc | 1127 | 3 |
Dickens.spc | zipfR | Dickens' Frequency Data (zipfR) | spc | 931 | 2 |
DickensGreatExpectations.emp.vgc | zipfR | Dickens' Frequency Data (zipfR) | vgc | 933 | 3 |
DickensGreatExpectations.spc | zipfR | Dickens' Frequency Data (zipfR) | spc | 273 | 2 |
DickensOliverTwist.emp.vgc | zipfR | Dickens' Frequency Data (zipfR) | vgc | 787 | 3 |
DickensOliverTwist.spc | zipfR | Dickens' Frequency Data (zipfR) | spc | 251 | 2 |
DickensOurMutualFriend.emp.vgc | zipfR | Dickens' Frequency Data (zipfR) | vgc | 1642 | 3 |
DickensOurMutualFriend.spc | zipfR | Dickens' Frequency Data (zipfR) | spc | 364 | 2 |
EvertLuedeling2001 | zipfR | Samples of German Word Formation Affixes (zipfR) | list | | |
ItaRi.emp.vgc | zipfR | Italian Ri- and Ultra- Prefix Frequency Data (zipfR) | vgc | 1400 | 3 |
ItaRi.spc | zipfR | Italian Ri- and Ultra- Prefix Frequency Data (zipfR) | spc | 302 | 2 |
ItaUltra.emp.vgc | zipfR | Italian Ri- and Ultra- Prefix Frequency Data (zipfR) | vgc | 174 | 3 |
ItaUltra.spc | zipfR | Italian Ri- and Ultra- Prefix Frequency Data (zipfR) | spc | 43 | 2 |
TigerNP.emp.vgc | zipfR | Tiger NP and PP expansions (zipfR) | vgc | 1091 | 3 |
TigerNP.spc | zipfR | Tiger NP and PP expansions (zipfR) | spc | 169 | 2 |
TigerNP.tfl | zipfR | Tiger NP and PP expansions (zipfR) | tfl | 3522 | 3 |
TigerPP.emp.vgc | zipfR | Tiger NP and PP expansions (zipfR) | vgc | 911 | 3 |
TigerPP.spc | zipfR | Tiger NP and PP expansions (zipfR) | spc | 155 | 2 |
TigerPP.tfl | zipfR | Tiger NP and PP expansions (zipfR) | tfl | 2908 | 3 |
Bids | DGLMExtPois | Bids Received by U.S. Firms | data.frame | 126 | 12 |
CustomerProfile | DGLMExtPois | Customer profile for a household supplies company | data.frame | 110 | 6 |
.flaglist | ggflags | | list | | |
pokemon | Rokemon | Pokemon Data | tbl_df | 801 | 41 |
CESdata | nlWaldTest | Data for testing CES production function | data.frame | 25 | 3 |
calif | pps | California places | data.frame | 1077 | 6 |
califcty | pps | California counties | data.frame | 57 | 6 |
blacklists | ChIPexoQual | 'list' of 'GRanges' objects with the blacklists generated by the ENCODE and modENCODE projects. | list | | |
exoExample | ChIPexoQual | 'ExoData' results for FoxA1 ChIP-exo experiment | ExoData | | |
TUNA | DELTD | Data of Tuna fish | numeric | | |
KinaseFamily | PhosR | KinaseFamily | matrix | 425 | 6 |
PhosphoSite.human | PhosR | PhosphoSitePlus annotations for human | list | | |
PhosphoSite.mouse | PhosR | PhosphoSitePlus annotations for mouse | list | | |
PhosphoSite.rat | PhosR | PhosphoSitePlus annotations for rat | list | | |
SPSs | PhosR | A list of Stably Phosphorylated Sites (SPSs) | character | | |
hSEGs | PhosR | A list of Stably Expressed Genes (SEGs) | character | | |
mSEGs | PhosR | A list of Stably Expressed Genes (SEGs) | character | | |
motif.human.list | PhosR | List of human kinase motifs | list | | |
motif.mouse.list | PhosR | List of mouse kinase motifs | list | | |
motif.rat.list | PhosR | List of rat kinase motifs | list | | |
phospho.L6.ratio | PhosR | phospho.L6.ratio | matrix | 6660 | 12 |
phospho.L6.ratio.pe | PhosR | phospho_L6_ratio_pe | PhosphoExperiment | | |
phospho.cells.Ins | PhosR | phospho.cells.Ins | matrix | 5000 | 24 |
phospho.cells.Ins.pe | PhosR | phospho.cells.Ins | PhosphoExperiment | | |
phospho.liver.Ins.TC.ratio.RUV | PhosR | phospho_liverInsTC_RUV_sample | matrix | 800 | 90 |
phospho.liver.Ins.TC.ratio.RUV.pe | PhosR | phospho.liver.Ins.TC.ratio.RUV.pe | PhosphoExperiment | | |
ropers | Pirat | Ropers dataset | list | | |
subbouyssie | Pirat | Sub-Bouyssie dataset | list | | |
subropers | Pirat | Sub-Ropers dataset | list | | |
tdata | genefilter | A small test dataset of Affymetrix Expression data. | data.frame | 500 | 26 |
spas | grwat | Spas-Zagorye daily runoff data | spec_tbl_df | 23742 | 4 |
DefaultColorSequence | ProjectionBasedClustering | Default color sequence for plots | character | | |
Hepta | ProjectionBasedClustering | Hepta is part of the Fundamental Clustering Problem Suit (FCPS) [Thrun/Ultsch, 2020]. | list | | |
Week3_otu | MicrobiomeSurv | OTU table at week 3. | tbl_df | 81 | 2724 |
Week3_response | MicrobiomeSurv | Response datase. | tbl_df | 81 | 5 |
data_zero_per_group_otu_w3 | MicrobiomeSurv | Zero per treatment groups. | tbl_df | 2720 | 10 |
fam_info_w3 | MicrobiomeSurv | Information at family level. | tbl_df | 2720 | 2 |
fam_shan_trim_w3 | MicrobiomeSurv | Dataset at family level. | tbl_df | 6 | 82 |
metadata_taxonomy | MicrobiomeSurv | Metadata taxonomy. | data.frame | 2720 | 3 |
gse87795_subset_sce | scReClassify | GSE827795 subset data | SingleCellExperiment | | |
faahko_se | qmtools | FAAH knockout LC/MS data SummarizedExperiment | SummarizedExperiment | | |
exSeuratObj | CatsCradle | exSeuratObj | Seurat | | |
humanLRN | CatsCradle | humanLRN | tbl_df | 12019 | 2 |
ligandReceptorResults | CatsCradle | ligandReceptorResults | list | | |
moransI | CatsCradle | moransI | data.frame | 248 | 2 |
moransILigandReceptor | CatsCradle | moransILigandReceptor | data.frame | 28 | 2 |
mouseLRN | CatsCradle | mouseLRN | tbl_df | 11592 | 2 |
seuratCells | CatsCradle | seuratCells | integer | | |
seuratGenes | CatsCradle | seuratGenes | character | | |
smallXenium | CatsCradle | smallXenium | Seurat | | |
xeniumCells | CatsCradle | xeniumCells | character | | |
baby | ddalpha | Data for Classification | data.frame | 247 | 6 |
banknoten | ddalpha | Data for Classification | data.frame | 200 | 7 |
biomed | ddalpha | Data for Classification | data.frame | 194 | 5 |
bloodtransfusion | ddalpha | Data for Classification | data.frame | 748 | 4 |
breast_cancer_wisconsin | ddalpha | Data for Classification | data.frame | 699 | 10 |
bupa | ddalpha | Data for Classification | data.frame | 345 | 7 |
chemdiab_1vs2 | ddalpha | Data for Classification | data.frame | 112 | 6 |
chemdiab_1vs3 | ddalpha | Data for Classification | data.frame | 69 | 6 |
chemdiab_2vs3 | ddalpha | Data for Classification | data.frame | 109 | 6 |
cloud | ddalpha | Data for Classification | data.frame | 108 | 7 |
crabB_MvsF | ddalpha | Data for Classification | data.frame | 100 | 6 |
crabF_BvsO | ddalpha | Data for Classification | data.frame | 100 | 6 |
crabM_BvsO | ddalpha | Data for Classification | data.frame | 100 | 6 |
crabO_MvsF | ddalpha | Data for Classification | data.frame | 100 | 6 |
crab_BvsO | ddalpha | Data for Classification | data.frame | 200 | 6 |
crab_MvsF | ddalpha | Data for Classification | data.frame | 200 | 6 |
cricket_CvsP | ddalpha | Data for Classification | data.frame | 156 | 5 |
diabetes | ddalpha | Data for Classification | data.frame | 768 | 9 |
ecoli_cpvsim | ddalpha | Data for Classification | data.frame | 220 | 6 |
ecoli_cpvspp | ddalpha | Data for Classification | data.frame | 195 | 6 |
ecoli_imvspp | ddalpha | Data for Classification | data.frame | 129 | 6 |
gemsen_MvsF | ddalpha | Data for Classification | data.frame | 1349 | 7 |
geneexp | ddalpha | Gene Expression Profile Data | functional | | |
glass | ddalpha | Data for Classification | data.frame | 146 | 10 |
groessen_MvsF | ddalpha | Data for Classification | data.frame | 230 | 4 |
growth | ddalpha | Berkeley Growth Study Data | functional | | |
haberman | ddalpha | Data for Classification | data.frame | 306 | 4 |
heart | ddalpha | Data for Classification | data.frame | 270 | 14 |
hemophilia | ddalpha | Data for Classification | data.frame | 75 | 3 |
indian_liver_patient_1vs2 | ddalpha | Data for Classification | data.frame | 579 | 11 |
indian_liver_patient_FvsM | ddalpha | Data for Classification | data.frame | 579 | 10 |
iris_setosavsversicolor | ddalpha | Data for Classification | data.frame | 100 | 5 |
iris_setosavsvirginica | ddalpha | Data for Classification | data.frame | 100 | 5 |
iris_versicolorvsvirginica | ddalpha | Data for Classification | data.frame | 100 | 5 |
irish_ed_MvsF | ddalpha | Data for Classification | data.frame | 500 | 6 |
kidney | ddalpha | Data for Classification | data.frame | 76 | 6 |
medflies | ddalpha | Relationship of Age Patterns of Fecundity to Mortality for Female Medflies. | functional | | |
pima | ddalpha | Data for Classification | data.frame | 200 | 8 |
plasma_retinol_MvsF | ddalpha | Data for Classification | data.frame | 315 | 14 |
population | ddalpha | World Historical Population-by-Country Dataset | functional | | |
population2010 | ddalpha | World Historical Population-by-Country Dataset (2010 Revision) | functional | | |
segmentation | ddalpha | Data for Classification | data.frame | 660 | 11 |
socmob_IvsNI | ddalpha | Data for Classification | data.frame | 1156 | 6 |
socmob_WvsB | ddalpha | Data for Classification | data.frame | 1156 | 6 |
tae | ddalpha | Data for Classification | data.frame | 151 | 6 |
tecator | ddalpha | Functional Data Set Spectrometric Data (Tecator) | functional | | |
tennis_MvsF | ddalpha | Data for Classification | data.frame | 87 | 16 |
tips_DvsN | ddalpha | Data for Classification | data.frame | 244 | 7 |
tips_MvsF | ddalpha | Data for Classification | data.frame | 244 | 7 |
uscrime_SvsN | ddalpha | Data for Classification | data.frame | 47 | 14 |
vertebral_column | ddalpha | Data for Classification | data.frame | 310 | 7 |
veteran_lung_cancer | ddalpha | Data for Classification | data.frame | 137 | 8 |
vowel_MvsF | ddalpha | Data for Classification | data.frame | 990 | 14 |
wine_1vs2 | ddalpha | Data for Classification | data.frame | 130 | 14 |
wine_1vs3 | ddalpha | Data for Classification | data.frame | 107 | 14 |
wine_2vs3 | ddalpha | Data for Classification | data.frame | 119 | 14 |
edgelist.humannet | SANTA | Pre-processed dataset for the SANTA vignette | data.frame | 58636 | 3 |
edgelist.intact | SANTA | Pre-processed dataset for the SANTA vignette | data.frame | 21291 | 2 |
g.bandyopadhyay.treated | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
g.bandyopadhyay.untreated | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
g.costanzo.cor | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
g.costanzo.raw | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
g.srivas.high | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
g.srivas.untreated | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
go.entrez | SANTA | Pre-processed dataset for the SANTA vignette | character | | |
rnai.cheung | SANTA | Pre-processed dataset for the SANTA vignette | matrix | 10690 | 6 |
lizards | cauphy | Greater Antillean Anolis lizard dataset | list | | |
exampleRprimerAlignment | rprimer | Example datasets | DNAMultipleAlignment | | |
exampleRprimerAssay | rprimer | Example datasets | RprimerAssay | | |
exampleRprimerMatchAssay | rprimer | Example datasets | RprimerMatchAssay | | |
exampleRprimerMatchOligo | rprimer | Example datasets | RprimerMatchOligo | | |
exampleRprimerOligo | rprimer | Example datasets | RprimerOligo | | |
exampleRprimerProfile | rprimer | Example datasets | RprimerProfile | | |
Asnps | snpStats | Test data for the snpStats package | data.frame | 9445 | 2 |
Autosomes | snpStats | Test data for the snpStats package | SnpMatrix | 400 | 9445 |
Xchromosome | snpStats | Test data for the snpStats package | XSnpMatrix | 400 | 155 |
Xsnps | snpStats | Test data for the snpStats package | data.frame | 155 | 1 |
ceph.1mb | snpStats | Datasets to illustrate calculation of linkage disequilibrium statistics | SnpMatrix | 90 | 603 |
genotypes | snpStats | Test data for family association tests | SnpMatrix | 3017 | 43 |
pedData | snpStats | Test data for family association tests | data.frame | 3017 | 6 |
snp.support | snpStats | Data for exercise in use of the snpStats package | data.frame | 28501 | 4 |
snps.10 | snpStats | Data for exercise in use of the snpStats package | SnpMatrix | 1000 | 28501 |
subject.data | snpStats | Test data for the snpStats package | data.frame | 400 | 4 |
subject.support | snpStats | Data for exercise in use of the snpStats package | data.frame | 1000 | 2 |
support.ld | snpStats | Datasets to illustrate calculation of linkage disequilibrium statistics | data.frame | 603 | 5 |
yri.1mb | snpStats | Datasets to illustrate calculation of linkage disequilibrium statistics | SnpMatrix | 90 | 603 |
biofeedback | coxphw | Biofeedback Treatment Data | data.frame | 33 | 6 |
gastric | coxphw | Gastric Cancer Data | data.frame | 90 | 4 |
ArcticLake | Ball | Arctic lake sediment samples of different water depth | list | | |
bdvmf | Ball | Simulated von Mises-Fisher Data | list | | |
genlung | Ball | Lung cancer genomic data | list | | |
macaques | Ball | Male and Female macaque data | list | | |
meteorology | Ball | meteorological data | list | | |
MDRS2016 | EffectLiteR | Dataset MDRS2016. | data.frame | 1000 | 10 |
elrdata_categorical_items | EffectLiteR | Dataset elrdata_categorical_items. | data.frame | 500 | 13 |
elrdata_kieferetal2024 | EffectLiteR | Dataset elrdata_kieferetal2024. | data.frame | 600 | 3 |
elrdata_logreg | EffectLiteR | Dataset elrdata_logreg. | data.frame | 1000 | 6 |
example01 | EffectLiteR | Dataset example01. | data.frame | 2000 | 7 |
example02lv | EffectLiteR | Dataset example02lv. | data.frame | 300 | 6 |
example_multilevel | EffectLiteR | Dataset example_multilevel. | data.frame | 800 | 7 |
nonortho | EffectLiteR | Dataset nonortho. | data.frame | 500 | 3 |
sophonet_data_simulated | EffectLiteR | Dataset sophonet_data_simulated. | data.frame | 328 | 24 |
dataB | UHM | Simulated data from zero-inflated Beta regression model | data.frame | 500 | 3 |
dataC | UHM | Simulated data from zero-inflated Gaussian regression model | data.frame | 500 | 3 |
dataD | UHM | Simulated data from zero-inflated Poisson regression model | data.frame | 500 | 3 |
dataI | UHM | Simulated data from zero-inflated exponential regression model | data.frame | 500 | 3 |
dataP | UHM | Simulated data from zero-inflated exponential regression model | data.frame | 500 | 3 |
multimodalDat | Dino | Plot data from simulated expression | list | | |
pbmcSmall | Dino | Subset of 500 peripheral blood mononuclear cells (PBMCs) from a healthy donor | dgCMatrix | | |
unimodalDat | Dino | Plot data from simulated expression | list | | |
BDIdata | npmlda | BDIdata dataset | data.frame | 7117 | 5 |
BMACS | npmlda | BMACS CD4 dataset | data.frame | 1817 | 6 |
HSCT | npmlda | HSCT dataset | data.frame | 271 | 8 |
NGHS | npmlda | NGHS dataset | data.frame | 19701 | 12 |
Agriculture | MSMwRA | Agriculture | data.frame | 9 | 4 |
PISA | MSMwRA | The PISA 2012 Results of OECD Countries | data.frame | 34 | 4 |
SER | MSMwRA | Sex, Education and Reading Data | data.frame | 21 | 3 |
Sim1 | MSMwRA | Simulated Data 1 | data.frame | 10 | 3 |
Sim2 | MSMwRA | Simulated Data 2 | data.frame | 10 | 2 |
ecodata | MSMwRA | Economic Indicators of Cities in Turkey | data.frame | 81 | 4 |
happiness | MSMwRA | The Happiness Data of OECD Countries | data.frame | 155 | 3 |
ssl | MSMwRA | Security and Social Life Indicators of Cities in Turkey | data.frame | 81 | 9 |
example_bin_se | diffUTR | Example bin-level 'RangedSummarizedExperiment' | RangedSummarizedExperiment | | |
example_gene_annotation | diffUTR | Example gene annotation | GRanges | | |
rn6_PAS | diffUTR | Poly-A sites compendium for Rattus Norvegicus (Rno6) | GRanges | | |
spike_df | monocle | Spike-in transcripts data. | data.frame | 92 | 7 |
example_SCE | GloScope | SingleCellExperiment containing example inputs to GloScope | SingleCellExperiment | | |
example_SCE_small | GloScope | SingleCellExperiment containing example inputs to GloScope | SingleCellExperiment | | |
multiOmics | MOSClip | Omics class object with TCGA ovarian data | Omics | | |
multiOmicsTopo | MOSClip | Omics class object with TCGA ovarian data for topological analysis | Omics | | |
ovarianDataset | MOSClip | ExperimentList class object with TCGA ovarian data | ExperimentList | | |
reactSmall | MOSClip | PathwayList of pathways from Reactome | PathwayList | | |
menetRNASeqMouseLiver | rain | Time courses of gene expression in mouse liver | data.frame | 20069 | 12 |
data.tdcm01 | TDCM | Several data sets for the 'TDCM' package. | list | | |
data.tdcm02 | TDCM | Several data sets for the 'TDCM' package. | list | | |
data.tdcm03 | TDCM | Several data sets for the 'TDCM' package. | list | | |
data.tdcm04 | TDCM | Several data sets for the 'TDCM' package. | list | | |
data.tdcm05 | TDCM | Several data sets for the 'TDCM' package. | list | | |
simdata | rnaseqcomp | Example of Quantifications on Simulation Data | list | | |
power.test | magpie | Power calculation results for GSE46705 | list | | |
power.test | magpie | Power calculation results for GSE46705 | list | | |
power.test | magpie | Power calculation results for GSE46705 | list | | |
power.test | magpie | Power calculation results for GSE46705 | list | | |
power.test | magpie | Power calculation results for GSE46705 | list | | |
power.test | magpie | Power calculation results for GSE46705 | list | | |
RocheBT | OVESEG | mRNA expression data downsampled from GSE28490 (Roche) | list | | |
countBT | OVESEG | RNAseq count data downsampled from GSE60424 | list | | |
se | MICSQTL | Example data | SummarizedExperiment | | |
sampleexp | IVAS | CEU expression data | data.frame | 64 | 78 |
samplesnp | IVAS | CEU genotype data | data.frame | 11 | 78 |
samplesnplocus | IVAS | snplocus | data.frame | 11 | 3 |
stations | traveltime | Singapore MRT and LRT data | matrix | 563 | 2 |
testSimulationData | retrofit | simulation data | list | | |
vignetteColonData | retrofit | colon vignette | list | | |
vignetteSimulationData | retrofit | simulation vignette | list | | |
stackepi | epistack | epistack example and test dataset | RangedSummarizedExperiment | | |
stackepi_gr | epistack | epistack backward compatibility dataset | GRanges | | |
UCICreditCard | creditmodel | UCI Credit Card data | data.frame | 30000 | 26 |
ewm_data | creditmodel | Entropy Weight Method Data | data.frame | 10 | 14 |
lendingclub | creditmodel | Lending Club data | data.frame | 31766 | 45 |
BrCa443 | sigsquared | Breast Cancer 443 Data Set | ExpressionSet | | |
Sales | ExcelFunctionsR | Random Sales Data | tbl_df | 24 | 4 |
Streets | ExcelFunctionsR | Random Salesman Streets Data | tbl_df | 4 | 2 |
matTesting | switchBox | Gene expression matrix for test set data | matrix | 70 | 307 |
matTraining | switchBox | Gene expression matrix for training set data | matrix | 70 | 78 |
testingGroup | switchBox | Testing set phenotypes | factor | | |
trainingGroup | switchBox | Training set phenotypes | factor | | |
CancerDetector.markers | cfTools | Cancer-specific marker parameter | data.frame | 1266 | 3 |
CancerDetector.reads | cfTools | Fragment-level methylation state for cancer detection | data.frame | 9991 | 2 |
CpG_OB_demo | cfTools | Methylation information for CpG on the original bottom strand (OB) | data.frame | 2224 | 5 |
CpG_OT_demo | cfTools | Methylation information for CpG on the original top strand (OT) | data.frame | 2556 | 5 |
beta_matrix | cfTools | Beta value matrix | data.frame | 20 | 3 |
cfDeconvolve.markers | cfTools | Tissue-specific marker parameter | data.frame | 10 | 8 |
cfDeconvolve.reads | cfTools | Fragment-level methylation state for tissue deconvolution | data.frame | 942 | 2 |
cfsort_markers | cfTools | cfSort markers | data.frame | 51035 | 4 |
cfsort_reads | cfTools | Fragment-level methylation state for cfSort tissue deconvolution | data.frame | 99999 | 2 |
demo.fragment_level.meth.bed | cfTools | Fragment-level methylation information | data.frame | 552 | 9 |
demo.refo_frag.bed | cfTools | Fragment-level information | data.frame | 559 | 6 |
demo.refo_meth.bed | cfTools | Methylation information on fragments | data.frame | 552 | 8 |
demo.sorted.bed | cfTools | Paired-end sequencing reads | data.frame | 1117 | 6 |
marker_index | cfTools | Marker name | data.frame | 3 | 1 |
markers.bed | cfTools | Genomic postions of markers | data.frame | 3 | 4 |
sample_type | cfTools | Sample type | data.frame | 20 | 1 |
beatenberg | extremis | Beatenberg | data.frame | 2839 | 2 |
lse | extremis | Selected Stocks from the London Stock Exchange | data.frame | 6894 | 27 |
sp500 | extremis | Standard & Poor 500 | data.frame | 5043 | 2 |
aflw | mulgar | AFLW player statistics | tbl_df | 381 | 35 |
anomaly1 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
anomaly2 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
anomaly3 | mulgar | Data sets with anomalies | tbl_df | 97 | 4 |
anomaly4 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
anomaly5 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
assoc1 | mulgar | Data sets with different types of association | tbl_df | 136 | 4 |
assoc2 | mulgar | Data sets with different types of association | tbl_df | 322 | 4 |
assoc3 | mulgar | Data sets with different types of association | tbl_df | 175 | 4 |
box | mulgar | 3D plane in 5D | data.frame | 200 | 5 |
bushfires | mulgar | Australian bushfires 2019-2020 | tbl_df | 1021 | 60 |
c1 | mulgar | Cluster challenge data sets | tbl_df | 300 | 6 |
c2 | mulgar | Cluster challenge data sets | tbl_df | 600 | 6 |
c3 | mulgar | Cluster challenge data sets | tbl_df | 729 | 10 |
c4 | mulgar | Cluster challenge data sets | tbl_df | 400 | 7 |
c5 | mulgar | Cluster challenge data sets | tbl_df | 200 | 10 |
c6 | mulgar | Cluster challenge data sets | tbl_df | 1500 | 4 |
c7 | mulgar | Cluster challenge data sets | tbl_df | 1300 | 6 |
clusters | mulgar | Three clusters in 5D | data.frame | 300 | 6 |
clusters_nonlin | mulgar | Four unusually shaped clusters in 4D | data.frame | 1268 | 4 |
multicluster | mulgar | Multiple clusters of different sizes, shapes and distance from each other | tbl_df | 400 | 11 |
pisa | mulgar | PISA scores | tbl_df | 26371 | 31 |
plane | mulgar | 2D plane in 5D | data.frame | 100 | 5 |
plane_nonlin | mulgar | Non-linear relationship in 5D | data.frame | 100 | 5 |
simple_clusters | mulgar | Two clusters in 2D | data.frame | 137 | 3 |
sketches_test | mulgar | Images of sketches for testing | tbl_df | 1200 | 786 |
sketches_train | mulgar | Images of sketches for training | tbl_df | 5998 | 786 |
code | IDCard | Administrative area code | data.frame | 3507 | 2 |
example_obs | landest | Hypothetical data from an observational study | data.frame | 4000 | 6 |
example_rct | landest | Hypothetical data from a randomized trial | data.frame | 3000 | 6 |
aSAH | reportROC | Subarachnoid hemorrhage data | data.frame | 113 | 7 |
lss_county_xwalk | marylandedu | Maryland Local School System-County Crosswalk | tbl_df | 25 | 7 |
md_nces_directory | marylandedu | Maryland NCES Directory (SY 2003-2023) | tbl_df | 31579 | 14 |
msde_attendance | marylandedu | Maryland Public School Attendance (SY 2003-2022) | tbl_df | 35182 | 18 |
msde_enrollment | marylandedu | Maryland Public School Enrollment (SY 2003-2023) | tbl_df | 247740 | 10 |
msde_student_mobility | marylandedu | Maryland Student Mobility (SY 2003-2022) | tbl_df | 35184 | 14 |
cdesc_char | cmapR | An example table of metadata, as would be parsed from or parse.gctx. Initially all the columns are of type character. | data.frame | 368 | 8 |
ds | cmapR | An example of a GCT object with row and column metadata and gene expression values in the matrix. | GCT | | |
gene_set | cmapR | An example collection of gene sets as used in the Lamb 2006 CMap paper. | list | | |
kd_gct | cmapR | An example GCT object of knockdown experiments targeting a subset of landmark genes. | GCT | | |
positions | BEAT | Sample dataset of CpG positions for a single cell BS-seq sample | data.frame | 235935 | 4 |
positions.reference | BEAT | Sample dataset of CpG positions for a reference BS-Seq sample | data.frame | 237531 | 4 |
cytoHDBMW | cytoKernel | Example of processed dimensionally reduced flow cytometry (marker median intensities) Bodenmiller_BCR_XL_flowSet() expression dataset from HDCytoData Bioconductor data package. | SummarizedExperiment | | |
dipdoubletGate | flowTime | A gate for the set of all diploid doublets | polygonGate | | |
dipsingletGate | flowTime | A gate for the set of all diploid singlet yeast cells | polygonGate | | |
hapdoubletGate | flowTime | A gate for the set of all haploid doublets | polygonGate | | |
hapsingletGate | flowTime | A gate for the set of all haploid singlets | polygonGate | | |
yeastGate | flowTime | A gate for the set of all yeast cells | polygonGate | | |
PBT_gmt | dearseq | PBT gene sets related to kidney transplant | list | | |
baduel_gmt | dearseq | Small portion of RNA-seq data from plant physiology study. | list | | |
design | dearseq | Small portion of RNA-seq data from plant physiology study. | data.frame | 48 | 11 |
expr_norm_corr | dearseq | Small portion of RNA-seq data from plant physiology study. | matrix | 2454 | 48 |
example.gbm.mae | MOMA | Glioblastoma (GBM) Example Dataset | MultiAssayExperiment | | |
gbm.pathways | MOMA | Glioblastoma (GBM) Pathways | list | | |
gene.map | MOMA | Gene Location Mapping | data.frame | 41703 | 4 |
mutSig | MOMA | MutSig Blacklisted genes | character | | |
beads1 | flowBeads | Dako beads on day 1 | BeadFlowFrame | | |
beads2 | flowBeads | Dako beads on day 2 | BeadFlowFrame | | |
cytocalmef | flowBeads | Cytocal config file | data.frame | 6 | 11 |
dakomef | flowBeads | Dako config file | data.frame | 6 | 5 |
melissa_encode_dt | Melissa | Synthetic ENCODE single cell methylation data | melissa_data_obj | | |
melissa_synth_dt | Melissa | Synthetic single cell methylation data | melissa_data_obj | | |
OscopeExampleData | Oscope | Simulated gene level data set with 600 genes and 30 cells. | matrix | 500 | 30 |
hm | heatmaps | Data for man page examples | Heatmap | | |
hm2 | heatmaps | Data for man page examples | Heatmap | | |
mat | heatmaps | Data for man page examples | matrix | 500 | 200 |
rle_list | heatmaps | Data for man page examples | CompressedRleList | | |
string_set | heatmaps | Data for man page examples | DNAStringSet | | |
tata_pwm | heatmaps | Data for man page examples | matrix | 4 | |
windows | heatmaps | Data for man page examples | GRanges | | |
ensg_hgnc | HIPPO | A reference data frame that matches ENSG IDs to HGNC symbols | data.frame | 46606 | 2 |
toydata | HIPPO | A sample single cell sequencing data subsetted from Zheng2017 | SingleCellExperiment | | |
hmel.dat | doseR | Hmel data set | list | | |
se | doseR | Hmel data set | SummarizedExperiment | | |
classification_scheme | planttfhunter | Data frame of TF family classification scheme | data.frame | 58 | 5 |
gsu | planttfhunter | Protein sequences of the algae species Galdieria sulphuraria | AAStringSet | | |
gsu_annotation | planttfhunter | Domain annotation for the algae species Galdieria sulphuraria The data set was created using the funcion 'annotate_pfam()' in local mode. | data.frame | 449 | 2 |
gsu_families | planttfhunter | TFs families of the algae species Galdieria sulphuraria The data set was created using the funcion 'classify_tfs()'. | data.frame | 125 | 2 |
tf_counts | planttfhunter | TF counts per family in 4 simulated species | SummarizedExperiment | | |
data_ge | rYWAASB | Dataset2: a tibble containing ENV, GEN, REP factors and GY(grain yield) and HM agronomic traits from the 'metan' package. | tbl_df | 420 | 5 |
maize | rYWAASB | Dataset1: a tibble containing GEN, Trait, 'WAASB' and 'WAASBY' indexes. | tbl_df | 20 | 4 |
BacteriaPI | fdadensity | pH distribution of 813 bacterial organisms | matrix | 813 | |
dat_ckid | hrcomprisk | CKID dataset | data.frame | 626 | 13 |
enriched.rd | phosphonormalizer | Enriched dataset | data.frame | 4099 | 17 |
non.enriched.rd | phosphonormalizer | Non-enriched dataset | data.frame | 16982 | 17 |
scenario.set | portfolio.optimization | | matrix | 251 | 30 |
sp100w17 | portfolio.optimization | | xts | 251 | 101 |
sp100w17av | portfolio.optimization | | numeric | | |
BUSexample_data | BUScorrect | A simulated data set | list | | |
cnodata | CNORode | A cnodata from CellNoptR | CNOlist | | |
cnolist | CNORode | A cnolist from CellNoptR | CNOlist | | |
cnolistCNORodeExample | CNORode | A cnolist from CellNoptR | list | | |
indices | CNORode | Indices that relate cnolist to model | list | | |
model | CNORode | A model from CellNoptR | list | | |
pknmodel | CNORode | A pknmodel from CellNoptR | list | | |
pknmodel | CNORode | A pknmodel from CellNoptR | list | | |
aafExpr | annaffy | Sample ExpressionSet used for demonstration purposes | ExpressionSet | | |
missing_dataset | ICIKendallTau | Example Dataset With Missingness | matrix | 1000 | |
yeast_missing | ICIKendallTau | Example RNA-Seq Dataset With Missingness | matrix | 6887 | 96 |
fhcrcData | microsimulation | Old data used in the prostata model | list | | |
df.compare1 | metaConvert | Fictitious dataset 1 | data.frame | 5 | 7 |
df.compare2 | metaConvert | Fictitious dataset 2 | data.frame | 6 | 7 |
df.haza | metaConvert | Meta-analytic dataset inspired from Haza and colleagues (2024) | data.frame | 170 | 106 |
df.short | metaConvert | Short version of the df.haza dataset | grouped_df | 37 | 109 |
gma_grn | magrene | Sample soybean GRN | data.frame | 137866 | 3 |
gma_paralogs | magrene | Soybean (Glycine max) duplicated genes | data.frame | 80763 | 3 |
gma_ppi | magrene | Sample soybean PPI network | data.frame | 18522 | 2 |
nulls | magrene | Null distribution of motif frequencies for vignette data set | list | | |
two_factor_crossed | contrast | Complete Two-Factor Experiment | data.frame | 24 | 3 |
two_factor_incompl | contrast | Incomplete Two-Factor Experiment with Repeated Measurments | data.frame | 23 | 5 |
onze_intercepts | nzilbb.vowels | Speaker random intercepts from GAMMs for 100 ONZE speakers | spec_tbl_df | 100 | 21 |
onze_intercepts_full | nzilbb.vowels | Speaker random intercepts for 418 ONZE speakers | spec_tbl_df | 481 | 21 |
onze_vowels | nzilbb.vowels | Monophthong data for random sample of speakers from the ONZE corpus | data.frame | 101572 | 8 |
onze_vowels_full | nzilbb.vowels | Monophthong data for speakers from the ONZE corpus | data.frame | 414679 | 8 |
qb_intervals | nzilbb.vowels | Formant and amplitude for intervals of QuakeBox monologues | grouped_df | 845 | 25 |
qb_vowels | nzilbb.vowels | Formants from QuakeBox 1 | grouped_df | 26331 | 14 |
sim_matrix | nzilbb.vowels | Similarity matrix from online perception test. | matrix | 38 | 38 |
desx | M3C | GBM clinical annotation data | data.frame | 50 | 2 |
mydata | M3C | GBM expression data | data.frame | 1740 | 50 |
dir_intnet_chicago | intensitynet | This data is an intensitynet object containing a directed network. The base data used is from Chicago, extracted from the spatstat package. | intensitynetDir | | |
mix_intnet_chicago | intensitynet | This data is an intensitynet object containing an mixed network. The base data used is from Chicago, extracted from the spatstat package. | intensitynetMix | | |
und_intnet_chicago | intensitynet | This data is an intensitynet object containing an undirected network. The base data used is from Chicago, extracted from the spatstat package. | intensitynetUnd | | |
GSP | gscramble | Example Genomic Simulation Pedigree, GSP, with 13 members | tbl_df | 13 | 11 |
GSP_opts | gscramble | A list of tibbles specifying the pedigrees available from 'createGSP()' | list | | |
Geno | gscramble | Genotype matrix of 78 individuals and 100 SNP markers | matrix | 78 | |
I_meta | gscramble | Metadata for 78 individuals | tbl_df | 78 | 2 |
M_meta | gscramble | Metadata for 100 molecular markers | spec_tbl_df | 100 | 3 |
RecRates | gscramble | Recombination rate data for many roughly 1 Mb bins | tbl_df | 198 | 5 |
RepPop1 | gscramble | A simple example of a reppop table | tbl_df | 4 | 3 |
RepPop4 | gscramble | Another simple example of a reppop table | tbl_df | 12 | 3 |
example_chrom_lengths | gscramble | Lengths of the three chromosomes used in the example data set | tbl_df | 3 | 2 |
example_segments | gscramble | Example of a segments tibble | tbl_df | 286 | 14 |
gsp3 | gscramble | Tibble holding specification for a 5 member genomic permutation pedigree. | spec_tbl_df | 5 | 11 |
gsp4 | gscramble | Tibble holding specification for a 7 member genomic permutation pedigree. | tbl_df | 7 | 11 |
VDJgermlines | VDJgermlines | VDJgermlines | data.frame | 7285 | 8 |
hearth | ordinalForest | Data on Coronary Artery Disease | data.frame | 294 | 11 |
ISOdata | maSigPro | RNA-Seq dataset example for isoforms | data.frame | 2782 | 37 |
ISOdesign | maSigPro | Experimental design for ISOdata dataset example | data.frame | 36 | 4 |
NBdata | maSigPro | RNA-Seq dataset example | matrix | 100 | 36 |
NBdesign | maSigPro | Experimental design for RNA-Seq example | matrix | 36 | 4 |
data.abiotic | maSigPro | Gene expression data potato abiotic stress | data.frame | 1000 | 36 |
edesign.abiotic | maSigPro | Experimental design potato abiotic stress | matrix | 36 | 6 |
edesignCT | maSigPro | Experimental design with a shared time | matrix | 32 | 7 |
edesignDR | maSigPro | Experimental design with different replicates | matrix | 54 | 7 |
expression | GSALightning | Breast Cancer Data from The Cancer Genome Atlas (TCGA) | matrix | 909 | 1218 |
sampleInfo | GSALightning | Sample Information for the Breast Cancer Data from The Cancer Genome Atlas (TCGA) | data.frame | 1218 | 3 |
targetGenes | GSALightning | Target Genes of Distal Regulatory Elements | list | | |
servosphere | servosphereR | Example servosphere data | list | | |
roi_volume | efast | Synthesised volume data for DKT-atlas ROIs. | data.frame | 647 | 68 |
ColDePalluel | DAIME | Microcpaleontological, palynological, and geochemical data from the Col de Palluel, SE France (Late Albanian) | list | | |
KPgLastOccurrences | DAIME | Last occurrences for the K/Pg boundary and a potential influence of Deccan volcanism | list | | |
LakeSuperior | DAIME | 210Pb measurements and derived age models from Lake Superior | list | | |
SeymourIslandAgeModels | DAIME | Age models for the K/Pg boundary on Seymour Island, Antarctica | list | | |
SeymourIslandBins | DAIME | Sampling bins approaching and covering the K/Pg boundary on Seymour Island, Antarctica | list | | |
us_cities | webmap | US Major Cities | data.frame | 1005 | 4 |
example_design_tidy | gcplyr | Design for example growth curve data A tidy-shaped dataset with the experimental design (i.e. plate layout) for the example data included with 'gcplyr'. | data.frame | 96 | 3 |
example_widedata | gcplyr | Example noisy growth curve data in wide format | data.frame | 97 | 97 |
example_widedata_noiseless | gcplyr | Example growth curve data in wide format | data.frame | 97 | 97 |
SoilP | BLA | Soil Phosphorus data | data.frame | 6020 | 2 |
SoilpH | BLA | Soil pH data | data.frame | 6047 | 2 |
evapotranspiration | BLA | Evapotranspiration data | data.frame | 691 | 3 |
soil | BLA | Soil survey data | data.frame | 6110 | 3 |
followers | rgexf | Edge list with attributes | data.frame | 6064 | 6 |
twitteraccounts | rgexf | Twitter accounts of Chilean Politicians and Journalists (sample) | data.frame | 148 | 5 |
A | ngspatial | Adjacency matrix for the infant mortality data. | matrix | 3071 | |
infant | ngspatial | Infant mortality data. | data.frame | 3071 | 12 |
boys7482 | AGD | Growth of Dutch boys | data.frame | 7482 | 9 |
cdc.bmi | AGD | Reference tables from CDC 2000 | data.frame | 438 | 7 |
cdc.hgt | AGD | Reference tables from CDC 2000 | data.frame | 490 | 7 |
cdc.wgt | AGD | Reference tables from CDC 2000 | data.frame | 486 | 7 |
nl3.bmi | AGD | Reference tables from Third Dutch Growth Study 1980 | data.frame | 132 | 7 |
nl4.bmi | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 414 | 7 |
nl4.hdc | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 264 | 7 |
nl4.hgt | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 414 | 7 |
nl4.hip | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 177 | 7 |
nl4.lgl | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 173 | 7 |
nl4.shh | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 173 | 7 |
nl4.sit | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 173 | 7 |
nl4.wfh | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 994 | 7 |
nl4.wgt | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 414 | 7 |
nl4.whr | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 177 | 7 |
nl4.wst | AGD | Reference tables from Fourth Dutch Growth Study 1997 | data.frame | 177 | 7 |
who.bmi | AGD | References WHO | data.frame | 4052 | 7 |
who.hdc | AGD | References WHO | data.frame | 3714 | 7 |
who.hgt | AGD | References WHO | data.frame | 4052 | 7 |
who.wfh | AGD | References WHO | data.frame | 1102 | 7 |
who.wfl | AGD | References WHO | data.frame | 1302 | 7 |
who.wgt | AGD | References WHO | data.frame | 4052 | 7 |
riceCH_eg | phenolocrop | Rice canopy height example data | tbl_df | 16 | 4 |
BiopsyTrees | dowser | Example Ig lineage trees with biopsy reconstructions. | tbl_df | 8 | 5 |
ExampleAirr | dowser | Example AIRR database | tbl_df | 389 | 30 |
ExampleClones | dowser | Example Ig lineage trees | tbl_df | 89 | 5 |
ExampleDbChangeo | dowser | Example Change-O database | tbl_df | 2000 | 15 |
ExampleMixedClones | dowser | Example Multiple Partition Trees | tbl_df | 4 | 6 |
ExampleMixedDb | dowser | Example Change-O database | tbl_df | 38 | 56 |
IsotypeTrees | dowser | Example Ig lineage trees with isotype reconstructions. | tbl_df | 34 | 5 |
TimeTrees | dowser | Example Ig lineage trees sampled over time. | tbl_df | 6 | 12 |
MinMaxFreq | AncestryMapper | Allele Variants for Demo SNPs According to dbSNP. | matrix | 1000 | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
arithmeticRefsMedoids | AncestryMapper | Object each reference file is assigned as it is loaded in or read. | list | | |
nrsf | iSeq | nrsf data | list | | |
agaricus.test | lightgbm | Test part from Mushroom Data Set | list | | |
agaricus.train | lightgbm | Training part from Mushroom Data Set | list | | |
bank | lightgbm | Bank Marketing Data Set | data.table | 4521 | 17 |
envdata | forestHES | Survey data of Environmental factors for pine-oak mixed forests | data.frame | 20 | 16 |
herbdata | forestHES | Survey data of undergrowth herb cluster for pine-oak mixed forests | data.frame | 229 | 4 |
indexSystem | forestHES | The national forest health evaluation system | data.frame | 21 | 9 |
testIndex | forestHES | Test Indices for forest health evaluation system | data.frame | 30 | 21 |
treedata | forestHES | Survey data of individual trees for pine-oak mixed forests | data.frame | 1054 | 5 |
encyclopedists | plume | Famous encyclopedists | tbl_df | 4 | 10 |
encyclopedists_fr | plume | Famous encyclopedists | tbl_df | 4 | 10 |
SCE | scCAN | SCE | list | | |
Silky_data | SharkDemography | Example life history data for Silky Sharks | Demography.inputs | | |
isocountry | isocountry | Country names with ISO country codes | tbl_df | 249 | 13 |
isocurrency | isocountry | ISO currency codes | tbl_df | 240 | 6 |
GSM | secr | Black Bear Hair Snag Dataset | sfc_POLYGON | | |
LStraps | secr | Skink Pitfall Data | traps | 462 | 2 |
OVpossumCH | secr | Orongorongo Valley Brushtail Possums | capthist | | |
blackbear.0 | secr | Black Bear Hair Snag Dataset | secr | | |
blackbear.h2bk | secr | Black Bear Hair Snag Dataset | secr | | |
blackbearCH | secr | Black Bear Hair Snag Dataset | capthist | | |
captXY | secr | SECR Models Fitted to Demonstration Data | data.frame | 235 | 5 |
captdata | secr | SECR Models Fitted to Demonstration Data | capthist | | |
deermouse.ESG | secr | Deermouse Live-trapping Datasets | capthist | | |
deermouse.WSG | secr | Deermouse Live-trapping Datasets | capthist | | |
hornedlizardCH | secr | Flat-tailed Horned Lizard Dataset | capthist | | |
housemouse | secr | House mouse live trapping data | capthist | | |
infraCH | secr | Skink Pitfall Data | capthist | | |
lineoCH | secr | Skink Pitfall Data | capthist | | |
ovenCH | secr | Ovenbird Mist-netting Dataset | capthist | | |
ovenCHp | secr | Ovenbird Mist-netting Dataset | capthist | | |
ovenbird.model.1 | secr | Ovenbird Mist-netting Dataset | secr | | |
ovenbird.model.D | secr | Ovenbird Mist-netting Dataset | secr | | |
ovenmask | secr | Ovenbird Mist-netting Dataset | mask | | |
ovensong.model.1 | secr | Ovenbird Acoustic Dataset | secr | | |
ovensong.model.2 | secr | Ovenbird Acoustic Dataset | secr | | |
possum.model.0 | secr | Brushtail Possum Trapping Dataset | secr | | |
possum.model.Ds | secr | Brushtail Possum Trapping Dataset | secr | | |
possumCH | secr | Brushtail Possum Trapping Dataset | capthist | | |
possumarea | secr | Brushtail Possum Trapping Dataset | data.frame | 152 | 2 |
possummask | secr | Brushtail Possum Trapping Dataset | mask | 5120 | 2 |
possumremovalarea | secr | Brushtail Possum Trapping Dataset | data.frame | 116 | 2 |
secrdemo.0 | secr | SECR Models Fitted to Demonstration Data | secr | | |
secrdemo.CL | secr | SECR Models Fitted to Demonstration Data | secr | | |
secrdemo.b | secr | SECR Models Fitted to Demonstration Data | secr | | |
signalCH | secr | Ovenbird Acoustic Dataset | capthist | | |
stoat.model.EX | secr | Stoat DNA Data | secr | | |
stoat.model.HN | secr | Stoat DNA Data | secr | | |
stoatCH | secr | Stoat DNA Data | capthist | | |
trapXY | secr | SECR Models Fitted to Demonstration Data | data.frame | 100 | 3 |
bp10k | clustra | Simulated blood pressure data | data.table | 167277 | 4 |
maize | ProbBreed | Maize real data set | data.frame | 1152 | 6 |
soy | ProbBreed | Soybean real data set | data.frame | 435 | 3 |
nlmixr2Keywords | nlmixr2est | A list and description of the fields in the nlmxir2 object | data.frame | 41 | 2 |
df_binom | projpred | Binomial toy example | data.frame | 100 | 2 |
df_gaussian | projpred | Gaussian toy example | data.frame | 100 | 2 |
mesquite | projpred | Mesquite data set | data.frame | 46 | 8 |
ClassificationExample | TSLA | Synthesic for the classification example | list | | |
RegressionExample | TSLA | Synthesic for the regression example | list | | |
alos_palsar_band_mapping | rsi | ALOS PALSAR band mapping | list | | |
dem_band_mapping | rsi | Landsat band mapping | list | | |
landsat_band_mapping | rsi | Landsat band mapping | list | | |
sentinel1_band_mapping | rsi | Sentinel-1 band mapping | list | | |
sentinel2_band_mapping | rsi | Sentinel-2 band mapping | list | | |
guerry | waywiser | Guerry "Moral Statistics" (1830s) | sf | 85 | 27 |
ny_trees | waywiser | Number of trees and aboveground biomass for Forest Inventory and Analysis plots in New York State | sf | 5303 | 5 |
worldclim_simulation | waywiser | Simulated data based on WorldClim Bioclimatic variables | sf | 10000 | 6 |
GRCh37 | CancerEvolutionVisualization | GRCh37 Chromosom Information | data.table | 24 | 3 |
GRCh38 | CancerEvolutionVisualization | GRCh38 Chromosom Information | data.table | 24 | 5 |
colours | CancerEvolutionVisualization | Colour scheme vector | character | | |
snv | CancerEvolutionVisualization | SNV dataframe | data.table | 5401 | 6 |
formats | pmeasyr | Table des formats | tbl_df | 29220 | 14 |
codata_combo2 | OncoBayes2 | Dataset: historical and concurrent data on a two-way combination | tbl_df | 27 | 6 |
dose_info_combo2 | OncoBayes2 | Dataset: trial dose information for a dual-agent combination study | tbl_df | 42 | 4 |
drug_info_combo2 | OncoBayes2 | Dataset: drug information for a dual-agent combination study | tbl_df | 2 | 4 |
hist_SA | OncoBayes2 | Single-agent example | tbl_df | 5 | 4 |
hist_combo2 | OncoBayes2 | Dataset: historical data on two single-agents to inform a combination study | tbl_df | 11 | 6 |
hist_combo3 | OncoBayes2 | Dataset: historical and concurrent data on a three-way combination | tbl_df | 18 | 7 |
AchievementAwardsRCT | clubSandwich | Achievement Awards Demonstration program | tbl_df | 16526 | 20 |
MortalityRates | clubSandwich | State-level annual mortality rates by cause among 18-20 year-olds | data.frame | 5508 | 12 |
SATcoaching | clubSandwich | Randomized experiments on SAT coaching | data.frame | 67 | 11 |
dropoutPrevention | clubSandwich | Dropout prevention/intervention program effects | data.frame | 385 | 18 |
erddapList | PAMmisc | A list of edinfo objects from ERDDAP data sources | list | | |
hycomList | PAMmisc | A list of edinfo objects from HYCOM data sources | hycomList | | |
boot.wmt | EGAnet | 'bootEGA' Results of 'wmt2'Data | bootEGA | | |
depression | EGAnet | Depression Data | data.frame | 574 | 78 |
dnn.weights | EGAnet | Loadings Comparison Test Deep Learning Neural Network Weights | list | | |
ega.wmt | EGAnet | 'EGA' Network of 'wmt2'Data | EGA | | |
intelligenceBattery | EGAnet | Intelligence Data | data.frame | 1152 | 125 |
optimism | EGAnet | Optimism Data | data.frame | 282 | 10 |
prime.num | EGAnet | Prime Numbers through 100,000 | integer | | |
sim.dynEGA | EGAnet | sim.dynEGA Data | data.frame | 5000 | 26 |
wmt2 | EGAnet | WMT-2 Data | data.frame | 1185 | 24 |
air | funcharts | Air quality data | list | | |
master_est | baRulho | Extended selection table of master acoustic data | extended_selection_table | 7 | 7 |
test_sounds_est | baRulho | Extended selection table with re-recorded playbacks | extended_selection_table | 25 | 9 |
bad_link_text | shinyGovstyle | Lookup for bad link text | data.frame | 54 | 1 |
BM14_M | dfms | Euro Area Macroeconomic Data from Banbura and Modugno 2014 | xts | 357 | 92 |
BM14_Models | dfms | Euro Area Macroeconomic Data from Banbura and Modugno 2014 | data.frame | 101 | 8 |
BM14_Q | dfms | Euro Area Macroeconomic Data from Banbura and Modugno 2014 | xts | 119 | 9 |
Demo.growth | lavaan | Demo dataset for a illustrating a linear growth model. | data.frame | 400 | 10 |
Demo.twolevel | lavaan | Demo dataset for a illustrating a multilevel CFA. | data.frame | 2500 | 12 |
FacialBurns | lavaan | Dataset for illustrating the InformativeTesting function. | data.frame | 77 | 6 |
HolzingerSwineford1939 | lavaan | Holzinger and Swineford Dataset (9 Variables) | data.frame | 301 | 15 |
PoliticalDemocracy | lavaan | Industrialization And Political Democracy Dataset | data.frame | 75 | 11 |
favara_imbs | DIDmultiplegtDYN | Favara and Imbs (2015) | tbl_df | 1157 | 7 |
goby_data | eDNAjoint | goby_data | list | | |
green_crab_data | eDNAjoint | green_crab_data | list | | |
ClusterExercise | Distance | Simulated minke whale data with cluster size | data.frame | 99 | 9 |
ClusterExercise_units | Distance | Simulated minke whale data with cluster size | data.frame | 3 | 3 |
CueCountingExample | Distance | Cue counts of whale blows | data.frame | 109 | 15 |
CueCountingExample_units | Distance | Cue counts of whale blows | data.frame | 2 | 3 |
DuikerCameraTraps | Distance | Duiker camera trap survey | data.frame | 6277 | 7 |
ETP_Dolphin | Distance | Eastern Tropical Pacific spotted dolphin survey | data.frame | 1090 | 13 |
ETP_Dolphin_units | Distance | Eastern Tropical Pacific spotted dolphin survey | data.frame | 3 | 3 |
LTExercise | Distance | Simulated line transect survey data | data.frame | 106 | 7 |
PTExercise | Distance | Simulated point transect survey data | data.frame | 144 | 7 |
PTExercise_units | Distance | Simulated point transect survey data | data.frame | 2 | 3 |
Savannah_sparrow_1980 | Distance | Savanna sparrow point transects | data.frame | 468 | 7 |
Savannah_sparrow_1980_units | Distance | Savanna sparrow point transects | data.frame | 2 | 3 |
Savannah_sparrow_1981 | Distance | Savanna sparrow point transects | data.frame | 448 | 7 |
Savannah_sparrow_1981_units | Distance | Savanna sparrow point transects | data.frame | 2 | 3 |
Stratify_example | Distance | Simulated minke whale data | data.frame | 99 | 7 |
Stratify_example_units | Distance | Simulated minke whale data | data.frame | 3 | 3 |
Systematic_variance_1 | Distance | Simulation of encounter rate variance | data.frame | 253 | 7 |
Systematic_variance_1_units | Distance | Simulation of encounter rate variance | data.frame | 3 | 3 |
Systematic_variance_2 | Distance | Simulation of encounter rate variance | data.frame | 256 | 7 |
Systematic_variance_2_units | Distance | Simulation of encounter rate variance | data.frame | 3 | 3 |
amakihi | Distance | Hawaiian amakihi point transect data | data.frame | 1487 | 12 |
amakihi_units | Distance | Hawaiian amakihi point transect data | data.frame | 2 | 3 |
capercaillie | Distance | Capercaillie in Monaughty Forest | data.frame | 112 | 9 |
capercaillie_units | Distance | Capercaillie in Monaughty Forest | data.frame | 3 | 3 |
ducknest | Distance | Ducknest line transect survey data | data.frame | 534 | 7 |
ducknest_units | Distance | Ducknest line transect survey data | data.frame | 3 | 3 |
golftees | Distance | Golf tee data | data.frame | 324 | 12 |
golftees_units | Distance | Golf tee data | data.frame | 3 | 3 |
minke | Distance | Simulated minke whale data | data.frame | 99 | 6 |
sikadeer | Distance | Sika deer pellet data from southern Scotland | data.frame | 1923 | 11 |
sikadeer_units | Distance | Sika deer pellet data from southern Scotland | data.frame | 3 | 3 |
unimak | Distance | Simulated line transect survey data with covariates | data.frame | 60 | 9 |
unimak_units | Distance | Simulated line transect survey data with covariates | data.frame | 3 | 3 |
wren_5min | Distance | Steve Buckland's winter wren surveys | data.frame | 134 | 8 |
wren_cuecount | Distance | Steve Buckland's winter wren surveys | data.frame | 774 | 9 |
wren_cuecount_units | Distance | Steve Buckland's winter wren surveys | data.frame | 2 | 3 |
wren_lt | Distance | Steve Buckland's winter wren surveys | data.frame | 156 | 7 |
wren_lt_units | Distance | Steve Buckland's winter wren surveys | data.frame | 3 | 3 |
wren_snapshot | Distance | Steve Buckland's winter wren surveys | data.frame | 119 | 7 |
wren_snapshot_units | Distance | Steve Buckland's winter wren surveys | data.frame | 2 | 3 |
Wenchuan | bgms | Post-traumatic stress disorder symptoms of Wenchuan earthquake survivors | matrix | 362 | 17 |
IBScovars | DoseFinding | Irritable Bowel Syndrome Dose Response data with covariates | data.frame | 369 | 3 |
biom | DoseFinding | Biometrics Dose Response data | data.frame | 100 | 2 |
glycobrom | DoseFinding | Glycopyrronium Bromide dose-response data | data.frame | 5 | 4 |
migraine | DoseFinding | Migraine Dose Response data | data.frame | 8 | 3 |
neurodeg | DoseFinding | Neurodegenerative disease simulated longitudinal dose-finding data set | data.frame | 1250 | 4 |
clock_iso_weekdays | clock | Integer codes | environment | | |
clock_months | clock | Integer codes | environment | | |
clock_weekdays | clock | Integer codes | environment | | |
dwellings | sdcSpatial | Simulated dwellings data set | data.frame | 90603 | 4 |
enterprises | sdcSpatial | Simulated data set with enterprise locations. | SpatialPointsDataFrame | | |
chinook | zoid | Data from Satterthwaite, W.H., Ciancio, J., Crandall, E., Palmer-Zwahlen, M.L., Grover, A.M., O’Farrell, M.R., Anson, E.C., Mohr, M.S. & Garza, J.C. (2015). Stock composition and ocean spatial distribution from California recreational chinook salmon fisheries using genetic stock identification. Fisheries Research, 170, 166–178. The data genetic data collected from port-based sampling of recreationally-landed Chinook salmon in California from 1998-2002. | data.frame | 60 | 10 |
coddiet | zoid | Data from Magnussen, E. 2011. Food and feeding habits of cod (Gadus morhua) on the Faroe Bank. – ICES Journal of Marine Science, 68: 1909–1917. The data here are Table 3 from the paper, with sample proportions (columns w) multiplied by total weight to yield total grams (g) for each sample-diet item combination. Dashes have been replaced with 0s. | data.frame | 10 | 32 |
Galapagos | netCoin | Data: Finches' presence in Galapagos Islands. | data.frame | 17 | 13 |
dice | netCoin | Data: Roll a die (100 times). | data.frame | 100 | 11 |
ess | netCoin | Data: European Social Survey, Round-8. | data.frame | 1000 | 5 |
events | netCoin | Data: Attributes of the dice events. | data.frame | 10 | 4 |
families | netCoin | Data: Italian families in the Renaissance. | data.frame | 16 | 6 |
finches | netCoin | Data: Finches'attributes in Galapagos islands. | data.frame | 13 | 4 |
links | netCoin | Data: Links between Italian families in the Renaissance. | data.frame | 36 | 17 |
sociologists | netCoin | Data: Classical sociologists. | data.frame | 16 | 11 |
works | netCoin | Data: Classical sociological works. | tbl_df | 54 | 4 |
mip_example_data | mip | mip_example_data | magpie | | |
milk | pwr4exp | An exemplary dataset of a 4x4 crossover design with 2 squares | data.frame | 32 | 4 |
data_cary | OxSR | Diffuse soil reflectance via Cary equipment | tbl_df | 5417 | 50 |
soil_refle | OxSR | Diffuse reflectance of soils from Brazil | tbl_df | 4241 | 24 |
GDP | DATAstudio | GDP of the US Economy | ts | 240 | 1 |
GDPIP | DATAstudio | A Real-time Vintage of GDP and IP for the US Economy | mts | 268 | 2 |
alps | DATAstudio | Swiss Alps Temperature Data | data.frame | 3190 | 5 |
beatenberg | DATAstudio | Beatenberg Forest Temperature Data (In Unit Fréchet Scale) | data.frame | 2839 | 2 |
brainwave | DATAstudio | Brainwave data | data.frame | 7506 | 10 |
brexit | DATAstudio | Brexit Poll Tracker | data.frame | 272 | 6 |
california | DATAstudio | California Fire Perimeters | data.frame | 16577 | 2 |
challenger | DATAstudio | Space Shuttle Challenger Data | data.frame | 23 | 2 |
claims | DATAstudio | Initial Claims of Unemployment | tis | | |
cortical | DATAstudio | Brain Shape Data | list | | |
diabetes | DATAstudio | Diabetes Diagnosis Data | data.frame | 286 | 3 |
ecg200 | DATAstudio | Electrocardiogram Data | tbl_df | 200 | 97 |
fire | DATAstudio | Danish Fire Insurance Claims Database | data.frame | 1502 | 5 |
hongkong | DATAstudio | Daily Maximum Temperature in Hong Kong | data.frame | 48516 | 2 |
hurricane | DATAstudio | Hurricane Tracking Data | spec_tbl_df | 43122 | 8 |
lisbon | DATAstudio | Rainfall Data from Lisbon, Portugal | tbl_df | 56503 | 2 |
lse | DATAstudio | Selected Stocks from the London Stock Exchange | data.frame | 6894 | 27 |
lungcancer | DATAstudio | Lung Cancer Diagnosis | data.frame | 241 | 4 |
madeira | DATAstudio | Rainfall Data from Madeira, Portugal | data.frame | 544 | 8 |
marketsUS | DATAstudio | NASDAQ and NYSE Indices | data.frame | 12562 | 3 |
merval | DATAstudio | MERVAL Stock Market Data | data.frame | 353 | 5 |
metsynd | DATAstudio | Metabolic Syndrome Data | list | | |
passengers | DATAstudio | International Airline Traffic Data | ts | 144 | 1 |
psa | DATAstudio | Prostate Cancer Diagnosis Data | data.frame | 683 | 6 |
santiago | DATAstudio | Santiago Temperature Data | mpp | | |
sp500 | DATAstudio | Standard & Poor's 500 | data.frame | 5043 | 2 |
tmt | DATAstudio | Trail Making Test | data.frame | 245 | 2 |
unemployment | DATAstudio | US Unemployment Rate | ts | | |
wildfire | DATAstudio | Portugal Wildfire Data | data.frame | 14609 | 11 |
closure_data | daedalus | Pandemic response strategy data for DAEDALUS | list | | |
country_codes_iso2c | daedalus | Country names and ISO codes for DAEDALUS | character | | |
country_codes_iso3c | daedalus | Country names and ISO codes for DAEDALUS | character | | |
country_data | daedalus | Country demographic data for DAEDALUS | list | | |
country_gni | daedalus | Life expectancies and values | list | | |
country_names | daedalus | Country names and ISO codes for DAEDALUS | character | | |
econ_sector_names | daedalus | Economic sector names | character | | |
economic_contacts | daedalus | Economic sector contacts data for DAEDALUS | list | | |
epidemic_names | daedalus | Infection characteristics for model epidemics | character | | |
infection_data | daedalus | Infection characteristics for model epidemics | list | | |
infection_parameter_names | daedalus | Infection characteristics for model epidemics | character | | |
life_expectancy | daedalus | Life expectancies and values | list | | |
life_value | daedalus | Life expectancies and values | list | | |
vaccination_parameter_names | daedalus | Vaccine investment scenario parameters | character | | |
vaccination_scenario_data | daedalus | Vaccine investment scenario parameters | list | | |
vaccination_scenario_names | daedalus | Vaccine investment scenario parameters | character | | |
NIRsoil | prospectr | NIRSoil | data.frame | 825 | 5 |
berlin_gtfs | gtfsrouter | berlin_gtfs | list | | |
data_censored | TrialEmulation | Example of longitudinal data for sequential trial emulation containing censoring | data.frame | 725 | 12 |
te_data_ex | TrialEmulation | Example of a prepared data object | TE_data_prep_dt | | |
te_model_ex | TrialEmulation | Example of a fitted marginal structural model object | TE_msm | | |
trial_example | TrialEmulation | Example of longitudinal data for sequential trial emulation | data.frame | 48400 | 11 |
vignette_switch_data | TrialEmulation | Example of expanded longitudinal data for sequential trial emulation | data.frame | 1939053 | 13 |
model_suite | campsismod | CAMPSIS model suite. | list | | |
out_list_demo | WhiteLabRt | Sample back-calculation output | backnow | | |
sample_cases | WhiteLabRt | Sample cases | numeric | | |
sample_dates | WhiteLabRt | Sample dates | Date | | |
sample_location | WhiteLabRt | Sample location | character | | |
sample_m_hier | WhiteLabRt | Sample hierachical model output | stanfit | | |
sample_multi_site | WhiteLabRt | Sample multi site | data.frame | 80 | 3 |
sample_onset_dates | WhiteLabRt | Sample onset dates | Date | | |
sample_report_dates | WhiteLabRt | Sample report dates | Date | | |
transfer_matrix | WhiteLabRt | Transfer matrix | matrix | 160 | 2 |
dfPrix_SP95_2016 | btb | Unleaded 95 price in France in 2016 | data.frame | 5573 | 3 |
dfRestaurantParis | btb | Parisian restaurants | data.frame | 13823 | 17 |
pixel_france | btb | France grid with 1km square tiles | data.frame | 548495 | 2 |
reunion | btb | Households of Reunion | data.frame | 14076 | 4 |
ae | pharmaversesdtm | Adverse Events | tbl_df | 1191 | 35 |
ae_ophtha | pharmaversesdtm | Adverse Events for Ophthalmology | tbl_df | 1191 | 37 |
ce_vaccine | pharmaversesdtm | Clinical Events for Vaccine | tbl_df | 44 | 29 |
cm | pharmaversesdtm | Concomitant Medication | tbl_df | 7510 | 22 |
dm | pharmaversesdtm | Demography | tbl_df | 306 | 25 |
dm_metabolic | pharmaversesdtm | Demographic Dataset-metabolic | tbl_df | 5 | 25 |
dm_peds | pharmaversesdtm | Demographic Dataset-pediatrics | tbl_df | 5 | 26 |
dm_vaccine | pharmaversesdtm | Demographics for Vaccine | tbl_df | 2 | 28 |
ds | pharmaversesdtm | Disposition | tbl_df | 850 | 13 |
eg | pharmaversesdtm | Electrocardiogram | tbl_df | 26717 | 23 |
ex | pharmaversesdtm | Exposure | tbl_df | 591 | 17 |
ex_ophtha | pharmaversesdtm | Exposure for Ophthalmology | tbl_df | 591 | 19 |
ex_vaccine | pharmaversesdtm | Exposures for Vaccine | tbl_df | 4 | 21 |
face_vaccine | pharmaversesdtm | Findings About Clinical Events for Vaccine | tbl_df | 307 | 30 |
is_vaccine | pharmaversesdtm | Immunogenicity Specimen Assessments for Vaccine | tbl_df | 16 | 24 |
lb | pharmaversesdtm | Laboratory Measurements | tbl_df | 59580 | 23 |
mh | pharmaversesdtm | Medical History | tbl_df | 1818 | 28 |
oe_ophtha | pharmaversesdtm | Ophthalmic Examinations for Ophthalmology | grouped_df | 15344 | 25 |
pc | pharmaversesdtm | Pharmacokinetic Concentrations | tbl_df | 4572 | 20 |
pp | pharmaversesdtm | Pharmacokinetic Parameters | data.frame | 2688 | 14 |
qs_metabolic | pharmaversesdtm | Questionnaire Dataset-metabolic | tbl_df | 966 | 18 |
qs_ophtha | pharmaversesdtm | Questionnaire for Ophthalmology | tbl_df | 75922 | 20 |
rs_onco | pharmaversesdtm | Disease Response for Oncology | tbl_df | 5808 | 19 |
rs_onco_ca125 | pharmaversesdtm | Disease Response (GCIG) | tbl_df | 66 | 13 |
rs_onco_imwg | pharmaversesdtm | Disease Response (IMWG) | tbl_df | 65 | 17 |
rs_onco_irecist | pharmaversesdtm | Disease Response (iRECIST) for Oncology | tbl_df | 376 | 19 |
rs_onco_recist | pharmaversesdtm | Disease Response (RECIST 1.1) for Oncology | tbl_df | 66 | 14 |
sc_ophtha | pharmaversesdtm | Subject Characteristic for Ophthalmology | tbl_df | 254 | 12 |
sdg_db | pharmaversesdtm | SDG | tbl_df | 16 | 5 |
smq_db | pharmaversesdtm | Standardized MedDRA Queries | tbl_df | 44 | 6 |
suppae | pharmaversesdtm | Supplemental Adverse Events | tbl_df | 1191 | 10 |
suppce_vaccine | pharmaversesdtm | Supplemental Qualifiers for Clinical Events for Vaccine | tbl_df | 4 | 9 |
suppdm | pharmaversesdtm | Supplemental Demography | tbl_df | 1197 | 10 |
suppdm_vaccine | pharmaversesdtm | Supplemental Qualifiers for Demographics for Vaccine | tbl_df | 2 | 9 |
suppds | pharmaversesdtm | Supplemental Disposition | tbl_df | 3 | 9 |
suppex_vaccine | pharmaversesdtm | Supplemental Qualifiers for Exposures for Vaccine | tbl_df | 4 | 9 |
suppface_vaccine | pharmaversesdtm | Supplemental Qualifiers for Findings About for Clinical Events for Vaccine | tbl_df | 4 | 9 |
suppis_vaccine | pharmaversesdtm | Supplemental Qualifiers for Immunogenicity Specimen Assessments for Vaccine | tbl_df | 16 | 10 |
supprs_onco_ca125 | pharmaversesdtm | Supplemental Qualifiers for RS_ONCO_CA125 | tbl_df | 49 | 9 |
supprs_onco_imwg | pharmaversesdtm | Supplemental Qualifiers for RS_ONCO_IMWG | tbl_df | 19 | 9 |
supptr_onco | pharmaversesdtm | Supplemental Tumor Results for Oncology | tbl_df | 55995 | 9 |
sv | pharmaversesdtm | Subject Visits | tbl_df | 3559 | 8 |
tr_onco | pharmaversesdtm | Tumor Results for Oncology | tbl_df | 55995 | 24 |
tr_onco_recist | pharmaversesdtm | Tumor Results (RECIST 1.1) for Oncology | tbl_df | 546 | 19 |
ts | pharmaversesdtm | Trial Design | tbl_df | 33 | 6 |
tu_onco | pharmaversesdtm | Tumor Identification for Oncology | tbl_df | 7734 | 18 |
tu_onco_recist | pharmaversesdtm | Tumor Identification (RECIST 1.1) for Oncology | tbl_df | 75 | 16 |
vs | pharmaversesdtm | Vital Signs | tbl_df | 29643 | 24 |
vs_metabolic | pharmaversesdtm | Vital signs Dataset-metabolic | tbl_df | 719 | 24 |
vs_peds | pharmaversesdtm | Vital signs Dataset-pediatrics | tbl_df | 164 | 26 |
vs_vaccine | pharmaversesdtm | Vital Signs for Vaccine | grouped_df | 28 | 23 |
HFD | bage | Components from Human Fertility Database | bage_ssvd | | |
HMD | bage | Components from Human Mortality Database | bage_ssvd | | |
LFP | bage | Components from OECD Labor Force Participation Data | bage_ssvd | | |
isl_deaths | bage | Deaths in Iceland | tbl_df | 5300 | 5 |
kor_births | bage | Births in South Korea | tbl_df | 1872 | 7 |
nld_expenditure | bage | Per Capita Health Expenditure in the Netherlands, 2003-2011 | tbl_df | 1296 | 4 |
nzl_divorces | bage | Divorces in New Zealand | tbl_df | 242 | 5 |
nzl_households | bage | People in One-Person Households in New Zealand | tbl_df | 528 | 5 |
nzl_injuries | bage | Fatal Injuries in New Zealand | tbl_df | 912 | 6 |
prt_deaths | bage | Deaths in Portugal | tbl_df | 3168 | 7 |
swe_infant | bage | Infant Mortality in Sweden | tbl_df | 441 | 4 |
usa_deaths | bage | Accidental Deaths in the USA | data.frame | 72 | 2 |
western_mediterranean | GIFT | Shape file of the western Mediterranean basin | sf | 1 | 2 |
economic_growth | bdsm | Economic Growth Data | tbl_df | 365 | 12 |
economic_growth_bma_params | bdsm | Example Approximate Summary of Parameters of Interest Based on Model Space | data.frame | 5 | 8 |
economic_growth_liks | bdsm | Example Approximate Likelihoods Summary based on Model Space | matrix | 13 | |
economic_growth_ms | bdsm | Example Model Space | matrix | 51 | |
economic_growth_ms_full_proj_const | bdsm | Full Model Space with Constant Projection Matrix | matrix | 106 | |
economic_growth_ms_full_proj_var | bdsm | Full Model Space with Varying Projection Matrix | matrix | 106 | |
ad_cost | SPORTSCausal | Advertising cost: a real experimental data under spillover effect | data.frame | 49 | 3 |
Scotch | bayesm | Survey Data on Brands of Scotch Consumed | data.frame | 2218 | 21 |
bank | bayesm | Bank Card Conjoint Data | list | | |
camera | bayesm | Conjoint Survey Data for Digital Cameras | list | | |
cheese | bayesm | Sliced Cheese Data | data.frame | 5555 | 4 |
customerSat | bayesm | Customer Satisfaction Data | data.frame | 1811 | 10 |
detailing | bayesm | Physician Detailing Data | list | | |
margarine | bayesm | Household Panel Data on Margarine Purchases | list | | |
orangeJuice | bayesm | Store-level Panel Data on Orange Juice Sales | list | | |
tuna | bayesm | Canned Tuna Sales Data | data.frame | 338 | 30 |
mort | jstable | DATASET_TITLE | data.frame | 17562 | 24 |
simulated_MLFA | MixLFA | simulated_MLFA: Simulated data from the MLFA model | list | | |
datatrade_EU | qPRAentry | Example Trade Data for the European Union | list | | |
datatrade_NorthAm | qPRAentry | Example Trade Data for Northern America | list | | |
flickr_userdays | VisitorCounts | Popularity of Flickr, in User-Days | ts | | |
forest_visitation | VisitorCounts | National Forest Visitation Photo-User-Days Data. | data.frame | 995 | 4 |
park_visitation | VisitorCounts | National Park Visitation Counts and Associated Photo-User-Days Data. | tbl_df | 3120 | 4 |
CT | CopulaREMADA | The computing tomography data | data.frame | 17 | 4 |
Down | CopulaREMADA | The down syndrome data | data.frame | 11 | 8 |
LAG | CopulaREMADA | The lymphangiography data | data.frame | 17 | 4 |
MK2016 | CopulaREMADA | The coronary CT angiography data in Menke and Kowalski (2016). | data.frame | 30 | 6 |
MRI | CopulaREMADA | The magnetic resonance imaging data | data.frame | 10 | 4 |
OGT | CopulaREMADA | The orale glucose tolerance data | data.frame | 10 | 4 |
Pap | CopulaREMADA | The Pap test data | data.frame | 59 | 4 |
arthritis | CopulaREMADA | The rheumatoid arthritis data | data.frame | 22 | 8 |
betaDG | CopulaREMADA | The beta-D-Glucan-data | data.frame | 8 | 4 |
coronary | CopulaREMADA | The coronary CT angiography data | data.frame | 26 | 6 |
mgrid | CopulaREMADA | A list containing four-dimensional arrays | list | | |
mgrid15 | CopulaREMADA | A list containing four-dimensional arrays | list | | |
mgrid30 | CopulaREMADA | A list containing four-dimensional arrays | list | | |
mgrid50 | CopulaREMADA | A list containing four-dimensional arrays | list | | |
mgrid5d15 | CopulaREMADA | A list containing five-dimensional arrays | list | | |
mgrid5d30 | CopulaREMADA | A list containing five-dimensional arrays | list | | |
telomerase | CopulaREMADA | The telomerase data | data.frame | 10 | 4 |
shp_mohinora | geoTS | SpatialPolygonsDataFrame for Cerro Mohinora | SpatialPolygonsDataFrame | | |
WeatherVienna | ShapleyOutlier | Weather data from Vienna | data.frame | 1804 | 25 |
darwin | robustmatrix | DARWIN (Diagnosis AlzheimeR WIth haNdwriting) | array | | |
weather | robustmatrix | Glacier weather data – Sonnblick observatory | array | | |
MEPS2001 | ssmrob | Ambulatory Expenditures Data | data.frame | 3328 | 12 |
MROZ.RAW | ssmrob | Wage Offer Data | data.frame | 753 | 22 |
IDM_cav | flexmsm | Cardiac allograft vasculopathy (CAV) data | data.frame | 2803 | 5 |
data1 | epr | data1: Sampaio (2010): page 134 | data.frame | 24 | 2 |
data2 | epr | data2: Kaps and Lamberson (2009): page 434 | data.frame | 25 | 3 |
data3 | epr | data3: fictional example | data.frame | 25 | 4 |
data4 | epr | data4: fictional example | data.frame | 50 | 5 |
data5 | epr | data5: fictional example | data.frame | 24 | 4 |
ama1c1cpg | asht | Three arm phase 1 malaria vaccine trial | data.frame | 58 | 2 |
milwaukee | pbr | Retailers in and around Milwaukee, WI | data.frame | 50 | 12 |
exdat | SSRA | Example data based on Takeya (1991) | data.frame | 10 | 5 |
example1.data | scaRabee | Datasets for scaRabee demo. | data.frame | 576 | 12 |
example1.initials | scaRabee | Datasets for scaRabee demo. | data.frame | 7 | 6 |
example2.data | scaRabee | Datasets for scaRabee demo. | data.frame | 8 | 11 |
example2.initials | scaRabee | Datasets for scaRabee demo. | data.frame | 2 | 6 |
example3.data | scaRabee | Datasets for scaRabee demo. | data.frame | 63 | 13 |
example3.initials | scaRabee | Datasets for scaRabee demo. | data.frame | 13 | 6 |
example4.data | scaRabee | Datasets for scaRabee demo. | data.frame | 14 | 11 |
example4.initials | scaRabee | Datasets for scaRabee demo. | data.frame | 8 | 6 |
example6.data | scaRabee | Datasets for scaRabee demo. | data.frame | 336 | 11 |
example6.initials | scaRabee | Datasets for scaRabee demo. | data.frame | 7 | 6 |
example8.data | scaRabee | Datasets for scaRabee demo. | data.frame | 8 | 11 |
example8.initials | scaRabee | Datasets for scaRabee demo. | data.frame | 14 | 6 |
cchsData | cchs | Data from a case-cohort study with stratified subcohort-selection | data.frame | 794 | 10 |
senate | LPGraph | Senate Vote Data | list | | |
enzyme | bssn | Enzymatic activity in the blood | data.frame | | 245 |
ozone | bssn | Daily ozone level measurements | data.frame | 116 | 1 |
Morris2023 | PoolDilutionR | Example time series data from a methane dilution pool experiment. | data.frame | 30 | 5 |
pdr_fractionation | PoolDilutionR | P and k fractionation values | data.frame | 1 | 5 |
data.r | MonoInc | Data range | data.frame | 121 | 3 |
decData.r | MonoInc | Data range(decreasing) | data.frame | 121 | 3 |
simDEC_data | MonoInc | Simulated Decreasing Data | data.frame | 5505 | 3 |
simulated_data | MonoInc | Simulated Data | data.frame | 5673 | 3 |
lung | RNAseqNet | RNA-seq expression from lung tissue (GTEx). | matrix | 221 | 100 |
thyroid | RNAseqNet | RNA-seq expression from thyroid tissue (GTEx). | matrix | 221 | 50 |
bcos | interval | Breast Cosmesis Data | data.frame | 94 | 3 |
Consist | irrNA | irrNA example data, showing perfect consistency between raters | data.frame | 6 | 6 |
ConsistNA | irrNA | irrNA example data, showing perfect consistency between raters and NAs | data.frame | 6 | 6 |
Ebel51 | irrNA | Example data, given by Ebel (1951, Table 2) | data.frame | 3 | 9 |
EbelFILL | irrNA | Example data, based on Ebel (1951, Table 2) | data.frame | 3 | 9 |
Indep | irrNA | irrNA example data, showing perfect independence among raters and objects | data.frame | 6 | 6 |
IndepNA | irrNA | irrNA example data, showing NAs and perfect independence among raters and objects | data.frame | 6 | 6 |
IndepW | irrNA | irrNA example data, showing perfect independence among raters and NAs | data.frame | 6 | 6 |
sim_nqc_data | vtype | Artificial data, that imitates non-quality controlled data | data.frame | 100 | 14 |
ehd | psy | Depressive Mood Scale | data.frame | 269 | 20 |
expsy | psy | Data set related to psychometry | data.frame | 30 | 16 |
sleep | psy | Ecological and Constitutional Data in Mammals | data.frame | 62 | 11 |
simpleREEMdata | REEMtree | Sample Data for RE-EM trees | data.frame | 600 | 5 |
mindfulness | multimedia | Mindfulness Dataset | phyloseq | | |
catch | capn | catch function of GOM dataset | function | | |
dsdotds | capn | first derivative function of sdot in GOM dataset | function | | |
dsdotdss | capn | second derivative function of sdot in GOM dataset | function | | |
dwds | capn | first derivative function of profit in GOM dataset | function | | |
dwdss | capn | second derivative function of profit in GOM dataset | function | | |
effort | capn | effort function of GOM dataset | function | | |
lvaproxdata | capn | Prey-Predator (Lotka-Volterra) example in LV dataset | matrix | 400 | 5 |
lvsimdata.time | capn | Prey-Predator (Lotka-Volterra) example in LV dataset | matrix | 101 | 3 |
param | capn | the parameter vector adopted in GOM dataset | data.frame | 1 | 13 |
profit | capn | profit function in GOM dataset | function | | |
sdot | capn | growth function of GOM dataset | function | | |
adjDurData | ACDm | Time Series Data Sets | durObj | 34767 | 6 |
defaultSplineObj | ACDm | Time Series Data Sets | list | | |
durData | ACDm | Time Series Data Sets | data.frame | 34767 | 5 |
transData | ACDm | Time Series Data Sets | data.frame | 96330 | 3 |
seal | argosfilter | Satellite tracking data from a ringed seal | data.frame | 1060 | 4 |
X | spselect | Input data X | data.frame | 20 | 5 |
X.3D | spselect | Input data X.3D | array | | |
y | spselect | Response data y | numeric | | |
arth800.descr | GeneNet | Time Series Expression Data for 800 Arabidopsis Thaliana Genes | character | | |
arth800.expr | GeneNet | Time Series Expression Data for 800 Arabidopsis Thaliana Genes | longitudinal | 22 | 800 |
arth800.mexpr | GeneNet | Time Series Expression Data for 800 Arabidopsis Thaliana Genes | longitudinal | 11 | 800 |
arth800.name | GeneNet | Time Series Expression Data for 800 Arabidopsis Thaliana Genes | character | | |
arth800.probe | GeneNet | Time Series Expression Data for 800 Arabidopsis Thaliana Genes | character | | |
arth800.symbol | GeneNet | Time Series Expression Data for 800 Arabidopsis Thaliana Genes | character | | |
ecoli | GeneNet | Microarray Time Series Data for 102 E. Coli Genes Genes | longitudinal | 9 | 102 |
Breastcancer | ggscidca | A survival data on breast cancer. | data.frame | 660 | 12 |
LIRI | ggscidca | A data for random forest analysis. | data.frame | 232 | 6 |
demo | ggscidca | A medical examination related data. | data.frame | 832 | 34 |
df_surv | ggscidca | A data for competitive risk modelling. | tbl_df | 750 | 9 |
modelA | astrochron | Example stratigraphic model series | data.frame | 499 | 2 |
crovData | crov | Real data example | data.frame | 500 | 6 |
hpa.data | TPACData | Gene expression data for normal human tissues from the Human Protein Atlas (HPA). This data was specially processed by the HPA group as FPKM values (file "HPA.normal.FPKM.GDCpipeline.csv") using a pipeline similar to that employed by GDC for the TCGA RNA-seq data. | data.table | 1088694 | 4 |
echinacea | aster2 | Life History Data on Echinacea angustifolia | asterdata | | |
hornworm | aster2 | Life History Data on Manduca sexta | asterdata | | |
test1 | aster2 | Test Data | data.frame | 100 | 8 |
juniperus | tgram | Traqueid Measurements in Juniperus thurifera | data.frame | 77 | 4 |
traq.profile | tgram | Light Throughout a Microscopic Section of Juniperus Wood | data.frame | 883 | 2 |
nasdaq | betategarch | Daily Apple stock returns | data.frame | 3215 | 2 |
PM10_2006 | MultiSkew | PM10_2006: dataset | data.frame | 257 | 5 |
growth | HDCurves | Berkeley Growth Study data. | list | | |
Data_spider | iNEXT.4steps | Spider abundance data | data.frame | 85 | 2 |
Data_woody_plant | iNEXT.4steps | Incidence raw data | list | | |
eco_us | sabre | Ecoregions of the United States | sf | 330 | 5 |
partitions1 | sabre | Red regionalization (raster version) | RasterLayer | | |
partitions2 | sabre | Blue regionalization (raster version) | RasterLayer | | |
regions1 | sabre | Red regionalization | sf | 4 | 2 |
regions2 | sabre | Blue regionalization | sf | 3 | 2 |
UN | wnominate | United Nations Vote Data | data.frame | 59 | 239 |
sen90 | wnominate | 90th U.S. Senate Roll Call Vote Matrix | rollcall | | |
sen90wnom | wnominate | 90th U.S. Senate Ideal Points | nomObject | | |
counts | ppcseq | counts | tbl_df | 394821 | 9 |
FF4_qc | TIGERr | Accompanying QC samples of KORA FF4 (demo data) | data.frame | 232 | 108 |
toydata | mvctm | Artificial 4-level data set | data.frame | 150 | 7 |
challengedescriptions | topChef | challengedescriptions | tbl_df | 666 | 17 |
challengewins | topChef | challengewins | tbl_df | 10053 | 10 |
chefdetails | topChef | chefdetails | tbl_df | 447 | 15 |
episodeinfo | topChef | episodeinfo | tbl_df | 373 | 8 |
judges | topChef | judges | tbl_df | 792 | 11 |
rewards | topChef | rewards | tbl_df | 350 | 9 |
beach_preferences | BayesMallows | Beach preferences | data.frame | 1442 | 3 |
bernoulli_data | BayesMallows | Simulated intransitive pairwise preferences | data.frame | 2280 | 3 |
cluster_data | BayesMallows | Simulated clustering data | matrix | 60 | |
potato_true_ranking | BayesMallows | True ranking of the weights of 20 potatoes. | numeric | | |
potato_visual | BayesMallows | Potato weights assessed visually | matrix | 12 | 20 |
potato_weighing | BayesMallows | Potato weights assessed by hand | matrix | 12 | 20 |
sushi_rankings | BayesMallows | Sushi rankings | matrix | 5000 | 10 |
ExampleDataBinary | pprof | Example data with binary outcomes | list | | |
ExampleDataLinear | pprof | Example data with continuous outcomes | list | | |
ecls_data | pprof | Early Childhood Longitudinal Study Dataset | tbl_df | 9101 | 5 |
multivrealdataset | samurais | Time series representing the three acceleration components recorded over time with body mounted accelerometers during the activity of a given person. | data.frame | 2253 | 4 |
multivtoydataset | samurais | A simulated non-stationary multidimensional time series with regime changes. | data.frame | 670 | 4 |
univrealdataset | samurais | Time series representing the electrical power consumption during a railway switch operation | data.frame | 562 | 3 |
univtoydataset | samurais | A simulated non-stationary time series with regime changes. | data.frame | 670 | 2 |
tcga_colon_blocks | epivizr | Example methylation data (blocks) for epivizr vignette. | GRanges | | |
tcga_colon_curves | epivizr | Example methylation data (smoothed methylation levels) for epivizr vignette | GRanges | | |
tcga_colon_expression | epivizr | Example exon-level RNAseq data from TCGA project for epivizr vignette. | RangedSummarizedExperiment | | |
R_0 | prioGene | the vector of initial disease risk scores for all genes | numeric | | |
dise_gene | prioGene | a vector of disease related genes | matrix | 79 | 1 |
edge_weight | prioGene | weights of edges of a net | matrix | 25 | 3 |
genes_mat | prioGene | a one-to-many matrix of GO term and gene | matrix | 45 | |
metabolic_net | prioGene | a matrix, Human metabolic network | matrix | 589199 | 2 |
net | prioGene | a network of genes | matrix | 2000 | 2 |
net_disease | prioGene | a network of disease related genes | matrix | 26 | 2 |
net_disease_term | prioGene | GO terms for each pair of nodes in the network | matrix | 25 | 4 |
node_weight | prioGene | a matrix, genes and their weights | matrix | 45 | |
terms_mat | prioGene | a matrix, GO terms and GO genes | matrix | 1172 | |
Seeds | iPRISM | Seed Node Names | character | | |
data.cell | iPRISM | data.cell | matrix | 121 | 21 |
data.path | iPRISM | data.path | matrix | 121 | 17 |
data_sig | iPRISM | data_sig | matrix | 121 | 31 |
genelist_cp | iPRISM | TME gene list after random walks | numeric | | |
genelist_hla | iPRISM | HLA gene list after random walks | numeric | | |
genelist_imm | iPRISM | ICI gene list after random walks | numeric | | |
path_list | iPRISM | path_list | list | | |
ppi | iPRISM | A protein-protein physical interaction network (PPI network) | igraph | | |
pred_value | iPRISM | Original Class Labels for Samples | character | | |
co | gear | Geochemical measurements for 960 sites in Colorado. | data.frame | 960 | 31 |
toydata | gear | A toy data set for examples | data.frame | 25 | 3 |
demo_loanbook | tilt.company.match | Demo loanbook entries | tbl_df | 12 | 5 |
demo_matched | tilt.company.match | Demo matched db entries | tbl_df | 18 | 13 |
demo_tilt | tilt.company.match | Demo tilt db entries | tbl_df | 11 | 5 |
ddpr_data | tidytof | CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy patient and 5,000 B-cell lineage cells from a B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) patient. | tof_tbl | 5000 | 21 |
ddpr_metadata | tidytof | Clinical metadata for each patient sample in Good & Sarno et al. (2018). | spec_tbl_df | 73 | 12 |
metal_masterlist | tidytof | A character vector of metal name patterns supported by tidytof. | character | | |
phenograph_data | tidytof | CyTOF data from 6,000 healthy immune cells from a single patient. | tof_tbl | 3000 | 25 |
beet | agriTutorial | Beet data for Example 2 | data.frame | 15 | 3 |
greenrice | agriTutorial | Rice data for Example 3 | data.frame | 64 | 5 |
rice | agriTutorial | Rice data for Example 1 | data.frame | 135 | 8 |
sorghum | agriTutorial | Sorghum data for Example 4 | data.frame | 100 | 7 |
turnip | agriTutorial | Turnip data for Example 5 | data.frame | 60 | 6 |
breast | randomForestSRC | Wisconsin Prognostic Breast Cancer Data | data.frame | 198 | 33 |
follic | randomForestSRC | Follicular Cell Lymphoma | data.frame | 541 | 7 |
hd | randomForestSRC | Hodgkin's Disease | data.frame | 865 | 8 |
housing | randomForestSRC | Ames Iowa Housing Data | data.frame | 2930 | 81 |
nutrigenomic | randomForestSRC | Nutrigenomic Study | list | | |
pbc | randomForestSRC | Primary Biliary Cirrhosis (PBC) Data | data.frame | 418 | 19 |
peakVO2 | randomForestSRC | Systolic Heart Failure Data | data.frame | 2231 | 41 |
vdv | randomForestSRC | van de Vijver Microarray Breast Cancer | data.frame | 78 | 4707 |
veteran | randomForestSRC | Veteran's Administration Lung Cancer Trial | data.frame | 137 | 8 |
wihs | randomForestSRC | Women's Interagency HIV Study (WIHS) | data.frame | 1164 | 6 |
wine | randomForestSRC | White Wine Quality Data | data.frame | 4898 | 12 |
bdstat | MESS | Danish live births and deaths | data.frame | 1356 | 4 |
bees | MESS | Bee data. Number of different types of bees caught. | data.frame | 72 | 7 |
clotting | MESS | Blood clotting for 158 rats | data.frame | 158 | 6 |
earthquakes | MESS | Earthquakes in 2015 | data.frame | 19777 | 7 |
greenland | MESS | Average yearly summer air temperature for Tasiilaq, Greenland | data.frame | 51 | 2 |
happiness | MESS | Happiness score and tax rates for 148 countries | data.frame | 148 | 6 |
icecreamads | MESS | Ice cream consumption and advertising | data.frame | 30 | 4 |
kwdata | MESS | Non-parametric Kruskal Wallis data example | data.frame | 18 | 3 |
lifeexpect | MESS | Estimated life expectancy for Danish newborns | data.frame | 70 | 4 |
matched | MESS | Flu hospitalization | tbl_df | 450 | 4 |
nh4 | MESS | Ammonia nitrogen found in river | data.frame | 120 | 3 |
picea | MESS | Ozone concentration damage to picea spruce | data.frame | 160 | 5 |
qpcr | MESS | Gene expression from real-time quantitative PCR | data.frame | 630 | 4 |
rainman | MESS | Perception of points in a swarm | data.frame | 30 | 6 |
smokehealth | MESS | Effect of smoking on self reported health | table | 5 | 4 |
soccer | MESS | Danish national soccer players | data.frame | 805 | 5 |
superroot2 | MESS | Gene expression data from two-color dye-swap experiment | data.frame | 258000 | 5 |
sq_trees | Quartet | Eighteen example trees | multiPhylo | | |
ufit | segtest | Genotype data from Cappai et al. (2020) | multidog | | |
ufit2 | segtest | Genotype data from Cappai et al. (2020) | multidog | | |
ufit3 | segtest | Genotype data from Cappai et al. (2020) | multidog | | |
indicatorsCH1 | bbw | Child Morbidity, Health Service Coverage, Anthropometry | data.frame | 3090 | 16 |
indicatorsCH2 | bbw | Infant and Child Feeding Index | data.frame | 2083 | 15 |
indicatorsHH | bbw | Mother Indicators Dataset | data.frame | 2135 | 26 |
somalia_population | bbw | Somalia regional population in 2022 | data.frame | 19 | 18 |
villageData | bbw | Cluster Population Weights Dataset | data.frame | 116 | 6 |
Icelandflu | epimdr | Monthly incidence of influenza-like illness in Iceland between 1980 and 2009. | data.frame | 360 | 3 |
SH9 | epimdr | Daily measures of malaria infected mice. | data.frame | 1300 | 11 |
black | epimdr | Black's measles seroprevalence data. | data.frame | 9 | 6 |
burnett | epimdr | Burnett's Parasitoid-Host data. | data.frame | 22 | 7 |
ccs | epimdr | UK measles CCS data. | data.frame | 954 | 9 |
cholera | epimdr | Dacca cholera death data. | data.frame | 600 | 4 |
dalziel | epimdr | Measles incidence across 40 US cities | data.frame | 44720 | 10 |
ebola | epimdr | Sierra-Leone Ebola 2015 data. | data.frame | 103 | 4 |
ferrari | epimdr | Ferrari et al. 2005 outbreak data. | data.frame | 15 | 7 |
filipendula | epimdr | Filipendula rust data. | data.frame | 162 | 4 |
fiv | epimdr | FIV infection in cats. | data.frame | 238 | 18 |
flu | epimdr | Boarding school influenza data. | data.frame | 14 | 2 |
gm | epimdr | Defoliated by gypsy moth each in northeast US 1975-2002. | data.frame | 1086 | 30 |
gonnet | epimdr | De et al. 2004 gonorrhea contact matrix | matrix | 89 | |
gra | epimdr | Euthamia graminifolia rust data. | data.frame | 360 | 8 |
litter | epimdr | Bordetella bronchiseptica in rabbit kittens. | data.frame | 494 | 8 |
magono | epimdr | Massachusetts gonorrhea data. | data.frame | 422 | 4 |
meas | epimdr | Bi-weekly measles incidence in London from 1944-65. | data.frame | 546 | 5 |
mossong | epimdr | POLYMOD contact-rate data by Age. | data.frame | 900 | 3 |
niamey | epimdr | Weekly measles incidence from 2003-04 in Niamey, Niger. | data.frame | 31 | 13 |
niamey_daily | epimdr | Day of appearance of each measles case from 2003-04 outbreak in Niamey, Niger. | data.frame | 10937 | 1 |
pagiard | epimdr | Weekly incidence of giardia in Pennsylvania between 2006 and 2014. | data.frame | 448 | 3 |
paili | epimdr | Weekly deaths from Influenza-like illness in Pennsylvania between 1972 and 1998. | data.frame | 1404 | 3 |
palymes | epimdr | Weekly incidence of Lymes disease in Pennsylvania between 2006 and 2014. | data.frame | 448 | 3 |
pameasle | epimdr | Weekly incidence of measles in Pennsylvania between 1928 and 1969. | data.frame | 2195 | 3 |
pertcop | epimdr | Weekly whooping cough incidence from 1900-1937 in Copenhagen, Denmark. | data.frame | 1982 | 8 |
peru | epimdr | Rubella in Peru data. | data.frame | 95 | 4 |
rabbit | epimdr | Rabbit _Bordetella brochiseptica_ data. | data.frame | 42 | 3 |
rabies | epimdr | Raccoon rabies data. | data.frame | 208 | 12 |
silene2 | epimdr | Antler smut on wild campion. | data.frame | 876 | 5 |
tydiphtheria | epimdr | Weekly incidence of diphtheria in Philadelphia between 1914 and 1947. | data.frame | 1774 | 4 |
tymeasles | epimdr | Weekly incidence of measles in Philadelphia between 1914 and 1947. | data.frame | 1774 | 4 |
tyscarlet | epimdr | Weekly incidence of scarlet fever in Philadelphia between 1914 and 1947. | data.frame | 1774 | 4 |
tywhooping | epimdr | Weekly incidence of whooping cough in Philadelphia between 1925 and 1947. | data.frame | 1200 | 5 |
usflu | epimdr | US 1975/76 ILI data. | data.frame | 49 | 7 |
ROSETTA.centroids | aqp | Average Hydraulic Parameters from the ROSETTA Model by USDA Soil Texture Class | data.frame | 12 | 13 |
SPC.with.overlap | aqp | Example SoilProfileCollection with Overlapping Horizons | SoilProfileCollection | | |
ca630 | aqp | Soil Data from the Central Sierra Nevada Region of California | list | | |
equivalent_munsell | aqp | Indices of "equivalent" Munsell chips in the 'munsell' data set | list | | |
jacobs2000 | aqp | Soil Morphologic Data from Jacobs et al. 2002. | SoilProfileCollection | | |
munsell | aqp | Munsell to sRGB Lookup Table for Common Soil Colors | data.frame | 10447 | 9 |
munsell.spectra | aqp | Spectral Library of Munsell Colors | data.frame | 136584 | 6 |
munsell.spectra.wide | aqp | Spectral Library of Munsell Colors | data.frame | 36 | 3795 |
munsellHuePosition | aqp | Munsell Hue Position Reference | data.frame | 41 | 4 |
osd | aqp | Example Output from soilDB::fetchOSD() | SoilProfileCollection | | |
reactionclass | aqp | pH Reaction Classes | data.frame | 11 | 3 |
rowley2019 | aqp | Soil Morphologic, Geochemical, and Mineralogy Data from Rowley et al. 2019. | SoilProfileCollection | | |
sierraTransect | aqp | Soil Physical and Chemical Data Related to Studies in the Sierra Nevada Mountains, CA, USA. | SoilProfileCollection | | |
soil_minerals | aqp | Munsell Colors of Common Soil Minerals | data.frame | 20 | 5 |
soiltexture | aqp | Lookup tables for sand, silt, clay, texture class, and textural modifiers. | list | | |
sp1 | aqp | Soil Profile Data Example 1 | data.frame | 60 | 18 |
sp2 | aqp | Honcut Creek Soil Profile Data | data.frame | 154 | 22 |
sp3 | aqp | Soil Profile Data Example 3 | data.frame | 46 | 17 |
sp4 | aqp | Soil Chemical Data from Serpentinitic Soils of California | data.frame | 30 | 13 |
sp5 | aqp | Sample Soil Database #5 | SoilProfileCollection | | |
sp6 | aqp | Soil Physical and Chemical Data from Manganiferous Soils | data.frame | 64 | 14 |
spectral.reference | aqp | Standard Illuminants and Observers | data.frame | 71 | 9 |
traditionalColorNames | aqp | Traditional Soil Color Names | data.frame | 482 | 2 |
us.state.soils | aqp | US State Soils | data.frame | 52 | 3 |
wilson2022 | aqp | Example Data from Wilson et al. 2022 | SoilProfileCollection | | |
kitchen_rolls | BayesTools | Kitchen Rolls data from Wagenmakers et al. (2015) replication study. | data.frame | 102 | 2 |
eezNC | StormR | EEZ of New Caledonia | sf | 1 | 1 |
overdoses | optic | OPTIC Overdoses example data. | tbl_df | 969 | 7 |
metadata | odbr | Metadata for the package | data.table | 15 | 4 |
od_sao_paulo_1977_not_harmonized_dictionary_en | odbr | Sao Paulo OD Survey Dictionary | data.table | 76 | 4 |
od_sao_paulo_1977_not_harmonized_dictionary_es | odbr | Sao Paulo OD Survey Dictionary | data.table | 76 | 4 |
od_sao_paulo_1977_not_harmonized_dictionary_pt | odbr | Sao Paulo OD Survey Dictionary | data.table | 76 | 4 |
od_sao_paulo_1987_not_harmonized_dictionary_en | odbr | Sao Paulo OD Survey Dictionary | data.table | 93 | 4 |
od_sao_paulo_1987_not_harmonized_dictionary_es | odbr | Sao Paulo OD Survey Dictionary | data.table | 93 | 4 |
od_sao_paulo_1987_not_harmonized_dictionary_pt | odbr | Sao Paulo OD Survey Dictionary | data.table | 93 | 4 |
od_sao_paulo_1997_not_harmonized_dictionary_en | odbr | Sao Paulo OD Survey Dictionary | data.table | 110 | 4 |
od_sao_paulo_1997_not_harmonized_dictionary_es | odbr | Sao Paulo OD Survey Dictionary | data.table | 76 | 4 |
od_sao_paulo_1997_not_harmonized_dictionary_pt | odbr | Sao Paulo OD Survey Dictionary | data.table | 110 | 4 |
od_sao_paulo_2007_not_harmonized_dictionary_en | odbr | Sao Paulo OD Survey Dictionary | data.table | 124 | 4 |
od_sao_paulo_2007_not_harmonized_dictionary_es | odbr | Sao Paulo OD Survey Dictionary | data.table | 76 | 4 |
od_sao_paulo_2007_not_harmonized_dictionary_pt | odbr | Sao Paulo OD Survey Dictionary | data.table | 124 | 4 |
od_sao_paulo_2017_not_harmonized_dictionary_en | odbr | Sao Paulo OD Survey Dictionary | data.table | 128 | 4 |
od_sao_paulo_2017_not_harmonized_dictionary_es | odbr | Sao Paulo OD Survey Dictionary | data.table | 76 | 4 |
od_sao_paulo_2017_not_harmonized_dictionary_pt | odbr | Sao Paulo OD Survey Dictionary | data.table | 128 | 4 |
assocref | insectDisease | Insect host-parasite interactions | data.frame | 11005 | 16 |
citation | insectDisease | Citation information for a subset of host-pathogen interactions | data.frame | 1966 | 7 |
hostTaxonomy | insectDisease | Cached version of host taxonomy (see vignette for code to build from scratch) | data.frame | 4489 | 7 |
hosts | insectDisease | Detailed information on insect host species | data.frame | 4392 | 21 |
negative | insectDisease | Information on negative host-parasite interactions | data.frame | 529 | 21 |
nemaref | insectDisease | Citation information for host-nematode interactions | data.frame | 338 | 16 |
nematode | insectDisease | Information on nematode parasite occurrences | data.frame | 234 | 25 |
new_asso | insectDisease | new_asso | data.frame | 19 | 25 |
newnema | insectDisease | new_asso | data.frame | 234 | 16 |
noassref | insectDisease | noassref | data.frame | 569 | 16 |
nvpassoc | insectDisease | Information on non-viral pathogens of insect hosts | data.frame | 7164 | 23 |
pathTaxonomy | insectDisease | Cached version of pathogen taxonomy (see vignette for code to build from scratch) | data.frame | 2282 | 7 |
pathogen | insectDisease | Information on pathogen species in the database | data.frame | 2041 | 9 |
viraref | insectDisease | Citation information for host-virus interactions | data.frame | 2124 | 16 |
viruses | insectDisease | Information on viral pathogen occurrences | data.frame | 1659 | 26 |
AUCO | patterncausality | Illapel Ecological Dataset | data.frame | 32 | 5 |
DJS | patterncausality | Dow Jones Stock Price Dataset | data.frame | 4510 | 30 |
climate_indices | patterncausality | Climate Indices Dataset | data.frame | 535 | 5 |
df_susenas_mar2020 | sae.projection | df_susenas_mar2020: Maret 2020 National Socio-Economic Survey (Susenas) Dataset for DKI Jakarta, Indonesia | data.frame | 18842 | 37 |
df_susenas_sep2020 | sae.projection | df_susenas_sep2020: September 2020 National Socio-Economic Survey (Susenas) Dataset for DKI Jakarta, Indonesia | data.frame | 3655 | 33 |
df_svy22 | sae.projection | df_svy22: August 2022 National Labor Force Survey Dataset for East Java, Indonesia. | data.frame | 74070 | 11 |
df_svy23 | sae.projection | df_svy23: August 2023 National Labor Force Survey Dataset for East Java, Indonesia. | data.frame | 66245 | 11 |
dat_external | CausalMetaR | External dataset | data.frame | 10083 | 13 |
dat_multisource | CausalMetaR | Multi-source dataset | data.frame | 3917 | 13 |
bnkpg_df | bruneimap | Brunei kampong data | tbl_df | 438 | 4 |
brn_sf | bruneimap | Simple feature objects to plot Brunei maps | sf | 17 | 2 |
census2021 | bruneimap | Brunei census data 2021 | tbl_df | 365 | 11 |
dis_sf | bruneimap | Simple feature objects to plot Brunei maps | sf | 4 | 7 |
enrolment | bruneimap | Data sets relating to schools in Brunei | tbl_df | 48 | 5 |
enrolment_MOE | bruneimap | Data sets relating to schools in Brunei | tbl_df | 84 | 5 |
fr_sf | bruneimap | Forest reserve areas in Brunei | sf | 20 | 6 |
healthcare | bruneimap | Hospitals and Clinics (Health Centres) in Brunei | spec_tbl_df | 19 | 6 |
kpg_sf | bruneimap | Simple feature objects to plot Brunei maps | sf | 438 | 9 |
masjid | bruneimap | Masjid (mosques) in Brunei | tbl_df | 121 | 13 |
mkm_sf | bruneimap | Simple feature objects to plot Brunei maps | sf | 39 | 8 |
sch_sf | bruneimap | Data sets relating to schools in Brunei | sf | 252 | 14 |
tchr | bruneimap | Data sets relating to schools in Brunei | tbl_df | 48 | 5 |
gripsYR1 | RCTRecruit | Daily recruitment data for the 1st year of the GRIPS study | data.frame | 159 | 2 |
gripsYR2 | RCTRecruit | Daily recruitment data for the 2nd year of the GRIPS study | data.frame | 292 | 2 |
gripsYR2Weekly | RCTRecruit | Weekly recruitment data for the 2nd year of the GRIPS study | data.frame | 52 | 4 |
cl.data | ern | Sample of aggregated clinical reports | tbl_df | 996 | 3 |
ww.data | ern | Sample of wastewater concentration | tbl_df | 31 | 2 |
bundData | NMOF | German Government Bond Data | list | | |
fundData | NMOF | Mutual Fund Returns | matrix | 500 | |
optionData | NMOF | Option Data | list | | |
andechs | isopam | Fen Meadows | matrix | 17 | 110 |
Jahn_CellReports_2018 | WeightedTreemaps | Data from the publication of Jahn et al., CellReports, 2018 | data.frame | 19790 | 13 |
rounded_rect | WeightedTreemaps | Coordinates to draw a rounded rectangle as parent cell for treemaps | data.frame | 50 | 2 |
demoForTransgenerationalAnalysis | methylInheritance | The methylation information from samples over three generations. Information for each generation is stored in a 'methylRawList' format (for demo purpose). | list | | |
methylInheritanceResults | methylInheritance | All observed and permutation results formatted in a 'methylInheritanceResults' class (for demo purpose). | methylInheritanceAllResults | | |
samplesForTransgenerationalAnalysis | methylInheritance | All samples information, formated by 'methylKit', in a 'methylRawList' format (for demo purpose). | list | | |
capitals | mapdeck | Capital cities for each country | data.frame | 200 | 4 |
city_trail | mapdeck | city_trail | sf | 1 | 3 |
geojson | mapdeck | Geojson | geojson | | |
melbourne | mapdeck | Polygons in and around Melbourne | sfencoded | 41 | 8 |
melbourne_mesh | mapdeck | Melbourne Mesh | mesh3d | | |
road_safety | mapdeck | road_safety | data.frame | 19139 | 2 |
roads | mapdeck | Roads in central Melbourne | sf | 18286 | 16 |
AIRRDb | tigger | | spec_tbl_df | 17559 | 26 |
GermlineIGHV | tigger | | character | | |
SampleDb | tigger | | data.frame | 17559 | 20 |
SampleGenotype | tigger | | data.frame | 9 | 5 |
SampleGermlineIGHV | tigger | | character | | |
SampleNovel | tigger | | data.frame | 12 | 30 |
barossa_obs | foreSIGHT | Multi-site rainfall observations in the Barossa Valley used in examples and vignette | list | | |
egClimData | foreSIGHT | Climate attributes from projections. | data.frame | 6 | 6 |
egMultiSiteSim | foreSIGHT | Output from call to generateScenarios() using multi-site model (see example 5 in generateScenarios). | list | | |
egScalPerformance | foreSIGHT | Performance metrics of the tank model using simple scaled scenarios. | list | | |
egScalSummary | foreSIGHT | Summary of a simple scaled scenario. | list | | |
egSimOATPerformance | foreSIGHT | Performance metrics of the tank model using OAT scenarios. | list | | |
egSimOATSummary | foreSIGHT | Summary of a OAT scenario. | list | | |
egSimPerformance | foreSIGHT | Performance metrics of the tank model using regGrid scenarios. | list | | |
egSimPerformance_systemB | foreSIGHT | Performance metrics of an alternate tank model using regGrid scenarios. | list | | |
egSimSummary | foreSIGHT | Summary of a regGrid scenario. | list | | |
tank_obs | foreSIGHT | Observations for demo tank model examples and vignette | data.frame | 3653 | 5 |
titanic | fastshap | Survival of Titanic passengers | data.frame | 1309 | 6 |
titanic_mice | fastshap | Survival of Titanic passengers | mild | | |
fdat | NMRphasing | This is an example data in NMRphasing | data.frame | 5891 | 2 |
admiralophtha_adbcva | admiralophtha | Best Corrected Visual Acuity Analysis Dataset | tbl_df | 7672 | 116 |
admiralophtha_adoe | admiralophtha | Ophthalmology Exam Analysis Dataset | tbl_df | 7672 | 98 |
admiralophtha_advfq | admiralophtha | Visual Function Questionnaire Analysis Dataset | tbl_df | 28798 | 41 |
X.class | caretEnsemble | data for classification | matrix | 150 | 6 |
X.reg | caretEnsemble | data for classification | matrix | 150 | 6 |
Y.class | caretEnsemble | data for classification | factor | | |
Y.reg | caretEnsemble | data for regression | numeric | | |
models.class | caretEnsemble | caretList of classification models | caretList | | |
models.reg | caretEnsemble | caretList of regression models | caretList | | |
CLAY.SALL.PAUL | ExtremalDep | Weekly maximum wind speed data collected over 4 stations across Oklahoma, USA, over the March-May preiod between 1996 and 2012. | matrix | 211 | 3 |
CLOU.CLAY.PAUL | ExtremalDep | Weekly maximum wind speed data collected over 4 stations across Oklahoma, USA, over the March-May preiod between 1996 and 2012. | matrix | 217 | 3 |
CLOU.CLAY.SALL | ExtremalDep | Weekly maximum wind speed data collected over 4 stations across Oklahoma, USA, over the March-May preiod between 1996 and 2012. | matrix | 212 | 3 |
CLOU.SALL.PAUL | ExtremalDep | Weekly maximum wind speed data collected over 4 stations across Oklahoma, USA, over the March-May preiod between 1996 and 2012. | matrix | 217 | 3 |
Leeds.frechet | ExtremalDep | Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998. | matrix | 590 | 5 |
Lingen | ExtremalDep | Hourly wind gust, wind speed and air pressure at Lingen (GER), Ossendorf (GER) and Parcay-Meslay (FRA). | data.frame | 1083 | 4 |
Milan.summer | ExtremalDep | Pollution data for summer and winter months in Milan, Italy. | data.frame | 1968 | 12 |
Milan.winter | ExtremalDep | Pollution data for summer and winter months in Milan, Italy. | data.frame | 1924 | 12 |
NSN | ExtremalDep | Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998. | matrix | 100 | 3 |
Ossendorf | ExtremalDep | Hourly wind gust, wind speed and air pressure at Lingen (GER), Ossendorf (GER) and Parcay-Meslay (FRA). | data.frame | 676 | 4 |
PNN | ExtremalDep | Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998. | matrix | 100 | 3 |
PNNS | ExtremalDep | Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998. | matrix | 200 | 4 |
PNS | ExtremalDep | Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998. | matrix | 100 | 3 |
ParcayMeslay | ExtremalDep | Hourly wind gust, wind speed and air pressure at Lingen (GER), Ossendorf (GER) and Parcay-Meslay (FRA). | data.frame | 2140 | 4 |
PrecipFrance | ExtremalDep | Weekly maxima of hourly rainfall in France | list | | |
heatdata | ExtremalDep | Summer temperature maxima in Melbourne, Australia between 1961 and 2010. | list | | |
locgrid | ExtremalDep | Summer temperature maxima in Melbourne, Australia between 1961 and 2010. | matrix | 10 | |
logReturns | ExtremalDep | Monthly maxima of log-return exchange rates of the Pound Sterling (GBP) against the US dollar (USD) and the Japanese yen (JPY), between March 1991 and December 2014. | data.frame | 286 | 4 |
mellat | ExtremalDep | Summer temperature maxima in Melbourne, Australia between 1961 and 2010. | numeric | | |
mellon | ExtremalDep | Summer temperature maxima in Melbourne, Australia between 1961 and 2010. | numeric | | |
scalegrid | ExtremalDep | Summer temperature maxima in Melbourne, Australia between 1961 and 2010. | matrix | 10 | |
shapegrid | ExtremalDep | Summer temperature maxima in Melbourne, Australia between 1961 and 2010. | matrix | 10 | |
winterdat | ExtremalDep | Air quality datasets containing daily maxima of air pollutants (PM10, NO, NO2, 03 and S02) recorded in Leeds (U.K.), during five winter seasons (November-Februrary) between 1994 and 1998. | data.frame | 601 | 5 |
muc_clim | treeclim | Monthly Mean Temperature and Total Precipitation for Forstenrieder Park, Munich | data.frame | 708 | 4 |
muc_fake | treeclim | Modeled Tree-Ring Chronology of a Spruce Population near Munich | matrix | 58 | 1 |
muc_spruce | treeclim | Tree-Ring Chronology of a Spruce Population near Munich | data.frame | 69 | 2 |
norw015 | treeclim | Tree-Ring Chronology of a Pine Population at Visdalen, Norway | crn | 384 | 2 |
norway_prec | treeclim | Monthly Precipitation Sums for Norway | data.frame | 100 | 13 |
norway_temp | treeclim | Monthly Temperature Means for Norway | data.frame | 100 | 13 |
rt_prec | treeclim | Monthly Precipitation Sums for Rothenburg ob der Tauber, Germany | data.frame | 61 | 13 |
rt_spruce | treeclim | Tree-Ring Chronology of a Spruce Population at Rothenburg ob der Tauber | data.frame | 91 | 2 |
rt_temp | treeclim | Monthly Temperature Means for Rothenburg ob der Tauber, Germany | data.frame | 61 | 13 |
spai020 | treeclim | Tree-Ring Chronology of a Pine Population at Penota, Spain | crn | 229 | 2 |
spain_prec | treeclim | Monthly Precipitation Sums for Spain | data.frame | 100 | 13 |
spain_temp | treeclim | Monthly Temperature Means for Spain | data.frame | 100 | 13 |
cc_data | ProcData | Data of item CP025Q01 (climate control item 1) in PISA 2012 | list | | |
act | nswgeo | Outlines of New South Wales and relevant territories. | sfc_MULTIPOLYGON | | |
australia | nswgeo | Geospatial data of the Australian state and territory administrative boundaries. | sfc_MULTIPOLYGON | | |
covid_cases_nsw | nswgeo | Small sample of COVID-19 cases in NSW for testing and demonstration. | tbl_df | 100 | 5 |
jbt | nswgeo | Outlines of New South Wales and relevant territories. | sfc_MULTIPOLYGON | | |
lga_nsw | nswgeo | Geospatial data of the New South Wales administrative boundaries. | sf | 131 | 9 |
lhd | nswgeo | Local Health Districts of NSW. | sf | 15 | 11 |
lhi | nswgeo | Outlines of New South Wales and relevant territories. | sfc_MULTIPOLYGON | | |
nsw | nswgeo | Outlines of New South Wales and relevant territories. | sfc_MULTIPOLYGON | | |
phn | nswgeo | Primary Health Network boundaries of New South Wales | sf | 10 | 9 |
poa_lhd_concordance | nswgeo | Concordance between postal areas and local health districts. | data.frame | 825 | 5 |
poa_nsw | nswgeo | Geospatial data of the New South Wales administrative boundaries. | sf | 644 | 7 |
postcodes | nswgeo | Postal codes and localities of New South Wales. | tbl_df | 5588 | 7 |
states | nswgeo | Geospatial data of the Australian state and territory administrative boundaries. | sf | 8 | 9 |
suburbs | nswgeo | Suburbs of New South Wales. | tbl_df | 4584 | 2 |
sydney | nswgeo | Outlines of New South Wales and relevant territories. | sfc_POLYGON | | |
short_tracks | trackdf | Trajectories of Two Goats Through the Namibian Desert (short version) | track | 120 | 4 |
tracks | trackdf | Trajectories of Two Goats Through the Namibian Desert | track | 7195 | 4 |
filges2015_dat | AIscreenR | RIS file data from Functional Family Therapy (FFT) systematic review | tbl_df | 270 | 6 |
model_prizes | AIscreenR | Model prize data (last updated November 5, 2024) | data.frame | 12 | 3 |
DublinWind | cobs | Daily Wind Speeds in Dublin | data.frame | 6574 | 2 |
USArmyRoofs | cobs | Roof Quality in US Army Bases | data.frame | 153 | 2 |
exHe | cobs | Small Dataset Example of He | data.frame | 10 | 2 |
globtemp | cobs | Annual Average Global Surface Temperature | ts | | |
mnData | micronutr | Micronutrient survey data | tbl_df | 19449 | 10 |
filter_verbs | cjar | Verbs available to be used in filter rules. | data.frame | 32 | 5 |
electrical_power_data | ReMFPCA | Electrical Power Dataset | list | | |
motion_sense_data | ReMFPCA | Motion Sense Dataset: Measurements of user acceleration and pitch attitude collected by smartphones from 24 individuals performing four distinct activities: jogging, walking, sitting, and standing. | list | | |
xSim | lokern | Simulated Linear plus Exponential Peak | numeric | | |
bastaCMRdat | BaSTA | Example of capture-mark-recapture data for BaSTA analysis. | data.frame | 500 | 25 |
bastaCMRout | BaSTA | Output from a Bayesian Survival Trajectory Analysis (BaSTA) analysis on a simulated capture-mark-recapture (CMR) dataset. | basta | | |
bastaCensDat | BaSTA | Example of census data for BaSTA analysis. | data.frame | 500 | 8 |
bastaCensOut | BaSTA | Output from a Bayesian Survival Trajectory Analysis (BaSTA) analysis on a simulated census dataset. | basta | | |
L | HEMDAG | Small real example datasets | matrix | 100 | 23 |
S | HEMDAG | Small real example datasets | matrix | 100 | 23 |
W | HEMDAG | Small real example datasets | matrix | 100 | 100 |
g | HEMDAG | Small real example datasets | graphNEL | | |
test.index | HEMDAG | Small real example datasets | integer | | |
source_zip_list | skm | source_zip_list | character | | |
zip2012 | skm | zip2012 | data.table | 28844 | 9 |
decoder | vein | Description data.frame for MOVES | list | | |
fe2015 | vein | Emission factors from Environmental Agency of Sao Paulo CETESB | data.frame | 288 | 10 |
fkm | vein | List of functions of mileage in km fro Brazilian fleet | list | | |
net | vein | Road network of the west part of Sao Paulo city | sf | 1505 | 10 |
pc_cold | vein | Profile of Vehicle start patterns | data.frame | 24 | 1 |
pc_profile | vein | Profile of traffic data 24 hours 7 n days of the week | data.frame | 24 | 7 |
pollutants | vein | Data.frame with pollutants names and molar mass used in VEIN | data.frame | 148 | 10 |
profiles | vein | Profile of traffic data 24 hours 7 n days of the week | list | | |
US_Trade_Deficit | ChangePointTaylor | US Trade Deficit Data: 1987-1988. | tbl_df | 24 | 2 |
AE98 | ddml | Random subsample from the data of Angrist & Evans (1991). | matrix | 5000 | 13 |
fruit | stringr | Sample character vectors for practicing string manipulations | character | | |
sentences | stringr | Sample character vectors for practicing string manipulations | character | | |
words | stringr | Sample character vectors for practicing string manipulations | character | | |
dec | combinedevents | Men's decathlon performances | data.frame | 23 | 24 |
Adult | bsnsing | | data.frame | 32561 | 14 |
BreastCancer | bsnsing | BreastCancer | data.frame | 699 | 10 |
Echocard | bsnsing | | data.frame | 61 | 12 |
Fertility | bsnsing | | data.frame | 100 | 10 |
Glass | bsnsing | | data.frame | 214 | 10 |
GlaucomaMVF | bsnsing | GlaucomaMVF | data.frame | 170 | 67 |
HTRU2 | bsnsing | | data.frame | 17898 | 9 |
Hayes | bsnsing | | data.frame | 132 | 5 |
ILPD | bsnsing | | data.frame | 583 | 11 |
Ionos | bsnsing | | data.frame | 351 | 35 |
Monks1 | bsnsing | | data.frame | 556 | 7 |
Monks2 | bsnsing | | data.frame | 601 | 7 |
Monks3 | bsnsing | | data.frame | 554 | 7 |
Mushroom | bsnsing | | data.frame | 8124 | 22 |
Qsar | bsnsing | | data.frame | 1055 | 42 |
SPECT | bsnsing | | data.frame | 267 | 23 |
Seeds | bsnsing | | data.frame | 210 | 8 |
Sonar | bsnsing | | data.frame | 208 | 61 |
WineQuality | bsnsing | | data.frame | 4898 | 12 |
acute1 | bsnsing | | data.frame | 120 | 7 |
acute2 | bsnsing | | data.frame | 120 | 7 |
auto | bsnsing | auto | data.frame | 392 | 8 |
balance | bsnsing | | data.frame | 625 | 5 |
banknote | bsnsing | | data.frame | 1372 | 5 |
birthwt | bsnsing | | data.frame | 189 | 10 |
circ | bsnsing | | data.frame | 600 | 3 |
climate | bsnsing | | data.frame | 540 | 19 |
compas | bsnsing | | data.frame | 7214 | 53 |
connect | bsnsing | | data.frame | 208 | 61 |
contra | bsnsing | | data.frame | 1473 | 10 |
credit | bsnsing | | data.frame | 690 | 16 |
derm | bsnsing | | data.frame | 366 | 35 |
diam | bsnsing | | data.frame | 600 | 3 |
ds.formula | bsnsing | | character | | |
ds.names | bsnsing | | character | | |
ds.respvar | bsnsing | | character | | |
ds.table | bsnsing | | data.frame | 74 | 6 |
dystrophy | bsnsing | | data.frame | 209 | 10 |
grid | bsnsing | | data.frame | 600 | 3 |
haberman | bsnsing | | data.frame | 306 | 4 |
heart | bsnsing | | data.frame | 303 | 14 |
heloc | bsnsing | | data.frame | 10459 | 24 |
hepatitis | bsnsing | | data.frame | 155 | 20 |
imgsegm | bsnsing | | data.frame | 210 | 20 |
iris | bsnsing | iris | data.frame | 150 | 5 |
magic04 | bsnsing | | data.frame | 19020 | 11 |
mammo | bsnsing | | data.frame | 830 | 6 |
norm3p10 | bsnsing | | data.frame | 600 | 11 |
norm3p5 | bsnsing | | data.frame | 600 | 6 |
nursery | bsnsing | | data.frame | 12960 | 9 |
obli | bsnsing | | data.frame | 600 | 3 |
optdigits | bsnsing | | data.frame | 5620 | 65 |
ozone1 | bsnsing | | data.frame | 2536 | 73 |
ozone8 | bsnsing | | data.frame | 2534 | 73 |
parkins | bsnsing | | data.frame | 195 | 23 |
pima | bsnsing | | data.frame | 768 | 9 |
relax | bsnsing | | data.frame | 182 | 13 |
retention | bsnsing | | data.frame | 10000 | 9 |
ring | bsnsing | | data.frame | 600 | 3 |
seismic | bsnsing | | data.frame | 1690 | 19 |
sh88 | bsnsing | | data.frame | 600 | 3 |
smiley | bsnsing | | data.frame | 500 | 3 |
soybean.l | bsnsing | | data.frame | 266 | 36 |
soybean.s | bsnsing | | data.frame | 47 | 36 |
spambase | bsnsing | | data.frame | 4601 | 58 |
spirals | bsnsing | | data.frame | 600 | 3 |
statlog.a | bsnsing | | data.frame | 690 | 15 |
tae | bsnsing | | data.frame | 151 | 6 |
thoraric | bsnsing | | data.frame | 470 | 17 |
thyroid | bsnsing | | data.frame | 3772 | 22 |
tictactoe | bsnsing | | data.frame | 958 | 10 |
titanic | bsnsing | | data.frame | 2201 | 4 |
trans | bsnsing | | data.frame | 748 | 5 |
votes | bsnsing | | data.frame | 435 | 17 |
wdbc | bsnsing | | data.frame | 569 | 31 |
wine | bsnsing | | data.frame | 178 | 14 |
wpbc | bsnsing | | data.frame | 198 | 34 |
xor3 | bsnsing | | data.frame | 600 | 4 |
language_test | regtomean | Language Test in High School | data.frame | 8 | 9 |
CPFs | eaf | Conditional Pareto fronts obtained from Gaussian processes simulations. | data.frame | 2967 | 3 |
HybridGA | eaf | Results of Hybrid GA on vanzyl and Richmond water networks | list | | |
SPEA2minstoptimeRichmond | eaf | Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network. | data.frame | 166 | 3 |
SPEA2relativeRichmond | eaf | Results of SPEA2 with relative time-controlled triggers on Richmond water network. | data.frame | 91 | 3 |
SPEA2relativeVanzyl | eaf | Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network. | data.frame | 107 | 3 |
gcp2x2 | eaf | Metaheuristics for solving the Graph Vertex Coloring Problem | data.frame | 3133 | 6 |
Tcomp_reproduction | Tcomp | Reproduction of selected tourism competition results | list | | |
tourism | Tcomp | Tourism competition data | Mcomp | | |
data | featurefinder | data | data.frame | 1860 | 5 |
LINE | rjags | Linear regression example | jags | | |
ER.limma | RedeR | Pre-processed dataset for RedeR case studies. | data.frame | 2900 | 12 |
hs.inter | RedeR | Pre-processed igraph object for RedeR case studies. | igraph | | |
mtf.alt | gravy | Species Data and Altitude from Mt. Field, Tasmania. | data.frame | 167 | 1 |
mtf01 | gravy | Species Data and Altitude from Mt. Field, Tasmania. | data.frame | 167 | 5 |
sampleData | tumgr | Example Patient Tumor Data | data.frame | 453 | 3 |
map | PAS | beef data | data.frame | 300 | 2 |
x | PAS | beef data | matrix | 836 | |
y | PAS | beef data | matrix | 836 | |
BCJ | astsa | Daily Returns of Three Banks | mts | 3243 | 3 |
EBV | astsa | Entire Epstein-Barr Virus (EBV) Nucleotide Sequence | character | | |
ENSO | astsa | El Nino - Southern Oscillation Index | ts | | |
EQ5 | astsa | Seismic Trace of Earthquake number 5 | ts | | |
EQcount | astsa | Earthquake Counts | ts | | |
EXP6 | astsa | Seismic Trace of Explosion number 6 | ts | | |
GDP | astsa | Quarterly U.S. GDP - updated to 2023 | ts | | |
GNP | astsa | Quarterly U.S. GNP - updated to 2023 | ts | | |
HCT | astsa | Hematocrit Levels | ts | | |
Hare | astsa | Snowshoe Hare | ts | | |
Lynx | astsa | Canadian Lynx | ts | | |
MEI | astsa | Multivariate El Nino/Southern Oscillation Index (version 1) | ts | | |
Months | astsa | Month Labels | character | | |
PLT | astsa | Platelet Levels | ts | | |
USpop | astsa | U.S. Population - 1900 to 2010 | ts | | |
UnempRate | astsa | U.S. Unemployment Rate | ts | | |
WBC | astsa | White Blood Cell Levels | ts | | |
ar1miss | astsa | AR with Missing Values | ts | | |
arf | astsa | Simulated ARFIMA | ts | | |
beamd | astsa | Infrasonic Signal from a Nuclear Explosion | data.frame | 2048 | 3 |
birth | astsa | U.S. Monthly Live Births | ts | | |
blood | astsa | Daily Blood Work with Missing Values | mts | 91 | 3 |
bnrf1ebv | astsa | Nucleotide sequence - BNRF1 Epstein-Barr | ts | | |
bnrf1hvs | astsa | Nucleotide sequence - BNRF1 of Herpesvirus saimiri | ts | | |
cardox | astsa | Monthly Carbon Dioxide Levels at Mauna Loa | ts | | |
chicken | astsa | Monthly price of a pound of chicken | ts | | |
climhyd | astsa | Lake Shasta inflow data | data.frame | 454 | 6 |
cmort | astsa | Cardiovascular Mortality from the LA Pollution study | ts | | |
cpg | astsa | Hard Drive Cost per GB | ts | | |
djia | astsa | Dow Jones Industrial Average | xts | 2518 | 5 |
econ5 | astsa | Five Quarterly Economic Series | mts | 161 | 5 |
eqexp | astsa | Earthquake and Explosion Seismic Series | data.frame | 2048 | 17 |
flu | astsa | Monthly pneumonia and influenza deaths in the U.S., 1968 to 1978. | ts | | |
fmri | astsa | fMRI - complete data set | list | | |
fmri1 | astsa | fMRI Data Used in Chapter 1 | mts | 128 | 9 |
gas | astsa | Gas Prices | ts | | |
gdp | astsa | Quarterly U.S. GDP | ts | | |
gnp | astsa | Quarterly U.S. GNP | ts | | |
gtemp.month | astsa | Monthly global average surface temperatures by year | data.frame | 12 | 49 |
gtemp_both | astsa | Global mean land and open ocean temperature deviations, 1850-2023 | ts | | |
gtemp_land | astsa | Global mean land temperature deviations, 1850-2023 | ts | | |
gtemp_ocean | astsa | Global mean ocean temperature deviations, 1850-2023 | ts | | |
hor | astsa | Hawaiian occupancy rates | ts | | |
jj | astsa | Johnson and Johnson Quarterly Earnings Per Share | ts | | |
lap | astsa | LA Pollution-Mortality Study | mts | 508 | 11 |
lap.xts | astsa | LA Pollution-Mortality Study: Sampled Daily | xts | 3652 | 11 |
lead | astsa | Leading Indicator | ts | | |
nyse | astsa | Returns of the New York Stock Exchange | ts | | |
oil | astsa | Crude oil, WTI spot price FOB | ts | | |
part | astsa | Particulate levels from the LA pollution study | ts | | |
polio | astsa | Poliomyelitis cases in US | ts | | |
prodn | astsa | Monthly Federal Reserve Board Production Index | ts | | |
qinfl | astsa | Quarterly Inflation | ts | | |
qintr | astsa | Quarterly Interest Rate | ts | | |
rec | astsa | Recruitment (number of new fish index) | ts | | |
sales | astsa | Sales | ts | | |
salmon | astsa | Monthly export price of salmon | ts | | |
salt | astsa | Salt Profiles | ts | | |
saltemp | astsa | Temperature Profiles | ts | | |
sleep1 | astsa | Sleep State and Movement Data - Group 1 | list | | |
sleep2 | astsa | Sleep State and Movement Data - Group 2 | list | | |
so2 | astsa | SO2 levels from the LA pollution study | ts | | |
soi | astsa | Southern Oscillation Index | ts | | |
soiltemp | astsa | Spatial Grid of Surface Soil Temperatures | matrix | 64 | |
sp500.gr | astsa | Returns of the S&P 500 | ts | | |
sp500w | astsa | Weekly Growth Rate of the Standard and Poor's 500 | xts | 509 | 1 |
speech | astsa | Speech Recording | ts | | |
star | astsa | Variable Star | ts | | |
sunspotz | astsa | Biannual Sunspot Numbers | ts | | |
tempr | astsa | Temperatures from the LA pollution study | ts | | |
unemp | astsa | U.S. Unemployment | ts | | |
varve | astsa | Annual Varve Series | ts | | |
C.Elegans | iGraphMatch | Chemical synapses and electrical synapses networks of roundworm | list | | |
Enron | iGraphMatch | Email communication networks of Enron Corporation | list | | |
Transportation | iGraphMatch | Britain Transportation Network | list | | |
aki_pt_data | epocakir | AKI Patient Data | tbl_df | 27 | 7 |
anemia_pt_data | epocakir | Anemia Patient Data | tbl_df | 10 | 3 |
clinical_obvs | epocakir | Clinical Patient Data | tbl_df | 3 | 9 |
eGFR_pt_data | epocakir | eGFR Patient Data | tbl_df | 51 | 10 |
EXA_C01_S04_01 | DanielBiostatistics10th | Example Data | data.frame | 189 | 2 |
EXA_C02_S05_05 | DanielBiostatistics10th | Example Data | data.frame | 20 | 1 |
EXA_C06_S02_04 | DanielBiostatistics10th | Example Data | data.frame | 35 | 1 |
EXA_C07_S02_03 | DanielBiostatistics10th | Example Data | data.frame | 17 | 2 |
EXA_C07_S03_02 | DanielBiostatistics10th | Example Data | data.frame | 10 | 2 |
EXA_C07_S04_01 | DanielBiostatistics10th | Example Data | data.frame | 12 | 2 |
EXA_C07_S07_01 | DanielBiostatistics10th | Example Data | data.frame | 17 | 1 |
EXA_C08_S02_01 | DanielBiostatistics10th | Example Data | data.frame | 144 | 2 |
EXA_C08_S03_01 | DanielBiostatistics10th | Example Data | data.frame | 15 | 3 |
EXA_C08_S04_01 | DanielBiostatistics10th | Example Data | data.frame | 72 | 3 |
EXA_C08_S04_02 | DanielBiostatistics10th | Example Data | data.frame | 25 | 6 |
EXA_C08_S05_02 | DanielBiostatistics10th | Example Data | data.frame | 80 | 3 |
EXA_C09_S03_01 | DanielBiostatistics10th | Example Data | data.frame | 109 | 3 |
EXA_C09_S07_01 | DanielBiostatistics10th | Example Data | data.frame | 155 | 2 |
EXA_C10_S03_01 | DanielBiostatistics10th | Example Data | data.frame | 71 | 3 |
EXA_C10_S06_01 | DanielBiostatistics10th | Example Data | data.frame | 29 | 3 |
EXA_C11_S01_01 | DanielBiostatistics10th | Example Data | data.frame | 25 | 3 |
EXA_C11_S01_02 | DanielBiostatistics10th | Example Data | data.frame | 15 | 4 |
EXA_C11_S02_01 | DanielBiostatistics10th | Example Data | data.frame | 100 | 4 |
EXA_C11_S02_03 | DanielBiostatistics10th | Example Data | data.frame | 36 | 3 |
EXA_C11_S03_01 | DanielBiostatistics10th | Example Data | data.frame | 30 | 7 |
EXA_C11_S04_02 | DanielBiostatistics10th | Example Data | data.frame | 184 | 2 |
EXA_C11_S05_01 | DanielBiostatistics10th | Example Data | data.frame | 45 | 4 |
EXA_C12_S02_03 | DanielBiostatistics10th | Example Data | data.frame | 90 | 2 |
EXA_C13_S03_02 | DanielBiostatistics10th | Example Data | data.frame | 12 | 3 |
EXA_C13_S05_01 | DanielBiostatistics10th | Example Data | data.frame | 16 | 2 |
EXA_C13_S06_01 | DanielBiostatistics10th | Example Data | data.frame | 15 | 2 |
EXA_C13_S07_01 | DanielBiostatistics10th | Example Data | data.frame | 36 | 1 |
EXA_C13_S08_02 | DanielBiostatistics10th | Example Data | data.frame | 10 | 5 |
EXA_C13_S09_01 | DanielBiostatistics10th | Example Data | data.frame | 9 | 4 |
EXA_C13_S09_02 | DanielBiostatistics10th | Example Data | data.frame | 16 | 5 |
EXA_C13_S10_01 | DanielBiostatistics10th | Example Data | data.frame | 20 | 3 |
EXA_C13_S10_02 | DanielBiostatistics10th | Example Data | data.frame | 35 | 3 |
EXA_C14_S03_01 | DanielBiostatistics10th | Example Data | data.frame | 39 | 4 |
EXA_C14_S05_01 | DanielBiostatistics10th | Example Data | data.frame | 40 | 5 |
EXR_C02_S03_01 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 1 |
EXR_C02_S03_02 | DanielBiostatistics10th | Exercise Data | data.frame | 159 | 1 |
EXR_C02_S03_03 | DanielBiostatistics10th | Exercise Data | data.frame | 29 | 1 |
EXR_C02_S03_04 | DanielBiostatistics10th | Exercise Data | data.frame | 53 | 1 |
EXR_C02_S03_05 | DanielBiostatistics10th | Exercise Data | data.frame | 45 | 1 |
EXR_C02_S03_06 | DanielBiostatistics10th | Exercise Data | data.frame | 60 | 1 |
EXR_C02_S03_07 | DanielBiostatistics10th | Exercise Data | data.frame | 155 | 1 |
EXR_C02_S03_08 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 1 |
EXR_C02_S03_09 | DanielBiostatistics10th | Exercise Data | data.frame | 50 | 2 |
EXR_C02_S03_11 | DanielBiostatistics10th | Exercise Data | data.frame | 216 | 1 |
EXR_C02_S03_12 | DanielBiostatistics10th | Exercise Data | data.frame | 109 | 1 |
EXR_C02_S05_03 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 1 |
EXR_C02_S05_06 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 1 |
EXR_C06_S02_05 | DanielBiostatistics10th | Exercise Data | data.frame | 16 | 1 |
EXR_C06_S04_10 | DanielBiostatistics10th | Exercise Data | data.frame | 32 | 2 |
EXR_C06_S09_07 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 1 |
EXR_C06_S10_07 | DanielBiostatistics10th | Exercise Data | data.frame | 26 | 2 |
EXR_C07_S02_13 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 1 |
EXR_C07_S02_15 | DanielBiostatistics10th | Exercise Data | data.frame | 50 | 1 |
EXR_C07_S02_16 | DanielBiostatistics10th | Exercise Data | data.frame | 21 | 1 |
EXR_C07_S03_03 | DanielBiostatistics10th | Exercise Data | data.frame | 63 | 2 |
EXR_C07_S03_04 | DanielBiostatistics10th | Exercise Data | data.frame | 174 | 2 |
EXR_C07_S03_05 | DanielBiostatistics10th | Exercise Data | data.frame | 82 | 2 |
EXR_C07_S03_10 | DanielBiostatistics10th | Exercise Data | data.frame | 24 | 2 |
EXR_C07_S03_11 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 2 |
EXR_C07_S03_12 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 2 |
EXR_C07_S04_01 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 2 |
EXR_C07_S04_02 | DanielBiostatistics10th | Exercise Data | data.frame | 66 | 2 |
EXR_C07_S04_03 | DanielBiostatistics10th | Exercise Data | data.frame | 11 | 2 |
EXR_C07_S04_04 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 2 |
EXR_C07_S08_07 | DanielBiostatistics10th | Exercise Data | data.frame | 23 | 2 |
EXR_C08_S02_01 | DanielBiostatistics10th | Exercise Data | data.frame | 329 | 2 |
EXR_C08_S02_02 | DanielBiostatistics10th | Exercise Data | data.frame | 96 | 2 |
EXR_C08_S02_03 | DanielBiostatistics10th | Exercise Data | data.frame | 113 | 2 |
EXR_C08_S02_04 | DanielBiostatistics10th | Exercise Data | data.frame | 164 | 2 |
EXR_C08_S02_05 | DanielBiostatistics10th | Exercise Data | data.frame | 29 | 2 |
EXR_C08_S02_06 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 2 |
EXR_C08_S02_07 | DanielBiostatistics10th | Exercise Data | data.frame | 178 | 2 |
EXR_C08_S03_01 | DanielBiostatistics10th | Exercise Data | data.frame | 96 | 3 |
EXR_C08_S03_02 | DanielBiostatistics10th | Exercise Data | data.frame | 10 | 5 |
EXR_C08_S03_03 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 3 |
EXR_C08_S03_04 | DanielBiostatistics10th | Exercise Data | data.frame | 16 | 3 |
EXR_C08_S03_05 | DanielBiostatistics10th | Exercise Data | data.frame | 12 | 3 |
EXR_C08_S04_01 | DanielBiostatistics10th | Exercise Data | data.frame | 40 | 3 |
EXR_C08_S04_02 | DanielBiostatistics10th | Exercise Data | data.frame | 35 | 3 |
EXR_C08_S04_03 | DanielBiostatistics10th | Exercise Data | data.frame | 48 | 3 |
EXR_C08_S04_06 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 6 |
EXR_C08_S05_01 | DanielBiostatistics10th | Exercise Data | data.frame | 24 | 3 |
EXR_C08_S05_02 | DanielBiostatistics10th | Exercise Data | data.frame | 72 | 3 |
EXR_C08_S05_03 | DanielBiostatistics10th | Exercise Data | data.frame | 44 | 3 |
EXR_C08_S05_04 | DanielBiostatistics10th | Exercise Data | data.frame | 13 | 3 |
EXR_C09_S03_02 | DanielBiostatistics10th | Exercise Data | data.frame | 10 | 2 |
EXR_C09_S03_03 | DanielBiostatistics10th | Exercise Data | data.frame | 17 | 2 |
EXR_C09_S03_04 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 2 |
EXR_C09_S03_06 | DanielBiostatistics10th | Exercise Data | data.frame | 22 | 2 |
EXR_C09_S03_07 | DanielBiostatistics10th | Exercise Data | data.frame | 27 | 2 |
EXR_C09_S07_01 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 2 |
EXR_C09_S07_02 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 2 |
EXR_C09_S07_04 | DanielBiostatistics10th | Exercise Data | data.frame | 18 | 2 |
EXR_C09_S07_05 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 2 |
EXR_C09_S07_06 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 2 |
EXR_C10_S03_01 | DanielBiostatistics10th | Exercise Data | data.frame | 35 | 3 |
EXR_C10_S03_02 | DanielBiostatistics10th | Exercise Data | data.frame | 100 | 4 |
EXR_C10_S03_03 | DanielBiostatistics10th | Exercise Data | data.frame | 10 | 3 |
EXR_C10_S03_04 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 3 |
EXR_C10_S03_05 | DanielBiostatistics10th | Exercise Data | data.frame | 25 | 3 |
EXR_C10_S03_06 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 8 |
EXR_C10_S06_01 | DanielBiostatistics10th | Exercise Data | data.frame | 40 | 4 |
EXR_C10_S06_02 | DanielBiostatistics10th | Exercise Data | data.frame | 12 | 3 |
EXR_C10_S06_03 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 3 |
EXR_C10_S06_04 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 6 |
EXR_C11_S02_01 | DanielBiostatistics10th | Exercise Data | data.frame | 44 | 3 |
EXR_C11_S02_02 | DanielBiostatistics10th | Exercise Data | data.frame | 100 | 3 |
EXR_C11_S02_03 | DanielBiostatistics10th | Exercise Data | data.frame | 17 | 3 |
EXR_C11_S02_04 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 3 |
EXR_C11_S03_01 | DanielBiostatistics10th | Exercise Data | data.frame | 100 | 8 |
EXR_C11_S03_02 | DanielBiostatistics10th | Exercise Data | data.frame | 35 | 7 |
EXR_C11_S03_03 | DanielBiostatistics10th | Exercise Data | data.frame | 68 | 7 |
EXR_C11_S04_01 | DanielBiostatistics10th | Exercise Data | data.frame | 4 | 3 |
EXR_C11_S04_02 | DanielBiostatistics10th | Exercise Data | data.frame | 184 | 2 |
EXR_C11_S05_01 | DanielBiostatistics10th | Exercise Data | data.frame | 45 | 4 |
EXR_C11_S05_02 | DanielBiostatistics10th | Exercise Data | data.frame | 45 | 5 |
EXR_C11_S05_04 | DanielBiostatistics10th | Exercise Data | data.frame | 90 | 3 |
EXR_C13_S05_01 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 2 |
EXR_C13_S05_02 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 2 |
EXR_C13_S06_01 | DanielBiostatistics10th | Exercise Data | data.frame | 70 | 2 |
EXR_C13_S06_02 | DanielBiostatistics10th | Exercise Data | data.frame | 17 | 2 |
EXR_C13_S06_03 | DanielBiostatistics10th | Exercise Data | data.frame | 83 | 2 |
EXR_C13_S07_02 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 1 |
EXR_C13_S08_01 | DanielBiostatistics10th | Exercise Data | data.frame | 232 | 2 |
EXR_C13_S08_02 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 2 |
EXR_C13_S08_03 | DanielBiostatistics10th | Exercise Data | data.frame | 53 | 2 |
EXR_C13_S08_04 | DanielBiostatistics10th | Exercise Data | data.frame | 22 | 2 |
EXR_C13_S08_05 | DanielBiostatistics10th | Exercise Data | data.frame | 44 | 2 |
EXR_C13_S08_06 | DanielBiostatistics10th | Exercise Data | data.frame | 22 | 3 |
EXR_C13_S09_01 | DanielBiostatistics10th | Exercise Data | data.frame | 9 | 4 |
EXR_C13_S09_02 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 11 |
EXR_C13_S09_03 | DanielBiostatistics10th | Exercise Data | data.frame | 10 | 6 |
EXR_C13_S10_01 | DanielBiostatistics10th | Exercise Data | data.frame | 15 | 3 |
EXR_C13_S10_02 | DanielBiostatistics10th | Exercise Data | data.frame | 10 | 3 |
EXR_C13_S10_03 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 2 |
EXR_C13_S10_04 | DanielBiostatistics10th | Exercise Data | data.frame | 20 | 2 |
EXR_C13_S10_05 | DanielBiostatistics10th | Exercise Data | data.frame | 30 | 2 |
EXR_C13_S10_06 | DanielBiostatistics10th | Exercise Data | data.frame | 17 | 3 |
EXR_C14_S03_01 | DanielBiostatistics10th | Exercise Data | data.frame | 53 | 3 |
EXR_C14_S03_02 | DanielBiostatistics10th | Exercise Data | data.frame | 62 | 2 |
EXR_C14_S04_03 | DanielBiostatistics10th | Exercise Data | data.frame | 50 | 4 |
LDS_C02_NCBIRTH800 | DanielBiostatistics10th | Large Data | data.frame | 800 | 14 |
LDS_C06_BABYWGTS | DanielBiostatistics10th | Large Data | data.frame | 1200 | 2 |
LDS_C06_BOYHGTS | DanielBiostatistics10th | Large Data | data.frame | 1000 | 2 |
LDS_C06_CHOLEST | DanielBiostatistics10th | Large Data | data.frame | 1000 | 2 |
LDS_C07_HEADCIRC | DanielBiostatistics10th | Large Data | data.frame | 1000 | 3 |
LDS_C07_HEMOGLOB | DanielBiostatistics10th | Large Data | data.frame | 1000 | 2 |
LDS_C07_MANDEXT | DanielBiostatistics10th | Large Data | data.frame | 1000 | 2 |
LDS_C07_PCKDATA | DanielBiostatistics10th | Large Data | data.frame | 1005 | 3 |
LDS_C07_PROTHROM | DanielBiostatistics10th | Large Data | data.frame | 1000 | 2 |
LDS_C08_CSFDATA | DanielBiostatistics10th | Large Data | data.frame | 300 | 6 |
LDS_C08_LSADATA | DanielBiostatistics10th | Large Data | data.frame | 350 | 5 |
LDS_C08_MEDSCORES | DanielBiostatistics10th | Large Data | data.frame | 582 | 4 |
LDS_C08_RBCDATA | DanielBiostatistics10th | Large Data | data.frame | 350 | 4 |
LDS_C08_SACEDATA | DanielBiostatistics10th | Large Data | data.frame | 400 | 5 |
LDS_C08_SERUMCHO | DanielBiostatistics10th | Large Data | data.frame | 347 | 4 |
LDS_C09_CALCIUM | DanielBiostatistics10th | Large Data | data.frame | 100 | 13 |
LDS_C09_CEREBRAL | DanielBiostatistics10th | Large Data | data.frame | 1050 | 3 |
LDS_C09_HYPERTEN | DanielBiostatistics10th | Large Data | data.frame | 1050 | 3 |
LDS_C10_LTEXER | DanielBiostatistics10th | Large Data | data.frame | 248 | 5 |
LDS_C10_RESPDIS | DanielBiostatistics10th | Large Data | data.frame | 1200 | 6 |
LDS_C10_RISKFACT | DanielBiostatistics10th | Large Data | data.frame | 1000 | 6 |
LDS_C10_STERLENGTH | DanielBiostatistics10th | Large Data | data.frame | 1162 | 4 |
LDS_C11_AQUATICS | DanielBiostatistics10th | Large Data | data.frame | 142 | 7 |
LDS_C11_TEACHERS | DanielBiostatistics10th | Large Data | data.frame | 212 | 7 |
LDS_C11_WGTLOSS | DanielBiostatistics10th | Large Data | data.frame | 1185 | 3 |
LDS_C12_SMOKING | DanielBiostatistics10th | Large Data | data.frame | 1200 | 6 |
LDS_C13_KLETTER | DanielBiostatistics10th | Large Data | data.frame | 168 | 3 |
REV_C02_13 | DanielBiostatistics10th | Review Exercise Data | data.frame | 50 | 1 |
REV_C02_15 | DanielBiostatistics10th | Review Exercise Data | data.frame | 28 | 1 |
REV_C02_16 | DanielBiostatistics10th | Review Exercise Data | data.frame | 53 | 2 |
REV_C02_19 | DanielBiostatistics10th | Review Exercise Data | data.frame | 22 | 1 |
REV_C02_29 | DanielBiostatistics10th | Review Exercise Data | data.frame | 107 | 1 |
REV_C06_22 | DanielBiostatistics10th | Review Exercise Data | data.frame | 27 | 2 |
REV_C06_23 | DanielBiostatistics10th | Review Exercise Data | data.frame | 28 | 2 |
REV_C06_28 | DanielBiostatistics10th | Review Exercise Data | data.frame | 110 | 2 |
REV_C07_18 | DanielBiostatistics10th | Review Exercise Data | data.frame | 107 | 3 |
REV_C07_19 | DanielBiostatistics10th | Review Exercise Data | data.frame | 107 | 3 |
REV_C07_22 | DanielBiostatistics10th | Review Exercise Data | data.frame | 76 | 2 |
REV_C07_24 | DanielBiostatistics10th | Review Exercise Data | data.frame | 37 | 2 |
REV_C07_29 | DanielBiostatistics10th | Review Exercise Data | data.frame | 12 | 2 |
REV_C07_40 | DanielBiostatistics10th | Review Exercise Data | data.frame | 8 | 3 |
REV_C07_41 | DanielBiostatistics10th | Review Exercise Data | data.frame | 11 | 3 |
REV_C07_42 | DanielBiostatistics10th | Review Exercise Data | data.frame | 10 | 6 |
REV_C07_43 | DanielBiostatistics10th | Review Exercise Data | data.frame | 31 | 2 |
REV_C07_44 | DanielBiostatistics10th | Review Exercise Data | data.frame | 98 | 4 |
REV_C07_45 | DanielBiostatistics10th | Review Exercise Data | data.frame | 15 | 11 |
REV_C07_46 | DanielBiostatistics10th | Review Exercise Data | data.frame | 17 | 2 |
REV_C07_47 | DanielBiostatistics10th | Review Exercise Data | data.frame | 66 | 2 |
REV_C07_48 | DanielBiostatistics10th | Review Exercise Data | data.frame | 51 | 2 |
REV_C07_49 | DanielBiostatistics10th | Review Exercise Data | data.frame | 22 | 2 |
REV_C07_50 | DanielBiostatistics10th | Review Exercise Data | data.frame | 28 | 4 |
REV_C07_51 | DanielBiostatistics10th | Review Exercise Data | data.frame | 22 | 2 |
REV_C07_52 | DanielBiostatistics10th | Review Exercise Data | data.frame | 24 | 2 |
REV_C07_53 | DanielBiostatistics10th | Review Exercise Data | data.frame | 55 | 2 |
REV_C07_54 | DanielBiostatistics10th | Review Exercise Data | data.frame | 17 | 2 |
REV_C07_55 | DanielBiostatistics10th | Review Exercise Data | data.frame | 50 | 2 |
REV_C08_13 | DanielBiostatistics10th | Review Exercise Data | data.frame | 75 | 2 |
REV_C08_14 | DanielBiostatistics10th | Review Exercise Data | data.frame | 91 | 2 |
REV_C08_15 | DanielBiostatistics10th | Review Exercise Data | data.frame | 48 | 3 |
REV_C08_16 | DanielBiostatistics10th | Review Exercise Data | data.frame | 36 | 3 |
REV_C08_17 | DanielBiostatistics10th | Review Exercise Data | data.frame | 52 | 3 |
REV_C08_18 | DanielBiostatistics10th | Review Exercise Data | data.frame | 74 | 4 |
REV_C08_19 | DanielBiostatistics10th | Review Exercise Data | data.frame | 162 | 3 |
REV_C08_20 | DanielBiostatistics10th | Review Exercise Data | data.frame | 56 | 2 |
REV_C08_21 | DanielBiostatistics10th | Review Exercise Data | data.frame | 30 | 3 |
REV_C08_22 | DanielBiostatistics10th | Review Exercise Data | data.frame | 54 | 3 |
REV_C08_23 | DanielBiostatistics10th | Review Exercise Data | data.frame | 31 | 2 |
REV_C08_24 | DanielBiostatistics10th | Review Exercise Data | data.frame | 30 | 3 |
REV_C08_25 | DanielBiostatistics10th | Review Exercise Data | data.frame | 60 | 3 |
REV_C08_31 | DanielBiostatistics10th | Review Exercise Data | data.frame | 28 | 1 |
REV_C08_32 | DanielBiostatistics10th | Review Exercise Data | data.frame | 48 | 1 |
REV_C08_33 | DanielBiostatistics10th | Review Exercise Data | data.frame | 25 | 2 |
REV_C08_39 | DanielBiostatistics10th | Review Exercise Data | data.frame | 18 | 10 |
REV_C08_40 | DanielBiostatistics10th | Review Exercise Data | data.frame | 30 | 3 |
REV_C08_41 | DanielBiostatistics10th | Review Exercise Data | data.frame | 24 | 2 |
REV_C08_42 | DanielBiostatistics10th | Review Exercise Data | data.frame | 14 | 8 |
REV_C08_43 | DanielBiostatistics10th | Review Exercise Data | data.frame | 24 | 5 |
REV_C08_44 | DanielBiostatistics10th | Review Exercise Data | data.frame | 16 | 14 |
REV_C08_45 | DanielBiostatistics10th | Review Exercise Data | data.frame | 90 | 2 |
REV_C08_46 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 5 |
REV_C08_47 | DanielBiostatistics10th | Review Exercise Data | data.frame | 18 | 3 |
REV_C08_48 | DanielBiostatistics10th | Review Exercise Data | data.frame | 34 | 2 |
REV_C08_49 | DanielBiostatistics10th | Review Exercise Data | data.frame | 24 | 3 |
REV_C08_50 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 5 |
REV_C08_51 | DanielBiostatistics10th | Review Exercise Data | data.frame | 36 | 3 |
REV_C08_52 | DanielBiostatistics10th | Review Exercise Data | data.frame | 65 | 2 |
REV_C08_53 | DanielBiostatistics10th | Review Exercise Data | data.frame | 11 | 2 |
REV_C08_54 | DanielBiostatistics10th | Review Exercise Data | data.frame | 22 | 8 |
REV_C08_55 | DanielBiostatistics10th | Review Exercise Data | data.frame | 41 | 2 |
REV_C08_56 | DanielBiostatistics10th | Review Exercise Data | data.frame | 14 | 8 |
REV_C08_57 | DanielBiostatistics10th | Review Exercise Data | data.frame | 119 | 2 |
REV_C08_58 | DanielBiostatistics10th | Review Exercise Data | data.frame | 173 | 2 |
REV_C08_59 | DanielBiostatistics10th | Review Exercise Data | data.frame | 192 | 2 |
REV_C08_60 | DanielBiostatistics10th | Review Exercise Data | data.frame | 24 | 2 |
REV_C08_61 | DanielBiostatistics10th | Review Exercise Data | data.frame | 32 | 2 |
REV_C08_62 | DanielBiostatistics10th | Review Exercise Data | data.frame | 14 | 12 |
REV_C08_63 | DanielBiostatistics10th | Review Exercise Data | data.frame | 30 | 2 |
REV_C08_64 | DanielBiostatistics10th | Review Exercise Data | data.frame | 60 | 2 |
REV_C08_65 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 2 |
REV_C08_66 | DanielBiostatistics10th | Review Exercise Data | data.frame | 15 | 2 |
REV_C09_16 | DanielBiostatistics10th | Review Exercise Data | data.frame | 29 | 2 |
REV_C09_17 | DanielBiostatistics10th | Review Exercise Data | data.frame | 89 | 2 |
REV_C09_18 | DanielBiostatistics10th | Review Exercise Data | data.frame | 31 | 5 |
REV_C09_19 | DanielBiostatistics10th | Review Exercise Data | data.frame | 19 | 2 |
REV_C09_20 | DanielBiostatistics10th | Review Exercise Data | data.frame | 16 | 2 |
REV_C09_21 | DanielBiostatistics10th | Review Exercise Data | data.frame | 15 | 2 |
REV_C09_22 | DanielBiostatistics10th | Review Exercise Data | data.frame | 12 | 2 |
REV_C09_23 | DanielBiostatistics10th | Review Exercise Data | data.frame | 16 | 2 |
REV_C09_24 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 2 |
REV_C09_25 | DanielBiostatistics10th | Review Exercise Data | data.frame | 10 | 2 |
REV_C09_29 | DanielBiostatistics10th | Review Exercise Data | data.frame | 85 | 2 |
REV_C09_30 | DanielBiostatistics10th | Review Exercise Data | data.frame | 27 | 2 |
REV_C09_31 | DanielBiostatistics10th | Review Exercise Data | data.frame | 25 | 2 |
REV_C09_32 | DanielBiostatistics10th | Review Exercise Data | data.frame | 9 | 3 |
REV_C09_33 | DanielBiostatistics10th | Review Exercise Data | data.frame | 25 | 2 |
REV_C09_34 | DanielBiostatistics10th | Review Exercise Data | data.frame | 19 | 5 |
REV_C09_35 | DanielBiostatistics10th | Review Exercise Data | data.frame | 62 | 2 |
REV_C09_36 | DanielBiostatistics10th | Review Exercise Data | data.frame | 23 | 2 |
REV_C09_37 | DanielBiostatistics10th | Review Exercise Data | data.frame | 23 | 2 |
REV_C09_38 | DanielBiostatistics10th | Review Exercise Data | data.frame | 32 | 2 |
REV_C09_39 | DanielBiostatistics10th | Review Exercise Data | data.frame | 48 | 5 |
REV_C09_40 | DanielBiostatistics10th | Review Exercise Data | data.frame | 13 | 2 |
REV_C09_41 | DanielBiostatistics10th | Review Exercise Data | data.frame | 66 | 2 |
REV_C09_42 | DanielBiostatistics10th | Review Exercise Data | data.frame | 102 | 7 |
REV_C09_43 | DanielBiostatistics10th | Review Exercise Data | data.frame | 14 | 4 |
REV_C09_44 | DanielBiostatistics10th | Review Exercise Data | data.frame | 44 | 2 |
REV_C09_45 | DanielBiostatistics10th | Review Exercise Data | data.frame | 172 | 2 |
REV_C09_46 | DanielBiostatistics10th | Review Exercise Data | data.frame | 33 | 4 |
REV_C10_06 | DanielBiostatistics10th | Review Exercise Data | data.frame | 114 | 3 |
REV_C10_07 | DanielBiostatistics10th | Review Exercise Data | data.frame | 15 | 3 |
REV_C10_08 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 4 |
REV_C10_09 | DanielBiostatistics10th | Review Exercise Data | data.frame | 15 | 3 |
REV_C10_10 | DanielBiostatistics10th | Review Exercise Data | data.frame | 21 | 3 |
REV_C10_11 | DanielBiostatistics10th | Review Exercise Data | data.frame | 15 | 5 |
REV_C10_17 | DanielBiostatistics10th | Review Exercise Data | data.frame | 26 | 13 |
REV_C10_18 | DanielBiostatistics10th | Review Exercise Data | data.frame | 34 | 7 |
REV_C10_19 | DanielBiostatistics10th | Review Exercise Data | data.frame | 96 | 8 |
REV_C11_14 | DanielBiostatistics10th | Review Exercise Data | data.frame | 70 | 5 |
REV_C11_15 | DanielBiostatistics10th | Review Exercise Data | data.frame | 28 | 3 |
REV_C11_16 | DanielBiostatistics10th | Review Exercise Data | data.frame | 27 | 3 |
REV_C11_17 | DanielBiostatistics10th | Review Exercise Data | data.frame | 40 | 3 |
REV_C11_18 | DanielBiostatistics10th | Review Exercise Data | data.frame | 31 | 4 |
REV_C11_22 | DanielBiostatistics10th | Review Exercise Data | data.frame | 12 | 3 |
REV_C11_23 | DanielBiostatistics10th | Review Exercise Data | data.frame | 47 | 3 |
REV_C11_24 | DanielBiostatistics10th | Review Exercise Data | data.frame | 129 | 3 |
REV_C11_25 | DanielBiostatistics10th | Review Exercise Data | data.frame | 30 | 3 |
REV_C11_26 | DanielBiostatistics10th | Review Exercise Data | data.frame | 18 | 3 |
REV_C11_27 | DanielBiostatistics10th | Review Exercise Data | data.frame | 37 | 3 |
REV_C11_28 | DanielBiostatistics10th | Review Exercise Data | data.frame | 41 | 10 |
REV_C11_29 | DanielBiostatistics10th | Review Exercise Data | data.frame | 19 | 9 |
REV_C13_06 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 2 |
REV_C13_08 | DanielBiostatistics10th | Review Exercise Data | data.frame | 303 | 2 |
REV_C13_09 | DanielBiostatistics10th | Review Exercise Data | data.frame | 10 | 4 |
REV_C13_10 | DanielBiostatistics10th | Review Exercise Data | data.frame | 34 | 2 |
REV_C13_13 | DanielBiostatistics10th | Review Exercise Data | data.frame | 70 | 2 |
REV_C13_15 | DanielBiostatistics10th | Review Exercise Data | data.frame | 12 | 2 |
REV_C13_16 | DanielBiostatistics10th | Review Exercise Data | data.frame | 43 | 5 |
REV_C13_17 | DanielBiostatistics10th | Review Exercise Data | data.frame | 67 | 2 |
REV_C13_18 | DanielBiostatistics10th | Review Exercise Data | data.frame | 18 | 3 |
REV_C13_19 | DanielBiostatistics10th | Review Exercise Data | data.frame | 50 | 2 |
REV_C13_20 | DanielBiostatistics10th | Review Exercise Data | data.frame | 31 | 3 |
REV_C13_21 | DanielBiostatistics10th | Review Exercise Data | data.frame | 45 | 3 |
REV_C13_22 | DanielBiostatistics10th | Review Exercise Data | data.frame | 18 | 2 |
REV_C13_23 | DanielBiostatistics10th | Review Exercise Data | data.frame | 44 | 5 |
REV_C13_24 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 6 |
REV_C13_25 | DanielBiostatistics10th | Review Exercise Data | data.frame | 20 | 3 |
REV_C13_26 | DanielBiostatistics10th | Review Exercise Data | data.frame | 18 | 3 |
REV_C13_27 | DanielBiostatistics10th | Review Exercise Data | data.frame | 9 | 6 |
REV_C13_28 | DanielBiostatistics10th | Review Exercise Data | data.frame | 31 | 2 |
REV_C13_29 | DanielBiostatistics10th | Review Exercise Data | data.frame | 17 | 2 |
REV_C14_11 | DanielBiostatistics10th | Review Exercise Data | data.frame | 55 | 4 |
REV_C14_12 | DanielBiostatistics10th | Review Exercise Data | data.frame | 77 | 4 |
tplyr_adae | Tplyr | ADAE Data | tbl_df | 276 | 55 |
tplyr_adas | Tplyr | ADAS Data | tbl_df | 1040 | 40 |
tplyr_adlb | Tplyr | ADLB Data | tbl_df | 311 | 46 |
tplyr_adpe | Tplyr | ADPE Data | tbl_df | 21 | 8 |
tplyr_adsl | Tplyr | ADSL Data | tbl_df | 254 | 49 |
MLM3 | TraitEnvMLMWA | MLM3-fit to Revisit data using trait C:N ratio and environmental variable TMG | glmmTMB | | |
MLM3_Aravo | TraitEnvMLMWA | quadratic MLM3-fit to Aravo data using SLA and Snow | glmerMod | | |
Revisit | TraitEnvMLMWA | Revisit data | data.frame | 3900 | 6 |
dm | sdtmval | Example SDTM Domain 'DM' | tbl_df | 2 | 4 |
edc_xx | sdtmval | Example EDC data for form/table 'XX' | data.frame | 10 | 5 |
spec_XX | sdtmval | Example domain specific tab from a SDTM specification .xlsx file | tbl_df | 12 | 6 |
spec_codelists | sdtmval | Example 'Codelists' tab from a SDTM specification .xlsx file | tbl_df | 3 | 3 |
spec_datasets | sdtmval | Example 'Datasets' tab from a SDTM specification .xlsx file | tbl_df | 1 | 4 |
vd | sdtmval | Example EDC data for form/table 'VD' | data.frame | 8 | 3 |
xx_no_meta_data | sdtmval | Example SDTM domain table XX without meta data | tbl_df | 10 | 11 |
ages_archosauria | paleoDiv | ages_archosauria | matrix | 13 | 2 |
archosauria | paleoDiv | archosauria | list | | |
diversity_table | paleoDiv | diversity_table | data.frame | 94 | 15 |
tree_archosauria | paleoDiv | tree_archosauria | phylo | | |
dat | EBcoBART | Exemplary Data Set | list | | |
MexicanHH_foodConsumption | censoredAIDS | National Survey of Household Income and Expenditures (ENIGH) | data.frame | 8777 | 17 |
assay | portfolio | Assay Research rankings as of 2004-12-31 | data.frame | 4000 | 25 |
dow.jan.2005 | portfolio | DJIA for January, 2005 | data.frame | 30 | 6 |
global.2004 | portfolio | Security data of large global companies for 2004 | data.frame | 6000 | 18 |
annotation | CpGassoc | Sample data from 'CpGassoc' | data.frame | 1228 | 3 |
samplecpg | CpGassoc | Sample data from 'CpGassoc' | matrix | 1228 | |
samplepheno | CpGassoc | Sample data from 'CpGassoc' | data.frame | 258 | 6 |
MEPS2001 | heckmanGE | Medical Expenditure Panel Survey (MEPS) Data | data.frame | 3328 | 22 |
pnadC_y2024q2 | heckmanGE | PNAD Continua de 2024, 2 trimestre | tbl_df | 326018 | 12 |
simulation | heckmanGE | Simulation dataset for the heckmanGE example | tbl_df | 10000 | 8 |
credit.data | rsubgroup | Statlog (German Credit Data) Data Set | data.frame | 1000 | 21 |
TtorquatusBreeding | Mapinguari | Breeding status of *Tropidurus torquatus* lizards at 15 locations in Brazil during each month of the year. | data.frame | 15 | 14 |
TtorquatusDistribution | Mapinguari | 359 occurrence records of *Tropidurus torquatus* in Brazil | data.frame | 359 | 3 |
TtorquatusGradient | Mapinguari | *Tropidurus torquatus* body temperatures at temperature gradient experiments. | data.frame | 3443 | 3 |
TtorquatusOperative | Mapinguari | Operative temperatures of multiple microhabitats at 6 localities in Brazil from 2014 to 2016. | data.frame | 915684 | 12 |
TtorquatusPerformance | Mapinguari | Running speed achieved by 72 *Tropidurus torquatus* lizards in 274 trials under different temperatures. | data.frame | 274 | 6 |
my_bingads_data | bingadsR | Sample of digital marketing data from bing Ads downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
schema_default | tabshiftr | Default template of a schema description | schema | | |
tabs2shift | tabshiftr | List of table types | list | | |
PakPMICS2018bh | PakPMICS2018bh | Multiple Indicator Cluster Survey (MICS) 2018 Child Questionnaire Data for Punjab, Pakistan | tbl_df | 157899 | 62 |
est21Ad | ExpDes.pt | Percevejos no milho: tratamento adicional. | numeric | | |
ex | ExpDes.pt | Videiras: parcelas subdivididas em DBC | data.frame | 24 | 4 |
ex1 | ExpDes.pt | Yacon: DIC | data.frame | 24 | 2 |
ex2 | ExpDes.pt | Barras alimenticias: DBC | data.frame | 350 | 3 |
ex3 | ExpDes.pt | Forrageiras: DQL | data.frame | 49 | 4 |
ex4 | ExpDes.pt | Compostagem: fatorial duplo em DIC | data.frame | 24 | 11 |
ex5 | ExpDes.pt | Barras alimenticias: fatorial duplo em DBC | data.frame | 160 | 4 |
ex6 | ExpDes.pt | Dados ficticios 1 | data.frame | 24 | 5 |
ex7 | ExpDes.pt | Estatura de plantas de milho 21 dias apos a emergencia. | data.frame | 80 | 4 |
ex8 | ExpDes.pt | Compostagem: fatorial duplo com um tratamento adicional em DIC | data.frame | 24 | 5 |
ex9 | ExpDes.pt | Coberturas vegetais: parcelas subdivididas em DIC | data.frame | 48 | 4 |
exnl | ExpDes.pt | Exemplo de massa de dados ficticios | data.frame | 30 | 3 |
respAd | ExpDes.pt | Dadaos ficticios: tratamento adicional | numeric | | |
secaAd | ExpDes.pt | Compostagem: tratamento adicional | numeric | | |
LM22 | FARDEEP | Siganature matrix | data.frame | 547 | 22 |
mixture | FARDEEP | Gene-expression data from 14 follicular lymphoma patients | data.frame | 19416 | 14 |
covid19_data | bets.covid19 | Confirmed cases of COVID-19 | data.table | 1460 | 19 |
wuhan_exported | bets.covid19 | COVID-19 exported from Wuhan | data.frame | 378 | 6 |
FTIR | cryst | FTIR Spectra of A-, B-, and C-Type Starch | data.frame | 1038 | 4 |
XRD | cryst | X-Ray Diffraction Patterns of A-, B-, and C-Type Starch | data.frame | 1527 | 4 |
xydat | RootsExtremaInflections | xydat | data.frame | 61 | 2 |
rnaCluster | CytoSimplex | Major cell type annotation of the example mouse bone marrow data | factor | | |
rnaRaw | CytoSimplex | Mouse bone marrow scRNAseq example data | dgCMatrix | | |
rnaVelo | CytoSimplex | Velocity graph of the example mouse bone marrow data | dgCMatrix | | |
comm_a | phyr | Example community data | data.frame | 15 | 15 |
comm_b | phyr | Example community data | data.frame | 15 | 9 |
envi | phyr | Example environmental data | data.frame | 15 | 5 |
oldfield | phyr | Phylogeny and community data from an Oldfield ecosystem in Southern Ontario, Canada | list | | |
phylotree | phyr | Example phylogeny | phylo | | |
traits | phyr | Example species traits data | data.frame | 18 | 4 |
RSA_step1 | RSAtools | Simulation data on needs-supplies fit processes (STEP1) | RSA | | |
sim_NSfit | RSAtools | Simulation data on needs-supplies fit processes (illustration) | data.frame | 500 | 3 |
Mnist | TensorTools | Subset of MNIST training and testing data. | list | | |
raytrace | TensorTools | Subset of raytrace data | list | | |
volume_aapl | intradayModel | 15-min Intraday Volume of AAPL | matrix | 26 | 124 |
volume_fdx | intradayModel | 15-min Intraday Volume of FDX | xts | 3299 | 1 |
fauxmatching | rpm | Faux Data on Heterosexual Matching | list | | |
metr.marks | bmetr | Metronomes Data | data.frame | 162 | 4 |
metr.neewer | bmetr | Metronomes Data | data.frame | 39 | 3 |
metr.params | bmetr | Metronomes Data | data.frame | 5 | 11 |
sym.duration | bmetr | Beethoven's Symphonies Data | data.frame | 1332 | 4 |
sym.marks | bmetr | Beethoven's Symphonies Data | data.frame | 61 | 10 |
sym.recordings | bmetr | Beethoven's Symphonies Data | data.frame | 36 | 8 |
sym.sample | bmetr | Beethoven's Symphonies Data | data.frame | 1332 | 7 |
sym.window | bmetr | Beethoven's Symphonies Data | data.frame | 253692 | 7 |
locality_dict | cms | Locality dictionary for 2020 Physician Fee Schedule | tbl_df | 113 | 5 |
mpfs20_oh | cms | 2020 Physician Fee Schedule, Ohio | tbl_df | 8994 | 15 |
carnivores | Biostatistics | Brain and body mass for carnivore species | data.frame | 199 | 7 |
cricket_song | Biostatistics | Cricket song dataset | data.frame | 568 | 5 |
finch_colours | Biostatistics | Finch colouration and mitochondrial function | data.frame | 36 | 5 |
gabon_diversity | Biostatistics | Data on relative animal abundances in Gabon | data.frame | 24 | 14 |
gnatocerus | Biostatistics | Weapon size and insulin-like signalling in the broad-horned flour beetle | data.frame | 144 | 3 |
height_immunity | Biostatistics | Data on the relationship between immune system functioning and body height in healthy people. | data.frame | 193 | 16 |
latitude_diversity | Biostatistics | Tree diversity data | data.frame | 24 | 12 |
longevity | Biostatistics | Data on maximum lifespan for 909 species of mammal and bird | data.frame | 909 | 9 |
malawi_carbon | Biostatistics | Carbon exposure and lung immunity | data.frame | 29 | 4 |
mammal_longevity | Biostatistics | Data on maximum lifespan for 375 species of mammal | data.frame | 375 | 10 |
mhc | Biostatistics | MHC promiscuity and pathogen diversity data | data.frame | 28 | 4 |
mouse_activity | Biostatistics | Locomotor activity in offspring of mice exposed to nicotine | data.frame | 54 | 3 |
parrots2 | Biostatistics | Parrot lifespan data | data.frame | 69 | 11 |
pinniped | Biostatistics | Pinniped brain sizes and mating system | data.frame | 33 | 6 |
quolls | Biostatistics | Data on physical performance in Northern Quolls | data.frame | 63 | 23 |
ragwort | Biostatistics | Data on how plant-soil feedback affects growth of ragwort | data.frame | 45 | 3 |
weaver | Biostatistics | Oxidative stress and group size in social weaver birds | data.frame | 34 | 6 |
zebra_bacteria | Biostatistics | Data on bacterial adaptation to host gut environments. | data.frame | 208 | 4 |
Catheter | meta4diag | The Catheter Segment Culture data. | data.frame | 33 | 7 |
Scheidler | meta4diag | Scheidler dataset. | data.frame | 44 | 6 |
Telomerase | meta4diag | Telomerase dataset. | data.frame | 10 | 6 |
table_cor | meta4diag | The example table prior for correlation. | data.frame | 1498 | 2 |
table_var | meta4diag | The example table prior for variance. | data.frame | 99999 | 2 |
bea2002_desc | concordance | BEA Description for year 2002 | spec_tbl_df | 430 | 2 |
bea2002_naics2002 | concordance | BEA2002-NAICS2002 Concordance | spec_tbl_df | 1335 | 6 |
bea2007_naics2007 | concordance | BEA2007-NAICS2007 Concordance | tbl_df | 1376 | 6 |
bea2012_desc | concordance | BEA Description for year 2012 | spec_tbl_df | 409 | 2 |
bea2012_naics2012 | concordance | BEA2012-NAICS2012 Concordance | spec_tbl_df | 1376 | 6 |
bec4_desc | concordance | BEC4 Description | data.frame | 50 | 2 |
hs0_bec4 | concordance | HS0-BEC4 Concordance | tbl_df | 5156 | 6 |
hs0_desc | concordance | HS0 Description | data.frame | 6380 | 2 |
hs0_isic2 | concordance | HS0-ISIC2 Concordance | tbl_df | 5018 | 7 |
hs0_isic3 | concordance | HS0-ISIC3 Concordance | tbl_df | 5018 | 7 |
hs0_naics | concordance | HS0-NAICS Concordance | tbl_df | 8058 | 8 |
hs0_sitc1 | concordance | HS0-SITC1 Concordance | spec_tbl_df | 5012 | 8 |
hs0_sitc2 | concordance | HS0-SITC2 Concordance | spec_tbl_df | 5017 | 8 |
hs0_sitc3 | concordance | HS0-SITC3 Concordance | spec_tbl_df | 5017 | 8 |
hs0_sitc4 | concordance | HS0-SITC4 Concordance | spec_tbl_df | 5018 | 8 |
hs1_bec4 | concordance | HS1-BEC4 Concordance | tbl_df | 5334 | 6 |
hs1_desc | concordance | HS1 Description | data.frame | 6473 | 2 |
hs1_hs0 | concordance | HS1-HS0 Concordance | tbl_df | 5130 | 6 |
hs1_isic2 | concordance | HS1-ISIC2 Concordance | tbl_df | 5113 | 7 |
hs1_isic3 | concordance | HS1-ISIC3 Concordance | tbl_df | 5113 | 7 |
hs1_naics | concordance | HS1-NAICS Concordance | tbl_df | 8297 | 8 |
hs1_sitc1 | concordance | HS1-SITC1 Concordance | spec_tbl_df | 5106 | 8 |
hs1_sitc2 | concordance | HS1-SITC2 Concordance | spec_tbl_df | 5111 | 8 |
hs1_sitc3 | concordance | HS1-SITC3 Concordance | spec_tbl_df | 5111 | 8 |
hs1_sitc4 | concordance | HS1-SITC4 Concordance | spec_tbl_df | 5111 | 8 |
hs2_bec4 | concordance | HS2-BEC4 Concordance | tbl_df | 5351 | 6 |
hs2_desc | concordance | HS2 Description | data.frame | 6568 | 2 |
hs2_hs0 | concordance | HS2-HS0 Concordance | tbl_df | 5223 | 6 |
hs2_hs1 | concordance | HS2-HS1 Concordance | tbl_df | 5223 | 6 |
hs2_isic2 | concordance | HS2-ISIC2 Concordance | tbl_df | 5224 | 7 |
hs2_isic3 | concordance | HS2-ISIC3 Concordance | tbl_df | 5224 | 7 |
hs2_naics | concordance | HS2-NAICS Concordance | tbl_df | 8609 | 8 |
hs2_sitc1 | concordance | HS2-SITC1 Concordance | spec_tbl_df | 5217 | 8 |
hs2_sitc2 | concordance | HS2-SITC2 Concordance | spec_tbl_df | 5222 | 8 |
hs2_sitc3 | concordance | HS2-SITC3 Concordance | spec_tbl_df | 5222 | 8 |
hs2_sitc4 | concordance | HS2-SITC4 Concordance | spec_tbl_df | 5220 | 8 |
hs3_bec4 | concordance | HS3-BEC4 Concordance | tbl_df | 5050 | 6 |
hs3_desc | concordance | HS3 Description | data.frame | 6372 | 2 |
hs3_hs0 | concordance | HS3-HS0 Concordance | tbl_df | 5053 | 6 |
hs3_hs1 | concordance | HS3-HS1 Concordance | tbl_df | 5052 | 6 |
hs3_hs2 | concordance | HS3-HS2 Concordance | tbl_df | 5052 | 6 |
hs3_isic2 | concordance | HS3-ISIC2 Concordance | tbl_df | 5052 | 7 |
hs3_isic3 | concordance | HS3-ISIC3 Concordance | tbl_df | 5052 | 7 |
hs3_naics | concordance | HS3-NAICS Concordance | tbl_df | 8545 | 8 |
hs3_sitc1 | concordance | HS3-SITC1 Concordance | spec_tbl_df | 5045 | 8 |
hs3_sitc2 | concordance | HS3-SITC2 Concordance | spec_tbl_df | 5050 | 8 |
hs3_sitc3 | concordance | HS3-SITC3 Concordance | spec_tbl_df | 5050 | 8 |
hs3_sitc4 | concordance | HS3-SITC4 Concordance | spec_tbl_df | 5050 | 8 |
hs4_bec4 | concordance | HS4-BEC4 Concordance | tbl_df | 5283 | 6 |
hs4_desc | concordance | HS4 Description | data.frame | 6528 | 2 |
hs4_hs0 | concordance | HS4-HS0 Concordance | tbl_df | 5206 | 6 |
hs4_hs1 | concordance | HS4-HS1 Concordance | tbl_df | 5206 | 6 |
hs4_hs2 | concordance | HS4-HS2 Concordance | tbl_df | 5206 | 6 |
hs4_hs3 | concordance | HS4-HS3 Concordance | tbl_df | 5205 | 6 |
hs4_isic2 | concordance | HS4-ISIC2 Concordance | tbl_df | 5205 | 7 |
hs4_isic3 | concordance | HS4-ISIC3 Concordance | tbl_df | 5205 | 7 |
hs4_naics | concordance | HS4-NAICS Concordance | tbl_df | 8790 | 8 |
hs4_sitc1 | concordance | HS4-SITC1 Concordance | tbl_df | 5199 | 8 |
hs4_sitc2 | concordance | HS4-SITC2 Concordance | tbl_df | 5205 | 8 |
hs4_sitc3 | concordance | HS4-SITC3 Concordance | tbl_df | 5206 | 8 |
hs4_sitc4 | concordance | HS4-SITC4 Concordance | tbl_df | 5205 | 8 |
hs5_bec4 | concordance | HS5-BEC4 Concordance | tbl_df | 5511 | 6 |
hs5_desc | concordance | HS5 Description | data.frame | 6708 | 2 |
hs5_hs0 | concordance | HS5-HS0 Concordance | tbl_df | 5388 | 6 |
hs5_hs1 | concordance | HS5-HS1 Concordance | tbl_df | 5388 | 6 |
hs5_hs2 | concordance | HS5-HS2 Concordance | tbl_df | 5388 | 6 |
hs5_hs3 | concordance | HS5-HS3 Concordance | tbl_df | 5388 | 6 |
hs5_hs4 | concordance | HS5-HS4 Concordance | tbl_df | 5388 | 6 |
hs5_isic2 | concordance | HS5-ISIC2 Concordance | tbl_df | 5388 | 7 |
hs5_isic3 | concordance | HS5-ISIC3 Concordance | tbl_df | 5388 | 7 |
hs5_naics | concordance | HS5-NAICS Concordance | tbl_df | 8973 | 8 |
hs5_sitc1 | concordance | HS5-SITC1 Concordance | tbl_df | 5381 | 8 |
hs5_sitc2 | concordance | HS5-SITC2 Concordance | tbl_df | 5387 | 8 |
hs5_sitc3 | concordance | HS5-SITC3 Concordance | tbl_df | 5387 | 8 |
hs5_sitc4 | concordance | HS5-SITC4 Concordance | tbl_df | 5387 | 8 |
hs6_bec4 | concordance | HS6-BEC4 Concordance | tbl_df | 5927 | 6 |
hs6_desc | concordance | HS6 Description | data.frame | 6939 | 2 |
hs6_hs0 | concordance | HS6-HS0 Concordance | tbl_df | 5613 | 6 |
hs6_hs1 | concordance | HS6-HS1 Concordance | tbl_df | 5613 | 6 |
hs6_hs2 | concordance | HS6-HS2 Concordance | tbl_df | 5613 | 6 |
hs6_hs3 | concordance | HS6-HS3 Concordance | tbl_df | 5613 | 6 |
hs6_hs4 | concordance | HS6-HS4 Concordance | tbl_df | 5613 | 6 |
hs6_hs5 | concordance | HS6-HS5 Concordance | tbl_df | 5613 | 6 |
hs6_isic2 | concordance | HS6-ISIC2 Concordance | tbl_df | 5613 | 7 |
hs6_isic3 | concordance | HS6-ISIC3 Concordance | tbl_df | 5613 | 7 |
hs6_naics | concordance | HS6-NAICS Concordance | tbl_df | 9451 | 8 |
hs6_sitc1 | concordance | HS6-SITC1 Concordance | tbl_df | 5605 | 8 |
hs6_sitc2 | concordance | HS6-SITC2 Concordance | tbl_df | 5611 | 8 |
hs6_sitc3 | concordance | HS6-SITC3 Concordance | tbl_df | 5611 | 8 |
hs6_sitc4 | concordance | HS6-SITC4 Concordance | tbl_df | 5611 | 8 |
hs_bec4 | concordance | HS-BEC4 Concordance | tbl_df | 7365 | 6 |
hs_desc | concordance | HS (Combined) Description | tbl_df | 8261 | 2 |
hs_isic2 | concordance | HS-ISIC2 Concordance | tbl_df | 6896 | 7 |
hs_isic3 | concordance | HS-ISIC3 Concordance | tbl_df | 6891 | 7 |
hs_isic31 | concordance | HS-ISIC3.1 Concordance | tbl_df | 7240 | 7 |
hs_isic4 | concordance | HS-ISIC4 Concordance | tbl_df | 24279 | 7 |
hs_naics | concordance | HS-NAICS Concordance | tbl_df | 10399 | 8 |
hs_sitc1 | concordance | HS-SITC1 Concordance | tbl_df | 8394 | 8 |
hs_sitc2 | concordance | HS-SITC2 Concordance | tbl_df | 9080 | 8 |
hs_sitc3 | concordance | HS-SITC3 Concordance | tbl_df | 7302 | 8 |
hs_sitc4 | concordance | HS-SITC4 Concordance | tbl_df | 7001 | 8 |
isic2_desc | concordance | ISIC2 Description | spec_tbl_df | 279 | 2 |
isic3.1_desc | concordance | ISIC3.1 Description | spec_tbl_df | 540 | 2 |
isic31_isic2 | concordance | ISIC3.1-ISIC2 Concordance | tbl_df | 636 | 8 |
isic31_isic3 | concordance | ISIC3.1-ISIC3 Concordance | tbl_df | 316 | 8 |
isic3_desc | concordance | ISIC3 Description | spec_tbl_df | 530 | 2 |
isic3_isic2 | concordance | ISIC3-ISIC2 Concordance | tbl_df | 585 | 8 |
isic4_desc | concordance | ISIC4 Description | spec_tbl_df | 766 | 2 |
isic4_isic2 | concordance | ISIC4-ISIC2 Concordance | tbl_df | 1706 | 8 |
isic4_isic3 | concordance | ISIC4-ISIC3 Concordance | tbl_df | 798 | 8 |
isic4_isic31 | concordance | ISIC4-ISIC3.1 Concordance | tbl_df | 737 | 8 |
naics2002_desc | concordance | NAICS 2002 Description | spec_tbl_df | 2341 | 2 |
naics2002_isic31 | concordance | NAICS2002-ISIC3.1 Concordance | tbl_df | 1960 | 9 |
naics2007_desc | concordance | NAICS 2007 Description | tbl_df | 2328 | 2 |
naics2007_isic4 | concordance | NAICS2007-ISIC4 Concordance | tbl_df | 1765 | 9 |
naics2007_naics2002 | concordance | NAICS2007-NAICS2002 Concordance | tbl_df | 1200 | 10 |
naics2012_desc | concordance | NAICS 2012 Description | tbl_df | 2229 | 2 |
naics2012_isic4 | concordance | NAICS2012-ISIC4 Concordance | tbl_df | 1663 | 9 |
naics2012_naics2002 | concordance | NAICS2012-NAICS2002 Concordance | tbl_df | 1209 | 10 |
naics2012_naics2007 | concordance | NAICS2012-NAICS2007 Concordance | tbl_df | 1184 | 10 |
naics2017_desc | concordance | NAICS 2017 Description | tbl_df | 2217 | 2 |
naics2017_isic4 | concordance | NAICS2017-ISIC4 Concordance | tbl_df | 1653 | 9 |
naics2017_naics2002 | concordance | NAICS2017-NAICS2002 Concordance | tbl_df | 1212 | 10 |
naics2017_naics2007 | concordance | NAICS2017-NAICS2007 Concordance | tbl_df | 1188 | 10 |
naics2017_naics2012 | concordance | NAICS2017-NAICS2012 Concordance | tbl_df | 1069 | 10 |
sigma_hs0 | concordance | Sigma Table (3-Digit HS0) | data.frame | 11293 | 4 |
sigma_sitc3 | concordance | Sigma Table (5-Digit SITC3) | tbl_df | 2716 | 7 |
sitc1_bec4 | concordance | SITC1-BEC4 Concordance | tbl_df | 1787 | 8 |
sitc1_desc | concordance | SITC1 Description | data.frame | 3024 | 2 |
sitc1_naics | concordance | SITC1-NAICS Concordance | tbl_df | 3797 | 10 |
sitc2_bec4 | concordance | SITC2-BEC4 Concordance | tbl_df | 2597 | 8 |
sitc2_desc | concordance | SITC2 Description | data.frame | 3988 | 2 |
sitc2_isic2 | concordance | SITC2-ISIC2 Concordance | tbl_df | 1687 | 9 |
sitc2_naics | concordance | SITC2-NAICS Concordance | tbl_df | 5065 | 10 |
sitc2_rauch | concordance | SITC2-Rauch Concordance | tbl_df | 1189 | 3 |
sitc2_sitc1 | concordance | SITC2-SITC1 Concordance | tbl_df | 1833 | 10 |
sitc3_bec4 | concordance | SITC3-BEC4 Concordance | tbl_df | 3404 | 8 |
sitc3_desc | concordance | SITC3 Description | data.frame | 5951 | 2 |
sitc3_isic3 | concordance | SITC3-ISIC3 Concordance | tbl_df | 3069 | 9 |
sitc3_naics | concordance | SITC3-NAICS Concordance | tbl_df | 6024 | 10 |
sitc3_sitc1 | concordance | SITC3-SITC1 Concordance | tbl_df | 3118 | 10 |
sitc3_sitc2 | concordance | SITC3-SITC2 Concordance | tbl_df | 3121 | 10 |
sitc4_bec4 | concordance | SITC4-BEC4 Concordance | tbl_df | 2972 | 8 |
sitc4_desc | concordance | SITC4 Description | data.frame | 7239 | 2 |
sitc4_naics | concordance | SITC4-NAICS Concordance | tbl_df | 5714 | 10 |
sitc4_sitc1 | concordance | SITC4-SITC1 Concordance | tbl_df | 3199 | 10 |
sitc4_sitc2 | concordance | SITC4-SITC2 Concordance | tbl_df | 3271 | 10 |
sitc4_sitc3 | concordance | A dataset containing concordances between SITC4 and SITC3 classification. | tbl_df | 3488 | 10 |
upstream | concordance | Upstreamness and Downstreamness Table | tbl_df | 24395 | 7 |
upstream_us_detailed | concordance | US Detailed Upstreamness Table | data.frame | 1236 | 4 |
wiod_2013 | concordance | ISIC3-WIOT2013 Concordance | tbl_df | 585 | 6 |
multi_omics_sd | coglasso | Multi-omics dataset of sleep deprivation in mouse | matrix | 30 | 238 |
multi_omics_sd_micro | coglasso | Multi-omics dataset of sleep deprivation in mouse | matrix | 30 | 6 |
multi_omics_sd_small | coglasso | Multi-omics dataset of sleep deprivation in mouse | matrix | 30 | 19 |
graph20 | NetworkDistance | 20 adjacency matrices from Erdős–Rényi models | list | | |
cohort_a | psHarmonize | Cohort A | tbl_df | 10000 | 9 |
cohort_b | psHarmonize | Cohort B | tbl_df | 5000 | 5 |
cohort_c | psHarmonize | Cohort C | tbl_df | 7000 | 5 |
error_harmonization_sheet_example | psHarmonize | Error harmonization sheet example | data.frame | 16 | 12 |
harmonization_sheet_example | psHarmonize | Harmonization sheet example | data.frame | 16 | 12 |
endometrial | detectseparation | Histology grade and risk factors for 79 cases of endometrial cancer | data.frame | 79 | 4 |
lizards | detectseparation | Habitat preferences of lizards | data.frame | 23 | 6 |
silvapulle1981 | detectseparation | Separation Example Presented in Silvapulle (1981) | data.frame | 35 | 2 |
resp | winr | Respiratory dataset | data.frame | 111 | 10 |
skin | winr | Dermatology dataset | tbl_df | 172 | 6 |
reti | ordDisp | Example Retinopathy | data.frame | 613 | 5 |
DeltaSimulatedPedigree | Jacquard | Estimates of Jacquard Coefficients for Simulated SNP data according to a Pedigree | list | | |
GTC | Jacquard | Joint Genotype Counts according to a Pedigree | list | | |
SimulatedPedigree | Jacquard | Simulated SNP data according to a Pedigree | data.frame | 111 | 20005 |
pinyin2 | pinyin | The Dictionary to Convert Chinese Characters to Pinyin. | data.frame | 20520 | 2 |
fibroblast | cumSeg | Fibroblast Cell Line dataset | data.frame | 2462 | 11 |
cesnames | cesr | Species names translation | data.frame | 666 | 14 |
ukdata | cesr | A sample CES dataset | ces | 4879 | 17 |
april | CircSpaceTime | April waves. | list | | |
may | CircSpaceTime | May waves. | list | | |
returns.alv | DCSmooth | Returns of Allianz SE | matrix | 1016 | 101 |
temp.nunn | DCSmooth | Temperatures from Nunn, CO | matrix | 366 | 288 |
temp.yuma | DCSmooth | Temperatures from Yuma, AZ | matrix | 366 | 288 |
volumes.alv | DCSmooth | Volumes of Allianz SE | matrix | 1016 | 102 |
wind.nunn | DCSmooth | Wind Speed from Nunn, CO | matrix | 366 | 288 |
wind.yuma | DCSmooth | Wind Speed from Yuma, AZ | matrix | 366 | 288 |
y.norm1 | DCSmooth | Single Gaussian Peak | matrix | 101 | |
y.norm2 | DCSmooth | Double Gaussian Peak | matrix | 101 | |
y.norm3 | DCSmooth | Double Gaussian Ridges | matrix | 101 | |
art | mfp2 | Artificial dataset with continuous response | data.frame | 250 | 11 |
gbsg | mfp2 | Breast cancer dataset used in the Royston and Sauerbrei (2008) book. | data.frame | 686 | 12 |
pima | mfp2 | Pima Indians dataset used in the Royston and Sauerbrei (2008) book. | tbl_df | 768 | 10 |
prostate | mfp2 | Prostate cancer dataset used in the Royston and Sauerbrei (2008) book. | tbl_df | 97 | 9 |
AtomWeight | shinyNORRRM | The standard atomic weights | data.frame | 92 | 2 |
Deccan | shinyNORRRM | Data collection of igneous rocks from the Deccan region (India) | data.frame | 7019 | 30 |
EAP | shinyNORRRM | Data collection of igneous rocks from Eastern Mexican Alkaline Province | data.frame | 46 | 28 |
IUGS | shinyNORRRM | Data collection of igneous rocks used in IUGSTAS software | data.frame | 37 | 28 |
MinWeight | shinyNORRRM | The oxides molecular weights of normative minerals | data.frame | 36 | 5 |
OxiWeight | shinyNORRRM | The molecular weights | data.frame | 26 | 2 |
TephraKam | shinyNORRRM | Data collection of igneous rocks from the Kamchatka volcanic arc (northwestern Pacific) | data.frame | 7596 | 32 |
cosmicCancer | miic | Genomic and ploidy alterations in breast tumors | data.frame | 807 | 176 |
cosmicCancer_stateOrder | miic | Genomic and ploidy alterations in breast tumors | data.frame | 176 | 3 |
covidCases | miic | Covid cases | data.frame | 171 | 6 |
hematoData | miic | Early blood development: single cell binary gene expression data | data.frame | 3934 | 33 |
occup | cat2cat | Occupational dataset | tbl_df | 69126 | 12 |
occup_small | cat2cat | Occupational dataset - small one | tbl_df | 8000 | 12 |
trans | cat2cat | trans dataset containing mappings (transitions) between old (2008) and new (2010) occupational codes. This table could be used to map encodings in both directions. | tbl_df | 2666 | 2 |
verticals | cat2cat | verticals dataset | data.frame | 21 | 4 |
verticals2 | cat2cat | verticals2 dataset | data.frame | 200 | 4 |
discrete_palettes | palettes | Cartography palettes | palettes_palette | | |
diverging_palettes | palettes | Cartography palettes | palettes_palette | | |
met_palettes | palettes | Metropolitan Museum of Art palettes | palettes_palette | | |
met_palettes_a11y | palettes | Metropolitan Museum of Art palettes | palettes_palette | | |
nord_palettes | palettes | Nord palettes | palettes_palette | | |
penguin_palettes | palettes | Palmer penguins palettes | palettes_palette | | |
performance_palettes | palettes | Performance palettes | palettes_palette | | |
pnw_palettes | palettes | Pacific Northwest palettes | palettes_palette | | |
sequential_palettes | palettes | Cartography palettes | palettes_palette | | |
viridis_palettes | palettes | Viridis palettes | palettes_palette | | |
gss | infer | Subset of data from the General Social Survey (GSS). | tbl_df | 500 | 11 |
SoilAggregate | soilphysics | Soil Aggregate Size Data Set | data.frame | 12 | 7 |
bulkDensity | soilphysics | Soil Bulk Density Data Set | data.frame | 20 | 3 |
compaction | soilphysics | Soil Compaction Data Set | data.frame | 50 | 4 |
skp1994 | soilphysics | LLWR Data Set | data.frame | 64 | 4 |
M | Cascade | Simulated M data for examples. | micro_array | | |
Net | Cascade | Simulated network data for examples. | network | | |
Net_inf | Cascade | Reverse-engineered network of the simulated data. | network | | |
Selection | Cascade | Selection of genes. | micro_array | | |
network | Cascade | A network object data. | network | | |
rats | dirichletprocess | Tumour incidences in rats | data.frame | 71 | 2 |
mads.data | mads | Example simulated data used to demonstrate the package functionality | list | | |
BLASTdata | RFLPtools | Example data set for BLAST data | data.frame | 4069 | 12 |
RFLPdata | RFLPtools | Example data set for RFLP data | data.frame | 737 | 4 |
RFLPref | RFLPtools | Example data set for RFLP reference | data.frame | 35 | 5 |
newDataGerm | RFLPtools | Example data set from GERM software | data.frame | 20 | 6 |
refDataGerm | RFLPtools | Example data set from GERM software | data.frame | 250 | 6 |
eeg | itsadug | Raw EEG data, single trial, 50Hz. | data.frame | 1504 | 5 |
simdat | itsadug | Simulated time series data. | data.frame | 75600 | 6 |
alsfrs_data | long2lstmarray | Clinical scale example data | tbl_df | 100 | 15 |
ants_L0_flat | ecocomDP | Joined and flat version of EDI data package knb-lter-hfr.118.33 | tbl_df | 2931 | 45 |
ants_L1 | ecocomDP | The ecocomDP (L1) version of EDI data package knb-lter-hfr.118.33 | list | | |
erotica | AATtools | AAT examining approach bias for erotic stimuli | data.frame | 18560 | 33 |
allometry | rorqual.morpho | Allometric equations for rorqual morphology | tbl_df | 48 | 5 |
ICHomes | MachineShop | Iowa City Home Sales Dataset | data.frame | 753 | 17 |
dataLH | Families | dataLH data | data.frame | 30151 | 29 |
dataLH_F | Families | dataLH_F data | data.frame | 2965 | 29 |
dpopus | Families | dpopus data Population of the United States in 2019 reported in the HMD (Population.txt file) | data.frame | 111 | 2 |
rates | Families | rates data | list | | |
carrillo | pre | Data on personality characteristics and depressive symptom severity | data.frame | 112 | 26 |
blood | FFTrees | Blood donation data | data.frame | 748 | 5 |
breastcancer | FFTrees | Breast cancer data | tbl_df | 683 | 10 |
car | FFTrees | Car acceptability data | data.frame | 1728 | 7 |
contraceptive | FFTrees | Contraceptive use data | data.frame | 1473 | 10 |
creditapproval | FFTrees | Credit approval data | data.frame | 690 | 16 |
fertility | FFTrees | Fertility data | data.frame | 100 | 10 |
forestfires | FFTrees | Forest fires data | data.frame | 517 | 13 |
heart.cost | FFTrees | Cue costs for the 'heartdisease' data | list | | |
heart.test | FFTrees | Heart disease testing data | tbl_df | 153 | 14 |
heart.train | FFTrees | Heart disease training data | tbl_df | 150 | 14 |
heartdisease | FFTrees | Heart disease data | tbl_df | 303 | 14 |
iris.v | FFTrees | Iris data | data.frame | 150 | 5 |
mushrooms | FFTrees | Mushrooms data | data.frame | 8124 | 23 |
sonar | FFTrees | Sonar data | data.frame | 208 | 61 |
titanic | FFTrees | Titanic survival data | data.frame | 2201 | 4 |
voting | FFTrees | Voting data | data.frame | 434 | 17 |
wine | FFTrees | Wine tasting data | data.frame | 6497 | 13 |
medfish | alien | Discoveries Of Native And Alien Fish Species In The Eastern Mediterranean Sea | data.frame | 60 | 4 |
sfestuary | alien | Discoveries Of Introduced Species In The San Francisco Estuary (California, USA) | integer | | |
db | ffp | Dataset used in Historical Scenarios with Fully Flexible Probabilities ('matrix' format). | matrix | 1083 | 9 |
db_tbl | ffp | Dataset used in Historical Scenarios with Fully Flexible Probabilities ('tibble' format). | tbl_df | 1083 | 9 |
IDR | MUVR2 | Subject identifiers for the rye metabolomics regression tutorial | integer | | |
IDR2 | MUVR2 | Subject identifiers for the rye metabolomics regression tutorial, using unique individuals | integer | | |
XRVIP | MUVR2 | Metabolomics data for the rye metabolomics regression tutorial | matrix | 112 | 1147 |
XRVIP2 | MUVR2 | Metabolomics data for the rye metabolomics regression tutorial, using unique individuals | data.frame | 58 | 1147 |
Xotu | MUVR2 | Microbiota composition in mosquitos for the classification tutorial | data.frame | 29 | 1678 |
YR | MUVR2 | Rye consumption for the rye metabolomics regression tutorial | numeric | | |
YR2 | MUVR2 | Rye consumption for the rye metabolomics regression tutorial, using unique individuals | numeric | | |
Yotu | MUVR2 | Village of capture of mosquitos for the classification tutorial | factor | | |
crispEM | MUVR2 | Effect matrix for the crisp multilevel tutorial | data.frame | 21 | 1508 |
qp_sim_power | qcapower | Data simulated power estimates | data.frame | 693 | 6 |
qp_sina_data | qcapower | Data simulated power estimates for plotting of 5%-quantiles | data.frame | 1000 | 6 |
flanker | autohrf | Datasets for autohrf examples Example datasets for use in 'autohrf' examples and vignettes. The datasets were extracted from the internal Mind and Brain Lab's (MBLab, <http://www.mblab.si> repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia. | data.frame | 192 | 3 |
flanker_autofit | autohrf | Datasets for autohrf examples Example datasets for use in 'autohrf' examples and vignettes. The datasets were extracted from the internal Mind and Brain Lab's (MBLab, <http://www.mblab.si> repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia. | list | | |
swm | autohrf | Datasets for autohrf examples Example datasets for use in 'autohrf' examples and vignettes. The datasets were extracted from the internal Mind and Brain Lab's (MBLab, <http://www.mblab.si> repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia. | data.frame | 11520 | 3 |
swm_autofit | autohrf | Datasets for autohrf examples Example datasets for use in 'autohrf' examples and vignettes. The datasets were extracted from the internal Mind and Brain Lab's (MBLab, <http://www.mblab.si> repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia. | list | | |
swm_autofit1 | autohrf | Datasets for autohrf examples Example datasets for use in 'autohrf' examples and vignettes. The datasets were extracted from the internal Mind and Brain Lab's (MBLab, <http://www.mblab.si> repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia. | list | | |
swm_autofit2 | autohrf | Datasets for autohrf examples Example datasets for use in 'autohrf' examples and vignettes. The datasets were extracted from the internal Mind and Brain Lab's (MBLab, <http://www.mblab.si> repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia. | list | | |
midsch | eeptools | A dataframe of aggregate test scores for schools in a Midwest state. | data.frame | 19985 | 16 |
stuatt | eeptools | Student Attributes from the Strategic Data Project Toolkit | data.frame | 87534 | 9 |
stulevel | eeptools | A synthetic data set of K-12 student attributes. | data.frame | 2700 | 32 |
landsat106 | stfit | Landsat data example | tbl_df | 990 | 963 |
landsat2 | stfit | Landsat data example | tbl_df | 990 | 963 |
AAPL | StockDistFit | Apple Inc. stock prices dataset | data.frame | 2599 | 7 |
AMZN | StockDistFit | Amazon.com Inc. Stock Prices Dataset | data.frame | 2599 | 7 |
GOOG | StockDistFit | Alphabet Inc Inc. Stock Prices Dataset | data.frame | 2599 | 7 |
TSLA | StockDistFit | Tesla Inc. Stock Prices Dataset | data.frame | 2599 | 7 |
gabriel1971 | bpca | Percentages of households having various facilities and appliances in East Jerusalem Arab areas, by quarters of the town | matrix | 8 | 9 |
gge2003 | bpca | A didactic matrix of genotypes (rows) and environments (columns) | matrix | 4 | 3 |
marina | bpca | Films shown at five festivals in Brazil from 2007 to 2011 | data.frame | 25 | 6 |
ontario | bpca | Ontario winter wheat (1993) | data.frame | 18 | 9 |
GNI2014 | treemap | GNI 2014 Data | data.frame | 188 | 5 |
business | treemap | Fictitious Business Statistics Data | data.frame | 603 | 9 |
primates | CRABS | Primates phylogenetic tree | phylo | | |
primates_ebd | CRABS | RevBayes Primates birth-death model | tbl_df | 100 | 3 |
primates_ebd_log | CRABS | Primates birth-death model | tbl_df | 251 | 604 |
primates_ebd_tess | CRABS | TESS Primates birth-death model | tbl_df | 100 | 3 |
primates_ebd_treepar | CRABS | TreePar Primates birth-death model | tbl_df | 100 | 3 |
derived_inductDelay | public.ctn0094extra | Derived Induction Delay Data | tbl_df | 2492 | 3 |
derived_raceEthnicity | public.ctn0094extra | Derived Patient Race and Ethnicity Data | tbl_df | 3560 | 4 |
derived_visitImputed | public.ctn0094extra | Imputed Patient Visit Data | tbl_df | 87891 | 3 |
derived_weeklyOpioidPattern | public.ctn0094extra | Patient UDS Opioid Weekly Pattern Data | tbl_df | 3560 | 8 |
derived_weeklyTLFBPattern | public.ctn0094extra | Patient TLFB Opioid Weekly Pattern Data | tbl_df | 3560 | 8 |
s2_data_example_wkt | s2 | Example Geometries | list | | |
s2_data_tbl_cities | s2 | Low-resolution world boundaries, timezones, and cities | data.frame | 243 | 3 |
s2_data_tbl_countries | s2 | Low-resolution world boundaries, timezones, and cities | data.frame | 177 | 3 |
s2_data_tbl_timezones | s2 | Low-resolution world boundaries, timezones, and cities | data.frame | 120 | 2 |
map_example | cspp | Sample Dataset for Working with generate_map() | tbl_df | 51 | 3 |
network_data | cspp | State Network data (IPPSR) | tbl_df | 2550 | 120 |
network_vars | cspp | State Network (IPPSR) Dataset Variable Names | data.frame | 118 | 2 |
var_names_db | cspp | Correlates of State Policy Project Dataset (IPPSR) Variable Names | data.frame | 2179 | 3 |
Bogota | FactoClass | Localities by Stratums in Bogota City | data.frame | 19 | 7 |
ColorAdjective | FactoClass | Associations between colors and adjectives. | data.frame | 89 | 11 |
DogBreeds | FactoClass | Dog Breeds | data.frame | 27 | 7 |
Vietnam | FactoClass | Student opinions about the Vietnam War | data.frame | 10 | 4 |
Whisky | FactoClass | Whisky example | data.frame | 35 | 5 |
admi | FactoClass | Admitted students to the seven careers of the Science Faculty | data.frame | 445 | 15 |
cafe | FactoClass | Cofee cups | data.frame | 12 | 16 |
icfes08 | FactoClass | Departmenst by Levels of Schools in Colombia | data.frame | 29 | 12 |
blob.stats | imagefx | Blob Statistics from Erebus Volcano, Antarctica | list | | |
erebus | imagefx | Image of Erebus Volcano, Antarctica | array | | |
erebus.40 | imagefx | Image of Erebus Volcano, Antarctica | array | | |
erebus.70 | imagefx | Image of Erebus Volcano, Antarctica | array | | |
erebus.90 | imagefx | Image of Erebus Volcano, Antarctica | array | | |
sakurajima | imagefx | Image of Sakurajima Volcano, Japan | array | | |
tux1 | imagefx | Image of Tux | matrix | 56 | |
tux2 | imagefx | Image of Tux | matrix | 56 | |
Example10MarkerFile | Map2NCBI | Example Marker File with 10 Markers on BTA 1 | data.frame | 10 | 3 |
GeneList_BTA1 | Map2NCBI | Output from GetGeneList function for BTA 1 | data.frame | 1267 | 20 |
bigdata | kronos | Snippet of example data to demonstrate the functionality of 'kronos' between and among three different groups | data.frame | 112 | 94 |
bigmeta | kronos | Descriptional metadata for the 'bigdata' object, for the purpose of demonstration. | data.frame | 94 | 3 |
groupdata | kronos | Snippet of example data to demonstrate the functionality of 'kronos' between and among three different groups | data.frame | 94 | 4 |
onevariable | kronos | Snippet of example data to demonstrate the functionality of 'kronos' in the most simple scenario. | data.frame | 31 | 3 |
twowaydata | kronos | Snippet of example data to demonstrate the functionality of 'kronos' in the two-factor design scenario. | data.frame | 150 | 9 |
syn_example_meta | volcano3D | PEAC synovial sample data | data.frame | 81 | 1 |
syn_example_rld | volcano3D | PEAC synovial gene expression data | matrix | 500 | 81 |
ovse | signifinder | Example expression data. | SummarizedExperiment | | |
binary | flps | binary.rda | data.frame | 4766 | 32 |
continuous | flps | continuous.rda | data.frame | 4766 | 32 |
example0 | flps | example0.rda | data.frame | 1000 | 18 |
example1 | flps | example1.rda | data.frame | 1000 | 14 |
example2 | flps | example2.rda | data.frame | 1000 | 14 |
example3 | flps | example3.rda | data.frame | 1000 | 18 |
graded | flps | graded.rda | data.frame | 4766 | 32 |
assays | CovidMutations | Assays for mutation detection using different primers and probes | data.frame | 10 | 7 |
chinalist | CovidMutations | A list of places in China | data.frame | 31 | 1 |
covid_annot | CovidMutations | Mutation annotation results produced by "indelSNP" function | data.frame | 3258 | 10 |
gene_position | CovidMutations | "GFF3" format gene position data for SARS-Cov-2 | data.frame | 26 | 10 |
gff3 | CovidMutations | "GFF3" format annotation data for SARS-Cov-2 | data.frame | 26 | 10 |
nucmer | CovidMutations | Mutation information derived from "nucmer" SNP analysis | data.frame | 3660 | 14 |
nucmerr | CovidMutations | Preprocessed "nucmer.snpss" file using "nucmerRMD" function | data.frame | 3660 | 10 |
refseq | CovidMutations | SARS-Cov-2 genomic reference sequence from NCBI | SeqFastadna | | |
data_ex_genind | graph4lg | data_ex_genind genetic dataset | genind | | |
data_ex_gstud | graph4lg | data_ex_gstud genetic dataset | data.frame | 200 | 22 |
data_ex_loci | graph4lg | data_ex_loci genetic dataset | loci | 200 | 21 |
data_simul_genind | graph4lg | data_simul_genind genetic dataset | genind | | |
data_tuto | graph4lg | data_tuto : data used to generate the vignette | list | | |
pts_pop_ex | graph4lg | pts_pop_ex : details on simulated populations | data.frame | 10 | 3 |
pts_pop_simul | graph4lg | pts_pop_simul : details on simulated populations | data.frame | 50 | 3 |
MTPL | insurancerating | Characteristics of 30,000 policyholders in a Motor Third Party Liability (MTPL) portfolio. | tbl_df | 30000 | 7 |
MTPL2 | insurancerating | Characteristics of 3,000 policyholders in a Motor Third Party Liability (MTPL) portfolio. | tbl_df | 3000 | 6 |
Ahola_Olli_betas | MiMIR | T2D-score Betas | data.frame | 7 | 3 |
BBMRI_hist | MiMIR | BBMRI_hist | list | | |
BBMRI_hist_scaled | MiMIR | BBMRI_hist_scaled | list | | |
CVD_score_betas | MiMIR | CVD-score betas | data.frame | 12 | 3 |
PARAM_metaboAge | MiMIR | PARAMETERS MetaboAge | list | | |
PARAM_surrogates | MiMIR | PARAMETERS surrogates | list | | |
acc_LOBOV | MiMIR | acc_LOBOV | list | | |
c21 | MiMIR | c21 | character | | |
covid_betas | MiMIR | COVID-score betas | data.frame | 25 | 3 |
metabo_names_translator | MiMIR | metabolomics feature nomenclatures | data.frame | 228 | 9 |
metabolites_subsets | MiMIR | metabolomics feature subsets | list | | |
mort_betas | MiMIR | Mortality score betas | data.frame | 14 | 3 |
phenotypes_names | MiMIR | phenotypic features names | list | | |
synthetic_metabolic_dataset | MiMIR | synthetic metabolomics dataset | data.frame | 500 | 229 |
synthetic_phenotypic_dataset | MiMIR | synthetic metabolomics dataset | data.frame | 500 | 24 |
CEES | tvReg | Standarised rates from a currency portfolio. | data.frame | 2855 | 8 |
FF5F | tvReg | Fama and French portfolio daily returns and factors for international markets. | data.frame | 314 | 41 |
OECD | tvReg | Variables related to the problem of healthcare spending. | data.frame | 680 | 7 |
RV | tvReg | Daily realized variance | data.frame | 4264 | 6 |
dem | rangeMapper | dem | RasterLayer | | |
tw_empty_image_metadata | tidywikidatar | A zero-rows tibble used internally when 'tw_get_image_metadata()' would not return any value. | tbl_df | | 19 |
tw_empty_item | tidywikidatar | A zero-rows tibble used internally when 'tw_get()' would not return any value. | tbl_df | | 4 |
tw_empty_qualifiers | tidywikidatar | A zero-rows tibble used internally when 'tw_get_qualifiers()' would not return any value. | tbl_df | | 8 |
tw_empty_search | tidywikidatar | A zero-rows tibble used internally when 'tw_search()' would not return any value. | tbl_df | | 4 |
tw_empty_wikipedia_category_members | tidywikidatar | A zero-rows tibble used internally when 'tw_empty_wikipedia_category_members()' would not return any value. | tbl_df | | 3 |
tw_empty_wikipedia_page | tidywikidatar | A zero-rows tibble used internally when 'tw_get_wikipedia_page_qid()' would not return any value. | tbl_df | | 7 |
tw_empty_wikipedia_page_links | tidywikidatar | A zero-rows tibble used internally when 'tw_get_wikipedia_page_links()' would not return any value. | tbl_df | | 8 |
tw_empty_wikipedia_page_sections | tidywikidatar | A zero-rows tibble used internally when 'tw_get_wikipedia_page_sections()' would not return any value. | tbl_df | | 8 |
tw_qid_airports | tidywikidatar | The Wikidata Q identifier of European airports found in Eurostat's 'avia_par_' dataset | tbl_df | 429 | 1 |
tw_qid_meps | tidywikidatar | The Wikidata Q identifier of all members of the European Parliament since its establishment | tbl_df | 4581 | 1 |
tw_test_items | tidywikidatar | A list mostly used for testing with some Wikidata items in the format resulting from 'WikidataR::get_item()' | wikidata | | |
RLdata10000 | RecordLinkage | Test data for Record Linkage | data.frame | 10000 | 7 |
RLdata500 | RecordLinkage | Test data for Record Linkage | data.frame | 500 | 7 |
identity.RLdata10000 | RecordLinkage | Test data for Record Linkage | numeric | | |
identity.RLdata500 | RecordLinkage | Test data for Record Linkage | numeric | | |
zarrineh | WRSS | data of Zarrineh-rud river basin | list | | |
NOAA_df_1990 | stxplore | National oceanic and atmospheric administration (NOAA) data from 1990 to 1993 | data.frame | 730486 | 10 |
SSTdatashort | stxplore | The data from of the Sea Surface Temperature (SST) dataset. A subset of the original dataset is used. | data.frame | 500 | 399 |
SSTlandmaskshort | stxplore | The land mask for the Sea Surface Temperature (SST) dataset. A subset of the original dataset is used. | numeric | | |
SSTlonlatshort | stxplore | The locations of the Sea Surface Temperatures (SST) dataset. A subset of the original dataset is used. | data.frame | 500 | 2 |
Times | stxplore | The time period in which the NOAA dataset was recorded. This spans from January 1990 to December 1993. | data.frame | 1461 | 4 |
Tmax | stxplore | The maximum temperature values used in the NOAA dataset in a wide dataframe format. | data.frame | 1461 | 328 |
aerosol_australia | stxplore | Data from of NASA Earth Observations at https://neo.gsfc.nasa.gov | stars | | |
aerosol_world | stxplore | Data from of NASA Earth Observations at https://neo.gsfc.nasa.gov | stars | | |
locs | stxplore | The locations used in the NOAA dataset. | data.frame | 328 | 3 |
GDPdata | GPP | 1960-2003 GDP dataset | data.frame | 748 | 14 |
BFI228 | EFAutilities | Ordinal Data of the Big Five Inventory (BFI) | matrix | 228 | 44 |
CPAI537 | EFAutilities | Composite Scores of the Chinese Personality Assessment Inventory (CPAI) | matrix | 537 | 28 |
HUR | gcmr | Hidden Unemployment in Sao Paulo | ts | | |
epilepsy | gcmr | Epilitic Seizures Data | data.frame | 295 | 6 |
malaria | gcmr | Gambia Malaria Data | data.frame | 65 | 10 |
polio | gcmr | Polio Time Series | data.frame | 168 | 6 |
scotland | gcmr | Scotland Lip Cancer Data | data.frame | 56 | 5 |
life_country | timelineS | Data for timelineGroup function example in timelineS package | data.frame | 256 | 6 |
life_exp | timelineS | Data for examples in timelineS package | data.frame | 64 | 5 |
mj_life | timelineS | Data for timelineS function example in timelineS package | data.frame | 8 | 2 |
Kovesi | spatstat.data | Colour Sequences with Uniform Perceptual Contrast | hyperframe | | |
amacrine | spatstat.data | Hughes' Amacrine Cell Data | ppp | | |
anemones | spatstat.data | Beadlet Anemones Data | ppp | | |
ants | spatstat.data | Harkness-Isham ants' nests data | ppp | | |
ants.extra | spatstat.data | Harkness-Isham ants' nests data | list | | |
austates | spatstat.data | Australian States and Mainland Territories | tess | | |
bdspots | spatstat.data | Breakdown Spots in Microelectronic Materials | ppplist | | |
bei | spatstat.data | Tropical rain forest trees | ppp | | |
bei.extra | spatstat.data | Tropical rain forest trees | imlist | | |
betacells | spatstat.data | Beta Ganglion Cells in Cat Retina | ppp | | |
bramblecanes | spatstat.data | Hutchings' Bramble Canes data | ppp | | |
bronzefilter | spatstat.data | Bronze gradient filter data | ppp | | |
btb | spatstat.data | Bovine Tuberculosis Data | ppp | | |
btb.extra | spatstat.data | Bovine Tuberculosis Data | ppplist | | |
cells | spatstat.data | Biological Cells Point Pattern | ppp | | |
cetaceans | spatstat.data | Point patterns of whale and dolphin sightings. | hyperframe | | |
cetaceans.extra | spatstat.data | Point patterns of whale and dolphin sightings. | list | | |
chicago | spatstat.data | Chicago Crime Data | lpp | | |
chorley | spatstat.data | Chorley-Ribble Cancer Data | ppp | | |
chorley.extra | spatstat.data | Chorley-Ribble Cancer Data | list | | |
clmfires | spatstat.data | Castilla-La Mancha Forest Fires | ppp | | |
clmfires.extra | spatstat.data | Castilla-La Mancha Forest Fires | list | | |
concrete | spatstat.data | Air Bubbles in Concrete | ppp | | |
copper | spatstat.data | Berman-Huntington points and lines data | list | | |
demohyper | spatstat.data | Demonstration Example of Hyperframe of Spatial Data | hyperframe | | |
demopat | spatstat.data | Artificial Data Point Pattern | ppp | | |
dendrite | spatstat.data | Dendritic Spines Data | lpp | | |
finpines | spatstat.data | Pine saplings in Finland. | ppp | | |
flu | spatstat.data | Influenza Virus Proteins | hyperframe | | |
ganglia | spatstat.data | Beta Ganglion Cells in Cat Retina, Old Version | ppp | | |
gordon | spatstat.data | People in Gordon Square | ppp | | |
gorillas | spatstat.data | Gorilla Nesting Sites | ppp | | |
gorillas.extra | spatstat.data | Gorilla Nesting Sites | imlist | | |
hamster | spatstat.data | Aherne's hamster tumour data | ppp | | |
heather | spatstat.data | Diggle's Heather Data | solist | | |
humberside | spatstat.data | Humberside Data on Childhood Leukaemia and Lymphoma | ppp | | |
humberside.convex | spatstat.data | Humberside Data on Childhood Leukaemia and Lymphoma | ppp | | |
hyytiala | spatstat.data | Scots pines and other trees at Hyytiala | ppp | | |
japanesepines | spatstat.data | Japanese Pines Point Pattern | ppp | | |
lansing | spatstat.data | Lansing Woods Point Pattern | ppp | | |
letterR | spatstat.data | Window in Shape of Letter R | owin | | |
longleaf | spatstat.data | Longleaf Pines Point Pattern | ppp | | |
meningitis | spatstat.data | Invasive Meningococcal Disease Cases in Germany | solist | | |
mucosa | spatstat.data | Cells in Gastric Mucosa | ppp | | |
mucosa.subwin | spatstat.data | Cells in Gastric Mucosa | owin | | |
murchison | spatstat.data | Murchison gold deposits | solist | | |
nbfires | spatstat.data | Point Patterns of New Brunswick Forest Fires | ppp | | |
nbfires.extra | spatstat.data | Point Patterns of New Brunswick Forest Fires | solist | | |
nbw.rect | spatstat.data | Point Patterns of New Brunswick Forest Fires | owin | | |
nbw.seg | spatstat.data | Point Patterns of New Brunswick Forest Fires | psp | | |
nztrees | spatstat.data | New Zealand Trees Point Pattern | ppp | | |
osteo | spatstat.data | Osteocyte Lacunae Data: Replicated Three-Dimensional Point Patterns | hyperframe | | |
paracou | spatstat.data | Kimboto trees at Paracou, French Guiana | ppp | | |
ponderosa | spatstat.data | Ponderosa Pine Tree Point Pattern | ppp | | |
ponderosa.extra | spatstat.data | Ponderosa Pine Tree Point Pattern | list | | |
pyramidal | spatstat.data | Pyramidal Neurons in Cingulate Cortex | hyperframe | | |
redwood | spatstat.data | California Redwoods Point Pattern (Ripley's Subset) | ppp | | |
redwood3 | spatstat.data | California Redwoods Point Pattern (Ripley's Subset) | ppp | | |
redwoodfull | spatstat.data | California Redwoods Point Pattern (Entire Dataset) | ppp | | |
redwoodfull.extra | spatstat.data | California Redwoods Point Pattern (Entire Dataset) | list | | |
residualspaper | spatstat.data | Data and Code From JRSS Discussion Paper on Residuals | list | | |
shapley | spatstat.data | Galaxies in the Shapley Supercluster | ppp | | |
shapley.extra | spatstat.data | Galaxies in the Shapley Supercluster | list | | |
shelling | spatstat.data | Artillery Impacts in Ukraine | ppp | | |
shelling2 | spatstat.data | Artillery Impacts in Ukraine | ppp | | |
simba | spatstat.data | Simulated data from a two-group experiment with replication within each group. | hyperframe | | |
simdat | spatstat.data | Simulated Point Pattern | ppp | | |
simplenet | spatstat.data | Simple Example of Linear Network | linnet | | |
spiders | spatstat.data | Spider Webs on Mortar Lines of a Brick Wall | lpp | | |
sporophores | spatstat.data | Sporophores Data | ppp | | |
spruces | spatstat.data | Spruces Point Pattern | ppp | | |
stonetools | spatstat.data | Palaeolithic Stone Tools | ppp | | |
swedishpines | spatstat.data | Swedish Pines Point Pattern | ppp | | |
urkiola | spatstat.data | Urkiola Woods Point Pattern | ppp | | |
vesicles | spatstat.data | Vesicles Data | ppp | | |
vesicles.extra | spatstat.data | Vesicles Data | solist | | |
waka | spatstat.data | Trees in Waka national park | ppp | | |
waterstriders | spatstat.data | Waterstriders data. Three independent replications of a point pattern formed by insects. | ppplist | | |
BS.chr22 | bsseq | Whole-genome bisulfite sequencing for chromosome 22 from Lister et al. | BSseq | | |
RaphNMR | rwavelet | Nuclear magnetic resonance (NMR) signal | numeric | | |
SLphantom | rwavelet | 3-d Shepp-Logan phantom | array | | |
cameraman | rwavelet | cameraman Image | matrix | 256 | |
lena | rwavelet | Lena Image | matrix | 512 | |
Nile_dataset | StructuralDecompose | Nile River Dataset | data.frame | 100 | 1 |
X | ZIPFA | A simulated data X. | matrix | 200 | 100 |
anorexia | granovaGG | Anorexia Data on Weight Change | data.frame | 72 | 3 |
anorexia.sub | granovaGG | Family Treatment Weight change data for young female anorexia patients (subset). | data.frame | 17 | 2 |
arousal | granovaGG | Arousal in Rats | data.frame | 10 | 4 |
blood_lead | granovaGG | Blood lead levels of lead workers' children matched with similar control children. | data.frame | 33 | 2 |
poison | granovaGG | Poison data from Biological Experiment | data.frame | 48 | 6 |
rat | granovaGG | Weight gains of rats fed different diets | data.frame | 60 | 3 |
shoes | granovaGG | Shoe wear data of Box, Hunter and Hunter | data.frame | 10 | 2 |
tobacco | granovaGG | Virus Preparation on Tobacco Leaves | data.frame | 8 | 2 |
sample_tcga | gapmap | Sample data matrix from the integrated pathway analysis of gastric cancer from the Cancer Genome Atlas (TCGA) study. | data.frame | 215 | 117 |
tra | QuantPsyc | Simulated Theory of Reasoned Action Data | data.frame | 1000 | 4 |
kwh_gdp | stevethemes | Kilowatt Hours per Capita and GDP per Capita, 2010 | tbl_df | 136 | 7 |
martel_ratings | stevethemes | CAGEMATCH Ratings of Rick Martel | tbl_df | 176 | 2 |
se_counties_gdppc | stevethemes | GDP per Capita of Swedish Counties, 2001-2020 | tbl_df | 420 | 4 |
steve_hex | stevethemes | Some Hex Triplets I Find Useful/Interesting/Fun | tbl_df | 14 | 2 |
dat | springer | simulated data for demonstrating the usage of springer | list | | |
ArticularlyWordRecognition | mlmts | ArticularyWordRecognition | character | | |
AtrialFibrillation | mlmts | AtrialFibrillation | list | | |
BasicMotions | mlmts | BasicMotions | list | | |
CharacterTrajectories | mlmts | CharacterTrajectories | character | | |
Cricket | mlmts | Cricket | character | | |
DuckDuckGeese_1 | mlmts | DuckDuckGeese_1 | character | | |
DuckDuckGeese_2 | mlmts | DuckDuckGeese_2 | character | | |
ERing | mlmts | ERing | list | | |
EigenWorms_1 | mlmts | EigenWorms_1 | character | | |
EigenWorms_2 | mlmts | EigenWorms_2 | character | | |
Epilepsy | mlmts | Epilepsy | list | | |
EthanolConcentration | mlmts | EthanolConcentration | character | | |
FinancialData | mlmts | FinancialData | list | | |
FingerMovements | mlmts | FingerMovements | character | | |
HandMovementDirection | mlmts | HandMovementDirection | character | | |
Handwriting | mlmts | Handwriting | character | | |
Heartbeat | mlmts | Heartbeat | character | | |
JapaneseVowels | mlmts | JapaneseVowels | character | | |
LSST | mlmts | LSST | character | | |
Libras | mlmts | Libras | list | | |
MotorImagery | mlmts | MotorImagery | character | | |
NATOPS | mlmts | NATOPS | character | | |
PEMS_SF_1 | mlmts | PEMS_SF_1 | character | | |
PEMS_SF_2 | mlmts | PEMS_SF_2 | character | | |
PenDigits | mlmts | PenDigits | list | | |
Phoneme | mlmts | Phoneme | character | | |
RacketSports | mlmts | RacketSports | list | | |
SelfRegulationSCP1 | mlmts | SelfRegulationSCP1 | character | | |
SelfRegulationSCP2 | mlmts | SelfRegulationSCP2 | character | | |
SpokenArabicDigits | mlmts | SpokenArabicDigits | character | | |
StandWalkJump | mlmts | StandWalkJump | list | | |
SyntheticData1 | mlmts | SyntheticData1 | list | | |
SyntheticData2 | mlmts | SyntheticData2 | list | | |
UWaveGestureLibrary | mlmts | UWaveGestureLibrary | character | | |
Goolam | scISR | Goolam | list | | |
MKMEP | cglasso | Megakaryocyte-Erythroid Progenitors | matrix | 48 | 63 |
MKMEP.Sim | cglasso | Simulated data for the cglasso vignette | matrix | 50 | 10 |
MM | cglasso | The Rule of miRNA in Multiple Myeloma | list | | |
MM.Sim | cglasso | Simulated data for the cglasso vignette | list | | |
mstate3_exdata | hesim | Example data for a reversible 3-state multi-state model | list | | |
multinom3_exdata | hesim | Example data for a 3-state multinomial model | list | | |
onc3 | hesim | Multi-state oncology data for 3-state model | data.table | 7510 | 12 |
onc3p | hesim | Multi-state panel oncology data for 3-state model | data.table | 7510 | 8 |
psm4_exdata | hesim | Example data for a 4-state partitioned survival model | list | | |
dem | landsat | Digital Elevation Model | SpatialGridDataFrame | | |
july1 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july2 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july3 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july4 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july5 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july61 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july62 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
july7 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov1 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov2 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov3 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov4 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov5 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov61 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov62 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
nov7 | landsat | Sample Landsat ETM+ data | SpatialGridDataFrame | | |
data.q1 | ArCo | A generated dataset used in the examples | matrix | 100 | 20 |
data.q2 | ArCo | A dataset used in the examples | list | | |
inflationNFP | ArCo | Dataset used on the empirical example by Carvalho, Masini and Medeiros (2016). | list | | |
spiders | PERMANOVA | Hunting Spiders Data | data.frame | 28 | 12 |
spidersb | PERMANOVA | Hunting Spiders Data | matrix | 28 | 12 |
wine | PERMANOVA | Wine data | data.frame | 45 | 21 |
subcortical | QRIpkg | Subcortical | data.frame | 8000 | 12 |
CnR_H3K27ac | EpiCompare | Example CUT&Run peak file | GRanges | | |
CnR_H3K27ac_picard | EpiCompare | Example Picard duplication metrics file 2 | data.frame | 1 | 10 |
CnT_H3K27ac | EpiCompare | Example CUT&Tag peak file | GRanges | | |
CnT_H3K27ac_picard | EpiCompare | Example Picard duplication metrics file 1 | data.frame | 1 | 10 |
encode_H3K27ac | EpiCompare | Example ChIP-seq peak file | GRanges | | |
hg19_blacklist | EpiCompare | Human genome hg19 blacklisted regions | GRanges | | |
hg38_blacklist | EpiCompare | Human genome hg38 blacklisted regions | GRanges | | |
mm10_blacklist | EpiCompare | Mouse genome mm10 blacklisted regions | GRanges | | |
mm9_blacklist | EpiCompare | Mouse genome mm9 blacklisted regions | GRanges | | |
sample_returns_small | portvine | A sample of log returns for 3 assets. | data.table | 1000 | 3 |
herbicide | bayesnec | Herbicide phytotoxicity data | data.frame | 580 | 3 |
manec_example | bayesnec | Example bayesmanecfit object | bayesmanecfit | | |
nec_data | bayesnec | Example data of non-linear decay | data.frame | 100 | 2 |
tasky | ssrhom | Dataset from Tasky et al. (2008) | data.frame | 70 | 5 |
gene_data | CNViz | Gene data for vignette example | data.frame | 5 | 7 |
meta_data | CNViz | Metadata for vignette example | data.frame | 1 | 2 |
probe_data | CNViz | Probe data for vignette example | data.frame | 5 | 6 |
segment_data | CNViz | Segment data for vignette example | data.frame | 5 | 5 |
variant_data | CNViz | Variant data for vignette example | data.frame | 5 | 4 |
PreDiabetes | MLDataR | PreDiabetes dataset | spec_tbl_df | 3059 | 9 |
care_home_incidents | MLDataR | Care Home Incidents | spec_tbl_df | 1216 | 12 |
csgo | MLDataR | csgo | spec_tbl_df | 1133 | 17 |
diabetes_data | MLDataR | Diabetes datasets | spec_tbl_df | 520 | 17 |
heartdisease | MLDataR | Heart disease dataset | tbl_df | 918 | 10 |
long_stayers | MLDataR | Long stayers dataset | tbl_df | 768 | 9 |
stroke_classification | MLDataR | Stroke Classification dataset | data.frame | 5110 | 11 |
thyroid_disease | MLDataR | Thyroid disease dataset | data.frame | 3772 | 28 |
GaussianT_BinaryY | CopSens | Dataset with Gaussian Treatments and Binary Outcomes | data.frame | 10000 | 11 |
GaussianT_GaussianY | CopSens | Dataset with Gaussian Treatments and Outcomes | data.frame | 10000 | 11 |
mice_est_nulltr | CopSens | Estimates of genes' effects on mice body weight using null treatments approach from Miao et al. (2020) | data.frame | 17 | 6 |
micedata | CopSens | Body weight and gene expressions of 287 mice | data.frame | 287 | 43 |
ph_crocs | pheble | Data from: The utility of cranial ontogeny for phylogenetic inference: a case study in crocodylians using geometric morphometrics | data.frame | 160 | 236 |
ph_stickleback | pheble | Data from: Sexually mediated phenotypic variation within and between sexes as a continuum structured by ecology: The mosaic nature of skeletal variation across body regions in Threespine stickleback (Gasterosteus aculeatus L.) | data.frame | 190 | 214 |
ozone20km | pargasite | Ozone concentration data | stars | | |
TrainingData | GENEAclassify | Example Training Data set | data.frame | 3500 | 26 |
trainingFit | GENEAclassify | Example classification tree | rpart | | |
anopheles | RiskMap | Anopheles mosquitoes in Southern Cameroon | data.frame | 116 | 7 |
galicia | RiskMap | Heavy metal biomonitoring in Galicia | data.frame | 132 | 3 |
italy_sim | RiskMap | Simulated data-set on the Italian peninsula | data.frame | 2000 | 7 |
liberia | RiskMap | River-blindness in Liberia | data.frame | 90 | 6 |
loaloa | RiskMap | Loa loa prevalence data from 197 village surveys | data.frame | 197 | 11 |
malkenya | RiskMap | Malaria Transmission in the Western Kenyan Highlands | data.frame | 8204 | 14 |
malnutrition | RiskMap | Malnutrition in Ghana | data.frame | 2671 | 10 |
tz_covariates | RiskMap | Covariates Dataset for Malaria Prediction in Tanzania | data.frame | 8740 | 8 |
tz_malaria | RiskMap | Malaria Dataset from Tanzania Demographic Health Surveys 2015 | data.frame | 387 | 20 |
ExampleData | PredictABEL | A hypothetical dataset that is used to demonstrate all functions. | data.frame | 10000 | 16 |
dat | gbs2ploidy | Simulated allele counts | list | | |
Data | r.jive | BRCA TCGA Dataset | list | | |
Results | r.jive | JIVE Results for Simulated Dataset | jive | | |
SimData | r.jive | Simulated Dataset | list | | |
clusts | r.jive | BRCA TCGA Dataset | numeric | | |
subjdemo_1d | grt | Sample dataset of a categorization experiment with 1D stimuli. | data.frame | 500 | 3 |
subjdemo_2d | grt | Sample dataset of a categorization experiment with 2D stimuli. | data.frame | 500 | 4 |
subjdemo_3d | grt | Sample dataset of a categorization experiment with 3D stimuli. | data.frame | 500 | 5 |
subjdemo_cj | grt | Sample dataset of a categorization experiment with 2D conjunctive stimuli. | data.frame | 100 | 4 |
dataglm | ProfileLikelihood | Example Data for a Profile Likelihood in Generalized Linear Models | data.frame | 100 | 5 |
datapolr | ProfileLikelihood | Example Data for a Profile Likelihood in Proportional Odds Models | data.frame | 66 | 5 |
R | RPEnsemble | A rotation matrix | matrix | 100 | |
isotopes | Rdisop | isotope information. | data.frame | 398 | 4 |
mono_masses | Rdisop | Monoisotopic mass information.. | numeric | | |
para_cp | HeritSeq | Parameter matrix obtained from simData by fitting CPMM. | matrix | 100 | 4 |
para_nb | HeritSeq | Parameter matrix obtained from simData by fitting NBMM. | matrix | 100 | 3 |
simData | HeritSeq | A simulated sequencing dataset. | matrix | 100 | 175 |
simData_voom | HeritSeq | Voom transformed version of simData. | matrix | 881 | 175 |
simData_vst | HeritSeq | Variance stabilize transformed version of simData. | matrix | 881 | 175 |
strains | HeritSeq | List of strain names for the samples. | character | | |
weights_voom | HeritSeq | Weights used in the voom transformation. | matrix | 881 | |
fmegen | mrMLM.GUI | Genotype data | matrix | 50 | |
fmegenraw | mrMLM.GUI | raw genotype data | matrix | 51 | 12 |
mrgen | mrMLM.GUI | Genotype data | matrix | 50 | |
mrgenraw | mrMLM.GUI | raw genotype data | matrix | 51 | 12 |
mrphe | mrMLM.GUI | phenotype data | matrix | 56 | |
Cor.Mat.Lomax | MBESS | Correlation matrix for Lomax (1983) data set | matrix | 14 | |
Cor.Mat.MM | MBESS | Correlation matrix for Maruyama & McGarvey (1980) data set | matrix | 13 | |
Gardner.LD | MBESS | The Gardner learning data, which was used by L.R. Tucker | data.frame | 504 | 4 |
HS | MBESS | Complete Data Set of Holzinger and Swineford's (1939) Study | data.frame | 301 | 34 |
prof.salary | MBESS | Cohen et. al. (2003)'s professor salary data set | data.frame | 62 | 6 |
Example | MantaID | ID example dataset. | data.frame | 5000 | 2 |
mi_data_attributes | MantaID | ID-related datasets in biomart. | data.frame | 65 | 3 |
mi_data_procID | MantaID | Processed ID data. | tbl_df | 4997 | 21 |
mi_data_rawID | MantaID | ID dataset for testing. | tbl_df | 5000 | 2 |
dataXY_df | SHAPforxgboost | Terra satellite data (X,Y) for running the xgboost model . | data.table | 10148 | 10 |
labels_within_package | SHAPforxgboost | labels_within_package: Some labels package auther defined to make his plot, mainly serve the paper publication. | list | | |
shap_int_iris | SHAPforxgboost | The interaction effect SHAP values example using iris dataset. | array | | |
shap_long_iris | SHAPforxgboost | The long-format SHAP values example using iris dataset. | data.table | 600 | 6 |
shap_score | SHAPforxgboost | SHAP values example from dataXY_df . | data.table | 10148 | 9 |
shap_values_iris | SHAPforxgboost | SHAP values example using iris dataset. | data.table | 150 | 4 |
Wilshire | ecm | FRED data on the Wilshire 5000 index and other economic factors | data.frame | 188 | 5 |
testlog | BetterReg | testlog | tbl_df | 164 | 11 |
testreg | BetterReg | testreg | data.frame | 1000 | 6 |
rbackupr_client | rbackupr | | gargle_oauth_client | | |
metaData | clinDR | Dose response data from several published meta-analyses | data.frame | 1797 | 40 |
example_activity_data | ActFrag | Activity/Wear Data from 50 Subjects from NHANES 2003 - 2006 | list | | |
alzheimer | MNB | Alzheimer data | data.frame | 240 | 4 |
seizures | MNB | Seizures data | data.frame | 295 | 5 |
Boik | phia | Contrived Data of Treatments for Hemophobia | data.frame | 72 | 3 |
Keselman1 | phia | Repeated-Measures Pyschopsychological Experiment | data.frame | 156 | 4 |
Keselman2 | phia | Repeated-Measures Pyschopsychological Experiment | data.frame | 120 | 4 |
Rosnow | phia | Rosnow's and Rosenthal's Baseball Performance Data | data.frame | 36 | 3 |
alon_data | EMMIXgene | Normalized gene expression values from Alon et al. (1999). | matrix | 2000 | 62 |
golub_data | EMMIXgene | Normalized gene expression values from Golub et al. (1999). | matrix | 3731 | 72 |
DictionaryGI | SentimentAnalysis | Dictionary with opinionated words from the Harvard-IV dictionary as used in the General Inquirer software | list | | |
DictionaryHE | SentimentAnalysis | Dictionary with opinionated words from Henry's Financial dictionary | list | | |
DictionaryLM | SentimentAnalysis | Dictionary with opinionated words from Loughran-McDonald Financial dictionary | list | | |
clinicalNames | TCGAutils | Clinical dataset names in TCGA | CompressedCharacterList | | |
diseaseCodes | TCGAutils | TCGA Cancer Disease Codes Table | data.frame | 37 | 4 |
sampleTypes | TCGAutils | Barcode Sample Type Table | data.frame | 22 | 3 |
ppmf_ex | ppmf | Example PPMF Data | spec_tbl_df | 10588 | 11 |
races | ppmf | Race Classifications | spec_tbl_df | 63 | 3 |
states | ppmf | State Rows | tbl_df | 52 | 4 |
satsolvers | llama | Example data for Leveraging Learning to Automatically Manage Algorithms | llama.data | | |
data_all | fusedMGM | An example of 2-group mixed data | data.frame | 500 | 100 |
data_mini | fusedMGM | A toy example of 2-group mixed data | data.frame | 500 | 10 |
ind_disc | fusedMGM | An example of 2-group mixed data | integer | | |
ind_disc_mini | fusedMGM | A toy example of 2-group mixed data | integer | | |
water | CopCTS | Water quality (Ammonia) data | data.frame | 524 | 4 |
Data | GNSSseg | Example of data | data.frame | 731 | 2 |
QMP | ANCOMBC | Quantitative Microbiome Project data | matrix | 106 | 91 |
CCLE_drts | pogos | compounds_v1: serialization of compounds info from PharmacoDb v1 | DRTraceSet | | |
cell_lines_v1 | pogos | compounds_v1: serialization of compounds info from PharmacoDb v1 | DFrame | | |
compounds_v1 | pogos | compounds_v1: serialization of compounds info from PharmacoDb v1 | DFrame | | |
datasets_v1 | pogos | compounds_v1: serialization of compounds info from PharmacoDb v1 | DFrame | | |
tissues_v1 | pogos | compounds_v1: serialization of compounds info from PharmacoDb v1 | DFrame | | |
sim_dat | scDDboost | scDDboost | SingleCellExperiment | | |
morse_lookup | remorse | Character to Morse Code Lookup | character | | |
dataset_racle | quantiseqr | An exemplary dataset with samples from four patients with metastatic melanoma | environment | | |
ti_quant_sim1700mixtures | quantiseqr | quanTIseq output for the simulation data of 1700 mixtures for RNA-seq data | data.frame | 1700 | 12 |
GTS2020 | isogeochem | Oxygen isotope stratigraphy from the Geologic Time Scale 2020: macrofossils | data.frame | 9676 | 6 |
LR04 | isogeochem | A Pliocene-Pleistocene benthic foraminifera d18O stack | data.frame | 2115 | 3 |
devilshole | isogeochem | Devils Hole carbonate d18O time series | data.frame | 442 | 4 |
meteoric_water | isogeochem | Oxygen isotope values for meteoric waters | data.frame | 156 | 4 |
detergent | MNP | Detergent Brand Choice | data.frame | 2657 | 7 |
japan | MNP | Voters' Preferences of Political Parties in Japan (1995) | data.frame | 418 | 7 |
AAPLStockMonthly | MMAC | Closing Stock Price of Apple Inc. | data.frame | 409 | 2 |
APCalculus | MMAC | Number of Students taking the AP Calculus Exam | data.frame | 61 | 2 |
APCalculus2 | MMAC | Number of Students taking the AP Calculus Exam | data.frame | 38 | 2 |
BlastData | MMAC | Blast Radius of Trinity Test | data.frame | 23 | 3 |
BodyMassMetabolicRate | MMAC | Closing Stock Price of Apple Inc. | data.frame | 1498 | 2 |
DJIACloseQuarterly | MMAC | Quarterly Closing Value of Dow Jones Industrial Average | data.frame | 320 | 2 |
EbolaSierraLeone | MMAC | Ebola Cases in Sierra Leone, Africa | data.frame | 110 | 2 |
ElectricBill | MMAC | Electric Bill | tbl_df | 37 | 2 |
ElectronicMailOrderSales | MMAC | US Electronic and Mail Order Sales | data.frame | 14 | 2 |
EngineRPM | MMAC | RPM of Different Engines | data.frame | 39 | 2 |
FacebookUsers | MMAC | Facebook Users | data.frame | 38 | 2 |
FordMarketVolume1 | MMAC | Ford Motors Market Volume | data.frame | 7 | 2 |
FordMarketVolume2 | MMAC | Ford Motors Market Volume | data.frame | 20 | 2 |
GenderRatio1 | MMAC | Gender Ratio in World Population | data.frame | 9 | 2 |
GenderRatio2 | MMAC | Gender Ratio in World Population | data.frame | 14 | 2 |
HSDropoutRate | MMAC | High School Dropout Rate | data.frame | 43 | 2 |
HSGradsInCollege | MMAC | High School Graduates in College | data.frame | 41 | 2 |
Hawaii | MMAC | Tidal Depths in Pearl Harbor, Hawaii | tbl_df | 31 | 2 |
HealthExpenditure | MMAC | Health Expenditures as a Percentage of U.S. GDP | data.frame | 18 | 2 |
HispanicPopulation | MMAC | Latino's Living in the United States | tbl_df | 5 | 2 |
LifeExpectancyPhysicians | MMAC | Life Expectancy in Different Countries | data.frame | 175 | 2 |
MaunaLoaCO2 | MMAC | Atmospheric Carbon Dioxide from Mauna Loa | tbl_df | 49 | 2 |
McDBurgers1 | MMAC | Burgers Sold by McDonalds | data.frame | 5 | 2 |
McDBurgers2 | MMAC | Burgers Sold by McDonalds | data.frame | 11 | 2 |
MonthlyUnemployment | MMAC | US Unemployment Rate | data.frame | 60 | 2 |
Mortgage15YrAnnual | MMAC | 15 Year Annual Mortgage Rates | data.frame | 23 | 2 |
Mortgage30YrAnnual | MMAC | 30 Year Annual Mortgage Rates | data.frame | 32 | 2 |
Mortgage30YrMonthly1 | MMAC | 30 Year Annual Mortgage Rates | data.frame | 265 | 2 |
Mortgage30YrMonthly2 | MMAC | 30 Year Annual Mortgage Rates | data.frame | 519 | 2 |
NASDAQQuarterly | MMAC | Closing NASDAQ Value | data.frame | 308 | 2 |
NaturalGasConsumption | MMAC | US Natural Gas Consumption | data.frame | 21 | 2 |
NaturalGasConsumption2 | MMAC | US Natural Gas Consumption | data.frame | 67 | 2 |
NetherlandsPopulation | MMAC | Population of the Netherlands | data.frame | 32 | 2 |
OilProductionAnnual1 | MMAC | Annual US Oil Production | data.frame | 38 | 2 |
OilProductionAnnual2 | MMAC | Annual US Oil Production | data.frame | 114 | 2 |
PollenCountLA | MMAC | Pollen Count in Los Angeles, CA | data.frame | 30 | 2 |
PopulationBelgium | MMAC | Population of Belgium | data.frame | 18 | 2 |
RunningSpeed | MMAC | Running Speed of Animals | data.frame | 12 | 2 |
SATMathKentucky | MMAC | SAT Math Scores in Kentucky | data.frame | 34 | 2 |
StudentDebt1 | MMAC | Average Student Debt | data.frame | 7 | 2 |
StudentDebt2 | MMAC | Average Student Debt | data.frame | 15 | 2 |
SunPositionAlaska | MMAC | Sun Position in Anchorage, Alaska | data.frame | 25 | 2 |
SunriseLA | MMAC | Sunrise in Los Angeles, CA | data.frame | 24 | 2 |
SunsetGreenwich | MMAC | Sunset in Greenwich, England | data.frame | 25 | 2 |
SunsetLA | MMAC | Sunset in Los Angeles, CA | data.frame | 48 | 2 |
SwimmingSpeed | MMAC | Swimming Speed of Various Animals | data.frame | 17 | 2 |
TemperaturesDanville | MMAC | Temperatures in Danville, KY | data.frame | 60 | 2 |
ToyotaMonthly | MMAC | Toyota Stock Prices | data.frame | 255 | 2 |
TwitterUsers | MMAC | Twitter Users | data.frame | 17 | 2 |
TwitterUsers1 | MMAC | Twitter Users | data.frame | 8 | 2 |
TwitterUsers2 | MMAC | Twitter Users | data.frame | 15 | 2 |
TwitterUsers3 | MMAC | Twitter Users | data.frame | 24 | 2 |
USCO2Emissions | MMAC | US CO2 Emissions | data.frame | 29 | 2 |
USRetailTax | MMAC | US Retail Tax | data.frame | 7 | 2 |
USTotalPopulation | MMAC | Total U.S. Population | data.frame | 7 | 3 |
WaterLevelsEastportMaine | MMAC | Water Levels in Eastport, Maine | data.frame | 477 | 2 |
WeightChange | MMAC | Weight Change during Pregnancy | data.frame | 129 | 2 |
WorldPopulation | MMAC | World Population | data.frame | 14 | 2 |
WorldPopulationChange | MMAC | World Population Change | data.frame | 10 | 2 |
YellowCards | MMAC | Yellow Cards in World Cup | data.frame | 11 | 2 |
jhu | ggsegJHU | JHU parcellation | brain_atlas | | |
jhu_3d | ggsegJHU | Parcellation from JHU | ggseg3d_atlas | 1 | 4 |
burndown | plan | Sample burndown dataset | burndown | | |
gantt | plan | Sample gantt dataset | gantt | | |
adni | ComBatFamQC | Harmonization Data | spec_tbl_df | 2515 | 104 |
age_df | ComBatFamQC | Age Trajectory Data | data.frame | 712 | 56 |
aus_accommodation | fpp3 | Australian accommodation data | tbl_ts | 592 | 5 |
aus_airpassengers | fpp3 | Air Transport Passengers Australia | tbl_ts | 47 | 2 |
aus_arrivals | fpp3 | International Arrivals to Australia | tbl_ts | 508 | 3 |
aus_births | fpp3 | Australian births data | tbl_ts | 4512 | 3 |
aus_fertility | fpp3 | Australian fertility rates | tbl_ts | 15120 | 4 |
aus_inbound | fpp3 | Monthly short term (<1 year) visitor arrivals to Australia | tbl_ts | 20748 | 4 |
aus_migration | fpp3 | Australian migration data | tbl_ts | 1360 | 3 |
aus_mortality | fpp3 | Australian mortality data | tbl_ts | 7740 | 5 |
aus_outbound | fpp3 | Monthly short term (<1 year) resident departures in Australia | tbl_ts | 11700 | 4 |
aus_tobacco | fpp3 | Australian cigarette and tobacco expenditure | tbl_ts | 1232 | 3 |
aus_vehicle_sales | fpp3 | Australian vehicle sales | tbl_ts | 864 | 3 |
bank_calls | fpp3 | Call volume for a large North American commercial bank | tbl_ts | 27716 | 2 |
boston_marathon | fpp3 | Boston marathon winning times since 1897 | tbl_ts | 265 | 5 |
canadian_gas | fpp3 | Monthly Canadian gas production | tbl_ts | 542 | 2 |
guinea_rice | fpp3 | Rice production (Guinea) | tbl_ts | 42 | 2 |
insurance | fpp3 | Insurance quotations and advertising expenditure | tbl_ts | 40 | 3 |
melb_walkers | fpp3 | Average daily total pedestrian count in Melbourne | tbl_ts | 1976 | 2 |
nsw_offences | fpp3 | Monthly offences in NSW | tbl_ts | 7308 | 3 |
ny_childcare | fpp3 | New York childcare data | tbl_ts | 412 | 2 |
otexts_views | fpp3 | OTexts page views | tbl_ts | 1561 | 2 |
prices | fpp3 | Price series for various commodities | tbl_ts | 198 | 7 |
souvenirs | fpp3 | Sales for a souvenir shop | tbl_ts | 84 | 2 |
us_change | fpp3 | Percentage changes in economic variables in the USA. | tbl_ts | 198 | 6 |
us_employment | fpp3 | US monthly employment data | tbl_ts | 143412 | 4 |
us_gasoline | fpp3 | US finished motor gasoline product supplied. | tbl_ts | 1355 | 2 |
genus_gram_stain | epidm | Bacterial Genus Gram Stain Lookup Table | data.frame | 250 | 4 |
group_ecds_discharge_destination | epidm | A&E attendance discharge destination | data.frame | 20 | 2 |
group_inpatient_admission_method | epidm | Inpatient admission methods | data.frame | 18 | 2 |
group_inpatient_discharge_destination | epidm | Inpatient discharge destination | data.frame | 22 | 2 |
lab_data | epidm | Synthetic Lab Data for epidm | data.frame | 10 | 13 |
respeciate_organism | epidm | Respeciated organisms | data.frame | 60 | 5 |
specimen_type_grouping | epidm | Specimen type grouping | data.frame | 143 | 2 |
Dunlap | ARPobservation | Dunlap et al.(1994) data | data.frame | 58 | 7 |
Moes | ARPobservation | Moes(1998) data | data.frame | 80 | 7 |
AHerringChile | FSAdata | Ages and lengths of Araucanian Herring from Chilean waters. | data.frame | 120 | 2 |
AfricanRivers | FSAdata | Characteristics of a sample of West African rivers. | data.frame | 39 | 6 |
AlewifeLH | FSAdata | Ages of Lake Huron Alewife assigned from otoliths and scales. | data.frame | 104 | 2 |
AnchovetaChile | FSAdata | Ages and lengths of Anchoveta from Chilean waters. | data.frame | 207 | 3 |
BGHRfish | FSAdata | Fish information from samples collected from Big Hill Reservoir, KS, 2014. | data.frame | 266 | 6 |
BGHRsample | FSAdata | Information for each electrofishing sample from Big Hill Reservoir, KS, 2014. | data.frame | 20 | 4 |
BSkateGB | FSAdata | Stock and recruitment data for Barndoor Skate from Georges Bank, 1966-2007. | data.frame | 31 | 4 |
BassFL | FSAdata | Catch-at-age for Suwanee and Largemouth Bass. | data.frame | 39 | 5 |
BlackDrum2001 | FSAdata | Biological data for Black Drum from Virginia waters of the Atlantic Ocean, 2001. | data.frame | 141 | 9 |
BloaterLH | FSAdata | Stock and recruitment data for Lake Huron Bloaters, 1981-1996. | data.frame | 16 | 3 |
BlueCatfish | FSAdata | Ages and lengths of Blue Catfish. | data.frame | 119 | 2 |
BlueCrab | FSAdata | Catch and effort data for male Blue Crabs. | data.frame | 12 | 2 |
BluefishAge | FSAdata | Ages of Bluefish assigned from otoliths by two readers. | data.frame | 445 | 2 |
BluegillIL | FSAdata | Length-at-marking and recapture and time-at-large of Bluegill. | data.frame | 61 | 5 |
BluegillLM | FSAdata | Lengths and weights for Bluegill from Lake Mary, MN. | data.frame | 100 | 5 |
BluntnoseIL1 | FSAdata | Subampled lengths of Bluntnose Minnows from Inch Lake, WI. | data.frame | 25 | 3 |
Bonito | FSAdata | Ages and lengths of Australian Bonito. | data.frame | 251 | 3 |
BrookTroutNC | FSAdata | Stock and recruitment data for Brook Trout from Ball Creek, NC, 1991-2004. | data.frame | 10 | 2 |
BrookTroutNEWP | FSAdata | Catches in removal events for Brook Trout in the Nashwaak Experimental Watersheds Project. | data.frame | 16 | 7 |
BrookTroutNEWP1 | FSAdata | Catches in removal events for Brook Trout in the Nashwaak Experimental Watersheds Project. | data.frame | 16 | 20 |
BrookTroutOnt | FSAdata | Summarized single mark-recapture data for Brook Trout across many years. | data.frame | 7 | 5 |
BrownTroutVC1 | FSAdata | Single census mark-recapture data with lengths for Brown Trout from Valley Creek, MN. | data.frame | 1014 | 3 |
BullTroutRML1 | FSAdata | Lengths and weights for Bull Trout from two Rocky Mountain lakes and two eras. | data.frame | 137 | 3 |
BullTroutRML2 | FSAdata | Ages and lengths of Bull Trout from two Rocky Mountain lakes at two times. | data.frame | 96 | 4 |
BullTroutTC | FSAdata | Catch-at-age for Bull Trout in Trestle Creek, ID. | data.frame | 6 | 2 |
CCatfishNB | FSAdata | Catch-at-age of Channel Catfish from two sections of the Platte River, NB. | data.frame | 26 | 3 |
Cabezon | FSAdata | Ages, lengths, and maturity for female Cabezon from Oregon. | data.frame | 525 | 5 |
Casselman1990 | FSAdata | Instantaneous growth rates for two calcified ageing structures. | data.frame | 12 | 4 |
ChinookKR | FSAdata | Stock and recruitment data for Klamath River Chinook Salmon, 1979-2000. | data.frame | 27 | 3 |
CiscoTL | FSAdata | Lengths, weights, and sex of Cisco from Trout Lake, WI. | data.frame | 8594 | 8 |
CrappieARMS | FSAdata | Stock and recruitment data for Crappies from four reservoirs in Arkansas and Mississippi, USA. | data.frame | 78 | 3 |
CreekChub | FSAdata | Ages (subsample) and lengths (all fish) for Creek Chub. | data.frame | 218 | 2 |
CreelMN | FSAdata | Results of a large number of creel surveys in Minnestoa lakes. | data.frame | 14550 | 2 |
Croaker1 | FSAdata | Ages of Atlantic Croaker assigned from otoliths by two readers. | data.frame | 317 | 2 |
Croaker2 | FSAdata | Ages, lengths, and sexes of Atlantic Croaker by sex. | data.frame | 318 | 3 |
CutthroatALf | FSAdata | Capture histories (9 samples) of Cutthroat Trout from Auke Lake. | data.frame | 46 | 2 |
DarterMahon | FSAdata | Catch and effort data for Fantail Darter. | data.frame | 7 | 2 |
DarterOnt | FSAdata | Ages and lengths of Channel Darters from two locations. | data.frame | 54 | 3 |
Deckeretal1999 | FSAdata | Catches in removal events of Cutthroat Trout and Coho Salmon in Little Stawamus Creek (British Columbia, Canada) in 1997. | data.frame | 36 | 10 |
EuroPerchTJ | FSAdata | Ages, lengths, and sexes of European Perch. | data.frame | 69 | 3 |
FHCatfish | FSAdata | Catch-at-age of Flathead Catfish from three southeastern rivers. | data.frame | 39 | 3 |
FHCatfishATL | FSAdata | Catch-at-age of Flathead Catfish from three Atlantic rivers. | data.frame | 44 | 3 |
FWDrumLE1 | FSAdata | Ages and lengths of Lake Erie Freshwater Drum. | data.frame | 1577 | 2 |
FWDrumLE2 | FSAdata | Ages (subsample) and lengths (all fish) for Freshwater Drum from Lake Erie. | data.frame | 1577 | 2 |
Ghats | FSAdata | Species accumulation data for fish of the Western Ghats of India. | data.frame | 350 | 2 |
GreensCreekMine | FSAdata | Catches in removal events of Coho Salmon and Dolly Varden Char at various locations near the Greens Creek (AK) Mine site. | data.frame | 66 | 8 |
Hake | FSAdata | Stock and recruitment data for Hake, 1982-1996. | data.frame | 15 | 3 |
HalibutPAC | FSAdata | Stock and recruitment data for Pacific Halibut, 1929-1991. | data.frame | 63 | 5 |
Herman | FSAdata | Lengths for Walleye, Yellow Perch, Black Crappie, and Black Bullheads from Lake Herman, SD. | data.frame | 5931 | 3 |
HerringBWE | FSAdata | Stock and recruitment data for Blackwater Estuary Herring, 1962-1997. | data.frame | 36 | 3 |
HerringISS | FSAdata | Stock and recruitment data for Icelandic summer spawning Herring, 1946-1996. | data.frame | 51 | 5 |
HumpbackWFCR | FSAdata | Capture histories (2 sample) of Humpback Whitefish. | data.frame | 1920 | 4 |
InchLake1 | FSAdata | Lengths for all fish captured in Inch Lake, WI, in two years | data.frame | 4894 | 5 |
InchLake2 | FSAdata | Lengths and weights for fish captured in Inch Lake | data.frame | 516 | 6 |
JobfishSIO | FSAdata | Catch and effort data for South Indian Ocean Jobfish. | data.frame | 13 | 2 |
JonesStockwell | FSAdata | Catches in removal events of Brown and Rainbow Trout at various locations. | data.frame | 40 | 10 |
Jonubi1 | FSAdata | Ages and lengths of male Jonubi. | data.frame | 410 | 2 |
Jonubi2 | FSAdata | Ages (subsample) and lengths (all fish) of Jonubi. | data.frame | 410 | 2 |
KingCrabAK | FSAdata | Stock and recruitment data for Red King Crab in Alaska, 1960-2004. | data.frame | 45 | 3 |
LJCisco | FSAdata | Ages and lengths of Longjaw Cisco from two locations in Lake Michigan. | data.frame | 378 | 3 |
LMBassBL | FSAdata | Lengths for Largemouth Bass from Boomer Lake, OK. | data.frame | 447 | 1 |
LMBassLCB | FSAdata | Lengths for Largemouth Bass from Lake Carl Blackwell, OK. | data.frame | 289 | 1 |
LakeTroutALTER | FSAdata | Biological data for Lake Trout from the Arctic LTER (AK). | data.frame | 86 | 8 |
LakeTroutEggs | FSAdata | Length and egg deposition of Lake Superior Lake Trout. | data.frame | 101 | 2 |
LakeTroutGIS | FSAdata | Stock and recruitment data for Lake Trout from Gull Island Shoal, Lake Superior, 1964-1991. | data.frame | 28 | 3 |
LakeTroutMI | FSAdata | Stock and recruitment data for Lake Trout in Lake Superior, 1971-1991. | data.frame | 105 | 5 |
Lizardfish | FSAdata | Stock and recruitment data for Greater Lizardfish, 1955-1964. | data.frame | 10 | 3 |
LobsterHI | FSAdata | Catch and effort data for Hawaiian Islands Slipper Lobster. | data.frame | 34 | 6 |
LobsterPEI | FSAdata | Catch and effort data for Prince Edward Island Lobster. | data.frame | 33 | 3 |
Menhaden1 | FSAdata | Catch-at-age for Gulf Menhaden, 1964-2004. | data.frame | 41 | 8 |
Morwong1 | FSAdata | Ages of Morwong assigned from otoliths by Reader A at two times. | data.frame | 217 | 2 |
Morwong2 | FSAdata | Ages of Morwong assigned from otoliths by Reader B at two times. | data.frame | 136 | 2 |
Morwong3 | FSAdata | Ages of Morwong assigned from otoliths by two readers. | data.frame | 58 | 2 |
Morwong4 | FSAdata | Ages and lengths of Morwong. | data.frame | 392 | 2 |
Morwong4a | FSAdata | Ages (subsample) and lengths (all fish) for Morwong from Morwong4. | data.frame | 392 | 2 |
Mosquitofish | FSAdata | Ages and lengths of Eastern Mosquitofish from ten locations from southern France to southern Spain. | data.frame | 9126 | 8 |
MulletBS | FSAdata | Ages of Red Mullet assigned from whole and broken-burnt otoliths. | data.frame | 51 | 2 |
MuskieSLR | FSAdata | Ages of Muskellunge assigned from scales and cleithra. | data.frame | 43 | 2 |
MuskieWI06MR | FSAdata | Summarized mark-recapture data for Muskellunge from many Wisconsin Lakes, 2006. | data.frame | 40 | 6 |
PSalmonAK | FSAdata | Stock and recruitment data for Alaskan Pink Salmon, 1960-1990. | data.frame | 34 | 5 |
Pallid | FSAdata | Lengths and weights for Pallid Sturgeon from four locations in the Missouri River. | data.frame | 30 | 7 |
Pathfinder | FSAdata | Catch and effort for three Snapper species in a depletion experiment. | data.frame | 13 | 5 |
PikeHL | FSAdata | Capture histories (2 samples) of Northern Pike from Harding Lake. | data.frame | 481 | 3 |
PikeIL | FSAdata | Catch and effort data for Northern Pike from Island Lake, NB. | data.frame | 9 | 3 |
PikeNYPartial2 | FSAdata | Capture histories (4 samples) of a subset of Northern Pike from Buckthorn Marsh. | data.frame | 68 | 2 |
PikeWindermere | FSAdata | Stock and recruitment data for Northern Pike from Lake Windermere, 1944-1981. | data.frame | 76 | 5 |
PygmyWFBC | FSAdata | Biological data for Pygmy Whitefish from Dina Lake #1 (British Columbia), 2000 and 2001. | data.frame | 690 | 13 |
RBSmeltErie | FSAdata | Recruitment time-series for Rainbow Smelt in Lake Erie, 1977-1996. | data.frame | 20 | 2 |
RBSmeltLM | FSAdata | Lengths for Rainbow Smelt from Lake Michigan, 1977. | data.frame | 3293 | 1 |
RBTroutKenai | FSAdata | Length-at-marking and recapture and time-at-large of Rainbow Trout. | data.frame | 102 | 3 |
RBTroutUNSP | FSAdata | Capture histories (2 sample) of Rainbow Trout. | data.frame | 173 | 2 |
RWhitefishAI | FSAdata | Ages and lengths of Round Whitefish. | data.frame | 995 | 2 |
RWhitefishIR | FSAdata | Ages and lengths of Round Whitefish. | data.frame | 103 | 2 |
RedDrum | FSAdata | Ages and lengths for Red Drum from the Atlantic Coast. | data.frame | 393 | 2 |
Riffleshell | FSAdata | Summarized multiple mark-recapture data for Tan Riffleshell. | data.frame | 6 | 4 |
RockBassCL | FSAdata | Catch-at-age of Cayuga Lake Rock Bass. | data.frame | 6 | 2 |
RockBassLO1 | FSAdata | Ages and lengths of Lake Ontario Rock Bass. | data.frame | 1288 | 2 |
RockBassLO2 | FSAdata | Ages (subsample) and lengths (all fish) for Rock Bass from Lake Ontario. | data.frame | 1288 | 2 |
RuffeSLRH92 | FSAdata | Biological data for Ruffe captured from the St. Louis River in 1992. | data.frame | 738 | 11 |
RuffeTL89 | FSAdata | Lengths of Ruffe captured from the St. Louis River in July, 1989. | data.frame | 236 | 1 |
SLampreyGL | FSAdata | Stock and recruitment data for Sea Lamprey in the Great Lakes, 1997-2007. | data.frame | 77 | 2 |
SalmonADP | FSAdata | Catches in removal events of salmon parr. | data.frame | 5 | 2 |
SalmonidsMCCA | FSAdata | Catches in removal events of Cutthroate Trout and Steelhead of various sizes in two reaches of McGarvey Creek (CA). | data.frame | 5 | 5 |
SardineChile | FSAdata | Ages and lengths of two year-classes of Sardine from Chilean waters. | data.frame | 196 | 3 |
SardineLK | FSAdata | Ages and lengths of larval Lake Tanganyika Sardine. | data.frame | 75 | 2 |
SardinesPacific | FSAdata | Stock and recruitment data for Pacific Sardines, 1935-1990. | data.frame | 34 | 3 |
SculpinALTER | FSAdata | Biological data for Slimy Sculpin from the Arctic LTER (AK). | data.frame | 117 | 3 |
ShadCR | FSAdata | Ages of American Shad assigned from scales by three readers at two times. | data.frame | 53 | 8 |
ShrimpGuam | FSAdata | Catch and effort data for Deepwater Caridean Shrimp. | data.frame | 15 | 4 |
SimonsonLyons | FSAdata | Catches in removal events of trout at various locations. | data.frame | 58 | 7 |
SiscowetMI2004 | FSAdata | Ages (subsample) and lengths (all fish) for male and female Siscowet Lake Trout captured at four locations in Michigan waters of Lake Superior. | data.frame | 780 | 8 |
Snapper | FSAdata | Lengths for Snapper from Australia. | data.frame | 256 | 1 |
SnapperHG1 | FSAdata | Age (subsample) and length (all fish) of Snapper from two survey locations. | data.frame | 18420 | 3 |
SnapperHG2 | FSAdata | Ages (subsample) and lengths (all fish) for Snapper. | data.frame | 6724 | 2 |
SockeyeKL | FSAdata | Stock and recruitment data for Sockeye Salmon from Karluk Lake, AK, 1921-1948. | data.frame | 28 | 3 |
SockeyeSR | FSAdata | Stock and recruitment data for Skeena River Sockeye Salmon, 1940-1967. | data.frame | 28 | 3 |
SpotVA2 | FSAdata | Ages (subsample) and lengths (all fish) for Spot. | data.frame | 403 | 2 |
SpottedSucker1 | FSAdata | Ages and lengths of Spotted Sucker. | data.frame | 95 | 2 |
StripedBass1 | FSAdata | Ages of Striped Bass assigned from scales and otoliths. | data.frame | 345 | 2 |
StripedBass2 | FSAdata | Ages and lengths of Atlantic Ocean Striped Bass. | data.frame | 1201 | 2 |
StripedBass3 | FSAdata | Ages (subsample) and lengths (all fish) for Striped Bass. | data.frame | 1201 | 2 |
StripedBass4 | FSAdata | Ages of Striped Bass assigned from scales by two readers. | data.frame | 1202 | 2 |
StripedBass5 | FSAdata | Ages of Striped Bass assigned from otoliths by two readers. | data.frame | 458 | 2 |
StripedBass6 | FSAdata | Ages of Striped Bass assigned from scales and otoliths. | data.frame | 451 | 2 |
SturgeonBL | FSAdata | Summarized multiple mark-recapture data for Lake Sturgeon. | data.frame | 24 | 4 |
SturgeonGB | FSAdata | Capture years and ages for Lake Sturgeon from Goulais Bay, Lake Superior, Ont. | data.frame | 436 | 2 |
SunfishIN | FSAdata | Summarized multiple mark-recapture data for Redear Sunfish. | data.frame | 14 | 4 |
SunfishLP | FSAdata | Catch-at-age for Bluegill and Redear Sunfish in Florida. | data.frame | 12 | 3 |
TPrawnsEG | FSAdata | Stock and recruitment data for Exmouth Gulf Tiger Prawn, 1970-83. | data.frame | 14 | 5 |
TroutADP | FSAdata | Catches in removal events of trout. | data.frame | 5 | 2 |
TroutBR | FSAdata | Ages and lengths of migratory Brown and Rainbow Trout. | data.frame | 851 | 3 |
TroutperchLM1 | FSAdata | Ages, lengths, and sexes of Troutperch. | data.frame | 431 | 3 |
TroutperchLM2 | FSAdata | Lengths for Troutperch from Lake Michigan, 1977. | data.frame | 3385 | 1 |
TroutperchLM3 | FSAdata | Subsampled lengths of Troutperch from Lake Michigan, 1977. | data.frame | 300 | 1 |
VendaceLP | FSAdata | Stock and recruitment data for Vendace from Lake Puulavesi, 1982-1996. | data.frame | 15 | 3 |
VendaceLP2 | FSAdata | Stock and recruitment data for Vendace from Lake Pyhajarvi. | data.frame | 9 | 2 |
WShrimpGA | FSAdata | Stock and recruitment data for White Shrimp off the coast of Georgia (USA), 1979-2000. | data.frame | 22 | 4 |
WalleyeConsumption | FSAdata | Consumption of prey by Walleye. | data.frame | 20 | 2 |
WalleyeEL | FSAdata | Stock and recruitment data for Walleye from Escanaba Lake, WI, 1958-1992. | data.frame | 39 | 5 |
WalleyeErie | FSAdata | Recruitment time-series for Walleye in Lake Erie, 1959-1972. | data.frame | 14 | 2 |
WalleyeErie2 | FSAdata | Biological data for Walleye from Lake Erie, 2003-2014. | data.frame | 33734 | 9 |
WalleyeKS | FSAdata | Catch-at-age for Walleye from eight Kansas reservoirs. | data.frame | 66 | 3 |
WalleyeML | FSAdata | Back-calculated lengths-at-age for Walleye from Lake Mille Lacs, 2000-2011. | data.frame | 14583 | 9 |
WalleyeMN06a | FSAdata | Catch-at-age for Walleye. | data.frame | 52 | 3 |
WalleyeMN06b | FSAdata | Summarized multiple mark-recapture data for Walleye from four lakes in Northern Minnesota. | data.frame | 20 | 5 |
WalleyePL | FSAdata | Summarized multiple mark-recapture data for YOY walleye. | data.frame | 33 | 5 |
WalleyePS | FSAdata | Ages of Walleye assigned from otoliths, scales, and spines. | data.frame | 60 | 4 |
WalleyeRL | FSAdata | Growth increment data for Red Lakes Walleye. | data.frame | 1543 | 13 |
WalleyeWad | FSAdata | Catches-at-age for male and female Walleye from Lake Winnebago, WI, 2010. | data.frame | 18 | 3 |
WalleyeWyrlng | FSAdata | Annual catches of yearling Walleye in bottom trawls from Lake Winnebago, WI, 1986-2010. | data.frame | 25 | 4 |
WhiteGrunt1 | FSAdata | Catch-at-age for White Grunt. | data.frame | 23 | 2 |
WhiteGrunt2 | FSAdata | Ages, lengths, and sexes of White Grunt. | data.frame | 465 | 3 |
WhitefishGSL | FSAdata | Catch-at-age of Great Slave Lake Whitefish (commercial) by area. | data.frame | 16 | 6 |
WhitefishLS | FSAdata | Landings and value of Lake Superior Lake Whitefish. | data.frame | 73 | 4 |
WhitefishMB | FSAdata | Ages of Lake Whitefish from four lakes assigned from scales and fin-rays. | data.frame | 859 | 3 |
WhitefishTB | FSAdata | Stock and recruitment data for Lake Whitefish in Thunder Bay, Lake Superior, 1975-1988. | data.frame | 14 | 5 |
YERockfish | FSAdata | Ages, lengths, and maturity for Yelloweye Rockfish. | data.frame | 158 | 5 |
YPerchCB1 | FSAdata | Catch-at-age for Yellow Perch from Chequamegon Bay, Lake Superior. | data.frame | 10 | 17 |
YPerchCB2 | FSAdata | Stock and recruitment data for Yellow Perch in Chequamegon Bay, 1975-1986. | data.frame | 12 | 3 |
YPerchGB | FSAdata | Recruitment time-series for Yellow Perch in Green Bay, 1978-1992. | data.frame | 15 | 2 |
YPerchGL | FSAdata | Lengths and weights of Yellow Perch from Grafton Lake (ME) by year. | data.frame | 100 | 3 |
YPerchRL | FSAdata | Recruitment time-series for Yellow Perch in Red Lakes, MN, 1942-1960. | data.frame | 19 | 2 |
YPerchSB | FSAdata | Stock and recruitment data for Yellow Perch from South Bay, Lake Huron, 1950-1983. | data.frame | 34 | 3 |
YPerchSB1 | FSAdata | Lengths for Yellow Perch from two locations in Saginaw Bay, Lake Michigan. | data.frame | 2074 | 2 |
YPerchTL | FSAdata | Lengths and weights for Yellow Perch from Trout Lake, WI. | data.frame | 7238 | 7 |
YTFlounder | FSAdata | Ages of Yellowtail Flounder assigned from scales and otoliths. | data.frame | 27 | 3 |
liver | poset | A dataset from a non-alcoholic fatty liver disease study | data.frame | 186 | 7 |
CO2data | isocalcR | CO2data | data.frame | 2023 | 3 |
piru13C | isocalcR | piru13C | grouped_df | 223 | 6 |
yield_data | plantphysioR | Example data | data.frame | 50 | 3 |
breast | beeswarm | Lymph-node-negative primary breast tumors | data.frame | 286 | 5 |
NOLHDRdesigns | DiceDesign | List of De Rainville's Nearly Orthogonal Latin Hypercubes designs | list | | |
NOLHdesigns | DiceDesign | List of Cioppa's Nearly Orthogonal Latin Hypercubes designs | list | | |
OA131 | DiceDesign | A 3D orthogonal array of strength 2 | data.frame | 49 | 3 |
OA131_scrambled | DiceDesign | A scrambled 3D orthogonal array of strength 2 | data.frame | 49 | 3 |
sim_dat | ofGEM | A simulated data example | list | | |
diplostomum_eyes_excl_lenses | aspi | Numbers of Diplostomum metacercariae recorded from the eyes (excluding lenses) of each of 50 ruffe. | data.frame | 50 | 2 |
diplostomum_lenses | aspi | Numbers of Diplostomum metacercariae recorded from the lenses of the eyes of each of 50 ruffe. | data.frame | 50 | 2 |
simulated_asymmetry_inconsistent_bias | aspi | Simulated data showing bilateral asymmetry with insconsistent bias | data.frame | 10 | 2 |
simulated_left_bias_heterogeneous_proportions | aspi | Simulated data showing left bias with heterogeneous proportions | data.frame | 10 | 2 |
simulated_left_bias_homogeneous_proportions | aspi | Simulated data for showing left bias with homogeneous proportions | data.frame | 10 | 2 |
simulated_symmetrical_infection | aspi | Simulated data showing bilateral symmetry | data.frame | 10 | 2 |
emma | janeaustenr | The text of Jane Austen's novel "Emma" | character | | |
mansfieldpark | janeaustenr | The text of Jane Austen's novel "Mansfield Park" | character | | |
northangerabbey | janeaustenr | The text of Jane Austen's novel "Northanger Abbey" | character | | |
persuasion | janeaustenr | The text of Jane Austen's novel "Persuasion" | character | | |
prideprejudice | janeaustenr | The text of Jane Austen's novel "Pride and Prejudice" | character | | |
sensesensibility | janeaustenr | The text of Jane Austen's novel "Sense and Sensibility" | character | | |
design_vec | shuffle | The design for an fMRI experiment | numeric | | |
fMRI_responses | shuffle | Responses for 30 voxels (of V1) to 1560 stimuli. | matrix | 30 | 1560 |
prediction_res | shuffle | Prediction results for V1 voxels as generated by the Gallant lab in UC Berkeley and published in Kay et al. (2008). | numeric | | |
test.design | recoup | Reference and genomic sample regions for recoup testing | data.frame | 100 | 2 |
test.exons | recoup | Reference and genomic sample regions for recoup testing | CompressedGRangesList | | |
test.genome | recoup | Reference and genomic sample regions for recoup testing | data.frame | 100 | 5 |
test.input | recoup | Reference and genomic sample regions for recoup testing | list | | |
ncoa7_cdss | wiggleplotr | Coding sequences from 9 protein coding transcripts of NCOA7 | CompressedGRangesList | | |
ncoa7_exons | wiggleplotr | Exons from 9 protein coding transcripts of NCOA7 | CompressedGRangesList | | |
ncoa7_metadata | wiggleplotr | Gene metadata for NCOA7 | tbl_df | 9 | 4 |
DatA | survey | Database of household expenses for two sampling frames | data.frame | 105 | 11 |
DatB | survey | Database of household expenses for two sampling frames | data.frame | 135 | 10 |
PiklA | survey | Database of household expenses for two sampling frames | matrix | 105 | |
PiklB | survey | Database of household expenses for two sampling frames | matrix | 135 | |
apiclus1 | survey | Student performance in California schools | data.frame | 183 | 39 |
apiclus2 | survey | Student performance in California schools | data.frame | 126 | 40 |
apipop | survey | Student performance in California schools | data.frame | 6194 | 37 |
apisrs | survey | Student performance in California schools | data.frame | 200 | 39 |
apistrat | survey | Student performance in California schools | data.frame | 200 | 39 |
crowd | survey | Household crowding | data.frame | 6 | 5 |
election | survey | US 2004 presidential election data at state or county level | data.frame | 4600 | 8 |
election_insample | survey | US 2004 presidential election data at state or county level | numeric | | |
election_jointHR | survey | US 2004 presidential election data at state or county level | matrix | 40 | |
election_jointprob | survey | US 2004 presidential election data at state or county level | matrix | 40 | |
election_pps | survey | US 2004 presidential election data at state or county level | data.frame | 40 | 9 |
fpc | survey | Small survey example | data.frame | 8 | 6 |
hospital | survey | Sample of obstetric hospitals | data.frame | 15 | 6 |
mu284 | survey | Two-stage sample from MU284 | data.frame | 15 | 5 |
myco | survey | Association between leprosy and BCG vaccination | data.frame | 516 | 6 |
nhanes | survey | Cholesterol data from a US survey | data.frame | 8591 | 7 |
salamander | survey | Salamander mating data set from McCullagh and Nelder (1989) | data.frame | 360 | 4 |
scd | survey | Survival in cardiac arrest | data.frame | 6 | 4 |
yrbs | survey | One variable from the Youth Risk Behaviors Survey, 2015. | data.frame | 15624 | 4 |
zh.data | tomoda | A raw read count matrix of zebrafish injured heart. | matrix | 16495 | 40 |
cat_pav | catR | Items parameters of the CAT_PAV data set (with item names) | data.frame | 96 | 3 |
tcals | catR | Items parameters of the TCALS 1998 data set and subgroups of items | data.frame | 85 | 5 |
lung.test | rDecode | Lung cancer test data set from Gordon et al. (2002) | data.frame | 36 | 1578 |
lung.train | rDecode | Lung cancer training data set from Gordon et al. (2002) | data.frame | 145 | 1578 |
adm_ancestries_test | ASAFE | Ancestries of 250 admixed individuals at 6 SNPs | data.frame | 6 | 501 |
adm_genotypes_test | ASAFE | Genotypes of 250 admixed individuals at 6 markers | data.frame | 6 | 251 |
allmeta | kgp | 1000 Genomes, SGDP, HGDP, and GGVP metadata | tbl_df | 212 | 8 |
kgp3 | kgp | 1000 Genomes Project sample data (Phase 3) | tbl_df | 2504 | 10 |
kgpe | kgp | 1000 Genomes Project sample data (Expanded) | tbl_df | 3202 | 11 |
kgpmeta | kgp | 1000 Genomes Project population metadata | tbl_df | 26 | 7 |
dol.count | mefa | The Dolina Dataset | data.frame | 297 | 4 |
dol.samp | mefa | The Dolina Dataset | data.frame | 24 | 2 |
dol.taxa | mefa | The Dolina Dataset | data.frame | 121 | 4 |
gss | icesAdvice | Greater Silver Smelt | data.frame | 24 | 17 |
shake | icesAdvice | Southern Hake Retro | data.frame | 8 | 6 |
Melbourne | ArArRedux | An example dataset | list | | |
ACNK | geostats | A-CN-K compositions | data.frame | 20 | 3 |
Britain | geostats | British coast | matrix | 512 | |
Corsica | geostats | rivers on Corsica | matrix | 512 | |
DZ | geostats | detrital zircon U-Pb data | list | | |
FAM | geostats | A-F-M data | data.frame | 1104 | 4 |
Finland | geostats | Finnish lake data | data.frame | 2327 | 5 |
catchments | geostats | properties of 20 river catchments | data.frame | 20 | 6 |
declustered | geostats | declustered earthquake data | data.frame | 28267 | 9 |
earthquakes | geostats | earthquake data | data.frame | 20000 | 9 |
fault | geostats | fault orientation data | data.frame | 10 | 2 |
forams | geostats | foram count data | matrix | 2 | 7 |
fractures | geostats | fractures | matrix | 512 | |
hills | geostats | hills | data.frame | 150 | 3 |
major | geostats | composition of Namib dune sand | data.frame | 16 | 10 |
meuse | geostats | Meuse river data set | data.frame | 155 | 6 |
palaeomag | geostats | palaeomagnetic data | data.frame | 10 | 2 |
pebbles | geostats | pebble orientations | numeric | | |
rbsr | geostats | Rb-Sr data | data.frame | 8 | 5 |
striations | geostats | directions of glacial striations | numeric | | |
test | geostats | composition of a further 147 oceanic basalts | data.frame | 147 | 9 |
training | geostats | composition of 646 oceanic basalts | data.frame | 646 | 9 |
worldpop | geostats | world population | data.frame | 22 | 2 |
data_efficient_portfolios_returns | DiversificationR | Efficient portfolios returns | matrix | 19 | 6 |
KSEAdb | pKSEA | KSEAdb | data.frame | 240749 | 6 |
NetworKINPred_db | pKSEA | NetworKINPred_db | data.frame | 450418 | 4 |
testInputBoundaries | CORE | A table of chromosome boundary positions for DNA copy number analysis | data.frame | 24 | 3 |
testInputCORE | CORE | A table of DNA copy number gain events observed in 100 individual tumor cells | data.frame | 2490 | 3 |
TCGA_A8_A0A7_maf | TPES | SNVsReadCountsFile for sample TCGA-A8-A0A7 | data.frame | 5000 | 6 |
TCGA_A8_A0A7_ploidy | TPES | Ploidy data for sample TCGA-A8-A0A7 | data.frame | 1 | 3 |
TCGA_A8_A0A7_seg | TPES | SEG file (segmented data) for sample TCGA-A8-A0A7 | data.frame | 362 | 6 |
TCGA_HT_8564_maf | TPES | SNVsReadCountsFile for sample TCGA-HT-8564 | data.frame | 5000 | 6 |
TCGA_HT_8564_ploidy | TPES | Ploidy data for sample TCGA-HT-8564 | data.frame | 1 | 3 |
TCGA_HT_8564_seg | TPES | SEG file (segmented data) for sample TCGA-HT-8564 | data.frame | 347 | 6 |
groupS | modTurPoint | A Real Experiment Dose Data | data.frame | 36 | 2 |
groupSN | modTurPoint | A Real Experiment Dose Data | data.frame | 38 | 2 |
data | GRS.test | Fama-French Data: 25 size-B/M portfolio and risk factors, obtained from French's library | data.frame | 630 | 32 |
Vandeputte | reconsi | Microbiomes of Crohn's disease patients and healthy controls | phyloseq | | |
CTSVexample_data | CTSV | A simulated data set | list | | |
data_algae | neonDivData | Periphyton, seston, and phytoplankton collection | tbl_df | 110273 | 25 |
data_beetle | neonDivData | Ground beetles sampled from pitfall traps | tbl_df | 81581 | 22 |
data_bird | neonDivData | Breeding landbird point counts data | tbl_df | 264617 | 35 |
data_fish | neonDivData | Fish survey data collected by NEON | tbl_df | 6566 | 27 |
data_herp_bycatch | neonDivData | Vertebrate Herpetofauna Bycatch sampled from pitfall traps | tbl_df | 2766 | 22 |
data_macroinvertebrate | neonDivData | Macroinvertebrate data | tbl_df | 117383 | 25 |
data_mosquito | neonDivData | Mosquitoes sampled from CO2 traps | tbl_df | 135296 | 24 |
data_plant | neonDivData | Plant survey data collected by NEON | tbl_df | 1148221 | 26 |
data_small_mammal | neonDivData | Small mammal box trap data collected by NEON | tbl_df | 19098 | 22 |
data_summary | neonDivData | Data product last modification time | spec_tbl_df | 12 | 15 |
data_tick | neonDivData | Ticks sampled using drag cloths | tbl_df | 426465 | 22 |
data_tick_pathogen | neonDivData | Tick-borne pathogen status | tbl_df | 12190 | 21 |
data_zooplankton | neonDivData | Zooplankton density data | tbl_df | 4902 | 16 |
neon_location | neonDivData | Information of all locations included in this data package. | tbl_df | 4274 | 8 |
neon_sites | neonDivData | Site information | spec_tbl_df | 81 | 10 |
neon_taxa | neonDivData | Taxanomic names of all groups | tbl_df | 12467 | 4 |
Cf.cytoband | idiogram | Cytogenetic Banding information | environment | | |
Hs.cytoband | idiogram | Cytogenetic Banding information | environment | | |
Mm.cytoband | idiogram | Cytogenetic Banding information | environment | | |
Rn.cytoband | idiogram | Cytogenetic Banding information | environment | | |
colo.eset | idiogram | data included for idiogram package examples | matrix | 2031 | 15 |
ucsf.chr | idiogram | data included for idiogram package examples | chromLocation | | |
vai.chr | idiogram | data included for idiogram package examples | chromLocation | | |
cz | ebci | Neighborhood effects data from Chetty and Hendren (2018) | data.frame | 741 | 10 |
birdrec | opticut | Bird Species Detections | list | | |
dolina | opticut | Land Snail Data Set | list | | |
warblers | opticut | Warblers Data Set | list | | |
ADH | ShiftShareSE | Dataset from Autor, Dorn and Hanson (2013) | list | | |
aseg_3d | ggseg3d | FreeSurfer automatic subcortical segmentation of a brain volume | tbl_df | 1 | 4 |
dk_3d | ggseg3d | Desikan-Killiany Cortical Atlas | ggseg3d_atlas | 4 | 4 |
Mono27ac | FLOPART | H3K27ac ChIP-seq data from one Monocyte sample | list | | |
Mono27ac.simple | FLOPART | Smaller H3K27ac ChIP-seq data from one Monocyte sample | list | | |
sim_dat | mvMISE | A Simulated Example data | list | | |
map | FlexScan | Shapefile | SpatialPolygonsDataFrame | | |
sample | FlexScan | Sample Data | data.frame | 123 | 2 |
MoreCamp | HappyCampR | Campground Data | tbl_df | 13011 | 22 |
goats | ResourceSelection | Mountain Goats Data Set | data.frame | 19014 | 8 |
guava | leafSTAR | Leaf angles of guava trees | data.frame | 239 | 11 |
olea | leafSTAR | Leaf angles of olive tree measured with 'Ahmes' 1.0 | data.frame | 24 | 15 |
olive | leafSTAR | Leaf angles of olive tree measured with a compass and inclinometer | data.frame | 20 | 10 |
orchids | leafSTAR | Leaf and pseudobulb angles of epiphytic orchids | data.frame | 500 | 7 |
tropical | leafSTAR | Leaf angles of tropical canopy trees measured with traditional instrumentation | data.frame | 41 | 4 |
pilo | SPINA | Demo Data Set from Pilo et al. 1990 | data.frame | 14 | 1 |
aids | KMsurv | data from Section 1.19 | data.frame | 295 | 3 |
alloauto | KMsurv | data from Section 1.9 | data.frame | 101 | 3 |
allograft | KMsurv | data from Exercise 13.1, p418 | data.frame | 34 | 4 |
azt | KMsurv | data from Exercise 4.7, p122 | data.frame | 45 | 4 |
baboon | KMsurv | data from Exercise 5.8, p147 | data.frame | 152 | 3 |
bcdeter | KMsurv | data from Section 1.18 | data.frame | 95 | 3 |
bfeed | KMsurv | data from Section 1.14 | data.frame | 927 | 10 |
bmt | KMsurv | data from Section 1.3 | data.frame | 137 | 22 |
bnct | KMsurv | data from Exercise 7.7, p223 | data.frame | 30 | 3 |
btrial | KMsurv | data from Section 1.5 | data.frame | 45 | 3 |
burn | KMsurv | data from Section 1.6 | data.frame | 154 | 18 |
channing | KMsurv | data from Section 1.16 | data.frame | 462 | 6 |
drug6mp | KMsurv | data from Section 1.2 | data.frame | 21 | 5 |
drughiv | KMsurv | data from Exercise 7.6, p222 | data.frame | 34 | 3 |
hodg | KMsurv | data from Section 1.10 | data.frame | 43 | 6 |
kidney | KMsurv | data from Section 1.4 | data.frame | 119 | 3 |
kidrecurr | KMsurv | Data on 38 individuals using a kidney dialysis machine | data.frame | 38 | 10 |
kidtran | KMsurv | data from Section 1.7 | data.frame | 863 | 6 |
larynx | KMsurv | data from Section 1.8 | data.frame | 90 | 5 |
lung | KMsurv | data from Exercise 4.4, p120 | data.frame | 25 | 4 |
pneumon | KMsurv | data from Section 1.13 | data.frame | 3470 | 15 |
psych | KMsurv | data from Section 1.15 | data.frame | 26 | 4 |
rats | KMsurv | data from Exercise 7.13, p225 | data.frame | 150 | 4 |
std | KMsurv | data from Section 1.12 | data.frame | 877 | 24 |
stddiag | KMsurv | data from Exercise 5.6, p146 | data.frame | 25 | 2 |
tongue | KMsurv | data from Section 1.11 | data.frame | 80 | 3 |
twins | KMsurv | data from Exercise 7.14, p225 | data.frame | 24 | 4 |
uscenpops | uscenpops | US Census and Intercensal Population Counts and Estimates | tbl_df | 10520 | 5 |
data_Wampold_1982 | LagSequential | data_Wampold_1982 | matrix | 200 | 1 |
data_Wampold_1984 | LagSequential | data_Wampold_1984 | matrix | 200 | |
data_seqgroups_numeric | LagSequential | data_seqgroups_numeric | matrix | 396 | |
data_seqgroups_strings | LagSequential | data_seqgroups_strings | matrix | 396 | |
data_sequential | LagSequential | data_sequential | matrix | 122 | |
de_block | cvap | Delaware Block Example Data | tbl_df | 24115 | 20 |
de_block_group | cvap | Delaware Block Group Example Data | tbl_df | 574 | 29 |
cm214_pfs | survRM2adapt | Sample Reconstructed Data of CheckMate214 Study | data.frame | 847 | 3 |
classification_cts | airt | A dataset containing classification algorithm performance data in a continuous format. | data.frame | 235 | 10 |
classification_poly | airt | A dataset containing classification algorithm performance data in a polytomous format. | matrix | 235 | 10 |
data_CANCOR | DFA.CANCOR | data_CANCOR | list | | |
data_DFA | DFA.CANCOR | data_DFA | list | | |
GO_gene_sets | gsEasy | GO term gene sets | list | | |
legistar_clients | legistarapi | Legistar Client Reference | data.frame | 286 | 2 |
legistar_methods | legistarapi | Legistar API Methods Reference | tbl_df | 124 | 8 |
LQcoef | MortCast | Coefficients for the Log-Quadratic Mortality Model | data.table | 72 | 6 |
MLT1Ylookup | MortCast | Model Life Tables Lookup | data.frame | 91962 | 8 |
MLTlookup | MortCast | Model Life Tables Lookup | data.frame | 19656 | 8 |
PMDadjcoef | MortCast | Coefficients for Sex Ratio Adjustments in the PMD Method | data.frame | 13 | 6 |
RhoFemales | MortCast | Pattern Mortality Decline Lookup Tables | tbl_df | 22 | 17 |
RhoMales | MortCast | Pattern Mortality Decline Lookup Tables | tbl_df | 22 | 17 |
altfuel | altfuelr | Alternate Fuel Stations in the US. | tbl_df | 40279 | 55 |
data_state_variables | SystemicR | State variables | data.frame | 5030 | 7 |
data_stock_returns | SystemicR | Financial institutions (banks, insurers and asset managers) stock returns | data.frame | 5030 | 74 |
catheter | rmeta | Meta-analysis of antibacterial catheter coating | data.frame | 15 | 7 |
cochrane | rmeta | Data for Cochrane Collaboration logo | data.frame | 7 | 5 |
svs | SPYvsSPY | Spy vs. Spy Data | tbl_df | 248 | 9 |
dailydata | nser | Daily data of a stock | spec_tbl_df | 499 | 7 |
futurama | tRakt | Futurama episodes | tbl_df | 144 | 17 |
gameofthrones | tRakt | Game of Thrones episodes | tbl_df | 73 | 18 |
trakt_certifications | tRakt | Cached filter datasets | tbl_df | 12 | 5 |
trakt_countries | tRakt | Cached filter datasets | tbl_df | 377 | 3 |
trakt_genres | tRakt | Cached filter datasets | tbl_df | 64 | 3 |
trakt_languages | tRakt | Cached filter datasets | tbl_df | 297 | 3 |
trakt_networks | tRakt | Cached filter datasets | tbl_df | 4526 | 5 |
bmi_insulin | mrbayes | Dataset from Richmond et. al 2017 investigating the association of BMI on insulin resistance | data.frame | 14 | 5 |
dodata | mrbayes | Dataset from Do et al., Nat Gen, 2013 containing summary level data on associations of genotypes with lipid traits and the risk of coronary heart diseases | data.frame | 185 | 21 |
aegypti | ripserr | _Aedes aegypti_ occurrences in Brazil in 2013 | tbl_df | 4411 | 10 |
case_predictors | ripserr | State-level predictors of mosquito-borne illness in Brazil | data.frame | 27 | 4 |
global_temperature | demodelr | Measured average global temperature anomaly by year | tbl_df | 142 | 2 |
parks | demodelr | Visitor and resource usage to a national park | data.frame | 8 | 3 |
phosphorous | demodelr | Measured phosphorous of Daphnia and algae | data.frame | 6 | 2 |
precipitation | demodelr | Measured precipitation from a rainfall event | tbl_df | 56 | 5 |
snowfall | demodelr | Measured snowfall from a blizzard in April 2018 | data.frame | 16 | 5 |
wilson | demodelr | Measured Weight of a dog over time | data.frame | 19 | 2 |
yeast | demodelr | Measured Sacchromyces data (yeast) from Gause 1932 "Experimental studies on the struggle for coexistence" | data.frame | 7 | 2 |
england | footBayes | English league results 1888-2022 | data.frame | 203956 | 12 |
italy | footBayes | Italy league results 1934-2022 | data.frame | 27684 | 8 |
congruency | ssMousetrack | Mouse-tracking experiment of a memory task | data.frame | 1695 | 7 |
language | ssMousetrack | Mouse-tracking experiment of a lexical decision task | data.frame | 6060 | 6 |
power_studies_results | R2sample | power_studies_results | list | | |
pvaluecdf | R2sample | pvaluecdf | matrix | 250 | 3 |
logistic_data | ggquickeda | Simulated Exposure Response Data | spec_tbl_df | 749 | 15 |
sample_data | ggquickeda | Simulated Pharmacokinetic Concentration Data | tbl_df | 1800 | 10 |
countries110 | rnaturalearth | world country polygons from Natural Earth | sf | 177 | 169 |
df_layers_cultural | rnaturalearth | list of cultural layers available from Natural Earth | data.frame | 43 | 4 |
df_layers_physical | rnaturalearth | list of physical layers available from Natural Earth | data.frame | 29 | 4 |
dataAnova_1way | lessR | Data for a One-Way ANOVA | data.frame | 24 | 2 |
dataAnova_2way | lessR | Data for a Two-Way Balanced Factorial Design | data.frame | 48 | 3 |
dataAnova_rb | lessR | Data for a Randomized Block ANOVA | data.frame | 7 | 5 |
dataAnova_rbf | lessR | Data for a Randomized Block Factorial ANOVA | data.frame | 48 | 4 |
dataAnova_sp | lessR | Data for a Split-Plot ANOVA | data.frame | 56 | 4 |
dataBodyMeas | lessR | Data: Body Measurements | data.frame | 340 | 8 |
dataCars93 | lessR | Data: Cars93 | data.frame | 93 | 25 |
dataEmployee | lessR | Data: Employees | data.frame | 37 | 8 |
dataEmployee_lbl | lessR | VariableLabels: Employee Data Set | data.frame | 8 | 1 |
dataFreqTable99 | lessR | Data: Joint Frequency Table | data.frame | 4 | 4 |
dataJackets | lessR | Data: Motorcycle Type and Thickness of Jacket | data.frame | 1025 | 2 |
dataLearn | lessR | Data: Distributed vs Massed Practice | data.frame | 34 | 2 |
dataMach4 | lessR | Data: Machiavellianism | data.frame | 351 | 21 |
dataMach4_lbl | lessR | VariableLabels: Mach4 Data Set | data.frame | 20 | 1 |
dataReading | lessR | Data: Reading Ability | data.frame | 100 | 4 |
dataStockPrice | lessR | Data: Stock price of Apple, IBM and Intel from 1985 through May of 2024 | data.frame | 1449 | 4 |
dataWeightLoss | lessR | Data: WeightLoss | data.frame | 10 | 2 |
Sa | chromatographR | Raw goldenrod root chromatograms | list | | |
Sa_pr | chromatographR | Preprocessed goldenrod root chromatograms | list | | |
Sa_warp | chromatographR | Warped goldenrod root chromatograms. | list | | |
pk_tab | chromatographR | Goldenrod peak table | peak_table | | |
prosopis | GerminaR | Germination under different osmotic potentials and temperatures. | data.frame | 80 | 15 |
gpkg_creation_options | gpkg | GeoPackage Creation Options | data.frame | 32 | 4 |
gpkg_sqlite_tables | gpkg | GeoPackage Dataset | data.frame | 10 | 1 |
eager_sim_obs | AIPW | Simulated Observational Study | data.frame | 200 | 8 |
eager_sim_rct | AIPW | Simulated Randomized Trial | data.frame | 1228 | 8 |
vote2020 | RcensusPkg | vote2020 data | data.table | 61 | 22 |
castle | did2s | Data from Cheng and Hoekstra (2013) | data.frame | 550 | 6 |
df_het | did2s | Simulated data with two treatment groups and heterogenous effects | data.frame | 46500 | 14 |
df_hom | did2s | Simulated data with two treatment groups and homogenous effects | data.frame | 46500 | 15 |
workforceHistory | hR | Workforce history data for a sample team of employees and contractors. | data.table | 45 | 10 |
cards | DescTools | Datasets for Probabilistic Simulation | data.frame | 52 | 3 |
d.countries | DescTools | ISO 3166-1 Country Codes | data.frame | 249 | 9 |
d.diamonds | DescTools | Data diamonds | data.frame | 440 | 10 |
d.periodic | DescTools | Periodic Table of Elements | data.frame | 110 | 24 |
d.pizza | DescTools | Data pizza | data.frame | 1209 | 16 |
d.prefix | DescTools | Unit Conversion and Metrix Prefixes | data.frame | 20 | 3 |
d.units | DescTools | Unit Conversion and Metrix Prefixes | data.frame | 85 | 5 |
d.whisky | DescTools | Classification of Scotch Single Malts | data.frame | 86 | 18 |
day.abb | DescTools | Build-in Constants Extension | character | | |
day.name | DescTools | Build-in Constants Extension | character | | |
hblue | DescTools | Some Custom Palettes | character | | |
hecru | DescTools | Some Custom Palettes | character | | |
hgreen | DescTools | Some Custom Palettes | character | | |
horange | DescTools | Some Custom Palettes | character | | |
hred | DescTools | Some Custom Palettes | character | | |
hyellow | DescTools | Some Custom Palettes | character | | |
roulette | DescTools | Datasets for Probabilistic Simulation | data.frame | 37 | 7 |
tarot | DescTools | Datasets for Probabilistic Simulation | data.frame | 78 | 4 |
wdConst | DescTools | Word VBA Constants | list | | |
xlConst | DescTools | Word VBA Constants | list | | |
COVID19 | Dyn4cast | Dynamic Forecast of Five Models and their Ensembles | spec_tbl_df | 348 | 2 |
Data | Dyn4cast | Constrained Forecast of One-sided Integer Response Model | spec_tbl_df | 200 | 3 |
Quicksummary | Dyn4cast | Quick Formatted Summary of Machine Learning Data | spec_tbl_df | 103 | 29 |
Transform | Dyn4cast | Standardize 'data.frame' for comparable *Machine Learning* prediction and visualization | spec_tbl_df | 25 | 7 |
garrett_data | Dyn4cast | Garrett Ranking of Categorical Data | spec_tbl_df | 29 | 15 |
garrett_table | Dyn4cast | Garrett Ranking of Categorical Data | spec_tbl_df | 100 | 3 |
linearsystems | Dyn4cast | Linear Model and various Transformations for Efficiency | spec_tbl_df | 100 | 9 |
sample | Dyn4cast | Attach Per Cent Sign to Data | spec_tbl_df | 13 | 8 |
sampling | Dyn4cast | Linear Model and various Transformations for Efficiency | spec_tbl_df | 200 | 5 |
treatments | Dyn4cast | Enhanced Estimation of Treatment Effects of Binary Data from Randomized Experiments | spec_tbl_df | 500 | 3 |
BuildIt | jfa | BuildIt Construction Financial Statements | data.frame | 3500 | 3 |
accounts | jfa | Accounts Receivable | data.frame | 20 | 3 |
allowances | jfa | Legitimacy Audit | data.frame | 4076 | 5 |
benchmark | jfa | Benchmark Analysis of Sales Versus Cost of Sales | data.frame | 100 | 2 |
carrier | jfa | Carrier Company Financial Statements | data.frame | 202 | 12 |
compas | jfa | COMPAS Recidivism Prediction | data.frame | 6172 | 7 |
retailer | jfa | Retailer Group Audit | data.frame | 20 | 4 |
sanitizer | jfa | Factory Workers' use of Hand Sanitizer | data.frame | 1600 | 1 |
sinoForest | jfa | Sino Forest Corporation's Financial Statements. | data.frame | 772 | 1 |
RicePrepped.dat | growthPheno | Prepped data from an experiment to investigate a rice germplasm panel. | data.frame | 14784 | 32 |
RiceRaw.dat | growthPheno | Data for an experiment to investigate a rice germplasm panel | data.frame | 7392 | 34 |
indv.dat | growthPheno | A small data set to use in function examples | data.frame | 20 | 45 |
longi.dat | growthPheno | A small data set to use in function examples | data.frame | 280 | 37 |
raw.dat | growthPheno | A small data set to use in function examples | data.frame | 280 | 34 |
tomato.dat | growthPheno | Longitudinal data for an experiment to investigate tomato response to mycorrhizal fungi and zinc | data.frame | 1120 | 16 |
bird_move | gratia | Simulated bird migration data | data.frame | 840 | 4 |
gss_vocab | gratia | Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago | data.frame | 1858 | 3 |
ref_sims | gratia | Reference simulation data | list | | |
smallAges | gratia | Lead-210 age-depth measurements for Small Water | data.frame | 12 | 7 |
zooplankton | gratia | Madison lakes zooplankton data | data.frame | 5848 | 9 |
Crowther2003 | metasens | Aspirin after Myocardial Infarction | data.frame | 9 | 5 |
Moore1998 | metasens | NSAIDS in acute pain | data.frame | 37 | 5 |
eicat_acacia | impIndicator | EICAT data of acacia taxa An example of EICAT data containing species name, impact category and mechanism. | tbl_df | 138 | 3 |
southAfrica_sf | impIndicator | South African sf An example of region sf for impact indicator. | sf | 1 | 1 |
taxa_Acacia | impIndicator | GBIF occurrences data of acacia in South Africa An example of occurrence data from GBIF containing required column for impact indicator. | tbl_df | 19100 | 6 |
metaHIV_phy | dar | Phyloseq object from metaHIV project | phyloseq | | |
test_prep_rec | dar | PrepRecipe for metaHIV_phy data | PrepRecipe | | |
test_rec | dar | Recipe for metaHIV_phy data | Recipe | | |
inference | LACE | Results obtained with the function LACE on the provided input data from Rambow, Florian, et al. "Toward minimal residual disease-directed therapy in melanoma." Cell 174.4 (2018): 843-855. | list | | |
longitudinal_sc_variants | LACE | Mutation data from Rambow, Florian, et al. "Toward minimal residual disease-directed therapy in melanoma." Cell 174.4 (2018): 843-855. | list | | |
HP.links | ape | Test of host-parasite coevolution | matrix | 15 | 17 |
bird.families | ape | Phylogeny of the Families of Birds From Sibley and Ahlquist | phylo | | |
bird.orders | ape | Phylogeny of the Orders of Birds From Sibley and Ahlquist | phylo | | |
carnivora | ape | Carnivora body sizes and life history traits | data.frame | 112 | 17 |
chiroptera | ape | Bat Phylogeny | phylo | | |
cynipids | ape | NEXUS Data Example | list | | |
gopher.D | ape | Test of host-parasite coevolution | data.frame | 15 | 15 |
hivtree.newick | ape | Phylogenetic Tree of 193 HIV-1 Sequences | character | | |
hivtree.table | ape | Phylogenetic Tree of 193 HIV-1 Sequences | data.frame | 192 | 2 |
lice.D | ape | Test of host-parasite coevolution | data.frame | 17 | 17 |
lmorigin.ex1 | ape | Multiple regression through the origin | data.frame | 41 | 4 |
lmorigin.ex2 | ape | Multiple regression through the origin | data.frame | 17 | 2 |
mat3 | ape | Three Matrices | data.frame | 27 | 9 |
mat5M3ID | ape | Five Trees | data.frame | 250 | 50 |
mat5Mrand | ape | Five Independent Trees | data.frame | 250 | 50 |
woodmouse | ape | Cytochrome b Gene Sequences of Woodmice | DNAbin | 15 | |
inference | VERSO | Results obtained running VERSO on the provided input dataset. | list | | |
variants | VERSO | Mutation data obtained by variant calling from raw data of a selected set of SARS-CoV-2 samples available from NCBI BioProject PRJNA610428. | matrix | 15 | 6 |
defined_image | SPIAT | SPE object of a simulated image with defined cell types based on marker combinations. | SpatialExperiment | | |
image_no_markers | SPIAT | SPE object of a formatted image without marker intensities (simulated by 'spaSim' package) | SpatialExperiment | | |
simulated_image | SPIAT | SPE object of a formatted image (simulated by 'spaSim' package) | SpatialExperiment | | |
counts_obj | sccomp | counts_obj | tbl_df | 720 | 6 |
multipanel_theme | sccomp | multipanel_theme | theme | | |
no_significance_df | sccomp | no_significance_df | tbl_df | 34 | 4 |
sce_obj | sccomp | sce_obj | SingleCellExperiment | | |
seurat_obj | sccomp | seurat_obj | Seurat | | |
minnbreast | Pedixplorer | Minnesota Breast Cancer Study | data.frame | 28081 | 15 |
relped | Pedixplorer | Relped data | data.frame | 8 | 4 |
sampleped | Pedixplorer | Sampleped data | data.frame | 55 | 19 |
MetRef | KODAMA | Nuclear Magnetic Resonance Spectra of Urine Samples | list | | |
USA | KODAMA | State of the Union Data Set | list | | |
clinical | KODAMA | Clinical Data of a Cohort of Prostate Cancer Patiens | data.frame | 105 | 5 |
lymphoma | KODAMA | Lymphoma Gene Expression Dataset | list | | |
enrich_example | plotthis | A example of clusterProfiler enrichment result | data.frame | 57 | 12 |
enrich_multidb_example | plotthis | A example of clusterProfiler enrichment result with multiple databases | data.frame | 114 | 13 |
gsea_example | plotthis | A example of GSEA result from fgsea package | data.frame | 10 | 11 |
palette_list | plotthis | A list of palettes for use in data visualization | list | | |
words_excluded | plotthis | Excluded words in keyword enrichment analysis and extraction | character | | |
HomoMidSag | prWarp | HomoMidSag dataset | data.frame | 24 | 174 |
papionin | prWarp | papionin dataset | list | | |
acb_games_1718 | BAwiR | ACB games 2017-2018 | tbl_df | 3939 | 38 |
acb_games_2223_coach | BAwiR | ACB coaches in the 2022-2023 season. | tbl_df | 612 | 4 |
acb_games_2223_info | BAwiR | ACB games 2022-2023, days and codes. | tbl_df | 306 | 3 |
acb_players_1718 | BAwiR | ACB players 2017-2018 | tbl_df | 255 | 7 |
acb_shields | BAwiR | Shields of the ACB teams | tbl_df | 20 | 2 |
acb_vbc_cz_pbp_2223 | BAwiR | ACB play-by-play data, 2022-2023, Valencia Basket-Casademont Zaragoza | tbl_df | 466 | 9 |
acb_vbc_cz_sl_2223 | BAwiR | ACB starting lineups, 2022-2023, Valencia Basket-Casademont Zaragoza | tbl_df | 40 | 9 |
eurocup_games_1718 | BAwiR | Eurocup games 2017-2018 | tbl_df | 3604 | 38 |
eurocup_players_1718 | BAwiR | Eurocup players 2017-2018 | tbl_df | 351 | 7 |
euroleague_games_1718 | BAwiR | Euroleague games 2017-2018 | tbl_df | 3932 | 38 |
euroleague_players_1718 | BAwiR | Euroleague players 2017-2018 | tbl_df | 245 | 7 |
piecewise_exp_cc | BayesFBHborrow | Example data, simulated from a piecewise exponential model. | tbl_df | 250 | 3 |
piecewise_exp_hist | BayesFBHborrow | Example data, simulated from a piecewise exponential model. | tbl_df | 100 | 2 |
weibull_cc | BayesFBHborrow | Example data, simulated from a Weibull distribution. | tbl_df | 250 | 3 |
weibull_hist | BayesFBHborrow | Example data, simulated from a Weibull distribution | tbl_df | 100 | 2 |
sarcoma | npcurePK | Sarcoma Dataset | spec_tbl_df | 232 | 4 |
exmp_dataset1 | INSPIRE | Example Gene Expression Dataset-1 | matrix | 28 | 9056 |
exmp_dataset2 | INSPIRE | Example Gene Expression Dataset-2 | matrix | 42 | 4165 |
oasis | jointest | Longitudinal MRI data in nondemented and demented older adults | data.frame | 373 | 15 |
cgd | bayesSurv | Chronic Granulomatous Disease data | data.frame | 203 | 17 |
tandmob2 | bayesSurv | Signal Tandmobiel data, version 2 | data.frame | 4430 | 156 |
tandmobRoos | bayesSurv | Signal Tandmobiel data, version Roos | data.frame | 4394 | 71 |
crayweed | ecopower | Crayweed dataset | list | | |
fish | ecopower | Fish dataset | data.frame | 9 | 39 |
data_US | atRisk | Historical data for the US (GDP and Financial Conditions) from 1973:Q1 to 2020:Q1 | data.frame | 189 | 3 |
data_euro | atRisk | Historical data for the eurozone (GDP and Financial Conditions) from 2008:Q4 to 2022:Q3 | data.frame | 56 | 4 |
data_param_histo_US | atRisk | Historical parameters (skew-t) for the US from 1973:Q1 to 2020:Q1 | data.frame | 188 | 4 |
MCK_dataset | anabel | Simulated data of binding curve for MCK. | data.frame | 403 | 6 |
MCK_dataset_drift | anabel | Simulated data of binding curve for MCK with linear drift. | data.frame | 403 | 6 |
SCA_dataset | anabel | Simulated data for SCA method. | data.frame | 453 | 4 |
SCA_dataset_drift | anabel | Simulated data for SCA method with linear drift. | data.frame | 453 | 4 |
SCK_dataset | anabel | Simulated data of different binding curves for SCK method. | data.frame | 1091 | 2 |
SCK_dataset_decay | anabel | Simulated data of different binding curves for SCK method with exponential decay. | data.frame | 1091 | 2 |
Acupuncture | R4HCR | Acupuncture for Chronic Headache. | data.frame | 301 | 4 |
BCG | R4HCR | Trials of BCG Vaccine against Tuberculosis. | data.frame | 13 | 8 |
BMT | R4HCR | Bone Marrow Transplantation. | data.table | 137 | 3 |
CA19 | R4HCR | Diagnosis of Pancreatic Cancer with CA19-9 Biomarker. | data.table | 22 | 5 |
CBF | R4HCR | Ciliary Beat Frequency Measurement Using Two Methods. | data.frame | 15 | 2 |
Cotinine | R4HCR | Salivary Cotinine Measurements on Scottish Schoolchildren. | data.frame | 20 | 3 |
Doppler | R4HCR | Cardiac Output Measured by Doppler Echocardiography. | data.frame | 23 | 2 |
Duplex | R4HCR | Duplex Ultrasonography for Detecting Peripheral Aterial Disease. | data.table | 14 | 6 |
Earnings | R4HCR | Gelman and Hill's Earnings and Height Data. | data.table | 1192 | 4 |
Endometrial | R4HCR | Exogenous Oestrogens and Endometrial Cancer. | data.table | 126 | 8 |
FEV | R4HCR | Forced Expiratory Volume Data. | data.frame | 20 | 3 |
Facemasks | R4HCR | Face Masks while Exercising Trial (MERIT). | data.table | 216 | 3 |
Framingham | R4HCR | Framingham Heart Study Dataset | data.frame | 4240 | 16 |
Galton | R4HCR | Galton's Height Data. | data.table | 898 | 9 |
Glucose | R4HCR | Comparison of impedance to insulin-mediated glucose uptake | data.frame | 14 | 3 |
IPNs | R4HCR | Artificial intelligence for Assessment of Indeterminate Pulmonary Nodules. | data.table | 200 | 2 |
Innova | R4HCR | Rapid Antigen Detection for SARS-CoV-2 by Lateral Flow Assay. | data.frame | 8 | 3 |
LVD | R4HCR | Left Ventricular Diastolic Diameter (LVD). | list | | |
LungCa | R4HCR | Years of Smoking and Lung Cancer Deaths in Men. | data.frame | 63 | 4 |
Malformation | R4HCR | Infant Malformation and Mother's Alcohol Consumption Data. | data.frame | 5 | 4 |
MedSchools | R4HCR | Medical Humanities Teaching and World Ranking. | data.frame | 109 | 4 |
Milk | R4HCR | Fat Content of Human Milk by Two Methods. | data.frame | 45 | 2 |
NPguided | R4HCR | NP Guided Monitoring of Heart Failure. | data.frame | 18 | 7 |
Nodules | R4HCR | Incidental or Screen-Detected Lung Nodules. | data.table | 999 | 8 |
OXFIT | R4HCR | OXFIT data set | data.frame | 9999 | 10 |
PEFR | R4HCR | Peak Expiratory Flow Rate Measurement. | data.table | 20 | 7 |
PTX | R4HCR | Detecting Pneumothoraces. | data.frame | 200 | 6 |
PTXII | R4HCR | Confidence in Detecting Pneumothoraces. | data.frame | 300 | 2 |
Peptides | R4HCR | Measurements of a Neurotoxic Bioactive Peptide in Brain Samples. | data.frame | 38 | 2 |
PlasmaVolume | R4HCR | Measurements of Plasma Volume Using Two Sets of Normal Values. | data.frame | 99 | 2 |
Potency | R4HCR | Potency of four cardiac substances. | data.frame | 40 | 2 |
Remission | R4HCR | Effect of 6-mercaptopurine (6-MP) on the Duration of Remission in Acute Leukemia. | data.table | 42 | 5 |
SCAN | R4HCR | Suspected CANcer (SCAN) Pathway | data.frame | 750 | 8 |
Scotland | R4HCR | Scottish Death Registration data for 2021. | matrix | 5 | 42 |
Smartphone | R4HCR | Cervical cancer Screening with Smartphones. | data.table | 181 | 10 |
Systolic | R4HCR | Systolic Blood Pressure Measured by Two Observers and a Machine. | data.frame | 85 | 9 |
Thrombosis | R4HCR | Mortality from Coronary Thrombosis. | data.frame | 10 | 4 |
USCancerStats | R4HCR | Change in Cancer Incidence, Mortality and Survival Statistics. | data.frame | 20 | 4 |
VSA | R4HCR | Volatile Substance Abuse Mortality in Great Britain, 1971-83. | data.frame | 18 | 4 |
Vaccinated | R4HCR | Vaccination Uptake Among European Countries. | data.table | 15 | 3 |
Arthritis | vcd | Arthritis Treatment Data | data.frame | 84 | 5 |
Baseball | vcd | Baseball Data | data.frame | 322 | 25 |
BrokenMarriage | vcd | Broken Marriage Data | data.frame | 20 | 4 |
Bundesliga | vcd | Ergebnisse der Fussball-Bundesliga | data.frame | 14018 | 7 |
Bundestag2005 | vcd | Votes in German Bundestag Election 2005 | table | 16 | 5 |
Butterfly | vcd | Butterfly Species in Malaya | table | | |
CoalMiners | vcd | Breathlessness and Wheeze in Coal Miners | table | | |
DanishWelfare | vcd | Danish Welfare Study Data | data.frame | 180 | 5 |
Employment | vcd | Employment Status | table | | |
Federalist | vcd | 'May' in Federalist Papers | table | | |
Hitters | vcd | Hitters Data | data.frame | 154 | 4 |
HorseKicks | vcd | Death by Horse Kicks | table | | |
Hospital | vcd | Hospital data | table | 3 | 3 |
JobSatisfaction | vcd | Job Satisfaction Data | data.frame | 8 | 4 |
JointSports | vcd | Opinions About Joint Sports | data.frame | 40 | 5 |
Lifeboats | vcd | Lifeboats on the Titanic | data.frame | 18 | 8 |
MSPatients | vcd | Diagnosis of Multiple Sclerosis | array | | |
NonResponse | vcd | Non-Response Survey Data | data.frame | 12 | 4 |
OvaryCancer | vcd | Ovary Cancer Data | data.frame | 16 | 5 |
PreSex | vcd | Pre-marital Sex and Divorce | table | | |
Punishment | vcd | Corporal Punishment Data | data.frame | 36 | 5 |
RepVict | vcd | Repeat Victimization Data | table | 8 | 8 |
Rochdale | vcd | Rochdale Data | xtabs | | |
Saxony | vcd | Families in Saxony | table | | |
SexualFun | vcd | Sex is Fun | table | 4 | 4 |
SpaceShuttle | vcd | Space Shuttle O-ring Failures | data.frame | 24 | 6 |
Suicide | vcd | Suicide Rates in Germany | data.frame | 306 | 6 |
Trucks | vcd | Truck Accidents Data | data.frame | 24 | 5 |
UKSoccer | vcd | UK Soccer Scores | table | 5 | 5 |
VisualAcuity | vcd | Visual Acuity in Left and Right Eyes | data.frame | 32 | 4 |
VonBort | vcd | Von Bortkiewicz Horse Kicks Data | data.frame | 280 | 4 |
WeldonDice | vcd | Weldon's Dice Data | table | | |
WomenQueue | vcd | Women in Queues | table | | |
cost_parameters | optistock | Cost parameters for species used in examples | grouped_df | 16 | 5 |
growth_parameters | optistock | Growth parameters for species used in examples | data.frame | 6 | 7 |
Acidity | mixAK | Acidity index of lakes in North-Central Wisconsin | numeric | | |
Enzyme | mixAK | Enzymatic activity in the blood | numeric | | |
Faithful | mixAK | Old Faithful Geyser Data | data.frame | 272 | 2 |
Galaxy | mixAK | Velocities of distant galaxies | numeric | | |
PBC910 | mixAK | Subset of Mayo Clinic Primary Biliary Cholangitis (Cirrhosis) data | data.frame | 918 | 9 |
PBCseq | mixAK | Mayo Clinic Primary Biliary Cholangitis (Cirrhosis), sequential data | data.frame | 1945 | 38 |
SimData | mixAK | Simulated dataset | data.frame | 1157 | 7 |
Tandmob | mixAK | Signal Tandmobiel data | data.frame | 4430 | 156 |
TandmobEmer | mixAK | Signal Tandmobiel data - emergence times | data.frame | 4430 | 91 |
burkina.faso.females | popReconstruct | Data for the Vignette burkina-faso-females | list | | |
burkina.faso.prop.vars | popReconstruct | Data for the Vignette burkina-faso-females | list | | |
MQC_testdata | microbiomeMQC | MQC_testdata | data.frame | 26 | 6 |
combinedamgut | SOHPIE | A Subdata from the American Gut Project study data | data.frame | 268 | 145 |
combineddietswap | SOHPIE | A Subdata from the Diet Swap study data | data.frame | 74 | 116 |
activities | aspace | Demo Data: x and y coordinates of 10 specified point locations | data.frame | 10 | 2 |
activities2 | aspace | Demo Data: x and y coordinates of 10 specified point locations | data.frame | 10 | 2 |
centre | aspace | Demo Data: Coordinates of a single source, centre, location | numeric | | |
wts | aspace | Weights vector | matrix | 10 | 1 |
BioSample | texteffect | Sample from the Fong and Grimmer Wikipedia Biography Data | data.frame | 500 | 51 |
simScenario5 | oceCens | Simulated data from simulation scenario 5 | data.frame | 400 | 8 |
hdbm.data | hdbm | Synthetic example data for hdbm | data.frame | 1000 | 102 |
RCI_sample_data | LogisticRCI | Sample Data for RCI Calculation | data.frame | 100 | 5 |
data1 | easyanova | data1: Kaps and Lamberson(2009): page 252 | data.frame | 15 | 2 |
data10 | easyanova | data10: Kaps and Lamberson (2009): page 395 | data.frame | 15 | 4 |
data11 | easyanova | data11: Pimentel Gomes and Garcia (2002): page 199 | data.frame | 56 | 4 |
data12 | easyanova | data12: Pimentel Gomes and Garcia (2002): page 202 | data.frame | 42 | 4 |
data13 | easyanova | data13: Cruz and Carneiro (2006): page 575 | data.frame | 23 | 3 |
data14 | easyanova | data14: Sampaio (2009): page173 | data.frame | 28 | 4 |
data15 | easyanova | data15: Pimentel Gomes and Garcia (2002): page 211 | data.frame | 48 | 4 |
data16 | easyanova | data16: Sampaio (2010): page164 | data.frame | 36 | 4 |
data17 | easyanova | data17: Sanders and Gaynor (1987) | data.frame | 36 | 5 |
data18 | easyanova | data18: Ramalho et al. (2005): page 115 | data.frame | 60 | 4 |
data19 | easyanova | data19: Sampaio (2010): page 155 | data.frame | 32 | 5 |
data2 | easyanova | data2: Kaps and Lamberson (2009): page 313: randomizad block design | data.frame | 12 | 3 |
data3 | easyanova | data3: Kaps and Lamberson (2009): page 347 | data.frame | 16 | 4 |
data4 | easyanova | data4: Kaps and Lamberson (2009): page 349 | data.frame | 32 | 5 |
data5 | easyanova | data5: Kaps and Lamberson (2009): page 361 | data.frame | 20 | 3 |
data6 | easyanova | data6: Pimentel Gomes and Garcia (2002): page 127 | data.frame | 16 | 4 |
data7 | easyanova | data7: Kaps and Lamberson (2009): page 409 | data.frame | 68 | 4 |
data8 | easyanova | data8: Kaps and Lamberson (2009): page 386 | data.frame | 24 | 4 |
data9 | easyanova | data9: Sampaio (2010): page 67 | data.frame | 120 | 5 |
combinations | hiphop | List of triads to be ranked for parentage assignment. | data.frame | 24192 | 24 |
genotypes | hiphop | Genotypes of individuals scored at different loci | data.frame | 1407 | 1376 |
individuals | hiphop | List of individuals to be compared for parentage assignment. | data.frame | 2526 | 5 |
danish | tea | Danish Fire Insurance Claims | numeric | | |
C | PICBayes | Adjacency matrix of 46 South Carolina counties | matrix | 46 | |
da1 | PICBayes | Partly interva-censored data | matrix | 460 | 7 |
da2 | PICBayes | Clustered partly interva-censored data | matrix | 920 | 8 |
da3 | PICBayes | Clustered partly interva-censored data | matrix | 500 | 8 |
da4 | PICBayes | Clustered partly interva-censored data | matrix | 500 | 8 |
mCRC | PICBayes | Colorectal cancer data | matrix | 855 | 8 |
amc | flux | Climate station data from 2009 to 2011 in the Ahlenmoor peat bog, Northeast Germany | data.frame | 43197 | 8 |
amd | flux | Closed chamber fluxes from 2009 to 2011 in the Ahlenmoor peat bog, Northeast Germany | data.frame | 559 | 14 |
tt.flux | flux | Medium frequency concentration data and fluxes from non-steady state closed chamber measurements | data.frame | 104 | 28 |
tt.nee | flux | Medium frequency concentration data and fluxes from non-steady state closed chamber measurements | data.frame | 14388 | 18 |
tt.pre | flux | One day data from closed chamber measurements in the Trebeltal | data.frame | 118 | 17 |
Miyagi20030626 | mmpp | The aftershock data of 26th July 2003 earthquake of M6.2 at the northern Miyagi-Ken Japan | data.frame | 2305 | 5 |
spain_ccaas | jrrosell | spain_ccaas | sf | 19 | 4 |
spain_provinces | jrrosell | spain_provinces | sf | 60 | 4 |
all_birds | diverge | Example Tree | phylo | | |
hotspot.data | hotspot | Hotspot Data | list | | |
cdsdata | CreditRisk | CDS quotes from market | data.frame | 10 | 3 |
colcancer | dirttee | Colon Cancer Dataset | data.frame | 546 | 12 |
genea140 | GENLIB | Genealogical information for 140 individuals from the Quebec Reference Sample | data.frame | 41523 | 4 |
geneaJi | GENLIB | Highly inbred pedigree | data.frame | 29 | 4 |
pop140 | GENLIB | Population of origin of the 140 Quebec samples | data.frame | 140 | 2 |
artcog | sensitivitymult | Arthritis and cognition in the elderly. | data.frame | 657 | 5 |
tbmetaphase | sensitivitymult | Genetic damage from drugs used to treat TB | data.frame | 15 | 3 |
teeth | sensitivitymult | Smoking and Periodontal Disease. | data.frame | 882 | 5 |
gomms_test_data | gomms | Test Data | data.frame | 70 | 83 |
biblio | RAT | biblio file for testing. | data.frame | 174 | 65 |
map | RAT | Matrix matching country names, coordinates and GDP. | data.frame | 230 | 5 |
deff.mu | hbim | HBIM data | list | | |
deff.rho | hbim | HBIM data | list | | |
deff.sigma | hbim | HBIM data | list | | |
dpp.mu | hbim | HBIM data | list | | |
dpp.rho | hbim | HBIM data | list | | |
dpp.sigma | hbim | HBIM data | list | | |
irdata | hbim | Immune Response data | data.frame | 574 | 16 |
refs | hbim | Reference list | factor | | |
state | MKLE | Violent death in the USA | data.frame | 50 | 2 |
cnrm.nino34.cc | afc | Example Data of Continuous Observations and Continuous Forecasts | list | | |
cnrm.nino34.ce | afc | Example Data of Continuous Observations and Ensemble Forecasts | list | | |
cnrm.nino34.dc | afc | Example Data of Dichotomous Observations and Continuous Forecasts | list | | |
cnrm.nino34.dd | afc | Example Data of Dichotomous Observations and Dichotomous Forecasts | list | | |
cnrm.nino34.de | afc | Example Data of Dichotomous Observations and Ensemble Forecasts | list | | |
cnrm.nino34.dm | afc | Example Data of Dichotomous Observations and Polychotomous Forecasts | list | | |
cnrm.nino34.dp | afc | Example Data of Dichotomous Observations and Probabilistic Forecasts | list | | |
cnrm.nino34.mc | afc | Example Data of Polychotomous Observations and Continuous Forecasts | list | | |
cnrm.nino34.me | afc | Example Data of Polychotomous Observations and Ensembles Forecasts | list | | |
cnrm.nino34.mm | afc | Example Data of Polychotomous Observations and Polychotomous Forecasts | list | | |
cnrm.nino34.mp | afc | Example Data of Polychotomous Observations and Probabilistic Forecasts | list | | |
dataghs | MRQoL | Data of Global Health Status (GHS) dimension. | data.frame | 100 | 7 |
hapmap | hdpca | Example dataset - Hapmap Phase III | list | | |
DexNR3C1 | DegCre | DegCre input data for examples. | list | | |
cattle | OPDOE | Cattle data | matrix | 5 | |
had100 | OPDOE | Stored Hadmard matrices | matrix | 100 | |
had116 | OPDOE | Stored Hadmard matrices | matrix | 116 | |
had156 | OPDOE | Stored Hadmard matrices | matrix | 156 | |
had172 | OPDOE | Stored Hadmard matrices | matrix | 172 | |
had188 | OPDOE | Stored Hadmard matrices | matrix | 188 | |
had236 | OPDOE | Stored Hadmard matrices | matrix | 236 | |
had244 | OPDOE | Stored Hadmard matrices | matrix | 244 | |
had52 | OPDOE | Stored Hadmard matrices | matrix | 52 | |
had92 | OPDOE | Stored Hadmard matrices | matrix | 92 | |
heights | OPDOE | male / female heights data | data.frame | 7 | 2 |
hemp | OPDOE | Hemp data | data.frame | 14 | 2 |
morse | fechner | Rothkopf's Morse Code Data | data.frame | 36 | 36 |
noRegMin | fechner | Artificial Data: Regular Minimality Violated | data.frame | 10 | 10 |
regMin | fechner | Artificial Data: Regular Minimality In Non-canonical Form | data.frame | 10 | 10 |
wish | fechner | Wish's Morse-code-like Data | data.frame | 32 | 32 |
verbal | deltaPlotR | Verbal Aggression Data Set | data.frame | 316 | 26 |
age_freq | foodquotient | Frequency Factors for American Children with Age of Participant | data.frame | 32 | 86 |
freq | foodquotient | Frequency Factors for American Children | data.frame | 32 | 85 |
hsffq | foodquotient | Harvard Foood Frequency Questionnaire Nutrition Information | spec_tbl_df | 85 | 15 |
rainfall | evdbayes | Daily Aggregate Rainfall | numeric | | |
NBArankings | ExtMallows | A real example of rankings of NBA teams | data.frame | 30 | 34 |
simu1 | ExtMallows | Simulation data 1 | data.frame | 30 | 6 |
simu2 | ExtMallows | Simulation data 2 | data.frame | 40 | 6 |
simu3 | ExtMallows | Simulation data 3 | data.frame | 100 | 20 |
Bennett5 | NISTnls | Magentization modelling | data.frame | 154 | 2 |
Chwirut1 | NISTnls | Ultrasonic calibration study 1 | data.frame | 214 | 2 |
Chwirut2 | NISTnls | Ultrasonic calibration data 2 | data.frame | 54 | 2 |
DanielWood | NISTnls | Radiated energy | data.frame | 6 | 2 |
ENSO | NISTnls | Atmospheric pressure differences | data.frame | 168 | 2 |
Eckerle4 | NISTnls | Circular interference data | data.frame | 35 | 2 |
Gauss1 | NISTnls | Generated data | data.frame | 250 | 2 |
Gauss2 | NISTnls | Generated data | data.frame | 250 | 2 |
Gauss3 | NISTnls | Generated data | data.frame | 250 | 2 |
Hahn1 | NISTnls | Thermal expansion data | data.frame | 236 | 2 |
Kirby2 | NISTnls | Microscope line width standards | data.frame | 151 | 2 |
Lanczos1 | NISTnls | Generated data | data.frame | 24 | 2 |
Lanczos2 | NISTnls | Generated data | data.frame | 24 | 2 |
Lanczos3 | NISTnls | Generated data | data.frame | 24 | 2 |
MGH09 | NISTnls | More, Gabrow and Hillstrom example 9 | data.frame | 11 | 2 |
MGH10 | NISTnls | More, Gabrow and Hillstrom example 10 | data.frame | 16 | 2 |
MGH17 | NISTnls | More, Gabrow and Hillstrom example 17 | data.frame | 33 | 2 |
Misra1a | NISTnls | Monomolecular Absorption Data | data.frame | 14 | 2 |
Misra1b | NISTnls | Monomolecular Absorption Data | data.frame | 14 | 2 |
Misra1c | NISTnls | Monomolecular Absorption data | data.frame | 14 | 2 |
Misra1d | NISTnls | Monomolecular Absorption data | data.frame | 14 | 2 |
Nelson | NISTnls | Dialectric breakdown data | data.frame | 128 | 3 |
Ratkowsky2 | NISTnls | Pasture yield data | data.frame | 9 | 2 |
Ratkowsky3 | NISTnls | Onion growth data | data.frame | 15 | 2 |
Roszman1 | NISTnls | Quantum defects in iodine | data.frame | 25 | 2 |
Thurber | NISTnls | Electron mobility data | data.frame | 37 | 2 |
rxReservedKeywords | rxode2 | A list and description of rxode2 supported reserved keywords | data.frame | 31 | 3 |
rxResidualError | rxode2 | A description of Rode2 supported residual errors | data.frame | 181 | 6 |
rxSyntaxFunctions | rxode2 | A list and description of Rode supported syntax functions | data.frame | 102 | 3 |
Data0201 | FractalParameterEstimation | Data of simulation of random Sierpinski-Carpet with [p,p,p,q]-model and p = 0.2 and q = 0.1 | data.frame | 81 | 81 |
Data03025 | FractalParameterEstimation | Data of simulation of random Sierpinski-Carpet with [p,p,p,q]-model and p = 0.3 and q = 0.25 | data.frame | 81 | 81 |
Data0501 | FractalParameterEstimation | Data of simulation of random Sierpinski-Carpet with [p,p,p,q]-model and p = 0.5 and q = 0.1 | data.frame | 81 | 81 |
Data0603 | FractalParameterEstimation | Data of simulation of random Sierpinski-Carpet with [p,p,p,q]-model and p = 0.6 and q = 0.3 | data.frame | 81 | 81 |
galbraith | stylo | Table of word frequencies (Galbraith, Rowling, Coben, Tolkien, Lewis) | stylo.data | 26 | 3000 |
lee | stylo | Table of word frequencies (Lee, Capote, Faulkner, Styron, etc.) | matrix | 28 | 3000 |
novels | stylo | A selection of 19th-century English novels | stylo.corpus | | |
Java.sparrow.notes | ZLAvian | Java sparrow note duration and frequency. | data.frame | 22970 | 3 |
HNP | RRHO | RRHO comparison data sets. | data.frame | 15144 | 2 |
My | RRHO | RRHO comparison data sets. | data.frame | 15144 | 2 |
Sestan | RRHO | RRHO comparison data sets. | data.frame | 15144 | 2 |
rrain | sad | Random Rain | array | | |
spant_mpress_drift | spant | Example MEGA-PRESS data with significant B0 drift. | mrs_data | | |
L7_ETMs | stars | Landsat-7 bands for a selected region around Olinda, BR | stars_proxy | | |
bcsd_obs | stars | Monthly Gridded Meteorological Observations | stars_proxy | | |
stars_sentinel2 | stars | Sentinel-2 sample tile | stars_proxy | | |
caseControl | EmpiricalCalibration | Odds ratios from a case-control design | data.frame | 47 | 4 |
cohortMethod | EmpiricalCalibration | Relative risks from a new-user cohort design | data.frame | 31 | 4 |
grahamReplication | EmpiricalCalibration | Relative risks from an adjusted new-user cohort design | data.frame | 125 | 4 |
sccs | EmpiricalCalibration | Incidence rate ratios from Self-Controlled Case Series | data.frame | 46 | 4 |
southworthReplication | EmpiricalCalibration | Relative risks from an unadjusted new-user cohort design | data.frame | 173 | 4 |
time_to_million | censored | Number of days before a movie grosses $1M USD | tbl_df | 551 | 49 |
bmmc | rliger | liger object of bone marrow subsample data with RNA and ATAC modality | liger | | |
deg.marker | rliger | Data frame for example marker DEG test result | data.frame | 1992 | 7 |
deg.pw | rliger | Data frame for example pairwise DEG test result | data.frame | 1743 | 7 |
pbmc | rliger | liger object of PBMC subsample data with Control and Stimulated datasets | liger | | |
pbmcPlot | rliger | liger object of PBMC subsample data with plotting information available | liger | | |
XYdat | fitPoly | A data set containing SNP array data | data.frame | 17808 | 6 |
fitPoly_data | fitPoly | Small fitPoly input datasets for testing and examples | list | | |
scores | fitPoly | A data set with dosage scores generated by fitPoly | data.frame | 17013 | 12 |
pm2.5 | merror | PM 2.5 Concentrations from SCAMP Collocated Samplers | data.frame | 77 | 5 |
redshift | merror | Spectroscopic and Photometric Galaxy Redshift Measurements | data.frame | 1432 | 7 |
USlandfall | ppgam | Times of landfalling US hurricanes | data.frame | 61129 | 2 |
windstorm | ppgam | Locations of windstorm peaks and tracks over the North Atlantic | data.frame | 3133 | 4 |
AirPassengersDf | datarium | Air Passengers | data.frame | 144 | 2 |
antismoking | datarium | Anti-Smoking Emotive Communication Data for McNemar Test | tbl_df | 62 | 3 |
anxiety | datarium | Anxiety Data for Two-Way Mixed ANOVA | tbl_df | 45 | 5 |
depression | datarium | Depression Data for Two Way Mixed ANOVA | tbl_df | 24 | 6 |
genderweight | datarium | Weight Data By Gender for Two-Samples Mean Test | tbl_df | 40 | 3 |
headache | datarium | Headache Data for Three Way ANOVA | tbl_df | 72 | 5 |
heartattack | datarium | Heart Attack Data for Three Way ANOVA | tbl_df | 72 | 5 |
housetasks.raw | datarium | Housetasks | tbl_df | 1744 | 2 |
jobsatisfaction | datarium | Job Satisfaction Data for Two-Way ANOVA | tbl_df | 58 | 4 |
marketing | datarium | Marketing Data Set | data.frame | 200 | 4 |
mice | datarium | Mice Weight Data for One Sample Mean Test | tbl_df | 10 | 2 |
mice2 | datarium | Mice Weight Data for Paired-Samples Mean Test | data.frame | 10 | 3 |
performance | datarium | Performance Data for Three-Way Mixed ANOVA | tbl_df | 60 | 5 |
properties | datarium | Properties Data for Chi-square Test of Independence | tbl_df | 333 | 2 |
renalstone | datarium | Risk of Renal Stone Data for Cochran-Armitage Trend Test | tbl_df | 3513 | 3 |
selfesteem | datarium | Self-Esteem Score Data for One-way Repeated Measures ANOVA | tbl_df | 10 | 4 |
selfesteem2 | datarium | Self Esteem Score Data for Two-way Repeated Measures ANOVA | tbl_df | 24 | 5 |
stress | datarium | Stress Data for Two-Way ANCOVA | tbl_df | 60 | 5 |
taskachievment | datarium | Task Achievment Data for Cochran's Q Test | tbl_df | 73 | 4 |
titanic.raw | datarium | Survival of Passengers on the Titanic | data.frame | 2201 | 4 |
weightloss | datarium | Weight Loss Score Data for Three-way Repeated Measures ANOVA | tbl_df | 48 | 6 |
generated_Q3 | ppsbm | Example dataset | list | | |
generated_Q3_n20 | ppsbm | Example dataset | list | | |
generated_sol_hist | ppsbm | Output example of mainVEM | list | | |
generated_sol_kernel | ppsbm | Output example of mainVEM | list | | |
survey_ghl | ggFishPlots | Greenland halibut measurements from IMR surveys | tbl_df | 618779 | 5 |
nih_sample | tidylda | Abstracts and metadata from NIH research grants awarded in 2014 | tbl_df | 100 | 44 |
nih_sample_dtm | tidylda | Abstracts and metadata from NIH research grants awarded in 2014 | dgCMatrix | | |
healthyR_data | healthyR.data | Main data file for healthyR | tbl_df | 187721 | 17 |
coef.0.ls | chngpt | Simulation Study Parameters | list | | |
dat.mtct | chngpt | An Example Dataset | data.frame | 236 | 3 |
dat.mtct.2 | chngpt | An Example Dataset | data.frame | 248 | 2 |
lidar | chngpt | Light Detection and Ranging Data | data.frame | 221 | 2 |
nutrition | chngpt | Infant Nutrition Data | data.frame | 72 | 2 |
sim.alphas | chngpt | Simulation Parameters | list | | |
KrigingExampleData | LatticeKrig | Synthetic data for kriging examples | list | | |
equatorGrid | LatticeKrig | Data examples for the LatticeKrig Vignette | matrix | 4050 | 2 |
equatorGridValues | LatticeKrig | Data examples for the LatticeKrig Vignette | numeric | | |
equatorLocations | LatticeKrig | Data examples for the LatticeKrig Vignette | matrix | 450 | 2 |
equatorValues | LatticeKrig | Data examples for the LatticeKrig Vignette | numeric | | |
polarGrid | LatticeKrig | Data examples for the LatticeKrig Vignette | matrix | 4050 | |
polarGridValues | LatticeKrig | Data examples for the LatticeKrig Vignette | numeric | | |
polarLocations | LatticeKrig | Data examples for the LatticeKrig Vignette | matrix | 450 | |
polarValues | LatticeKrig | Data examples for the LatticeKrig Vignette | numeric | | |
race | BMA | Scottish Hill Racing data | matrix | 35 | 3 |
vaso | BMA | Vaso data | data.frame | 39 | 3 |
D24_2014 | mkin | Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 | mkindsg | | |
FOCUS_2006_A | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 8 | 3 |
FOCUS_2006_B | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 8 | 3 |
FOCUS_2006_C | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 9 | 3 |
FOCUS_2006_D | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 44 | 3 |
FOCUS_2006_DFOP_ref_A_to_B | mkin | Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) | data.frame | 15 | 8 |
FOCUS_2006_E | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 18 | 3 |
FOCUS_2006_F | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 27 | 3 |
FOCUS_2006_FOMC_ref_A_to_F | mkin | Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) | data.frame | 45 | 7 |
FOCUS_2006_HS_ref_A_to_F | mkin | Results of fitting the HS model to Datasets A to F of FOCUS (2006) | data.frame | 36 | 8 |
FOCUS_2006_SFO_ref_A_to_F | mkin | Results of fitting the SFO model to Datasets A to F of FOCUS (2006) | data.frame | 50 | 6 |
NAFTA_SOP_Appendix_B | mkin | Example datasets from the NAFTA SOP published 2015 | data.frame | 14 | 3 |
NAFTA_SOP_Appendix_D | mkin | Example datasets from the NAFTA SOP published 2015 | data.frame | 18 | 3 |
NAFTA_SOP_Attachment | mkin | Example datasets from Attachment 1 to the NAFTA SOP published 2015 | list | | |
dimethenamid_2018 | mkin | Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 | mkindsg | | |
ds_dfop | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
ds_dfop_sfo | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
ds_fomc | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
ds_hs | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
ds_sfo | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
experimental_data_for_UBA_2019 | mkin | Experimental datasets used for development and testing of error models | list | | |
focus_soil_moisture | mkin | FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar | matrix | 12 | 3 |
mccall81_245T | mkin | Datasets on aerobic soil metabolism of 2,4,5-T in six soils | data.frame | 141 | 4 |
schaefer07_complex_case | mkin | Metabolism data set used for checking the software quality of KinGUI | data.frame | 8 | 6 |
schaefer07_complex_results | mkin | Metabolism data set used for checking the software quality of KinGUI | data.frame | 14 | 5 |
synthetic_data_for_UBA_2014 | mkin | Synthetic datasets for one parent compound with two metabolites | list | | |
test_data_from_UBA_2014 | mkin | Three experimental datasets from two water sediment systems and one soil | list | | |
gameadd | seqimpute | Example data set: Game addiction | data.frame | 500 | 11 |
grevilleasf | eks | Geographical locations of Grevillea plants in Western Australia | sf | 22303 | 3 |
wa | eks | Geographical locations of Grevillea plants in Western Australia | sfc_POLYGON | | |
index | argoFloats | A Sample Index of Profiles | argoFloats | | |
indexBgc | argoFloats | A Sample Index of Biogeochemical-Argo Profiles | argoFloats | | |
indexDeep | argoFloats | A Sample Index of Deep Argo | argoFloats | | |
indexSynthetic | argoFloats | A Sample Index of Synthetic Profiles | argoFloats | | |
forstmann | pmwg | Forstmann et al.'s data | data.frame | 15818 | 5 |
sampled_forstmann | pmwg | A sampled object of a model of the Forstmann dataset | pmwgs | | |
Covariates | BayesDLMfMRI | Covariates related to the observed BOLD response | data.frame | 310 | 2 |
ffd | BayesDLMfMRI | MNI image used to plot posterior probability maps in the vignette examples. | nifti | | |
mask | BayesDLMfMRI | A 3D array that works as a brain of reference (MNI atlas). | nifti | | |
utsnow | remap | Snowpack at weather stations in Utah on April 1st, 2011. | sf | 394 | 8 |
utws | remap | Watershed polygons within the state of Utah. | sf | 4 | 2 |
co2_emissions | vchartr | CO2 emissions | data.frame | 1095 | 12 |
co2_world | vchartr | World CO2 emissions | sf | 172 | 5 |
countries_gdp | vchartr | Countries GDP | data.frame | 177 | 4 |
eco2mix | vchartr | Monthly electricity generation by source in France | data.frame | 151 | 10 |
eco2mix_long | vchartr | Monthly electricity generation by source in France (long format) | data.frame | 1359 | 3 |
electricity_mix | vchartr | Electricity mix for 10 countries | data.frame | 70 | 4 |
energy_sankey | vchartr | Data for Sankey Chart | data.frame | 68 | 3 |
meteo_paris | vchartr | Paris climate | data.frame | 12 | 8 |
temperatures | vchartr | Temperature data | data.frame | 366 | 5 |
top_cran_downloads | vchartr | Top CRAN downloads | data.frame | 100 | 5 |
top_generation | vchartr | Top electricity-generating countries | data.frame | 10 | 2 |
world_electricity | vchartr | World low carbon & fossil electricity generation 2014 - 2023 | data.frame | 70 | 4 |
gp | encryptr | General Practioner (family doctor) practices in Scotland 2018 | spec_tbl_df | 1212 | 12 |
galleries | bs4cards | Generative art galleries | tbl_df | 7 | 7 |
georgia | geostan | | sf | 159 | 26 |
sentencing | geostan | Florida state prison sentencing counts by county, 1905-1910 | sf | 47 | 9 |
virtualSp | SDMtune | Virtual Species | list | | |
ibd | microViz | IBD study data in phyloseq object. | phyloseq | | |
shao19 | microViz | Gut microbiota relative abundance data from Shao et al. 2019 | phyloseq | | |
BostonHomicide | strucchangeRcpp | Youth Homicides in Boston | data.frame | 77 | 8 |
DJIA | strucchangeRcpp | Dow Jones Industrial Average | zooreg | | |
GermanM1 | strucchangeRcpp | German M1 Money Demand | data.frame | 140 | 14 |
Grossarl | strucchangeRcpp | Marriages, Births and Deaths in Grossarl | data.frame | 200 | 10 |
PhillipsCurve | strucchangeRcpp | UK Phillips Curve Equation Data | mts | 131 | 8 |
RealInt | strucchangeRcpp | US Ex-post Real Interest Rate | ts | 103 | |
SP2001 | strucchangeRcpp | S&P 500 Stock Prices | zoo | 103 | 500 |
USIncExp | strucchangeRcpp | Income and Expenditures in the US | mts | 506 | 2 |
durab | strucchangeRcpp | US Labor Productivity | mts | 650 | 2 |
historyM1 | strucchangeRcpp | German M1 Money Demand | data.frame | 118 | 14 |
monitorM1 | strucchangeRcpp | German M1 Money Demand | data.frame | 22 | 14 |
scPublications | strucchangeRcpp | Structural Change Publications | data.frame | 835 | 9 |
homa76 | catlearn | Category breadth CIRP | data.frame | 36 | 5 |
krus96 | catlearn | Inverse Base-rate Effect AP | data.frame | 36 | 3 |
nosof88 | catlearn | Instantiation frequency CIRP | data.frame | 36 | 3 |
nosof94 | catlearn | Type I-VI category structure CIRP | data.frame | 96 | 3 |
shin92 | catlearn | Category size CIRP | data.frame | 46 | 4 |
thegrid | catlearn | Ordinal adequacy results for all catlearn simulations | data.frame | 6 | 6 |
economiccomplexity_output | economiccomplexity | Example Outputs of the Functions within the Package | list | | |
world_gdp_avg_1998_to_2000 | economiccomplexity | World Trade Per-Capita GDP for the Period 1998-2000 | tbl_df | 240 | 2 |
world_trade_avg_1998_to_2000 | economiccomplexity | World Trade Averages for the Period 1998-2000 | tbl_df | 124336 | 3 |
hobbs | plotly | Hobbs data | data.frame | 300 | 3 |
mic | plotly | Mic data | data.frame | 305 | 3 |
res_mn | plotly | Minnesotan Indian Reservation Lands | sf | 13 | 6 |
wind | plotly | Wind data | data.frame | 32 | 3 |
APSIMdat | LMMsolver | Simulated Biomass as function of time using APSIM wheat. | data.frame | 121 | 4 |
SeaSurfaceTemp | LMMsolver | Sea Surface Temperature | data.frame | 15607 | 4 |
multipop | LMMsolver | Simulated QTL mapping data set | data.frame | 180 | 6 |
random128a | transport | Images to Illustrate the Use of transport.pgrid | pgrid | | |
random128b | transport | Images to Illustrate the Use of transport.pgrid | pgrid | | |
random32a | transport | Images to Illustrate the Use of transport.pgrid | pgrid | | |
random32b | transport | Images to Illustrate the Use of transport.pgrid | pgrid | | |
random64a | transport | Images to Illustrate the Use of transport.pgrid | pgrid | | |
random64b | transport | Images to Illustrate the Use of transport.pgrid | pgrid | | |
example_migration | hhmR | example_migration | data.frame | 324 | 5 |
example_time_series | hhmR | example_time_series | tbl_df | 90 | 4 |
NCHS2022_sample | COMMA | Example data from the National Vital Statistics System of the National Center for Health Statistics (NCHS), 2022 | data.frame | 20000 | 18 |
data | DeductiveR | Monthly flow data from Estero Culebron DGA station | tbl_df | 480 | 5 |
EVS | ConsRankClass | European Values Studies (EVS) data | list | | |
Irish | ConsRankClass | Irish Election data set | list | | |
Univranks | ConsRankClass | University rankings dataset. | list | | |
bakshy | webtrackR | Bakshy Top500 Ideological alignment of 500 domains based on facebook data | data.table | 500 | 7 |
domain_list | webtrackR | Domain list classification of domains into news,portals, search, and social media | data.table | 663 | 2 |
fake_tracking | webtrackR | Fake data | data.frame | 500 | 3 |
news_types | webtrackR | News Types | data.table | 690 | 2 |
testdt_survey_l | webtrackR | Test survey | tbl_df | 15 | 7 |
testdt_survey_w | webtrackR | Test survey | data.frame | 5 | 8 |
testdt_tracking | webtrackR | Test data | data.frame | 49612 | 5 |
chaotic_logistic_series | setartree | | list | | |
web_traffic_test | setartree | | data.frame | 5 | 11 |
web_traffic_train | setartree | | data.frame | 120 | 12 |
APAFULL | ConsRank | American Psychological Association dataset, full version | matrix | 15449 | 5 |
APAred | ConsRank | American Psychological Association dataset, reduced version with only full rankings | matrix | 5738 | 5 |
BU | ConsRank | Brook and Upton data | matrix | 6 | 4 |
EMD | ConsRank | Emond and Mason data | matrix | 21 | 16 |
German | ConsRank | German political goals | matrix | 2262 | 4 |
Idea | ConsRank | Idea data set | matrix | 98 | 5 |
USAranks | ConsRank | USA rank data | matrix | 104 | 50 |
sports | ConsRank | sports data | matrix | 130 | 7 |
iciotest_data | exvatools | ICIO-type input-output table example data | matrix | 30 | 42 |
wiodtest_data | exvatools | WIOD-type input-output table example data | matrix | 18 | 30 |
bs.list.example | ciuupi | The list that specifies the CIUUPI for the example | list | | |
req.objects | InferenceSMR | Required objects to run some examples | list | | |
screening | InferenceSMR | A population of size 10,000 for a screening program | data.frame | 10000 | 12 |
dafforne_accounts | debkeepr | Accounts from the practice journal and ledger of Richard Dafforne | tbl_df | 46 | 5 |
dafforne_transactions | debkeepr | Transactions from the practice journal and ledger of Richard Dafforne | tbl_df | 177 | 8 |
aal116 | brainGraph | Coordinates for data from brain atlases | data.table | 116 | 7 |
aal2.120 | brainGraph | Coordinates for data from brain atlases | data.table | 120 | 7 |
aal2.94 | brainGraph | Coordinates for data from brain atlases | data.table | 94 | 7 |
aal90 | brainGraph | Coordinates for data from brain atlases | data.table | 90 | 7 |
brainnetome | brainGraph | Coordinates for data from brain atlases | data.table | 246 | 13 |
brainsuite | brainGraph | Coordinates for data from brain atlases | data.table | 74 | 7 |
craddock200 | brainGraph | Coordinates for data from brain atlases | data.table | 200 | 8 |
destrieux | brainGraph | Coordinates for data from brain atlases | data.table | 148 | 8 |
destrieux.scgm | brainGraph | Coordinates for data from brain atlases | data.table | 162 | 8 |
dk | brainGraph | Coordinates for data from brain atlases | data.table | 68 | 8 |
dk.scgm | brainGraph | Coordinates for data from brain atlases | data.table | 82 | 8 |
dkt | brainGraph | Coordinates for data from brain atlases | data.table | 62 | 8 |
dkt.scgm | brainGraph | Coordinates for data from brain atlases | data.table | 76 | 8 |
dosenbach160 | brainGraph | Coordinates for data from brain atlases | data.table | 160 | 8 |
gordon333 | brainGraph | Coordinates for data from brain atlases | data.table | 333 | 9 |
hcp_mmp1.0 | brainGraph | Coordinates for data from brain atlases | data.table | 360 | 9 |
hoa112 | brainGraph | Coordinates for data from brain atlases | data.table | 112 | 7 |
lpba40 | brainGraph | Coordinates for data from brain atlases | data.table | 56 | 7 |
power264 | brainGraph | Coordinates for data from brain atlases | data.table | 264 | 10 |
wheatdata | SpATS | Wheat yield in South Australia | data.frame | 330 | 7 |
nsr_testfile | NSR | Example NSR data | data.frame | 22 | 5 |
results_2010 | compareDF | Data set created set to show off the package capabilities - Results of students for 2010 | data.frame | 12 | 8 |
results_2011 | compareDF | Data set created set to show off the package capabilities - Results of students for 2011 | data.frame | 13 | 8 |
bed.example | StratigrapheR | Data for examples | data.frame | 36 | 6 |
boundary.example | StratigrapheR | Data for examples | data.frame | 7 | 2 |
chron.example | StratigrapheR | Data for examples | data.frame | 3 | 4 |
example.HB2000.svg | StratigrapheR | Data for examples | character | | |
example.ammonite | StratigrapheR | Data for examples | data.frame | 277 | 4 |
example.ammonite.svg | StratigrapheR | Data for examples | character | | |
example.belemnite | StratigrapheR | Data for examples | data.frame | 153 | 4 |
example.breccia | StratigrapheR | Data for examples | data.frame | 23 | 4 |
example.lense | StratigrapheR | Data for examples | data.frame | 27 | 4 |
example.liquefaction | StratigrapheR | Data for examples | data.frame | 55 | 4 |
fossil.example | StratigrapheR | Data for examples | data.frame | 4 | 2 |
irreg.example | StratigrapheR | Data for examples | data.frame | 274 | 2 |
log.loop.tex | StratigrapheR | Data for examples | character | | |
noise_emd | StratigrapheR | Data for examples | data.frame | 6000 | 11 |
oufti99 | StratigrapheR | Collections of symbols | list | | |
proxy.example | StratigrapheR | Data for examples | data.frame | 76 | 3 |
proxy.example.litho | StratigrapheR | Data for examples | data.frame | 76 | 4 |
tie.points.example | StratigrapheR | Data for examples | data.frame | 10 | 6 |
zeq_example | StratigrapheR | Data for examples | data.frame | 13 | 5 |
dummy | wyz.code.rdoc | Data set dummy | data.table | 9 | 2 |
family | wyz.code.rdoc | Data set family | data.frame | 3 | 2 |
votingJNP2014 | FCVAR | Aggregate support for Canadian political parties. | data.frame | 316 | 6 |
example_data | Observation | Sample data collected interactively with direct observation program. | data.frame | 10 | 15 |
dynCorrData | dynCorr | dynCorrData | data.frame | 648 | 5 |
BERDFc | binfunest | An example 'BERDF' dataframe created by 'simsigs()', a function in a forthcoming package 'coherent'. | BERDF | 54 | 7 |
lasData | itcSegment | LiDAR data point cloud acquired over a forest area | data.frame | 16907 | 3 |
expdata | Ac3net | Real E.coli Gene Expression Dataset | matrix | 1146 | 524 |
truenet | Ac3net | The known intreactions of E.coli from RegulonDB database. | data.table | 3792 | 4 |
zeroindx | Ac3net | The indices to force to zero correlations. | integer | | |
OTU | iZID | Bacterial OTUs. | data.frame | 229 | 354 |
Hedenfalk | sgof | Hedenfalk data | data.frame | 3170 | 1 |
communities | COR | The communities and crime data set | data.frame | 1994 | 128 |
ethylene_CO | COR | The chemical sensor data set | data.frame | 4001 | 19 |
murljobs | muRL | A sample dataframe of recipient information and addresses | data.frame | 8 | 15 |
zips | muRL | A .tab file of US ZIP code data for mapping recipients | data.frame | 33309 | 4 |
miss | mtsdi | Sample Dataset | data.frame | 24 | 5 |
RPP_filtered | PRP | Filtered RPP data | data.frame | 73 | 5 |
mortality | PRP | Cardiovascular disease impact on the mortality of COVID-19 | data.frame | 6 | 3 |
severity | PRP | Cardiovascular disease impact on the severe case rate of COVID-19 | data.frame | 6 | 3 |
breast | success | Survival after breast cancer surgery | data.frame | 2663 | 11 |
surgerydat | success | Simulated data set with data of surgery procedures performed at multiple hospitals. | data.frame | 32529 | 9 |
coating | AHM | Photoresist-coating experiment data | data.frame | 42 | 7 |
design_simplex_centroid_design_3_major_component | AHM | Design points for the simplex centroid design with 3 components | data.frame | 10 | 3 |
pringles_candidates2search | AHM | The candidate search points in the nonlinear optimization for the optimal value in the Pringles experiment | matrix | 707373 | 7 |
pringles_fat | AHM | Pringles experiment data set with the percent of Fat as the response | data.frame | 17 | 13 |
pringles_hardness | AHM | Pringles experiment data set with the Hardness as the response | data.frame | 17 | 13 |
strokedata | causalPAF | Fictional ischemic stroke data case control data with risk factors, exposures and confounders | data.frame | 16623 | 21 |
strokedata_smallSample | causalPAF | Fictional ischemic stroke data case control data with risk factors, exposures and confounders | data.frame | 5000 | 21 |
Arroyo | HiveR | Plant-Pollinator Data Sets in Hive Plot Data Format | HivePlotData | | |
HEC | HiveR | A HivePlotData Object of the Hair Eye Color Data Set | HivePlotData | | |
Safari | HiveR | Plant-Pollinator Data Sets in Hive Plot Data Format | HivePlotData | | |
example_data | epiomics | Example data with multiple exposures, multiple outcomes, | data.frame | 400 | 116 |
for_bus_gtfs | GTFSwizard | GTFS Data for Fortaleza (Bus System), Brazil. | wizardgtfs | | |
for_gtfs | GTFSwizard | GTFS Data for Fortaleza, Brazil. | wizardgtfs | | |
for_rail_gtfs | GTFSwizard | GTFS Data for Fortaleza (Rail System), Brazil | wizardgtfs | | |
cchs2001_p | cchsflow | 2001 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 614 |
cchs2003_p | cchsflow | 2003 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1068 |
cchs2005_p | cchsflow | 2005 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1284 |
cchs2007_2008_p | cchsflow | 2007-2008 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1195 |
cchs2009_2010_p | cchsflow | 2009-2010 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1254 |
cchs2009_s | cchsflow | 2009 CCHS synthetic subset data (200 respondents) | data.frame | 200 | 1845 |
cchs2010_p | cchsflow | 2010 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1325 |
cchs2010_s | cchsflow | 2010 CCHS synthetic subset data (200 respondents) | data.frame | 200 | 1955 |
cchs2011_2012_p | cchsflow | 2011-2012 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1207 |
cchs2012_p | cchsflow | 2012 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1381 |
cchs2012_s | cchsflow | 2012 CCHS synthetic subset data (200 respondents) | data.frame | 200 | 1815 |
cchs2013_2014_p | cchsflow | 2013-2014 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1003 |
cchs2014_p | cchsflow | 2014 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1129 |
cchs2015_2016_p | cchsflow | 2015-2016 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1283 |
cchs2017_2018_p | cchsflow | 2017-2018 CCHS PUMF subset data (200 respondents) | data.frame | 200 | 1051 |
variable_details | cchsflow | variable_details.csv | data.frame | 3266 | 16 |
variables | cchsflow | variables.csv | data.frame | 337 | 10 |
params_sinaica | rsinaica | Valid air quality parameters | data.frame | 55 | 2 |
stations_sinaica | rsinaica | Air quality measuring stations in Mexico | data.frame | 365 | 26 |
polblogs | nett | Political blogs network | igraph | | |
cholera_cases | SnowData | Cholera Cases Dataset | spec_tbl_df | 317 | 5 |
pump_locations | SnowData | Water Pump Locations Dataset | spec_tbl_df | 13 | 3 |
streets | SnowData | Streets Dataset | tbl_df | 436 | 8 |
hayden96flu | DSAIRM | Influenza virus load data | data.frame | 27 | 4 |
schirm20strep | DSAIRM | Streptococcus pneumoniae infection data | data.frame | 99 | 3 |
FIFA2018 | distributions3 | Goals scored in all 2018 FIFA World Cup matches | data.frame | 128 | 7 |
motor | dbnR | Multivariate time series dataset on the temperature of an electric motor | data.table | 3000 | 11 |
exampleData | trimr | Example response time data set | data.frame | 20518 | 4 |
linearInterpolation | trimr | SDs used for the recursive / moving criterion trimming methods | data.frame | 97 | 3 |
AML43 | Canopy | SNA input for primary tumor and relapse genome of leukemia patient from Ding et al. Nature 2012. | list | | |
MDA231 | Canopy | Dataset for project MDA231 | list | | |
MDA231_sampchain | Canopy | List of pre-sampled trees | list | | |
MDA231_tree | Canopy | Most likely tree from project MDA231 | phylo | | |
toy | Canopy | Toy dataset for Canopy | list | | |
toy2 | Canopy | Toy dataset 2 for Canopy | list | | |
toy3 | Canopy | Toy dataset 3 for Canopy | list | | |
agadullina2018 | psymetadata | Studies on Out-Group Entitativity and Prejudice | data.frame | 85 | 9 |
aksayli2019 | psymetadata | Studies on the cognitive and academic benefits of Cogmed | data.frame | 637 | 15 |
barroso2021 | psymetadata | Studies on Math Anxiety and Math Achievement | data.frame | 747 | 11 |
coles2019 | psymetadata | Studies on the Facial Feedback Literature | data.frame | 286 | 13 |
gamble2019 | psymetadata | Meta-analytic data collected from studying on the specificity of future thinking in depression | data.frame | 89 | 20 |
gnambs2020 | psymetadata | Studies on the Color Red and Cognitive Performance | data.frame | 67 | 10 |
lowe2020 | psymetadata | Studies on the advantage of bilingualism in children: a meta-analytic review | tbl_df | 1194 | 20 |
maccann2020 | psymetadata | Studies examining whether student emotional intelligence is associated with academic performance | escalc | 1246 | 20 |
maldonado2020 | psymetadata | Studies on Age Differences in Executive Functioning | data.frame | 1268 | 13 |
manybabies2020 | psymetadata | Studies from ManyBabies 1: Infant-Directed Speech Preference. | data.frame | 108 | 10 |
manylabs2018 | psymetadata | Studies from the Many Labs 2 project. | data.frame | 1414 | 17 |
noble2019 | psymetadata | Studies on Shared Reading and Language Development | data.frame | 316 | 13 |
nuijten2020 | psymetadata | Data collected from meta-analyses on intelligence research | data.frame | 2443 | 14 |
sala2019 | psymetadata | Studies on the impact of working-memory training on near- and far-transfer measures | data.frame | 1555 | 10 |
schroeder2020 | psymetadata | Studies on the effects of transcranial direct current stimulation on inhibitory control | data.frame | 75 | 14 |
spaniol2020 | psymetadata | Studies on Executive function components in intellectual disability | escalc | 99 | 11 |
stasielowicz2019a | psymetadata | Studies on the association between goal orientation and performance adaptation | data.frame | 76 | 24 |
stasielowicz2019b | psymetadata | Studies on the association between goal orientation and performance adaptation | data.frame | 86 | 25 |
stasielowicz2020 | psymetadata | Studies on the importance of cognitive ability in performance adaptation | data.frame | 133 | 23 |
steffens2020 | psymetadata | Studies on Social Identity Theory and Leadership: Leader Group Prototypicality | data.frame | 251 | 10 |
stramaccia2021 | psymetadata | Studies on memory suppression and its deficiency in psychological disorders | escalc | 96 | 15 |
wibbelink2017 | psymetadata | Studies on juvenile recidivism | data.frame | 100 | 10 |
mc_attitudes | discrtr | McMaster Attitudes Data Set. | tbl_df | 1230 | 39 |
mc_commute_long | discrtr | McMaster Commuting Data Set (long format). | dfidx_mlogit | 5500 | 62 |
mc_commute_wide | discrtr | McMaster Commuting Data set (wide format). | data.frame | 1375 | 74 |
mc_modality | discrtr | McMaster Modality Data Set. | tbl_df | 4146 | 37 |
urban_types | discrtr | Hamilton City boundaries and urban types | sf | 3 | 2 |
Authors_stagflation | biblionetwork | List of Authors of the Articles and Books Explaining the 1970s US Stagflation. | data.frame | 231 | 3 |
Nodes_stagflation | biblionetwork | Articles and Books Explaining the 1970s US Stagflation. | data.table | 654 | 7 |
Ref_stagflation | biblionetwork | Articles and Books Explaining the 1970s US Stagflation. | data.table | 4416 | 6 |
onbabynames | onbabynames | Names of babies in Ontario Between 1917 and 2018 | tbl_df | 161703 | 4 |
uhecrauger2014 | SPADAR | Auger 2014 Ultra-high Energy Cosmic Ray Events | data.frame | 231 | 3 |
data_earthquake_6.5_7 | ERPeq | Earthquake dataset | numeric | | |
data_earthquake_6_6.5 | ERPeq | Earthquake dataset | numeric | | |
data_earthquake_7 | ERPeq | Earthquake dataset | numeric | | |
followup | ordinalRR | Followup data from experiment on soldered joints. | data.frame | 30 | 6 |
DEVICE1 | SpecDetec | DEVICE1 | data.frame | 375 | 2 |
DEVICE2 | SpecDetec | DEVICE2 | data.frame | 375 | 2 |
DEVICE3 | SpecDetec | DEVICE3 | data.frame | 375 | 2 |
DEVICE4 | SpecDetec | DEVICE4 | data.frame | 375 | 2 |
DEVICE5 | SpecDetec | DEVICE5 | data.frame | 375 | 2 |
DEVICE6 | SpecDetec | DEVICE6 | data.frame | 375 | 2 |
FTIR1 | SpecDetec | FTIR1 | data.frame | 60 | 2 |
FTIR2 | SpecDetec | FTIR2 | data.frame | 60 | 2 |
FTIR3 | SpecDetec | FTIR3 | data.frame | 60 | 2 |
FTIR4 | SpecDetec | FTIR4 | data.frame | 60 | 2 |
FTIR5 | SpecDetec | FTIR5 | data.frame | 60 | 2 |
FTIR6 | SpecDetec | FTIR6 | data.frame | 60 | 2 |
Rigaweb | diagram | Gulf of Riga autumn planktonic food web | matrix | 9 | 9 |
Takapotoweb | diagram | Takapoto atoll planktonic food web | matrix | 10 | 10 |
Teasel | diagram | Population dynamics model transition matrix of teasel | matrix | 6 | 6 |
city_population | heuristica | Population size of the 83 largest German cities. | data.frame | 83 | 12 |
city_population_original | heuristica | Original, uncorrected Population size of the 83 largest German cities. | data.frame | 83 | 12 |
highschool_dropout | heuristica | Chicago high school dropout rates. | data.frame | 63 | 23 |
example_model | dyngen | A (very!) small toy dyngen model | list | | |
realcounts | dyngen | A set of real single cell expression datasets | tbl_df | 111 | 9 |
realnets | dyngen | A set of gold standard gene regulatory networks | tbl_df | 32 | 2 |
envData | SMDIC | envData | environment | | |
xCell.data | SMDIC | xCell datasets | list | | |
MC15 | DNAmixturesLite | The MC15 DNA mixture | data.frame | 51 | 6 |
MC18 | DNAmixturesLite | The MC18 DNA mixture | data.frame | 52 | 6 |
NGM | DNAmixturesLite | NGM allele frequencies | list | | |
NGMDyes | DNAmixturesLite | Dyes used for NGM | list | | |
ProfilerDyes | DNAmixturesLite | Dyes used for Profiler plus | list | | |
SGMplusDyes | DNAmixturesLite | Dyes used for SGMplus | list | | |
UKCaucasian | DNAmixturesLite | Allele frequencies for UK Caucasians | data.frame | 140 | 4 |
USCaucasian | DNAmixturesLite | The data base of allele frequencies for 302 US Caucasian profiles. | data.frame | 153 | 3 |
dietary_survey_IBS | ClusterR | Synthetic data using a dietary survey of patients with irritable bowel syndrome (IBS) | data.frame | 400 | 43 |
mushroom | ClusterR | The mushroom data | data.frame | 8124 | 23 |
soybean | ClusterR | The soybean (large) data set from the UCI repository | data.frame | 307 | 36 |
example.data | BayesOrdDesign | Clinical ordinal endpoints and treatments assignment for 200 patient | data.frame | 201 | 2 |
IRTsim | PLmixed | Simulated multilevel IRT dataset. | data.frame | 2500 | 4 |
JUDGEsim | PLmixed | Simulated Multi-rater Multi-response dataset. | data.frame | 54462 | 7 |
KYPSitemsim | PLmixed | Simulated KYPS item-level dataset. | data.frame | 66947 | 6 |
KYPSsim | PLmixed | Simulated KYPS dataset. | data.frame | 11494 | 5 |
Liver_Expr | fuzzyforest | Liver Expression Data from Female Mice | data.frame | 66 | 3601 |
ctg | fuzzyforest | Cardiotocography Data Set | data.frame | 100 | 22 |
example_ff | fuzzyforest | Fuzzy Forest Example | fuzzy_forest | | |
bli | giniCI | OECD Better Life Index Indicators | data.frame | 144 | 13 |
bixi_spatial_features | BKTR | Spatial Features of Montreal BIXI Stations in 2019 | data.table | 587 | 14 |
bixi_spatial_locations | BKTR | Spatial Locations of Montreal BIXI Stations in 2019 | data.table | 587 | 3 |
bixi_station_departures | BKTR | Daily Departure from BIXI Stations in 2019 | data.table | 587 | 197 |
bixi_temporal_features | BKTR | Temporal Features in Montreal applicable to BIXI for 2019 | data.table | 196 | 6 |
bixi_temporal_locations | BKTR | Temporal indices for the 2019 BIXI season | data.table | 196 | 2 |
ggprism_data | ggprism | Palettes and theme data for ggprism | list | | |
wings | ggprism | Wing morphology of mutant flies | tbl_df | 120 | 4 |
example3Dtrees | treeDbalance | Examples of rooted 3D trees | list | | |
mydata | IFTPredictor | Example Dataset for DIFtree | data.frame | 500 | 24 |
simData | plsmselect | Simulated dataset to be used for gamlasso | data.frame | 100 | 23 |
HallOfFame | NBAloveR | HallOfFame data set | data.frame | 191 | 4 |
franchise | NBAloveR | franchise data set | data.frame | 30 | 13 |
players | NBAloveR | players data set | data.frame | 21282 | 13 |
cpi | acs | Consumer Price Index data (1913-2015). | numeric | | |
fips.american.indian.area | acs | FIPS codes and geographic names for use in searching and creating geographies in the acs package. | data.frame | 692 | 2 |
fips.county | acs | FIPS codes and geographic names for use in searching and creating geographies in the acs package. | data.frame | 3235 | 5 |
fips.county.subdivision | acs | FIPS codes and geographic names for use in searching and creating geographies in the acs package. | data.frame | 36642 | 7 |
fips.place | acs | FIPS codes and geographic names for use in searching and creating geographies in the acs package. | data.frame | 41414 | 7 |
fips.school | acs | FIPS codes and geographic names for use in searching and creating geographies in the acs package. | data.frame | 13710 | 5 |
fips.state | acs | FIPS codes and geographic names for use in searching and creating geographies in the acs package. | data.frame | 57 | 4 |
kansas07 | acs | County-level data from the 2007 American Community Survey for Kansas for use in examples of acs package. | acs | | |
kansas09 | acs | County-level data from the 2005-2009 American Community Survey for Kansas for use in examples of acs package. | acs | | |
lawrence10 | acs | Tract-level data from the 2006-2010 American Community Survey for Lawrence, MA for use in examples of acs package. | acs | | |
FactSheet | RoundAndRound | This is data to be included in my package | data.frame | 20 | 12 |
basemaps | mapboxer | A list of basemap style URLs | list | | |
motor_vehicle_collisions_nyc | mapboxer | Motor Vehicle Collisions in NYC | data.frame | 1601 | 6 |
Elpaso | bayesMRM | PM2.5 speciation data from El Paso, Texas, USA. | list | | |
IALS_data | mult.latent.reg | International Adult Literacy Survey (IALS) for 13 countries | data.frame | 26 | 5 |
fetal_covid_data | mult.latent.reg | A set of fetal movements data collected before and during the Covid-19 pandemic | data.frame | 40 | 7 |
trading_data | mult.latent.reg | A set of import and export data in 44 countries. | data.frame | 205 | 3 |
twins_data | mult.latent.reg | A set of fetal movements data in twins. | data.frame | 34 | 6 |
dna.obj | haplotypes | Example DNA sequence data | Dna | | |
example0 | rgTest | Example | list | | |
ciaaw.mass.2003 | CIAAWconsensus | Atomic masses of isotopes (IUPAC/CIAAW 2003) | data.frame | 269 | 4 |
ciaaw.mass.2012 | CIAAWconsensus | Atomic masses of isotopes (IUPAC/CIAAW 2012) | data.frame | 269 | 4 |
ciaaw.mass.2016 | CIAAWconsensus | Atomic masses of isotopes (IUPAC/CIAAW 2016) | data.frame | 269 | 4 |
iridium.data | CIAAWconsensus | Iridium isotope ratio data from various studies | data.frame | 5 | 7 |
platinum.data | CIAAWconsensus | Platinum isotope ratio data from various studies | data.frame | 20 | 7 |
asmbPLS.example | asmbPLS | Example data for asmbPLS algorithm | list | | |
asmbPLSDA.example | asmbPLS | Example data for asmbPLS-DA algorithm | list | | |
data_bookReviews | classmap | Amazon book reviews data | data.frame | 1000 | 2 |
data_floralbuds | classmap | Floral buds data | data.frame | 550 | 7 |
data_instagram | classmap | Instagram data | data.frame | 696 | 13 |
data_titanic | classmap | Titanic data | data.frame | 1309 | 13 |
synthetic_us_2010 | CausalGPS | Public data set for air pollution and health studies, case study: 2010 county-Level data set for the contiguous United States | data.frame | 3109 | 46 |
example.vectors.list | veccompare | Example Vectors List | list | | |
sero | viraldomain | Seropositive Data for Applicability Domain Testing | data.frame | 53 | 2 |
viral | viraldomain | Predictive Modeling Data for Viral Load and CD4 Lymphocyte Counts | data.frame | 35 | 6 |
dbEUF2018meta | convergEU | Metainformation on Eurofound dataset | tbl_df | 13 | 10 |
dbEurofound | convergEU | Eurofound dataset | tbl_df | 10964 | 17 |
dbMetaEUStat | convergEU | Eurostat metainformation | tbl_df | 56 | 10 |
emp_20_64_MS | convergEU | Dataset emp_20_64_MS | spec_tbl_df | 17 | 29 |
D | hot.deck | Example data for multiple hot deck imputation. | data.frame | 20 | 5 |
ampData | hot.deck | Example data for multiple hot deck imputation with ordinal variables. | data.frame | 1000 | 20 |
isq99 | hot.deck | Data from Poe, Tate and Keith 1999. | data.frame | 3222 | 13 |
EDR_data | ecoregime | Ecological Dynamic Regime data | list | | |
oasis | rsmatch | Longitudinal MRI data in nondemented and demented older adults. | tbl_df | 115 | 11 |
example_data | multibreakeR | Example MultibreakeR simulated data | matrix | 100 | 5 |
datenKapitel01 | LSAmitR | Illustrationsdaten zu Kapitel 1, Testkonstruktion | list | | |
datenKapitel02 | LSAmitR | Illustrationsdaten zu Kapitel 2, Stichprobenziehung | list | | |
datenKapitel03 | LSAmitR | Illustrationsdaten zu Kapitel 3, Standard-Setting | list | | |
datenKapitel04 | LSAmitR | Illustrationsdaten zu Kapitel 4, Differenzielles Itemfunktionieren in Subgruppen | list | | |
datenKapitel05 | LSAmitR | Illustrationsdaten zu Kapitel 5, Testdesign | list | | |
datenKapitel06 | LSAmitR | Illustrationsdaten zu Kapitel 6, Skalierung und Linking | list | | |
datenKapitel07 | LSAmitR | Illustrationsdaten zu Kapitel 7, Statistische Analysen produktiver Kompetenzen | list | | |
datenKapitel08 | LSAmitR | Illustrationsdaten zu Kapitel 8, Fehlende Daten und Plausible Values | list | | |
datenKapitel09 | LSAmitR | Illustrationsdaten zu Kapitel 9, Fairer Vergleich in der Rueckmeldung | data.frame | 244 | 50 |
datenKapitel10 | LSAmitR | Illustrationsdaten zu Kapitel 10, Reporting und Analysen | list | | |
orni | seewave | Song of the cicada Cicada orni | Wave | | |
peewit | seewave | Song of the bird Vanellus vanellus | Wave | | |
pellucens | seewave | Calling song of the tree cricket Oecanthus pellucens | Wave | | |
sheep | seewave | Sheep bleat | Wave | | |
tico | seewave | Song of the bird Zonotrichia capensis | Wave | | |
airports | carbonr | Table of airport detail data | tbl_df | 7698 | 17 |
example_clinical_theatre | carbonr | Clinical Theatre Data | tbl_df | 4386 | 6 |
seaports | carbonr | Dataset of different seaports | data.frame | 8736 | 6 |
stations | carbonr | Dataset of UK train stations | tbl_df | 2608 | 7 |
vignette_1_data | RWildbook | Data for the first vignette. | data.frame | 69 | 65 |
vignette_2_data | RWildbook | Data for the second vignette. | data.frame | 1016 | 65 |
foodtruck | utiml | Foodtruck multi-label dataset. | mldr | | |
toyml | utiml | Toy multi-label dataset. | mldr | | |
McAbeeExample | dilp | McAbee Example Data | spec_tbl_df | 192 | 18 |
climate_calibration_data | dilp | Climate Calibration Data | spec_tbl_df | 92 | 3 |
physiognomy_calibration_data | dilp | Physiognomy Calibration Data | spec_tbl_df | 92 | 12 |
ensembl_gene_lengths | ICBioMark | Gene Lengths from the Ensembl Database | data.frame | 19795 | 3 |
example_first_pred_tmb | ICBioMark | First-Fit Predictive Model Fitting on Example Data | list | | |
example_gen_model | ICBioMark | Generative Model from Simulated Data | list | | |
example_maf_data | ICBioMark | Simulated MAF Data | list | | |
example_predictions | ICBioMark | Example Predictions | list | | |
example_refit_panel | ICBioMark | Refitted Predictive Model Fitted on Example Data | list | | |
example_refit_range | ICBioMark | Refitted Predictive Models Fitted on Example Data | list | | |
example_tables | ICBioMark | Mutation Matrices from Simulated Data | list | | |
example_tib_tables | ICBioMark | Tumour Indel Burden of Example Train, Validation and Test Data. | list | | |
example_tmb_tables | ICBioMark | Tumour Mutation Burden of Example Train, Validation and Test Data. | list | | |
nsclc_maf | ICBioMark | Non-Small Cell Lung Cancer MAF Data | data.frame | 299855 | 6 |
nsclc_survival | ICBioMark | Non-Small Cell Lung Cancer Survival and Clinical Data | data.frame | 1144 | 23 |
bootstrap_iRAM_2node | pompom | Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the bivariate time-series (simts2node) | list | | |
bootstrap_iRAM_3node | pompom | Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the 3-variate time-series (simts) | list | | |
simts_2node | pompom | Simulated bivariate time-series data | data.frame | 200 | 2 |
simts_3node | pompom | Simulated 3-variate time-series data | data.frame | 100 | 3 |
true_beta_2node | pompom | The true beta matrix (4 by 4) used in simulation. | matrix | 4 | |
true_beta_3node | pompom | The true beta matrix (6 by 6) used in simulation. | matrix | 6 | |
usemmodelfit | pompom | Model fitbased on similated time-series by uSEM. | lavaan | | |
survival | multiplestressR | Survival data for 250 populations exposed to the stressors of temperature and pH. | data.frame | 250 | 12 |
datagvp1 | gluvarpro | clean data-set from Abbott continuous glucose monitoring | data.frame | 576 | 3 |
datagvp2 | gluvarpro | clean data-set from Medtronic continuous glucose monitoring | data.frame | 10368 | 3 |
datagvp3 | gluvarpro | clean data-set from Abbott continuous glucose monitoring | data.frame | 45696 | 3 |
datagvp4 | gluvarpro | raw data-set from Medtronic continuous glucose monitoring | data.frame | 4004 | 47 |
municipality | plotDK | Municipality data with keys and polygon-geoms for municipalities of Denmark | data.frame | 39230 | 7 |
municipality_info | plotDK | Information of Valid Municipality Names and Numbers | data.frame | 99 | 2 |
province | plotDK | Province data with keys and polygon-geoms for provinces of Denmark | data.frame | 4083 | 7 |
province_info | plotDK | Information of Valid Province Names and Numbers | data.frame | 11 | 2 |
region | plotDK | Region data with keys and polygon-geoms for regions of Denmark | data.frame | 32522 | 7 |
region_info | plotDK | Information of Valid Region Names and Numbers | data.frame | 5 | 2 |
zipcode_info | plotDK | Information of Valid Zipcodes | data.frame | 598 | 1 |
zipcodes | plotDK | Zipcode data with keys and polygon-geoms for zipcodes of Denmark | data.frame | 49322 | 7 |
df | easySimData | Example data from 'simdata' package | data.frame | 4709 | 7 |
exampledata | perturbR | Example, symmetric weighted count matrix | matrix | 25 | 25 |
colonIDM | presmTP | Chemotherapy for Stage B/C colon cancer. | data.frame | 929 | 15 |
CECERS | MCCM | Chinese Early Childhood Environment Rating Scale | spec_tbl_df | 1383 | 95 |
Parenteral_nutrition | MCCM | Parenteral_nutrition | tbl_df | 1086 | 29 |
goldcrest | poptrend | Data for goldcrest from the Swedish Bird Survey. | data.frame | 5728 | 8 |
greenfinch | poptrend | Data for greenfinch from the Swedish Bird Survey. | data.frame | 5728 | 8 |
fmulti_data | HDSpatialScan | Multivariate functional data | list | | |
funi_data | HDSpatialScan | Univariate functional data | matrix | 169 | 56 |
map_sites | HDSpatialScan | Spatial object corresponding to the sites of the data of the package HDSpatialScan | SpatialPolygonsDataFrame | | |
multi_data | HDSpatialScan | Multivariate non-functional data | matrix | 169 | |
RDMCCP16 | adiv | Theoretical Data Set used in Ricotta et al. (2016) | list | | |
RP15EI | adiv | Theoretical Data Set used in Ricotta and Pavoine (2015) in Ecological Indicators | list | | |
RP15JVS | adiv | Theoretical Data Set used in Ricotta and Pavoine (2015) in Journal of Vegetation Science | list | | |
RPP16EE | adiv | Theoretical Data Set used in Ricotta et al. (2016) in Ecology and Evolution | list | | |
RutorGlacier | adiv | Functional and phylogenetic composition of plant communities along a primary succession on glacial deposits. | list | | |
batcomm | adiv | Bat Abundance and Phylogeny Along a Disturbance Gradient in a Neotropical Rainforest | list | | |
birdData | adiv | Avian Communities along Successional Forest Gradients | list | | |
rockfish | adiv | Rockfish Phylogenetic Diversity in Southern California Bight | list | | |
Data_toy | FlexVarJM | Data_toy | data.frame | 1184 | 5 |
InadequacyData | PerFit | The NPV-J inadequacy scale data | data.frame | 806 | 28 |
IntelligenceData | PerFit | Intelligence data (number completion) | data.frame | 1000 | 26 |
PhysFuncData | PerFit | The SF-36 physical functioning data | data.frame | 714 | 10 |
SampleTest | qcQpcr | qPCR ChIP-seq Results | tbl_df | 64 | 11 |
brainvol | metaviz | Example data for a meta-analysis of correlations: Human brain volume and intelligence | data.frame | 83 | 8 |
exrehab | metaviz | Example data for a meta-analysis of dichotomous outcomes: Exercise-based cardiac rehabilitation | data.frame | 14 | 15 |
homeopath | metaviz | Example data for a meta-analysis of standardized mean differences: Homeopathic treatment vs. Placebo | data.frame | 54 | 4 |
mozart | metaviz | Example data for a meta-analysis of standardized mean differences: Mozart effect | data.frame | 38 | 6 |
pbc_gmfamm | gmfamm | Subset of PBC data set for GMFAMM | data.frame | 5943 | 10 |
RandomPhysician | InSilicoVA | 100 records of Sample Input together with two simulated physician codes | data.frame | 1000 | 250 |
RandomVA1 | InSilicoVA | 1000 records of Sample Input | data.frame | 1000 | 246 |
RandomVA2 | InSilicoVA | 100 records of Sample Input | data.frame | 1000 | 248 |
SampleCategory | InSilicoVA | Correspondence between InterVA causes and the physician coded cause categories | data.frame | 60 | 2 |
SamplePhysician | InSilicoVA | 100 records of Sample debiased physician codes | matrix | 100 | 7 |
causetext | InSilicoVA | Translation list of COD codes | matrix | 68 | 3 |
condprob | InSilicoVA | Conditional probability table used by InterVA-4 | matrix | 245 | 60 |
condprobnum | InSilicoVA | Conditional probability values used by InterVA-4 | matrix | 245 | 60 |
probbase | InSilicoVA | Conditional probability of InterVA4 | matrix | 246 | 81 |
probbase3 | InSilicoVA | Conditional probability of InterVA4.03 | matrix | 246 | 81 |
Lipid | nlrr | Lipid and diabetes | data.frame | 2000 | 4 |
data.immer01a | immer | Some Example Datasets for the 'immer' Package | data.frame | 23904 | 8 |
data.immer01b | immer | Some Example Datasets for the 'immer' Package | data.frame | 4244 | 8 |
data.immer02 | immer | Some Example Datasets for the 'immer' Package | data.frame | 6105 | 6 |
data.immer03 | immer | Some Example Datasets for the 'immer' Package | data.frame | 6466 | 6 |
data.immer04a | immer | Some Example Datasets for the 'immer' Package | data.frame | 25578 | 7 |
data.immer04b | immer | Some Example Datasets for the 'immer' Package | data.frame | 2975 | 7 |
data.immer05 | immer | Some Example Datasets for the 'immer' Package | data.frame | 21398 | 9 |
data.immer06 | immer | Some Example Datasets for the 'immer' Package | matrix | 1278 | |
data.immer07 | immer | Some Example Datasets for the 'immer' Package | data.frame | 1500 | 6 |
data.immer08 | immer | Some Example Datasets for the 'immer' Package | data.frame | 16 | 3 |
data.immer09 | immer | Some Example Datasets for the 'immer' Package | data.frame | 128 | 3 |
data.immer10 | immer | Some Example Datasets for the 'immer' Package | data.frame | 61 | 15 |
data.immer11 | immer | Some Example Datasets for the 'immer' Package | numeric | | |
data.immer12 | immer | Some Example Datasets for the 'immer' Package | data.frame | 180 | 7 |
data.ptam1 | immer | Example Datasets for Robitzsch and Steinfeld (2018) | data.frame | 9395 | 4 |
data.ptam2 | immer | Example Datasets for Robitzsch and Steinfeld (2018) | data.frame | 1043 | 21 |
data.ptam3 | immer | Example Datasets for Robitzsch and Steinfeld (2018) | data.frame | 523 | 25 |
data.ptam4 | immer | Example Datasets for Robitzsch and Steinfeld (2018) | data.frame | 592 | 5 |
data.ptam4long | immer | Example Datasets for Robitzsch and Steinfeld (2018) | data.frame | 1776 | 17 |
data.ptam4wide | immer | Example Datasets for Robitzsch and Steinfeld (2018) | data.frame | 40 | 11 |
DataHR | MetabolicSurv | Survival and Prognostic Data . | data.frame | 149 | 5 |
clayloam | envalysis | Hydrometer readings for a clay loam | data.frame | 7 | 4 |
din32645 | envalysis | Calibration data from DIN 32645 | data.frame | 20 | 2 |
icp | envalysis | ICP-AES calibration data | data.frame | 16 | 6 |
neitzel2003 | envalysis | Calibration data from Neitzel (2003) | data.frame | 20 | 2 |
phenolics | envalysis | Degradation of phenolic compounds by Steinmetz et al. (2019) | list | | |
mars_screw | ktaucenters | Intensity and saturation values of a picture from mars. | list | | |
CGdata | adproclus | Randomly generated data with underlying overlapping clusters. | data.frame | 100 | 15 |
Pbear | popdemo | Polar bear matrices | list | | |
Tort | popdemo | Desert tortoise matrix | matrix | 8 | 8 |
likingLong | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 2916 | 6 |
liking_a | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 54 | 54 |
liking_b | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 54 | 54 |
metaliking_a | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 54 | 54 |
metaliking_b | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 54 | 54 |
multiGroup | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 5114 | 5 |
multiLikingLong | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 500 | 7 |
multiNarc | TripleR | Triple R: Round-Robin Analyses Using R | data.frame | 194 | 2 |
pitts_emojis | ggDoubleHeat | Popular Emojis | character | | |
pitts_tg | ggDoubleHeat | Pittsburgh COVID-related Google & Twitter incidence rates | tbl_df | 270 | 6 |
states_tg | ggDoubleHeat | States' COVID-related Google & Twitter incidence rates | tbl_df | 1116 | 6 |
ConductorFailureTimes | TLCAR | Dataset: ConductorFailureTimes | numeric | | |
Tree_diameters | TLCAR | Dataset: Tree_diameters | numeric | | |
llsr_data | LLSR | LLSR's database | list | | |
peg4kslt | LLSR | Dataset of experimental binodal data of an ATPS | data.frame | 115 | 8 |
wavedata | oceanwaves | Example wave records | data.frame | 7200 | 4 |
BT_LW_H | heritability | Bolting time and leaf width for the Arabidopsis hapmap population. | data.frame | 1014 | 6 |
K_atwell | heritability | Marker-based relatedness matrices for 3 populations of Arabidopsis thaliana. | matrix | 199 | 199 |
K_hapmap | heritability | Marker-based relatedness matrices for 3 populations of Arabidopsis thaliana. | matrix | 350 | 350 |
K_swedish | heritability | Marker-based relatedness matrices for 3 populations of Arabidopsis thaliana. | matrix | 304 | 304 |
LA_H | heritability | _Arabidopsis_ leaf area data for the hapmap and Swedish regmap population. | data.frame | 1170 | 13 |
LA_S | heritability | _Arabidopsis_ leaf area data for the hapmap and Swedish regmap population. | data.frame | 970 | 13 |
LD | heritability | Flowering time data taken from Atwell _et al._ (2010). | data.frame | 914 | 8 |
LDV | heritability | Flowering time data taken from Atwell _et al._ (2010). | data.frame | 879 | 8 |
R_matrix_LD | heritability | Covariance matrix of the accession means for flowering time. | matrix | 167 | 167 |
R_matrix_LDV | heritability | Covariance matrix of the accession means for flowering time. | matrix | 168 | 168 |
means_LD | heritability | Flowering time from Atwell _et al._ (2010): accession means. | asremlPredict | 167 | 2 |
means_LDV | heritability | Flowering time from Atwell _et al._ (2010): accession means. | asremlPredict | 168 | 2 |
DS14 | mokken | DS14 | matrix | 541 | 16 |
SWMD | mokken | SWMD Data Subset | data.frame | 651 | 8 |
SWMDK | mokken | SWMDK Data Subset | data.frame | 639 | 14 |
acl | mokken | Adjective Checklist Data | matrix | 433 | 218 |
autonomySupport | mokken | Autonomy Support Data | data.frame | 259 | 8 |
balance | mokken | Balance Data | data.frame | 484 | 25 |
cavalini | mokken | Coping Strategies | data.frame | 828 | 17 |
mcmi | mokken | Millon Clinical Multiaxial Inventory | data.frame | 1208 | 44 |
transreas | mokken | Transitive Reasoning | data.frame | 425 | 13 |
transreas2 | mokken | Transitive Reasoning Data | data.frame | 606 | 16 |
trog | mokken | trog Data | data.frame | 210 | 80 |
polyads | TraMineRextras | Polyadic data example | data.frame | 30 | 11 |
LPdata | zCompositions | La Paloma data set | data.frame | 96 | 15 |
LPdataZM | zCompositions | La Paloma data set (incl. zeros and missing data) | data.frame | 96 | 15 |
Pigs | zCompositions | Pigs data set | data.frame | 29 | 6 |
Water | zCompositions | Water data set | data.frame | 100 | 4 |
mdl | zCompositions | Water data set: matrix of limits of detection | matrix | 100 | |
KidneyMimic | DepCens | KidneyMimic data set | data.frame | 200 | 9 |
dlpfc151510 | DR.SC | A human dorsolateral prefrontal cortex data | Seurat | | |
cattle_chip | MoBPS | Cattle chip | data.frame | 29 | 3 |
chicken_chip | MoBPS | chicken chip | data.frame | 36 | 3 |
ex_json | MoBPS | ex_json | list | | |
ex_pop | MoBPS | ex_pop | population | | |
maize_chip | MoBPS | maize chip | data.frame | 10 | 3 |
pig_chip | MoBPS | pig chip | data.frame | 18 | 3 |
sheep_chip | MoBPS | sheep chip | data.frame | 26 | 3 |
ALL_colosimo_table_4_1 | EstimationTools | Acute Lymphoblastic Leukemia, table 4.1 | data.frame | 17 | 4 |
ALL_colosimo_table_4_3 | EstimationTools | Acute Lymphoblastic Leukemia, table 4.3 | data.frame | 33 | 5 |
Fibers | EstimationTools | Tensile strengths | data.frame | 69 | 1 |
head_neck_cancer | EstimationTools | Head and neck cancer | data.frame | 96 | 3 |
reduction_cells | EstimationTools | Reduction cells | data.frame | 20 | 2 |
eight_schools | shinystan | 'ShinyStan' demo | shinystan | | |
hazardcross | survELtest | Simulated survival data with crossing hazard functions from the piecewise exponential distribution | data.frame | 100 | 3 |
hazardcross_Weibull | survELtest | Simulated survival data with crossing hazard functions from the Weibull distribution | data.frame | 100 | 3 |
hepatitis | survELtest | Severe alcoholic hepatitis data | data.frame | 174 | 3 |
threearm | survELtest | Time to first remission data | data.frame | 664 | 3 |
sud | OVtool | Longitudinal observational data from adolescents receiving SUD treatment. | spec_tbl_df | 4000 | 29 |
demo_notes | shinyNotes | Demo notes for testing 'shinynote' module. | tbl_df | 274 | 3 |
emojis | shinyNotes | Demo notes for testing 'shinynote' module. | list | | |
markdown_notes | shinyNotes | Demo notes formatted with markdown for testing 'shinynote' module. | tbl_df | 3 | 3 |
leukemia | LPKsample | Leukemia cancer gene expression data | list | | |
comment_example | highlightr | Comment Example Dataset | tbl_df | 125 | 2 |
transcript_example | highlightr | Transcript Example | tbl_df | 1 | 1 |
wiki_pages | highlightr | Wikipedia Edit History for "Highlighter" | data.frame | 300 | 1 |
Appalachia | rrcov | Annual maximum streamflow in central Appalachia | data.frame | 104 | 3 |
Cars | rrcov | Consumer reports car data: dimensions | data.frame | 111 | 11 |
Cascades | rrcov | Annual precipitation totals for the North Cascades region | data.frame | 19 | 3 |
OsloTransect | rrcov | Oslo Transect Data | data.frame | 360 | 38 |
bus | rrcov | Automatic vehicle recognition data | data.frame | 218 | 18 |
bushmiss | rrcov | Campbell Bushfire Data with added missing data items | data.frame | 190 | 6 |
diabetes | rrcov | Reaven and Miller diabetes data | data.frame | 145 | 6 |
fish | rrcov | Fish Catch Data Set | data.frame | 159 | 7 |
fruit | rrcov | Fruit data set | data.frame | 1096 | 257 |
hemophilia | rrcov | Hemophilia Data | data.frame | 75 | 3 |
ionosphere | rrcov | Johns Hopkins University Ionosphere database. | data.frame | 351 | 35 |
lmom32 | rrcov | Hosking and Wallis Data Set, Table 3.2 | data.frame | 18 | 3 |
lmom33 | rrcov | Hosking and Wallis Data Set, Table 3.3 | data.frame | 17 | 3 |
machines | rrcov | Computer Hardware | data.frame | 209 | 8 |
maryo | rrcov | Marona and Yohai Artificial Data | matrix | 20 | 2 |
octane | rrcov | Octane data | data.frame | 39 | 227 |
olitos | rrcov | Olive Oil Data | data.frame | 120 | 26 |
pottery | rrcov | Archaic Greek Pottery data | data.frame | 27 | 7 |
pottery.test | rrcov | Archaic Greek Pottery data | data.frame | 13 | 7 |
rice | rrcov | Rice taste data | data.frame | 105 | 6 |
salmon | rrcov | Salmon data | data.frame | 100 | 4 |
soil | rrcov | Exchangable cations in forest soil data set | data.frame | 116 | 7 |
un86 | rrcov | United Nations Data - 1986 | data.frame | 73 | 7 |
wages | rrcov | Wages and Hours | data.frame | 39 | 10 |
wolves | rrcov | Skull dimensions of the wolf _Canis lupus_ L. | data.frame | 25 | 12 |
ParamCubic | CluMP | Parameters of cubic model | data.frame | 2 | 8 |
ParamExpon | CluMP | Parameters of exponential model | data.frame | 2 | 7 |
ParamLinear | CluMP | Parameters of linear model | data.frame | 2 | 6 |
ParamQuadrat | CluMP | Parameters of quadratic model | data.frame | 2 | 7 |
GamWeightData | FuzzyClass | Gamma Weighted Data | data.frame | 600 | 4 |
HouseVotes84 | FuzzyClass | United States Congressional Voting Records 1984 | data.frame | 435 | 17 |
SimulatedData | FuzzyClass | Simulated Data | data.frame | 600 | 4 |
VirtualRealityData | FuzzyClass | Virtual Reality Simulator Data | data.frame | 600 | 4 |
ecoli | MoTBFs | Data set Ecoli: Protein Localization Sites | data.frame | 336 | 9 |
thyroid | MoTBFs | Data set Thyroid Disease (thyroid0387) | data.frame | 7200 | 22 |
.FC.CT | MixfMRI | Sets of controls in MixfMRI | list | | |
.MixfMRIEnv | MixfMRI | All General Internal Functions and Datasets | environment | | |
pstats | MixfMRI | Example datasets in MixfMRI | array | | |
pval.2d.complex | MixfMRI | Example datasets in MixfMRI | matrix | 128 | |
pval.2d.mag | MixfMRI | Example datasets in MixfMRI | matrix | 128 | |
shepp0fMRI | MixfMRI | Example datasets in MixfMRI | matrix | 256 | |
shepp1fMRI | MixfMRI | Example datasets in MixfMRI | matrix | 256 | |
shepp2fMRI | MixfMRI | Example datasets in MixfMRI | matrix | 256 | |
sheppAnat | MixfMRI | Example datasets in MixfMRI | matrix | 256 | |
toy1 | MixfMRI | Example datasets in MixfMRI | list | | |
toy2 | MixfMRI | Example datasets in MixfMRI | list | | |
Cal_elec | USgrid | Demand for California Independent System Operator (CISO) | tbl_ts | 118525 | 3 |
US_elec | USgrid | The US Hourly Demand and Supply for Electricity | tbl_ts | 100024 | 3 |
US_source | USgrid | The US Hourly Net Generation by Energy Source | tbl_ts | 189664 | 3 |
fairclough | codaredistlm | Data from Fairclough (2017). Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data | data.frame | 169 | 21 |
fat_data | codaredistlm | Randomly generated data to simulate child fat percentage regressed on time-use compositional data | data.frame | 100 | 8 |
simdata_lasso_binomial | svyVarSel | Simulated complex survey data | data.frame | 1720 | 54 |
df.data10 | clinmon | Test-data (10 Hz) | data.frame | 3361 | 4 |
df.data1000 | clinmon | Test-data (1000 Hz) | data.frame | 336029 | 4 |
df.deleter | clinmon | Test-deleter | data.frame | 88 | 2 |
Lienert1978 | confreq | The Lienert (1978) Data | data.frame | 12 | 4 |
LienertLSD | confreq | The Lienert LSD Data | data.frame | 8 | 4 |
lazar | confreq | The Data Example from Lazarsfeld and Henry | data.frame | 8 | 4 |
newborns | confreq | The Data Example from Stemmler 2020 | data.frame | 4 | 3 |
suicide | confreq | The Krauth & Lienert suicide Data | data.frame | 702 | 3 |
dat | malani | A matrix of expression values. | matrix | 100 | |
grp | malani | A vector of class labels for 'dat'. | numeric | | |
ais | ALDqr | Australian institute of sport data | data.frame | 202 | 14 |
BCI.count | asbio | Barro Colorado Island Tree Counts | data.frame | 50 | 225 |
BCI.plant | asbio | Tree presence/absence data from Barro Colorado island | data.frame | 43 | 9 |
C.isotope | asbio | Atmospheric carbon and D14C measurements | data.frame | 280 | 5 |
Fbird | asbio | Frigatebird drumming frequency data | data.frame | 18 | 2 |
Glucose2 | asbio | Glucose Levels Following Alcohol Ingestion | nmGroupedData | 196 | 4 |
K | asbio | Soil potassium analyses from 8 laboratories | data.frame | 72 | 2 |
PCB | asbio | PCBs and herring egg thickness | data.frame | 26 | 3 |
PM2.5 | asbio | PM 2.5 pollutant data from Pocatello Idaho | data.frame | 65 | 2 |
Rabino_CO2 | asbio | CSIRO d13C-CO2 data from Rubino et al., A revised 1000 year atmospheric 13C-CO2 record from Law Dome and South Pole, Antarctica | data.frame | 332 | 5 |
Rabino_del13C | asbio | CSIRO d13C-CO2 data from Rubino et al., A revised 1000 year atmospheric 13C-CO2 record from Law Dome and South Pole, Antarctica | data.frame | 156 | 4 |
SM.temp.moist | asbio | Alpine soil temperature and moisture time series | data.frame | 30 | 4 |
Semiconductor | asbio | Split plot computer chip data from Littell et al. (2006) | data.frame | 48 | 4 |
SexDeterm | asbio | Fern environmental sex determination data | data.frame | 156 | 11 |
agrostis | asbio | Agrostis variabilis cover measurements | data.frame | 25 | 2 |
aids | asbio | Aids and veterans dataset | data.frame | 338 | 3 |
alfalfa.split.plot | asbio | An agricultural split plot design | data.frame | 72 | 4 |
anolis | asbio | Anolis lizard contingency table data | data.frame | 8 | 4 |
anscombe | asbio | Anscombe's quartet | data.frame | 11 | 8 |
ant.dew | asbio | Ant honeydew data | data.frame | 72 | 3 |
asthma | asbio | Asthma repeated measures dataset from Littell et al. (2002) | data.frame | 72 | 11 |
baby.walk | asbio | Baby walking times experimental data | data.frame | 22 | 2 |
bats | asbio | Bat forearm length as a function of bat age | data.frame | 38 | 2 |
bear | asbio | Grizzly bear litter sizes | data.frame | 38 | 5 |
beetle | asbio | Wood boring beetle data | data.frame | 24 | 4 |
bighorn.sel | asbio | Datasets for resource use and availability | data.frame | 10 | 4 |
bighornAZ.sel | asbio | Datasets for resource use and availability | data.frame | 10 | 4 |
bombus | asbio | Bombus pollen data. | data.frame | 47 | 2 |
bone | asbio | Bone development data | data.frame | 14 | 3 |
bromus | asbio | Bromus tectorum dataset | data.frame | 13 | 3 |
caribou | asbio | Caribou count data | data.frame | 6 | 5 |
case0902 | asbio | Dataset of mammal traits from Ramsey and Schaefer (1997) | data.frame | 96 | 5 |
case1202 | asbio | Dataset of salary attributes for male and female workers from Ramsey and Schafer (1997) | data.frame | 93 | 7 |
chronic | asbio | Chronic ailment counts for urban and rural women in Australia | data.frame | 49 | 3 |
cliff.env | asbio | Environmental data for the community dataset cliff.sp | matrix | 54 | 2 |
cliff.sp | asbio | Yellowstone NP cliff community data | data.frame | 54 | 11 |
concrete | asbio | Concrete strength dataset for data mining | data.frame | 1030 | 9 |
corn | asbio | Corn yield data | data.frame | 40 | 4 |
crab.weight | asbio | crab gill and body weight data | data.frame | 12 | 2 |
crabs | asbio | Agresti crabs dataset | data.frame | 173 | 5 |
cuckoo | asbio | Tippett cuckoo egg data | data.frame | 16 | 3 |
dO2 | asbio | Dissolved levels in locations above and below a town | data.frame | 30 | 2 |
death.penalty | asbio | Florida state death penalty data | data.frame | 8 | 4 |
deer | asbio | Maternal deer data | data.frame | 36 | 6 |
deer.296 | asbio | Mule deer telemetry data | data.frame | 5423 | 7 |
depression | asbio | Hamilton depression scores before and after drug treatment | data.frame | 18 | 3 |
drugs | asbio | Contingency data for high school marijuana, alcohol, and cigarette use | data.frame | 8 | 4 |
e.cancer | asbio | Esophageal cancer data modified slightly to create a balanced three-way factorial design | data.frame | 80 | 4 |
elk.sel | asbio | Datasets for resource use and availability | data.frame | 4 | 5 |
enzyme | asbio | Enzymatic rate data for the phospholipase protein ExoU | data.frame | 10 | 3 |
exercise.repeated | asbio | Repeated measures data for an exercise experiment. | data.frame | 239 | 4 |
facebook | asbio | Facebook performance metrics for data mining and machine learning | data.frame | 500 | 19 |
fire | asbio | Fire data from Yellowstone National Park | data.frame | 10 | 2 |
fly.sex | asbio | Fly sex and longevity | data.frame | 125 | 3 |
frog | asbio | Australian frog calls following fire | data.frame | 18 | 3 |
fruit | asbio | Fruit weight data from Littell et al. (2002) | data.frame | 40 | 3 |
garments | asbio | Garment Latin square data from Littell et al. (2002) | data.frame | 16 | 4 |
goat.sel | asbio | Datasets for resource use and availability | data.frame | 8 | 4 |
goats | asbio | Mountain goat data from Yellowstone National Park | data.frame | 152 | 3 |
grass | asbio | Agricultural factorial design | data.frame | 90 | 3 |
heart | asbio | Heart rate data from Milliken and Johnson (2009) | data.frame | 96 | 4 |
ipomopsis | asbio | Ipomopsis fruit yield data | data.frame | 40 | 3 |
juniper.sel | asbio | Datasets for resource use and availability | data.frame | 14 | 5 |
larrea | asbio | Creosote bush counts | data.frame | 25 | 3 |
life.exp | asbio | Mouse life expectancy data | data.frame | 244 | 2 |
magnets | asbio | Magnet pain relief data | data.frame | 50 | 3 |
montane.island | asbio | Mountain island biogeographic data | data.frame | 27 | 3 |
moose.sel | asbio | Datasets for resource use and availability | data.frame | 4 | 4 |
mosquito | asbio | Mosquito wing length data | data.frame | 24 | 4 |
mule.sel | asbio | Datasets for resource use and availability | data.frame | 9 | 5 |
myeloma | asbio | Patient responses to myeloma drug treatments | data.frame | 20 | 2 |
pika | asbio | Nitrogen content of soils under pika haypiles | data.frame | 21 | 2 |
plantTraits | asbio | Plant traits for 136 species | data.frame | 136 | 31 |
polyamine | asbio | Polyamine data from Hollander and Wolfe (1999) | data.frame | 25 | 2 |
portneuf | asbio | Portneuf River longitudinal N and P data | data.frame | 172 | 3 |
potash | asbio | Potash/cotton strength data | data.frame | 15 | 3 |
potato | asbio | Fisher's Rothamsted potato data | data.frame | 108 | 4 |
prostate | asbio | Prostate cancer data | data.frame | 97 | 4 |
quail.sel | asbio | Datasets for resource use and availability | data.frame | 6 | 4 |
rat | asbio | Rat glycogen data from Sokal and Rohlf (2012) | data.frame | 36 | 5 |
refinery | asbio | Refinery CO dataset | data.frame | 40 | 3 |
rmvm | asbio | A multivariate normal dataset for data mining | data.frame | 500 | 16 |
savage | asbio | Mammalian BMR and biomass data from Savage et al. (2004) | data.frame | 1006 | 9 |
sc.twin | asbio | Matched pairs schizophrenia data | data.frame | 15 | 2 |
sedum.ts | asbio | CO2 exchange time series data | data.frame | 24 | 3 |
shad | asbio | American gizzard shad data | data.frame | 40 | 2 |
simberloff | asbio | Compilations of genus and species counts from Simberloff (1970) | data.frame | 204 | 12 |
snore | asbio | Snoring and heart disease contingency data | data.frame | 2484 | 3 |
so2.us.cities | asbio | SO2 data for 32 US cities with respect to 6 explanatory variables | data.frame | 32 | 8 |
starkey | asbio | DEM data from the Starkey experimental forest in NE Oregon | data.frame | 85969 | 20 |
suess | asbio | del14C in the atmosphere from 1700-1950 | data.frame | 159 | 3 |
trag | asbio | Salsify height dataset | data.frame | 20 | 1 |
veneer | asbio | Veneer data from Littell et al. (2002) | data.frame | 20 | 2 |
vs | asbio | Scandinavian site by species community matrix | data.frame | 24 | 44 |
wash.rich | asbio | Species richness and environmental variables from Mt Washburn | data.frame | 40 | 7 |
webs | asbio | Spider web length data | data.frame | 50 | 4 |
wheat | asbio | Agricultural randomized block design | data.frame | 60 | 4 |
whickham | asbio | Whickham contingency table data for smokers and survivorship | data.frame | 12 | 4 |
wildebeest | asbio | Wildebeest carcass categorical data | data.frame | 12 | 4 |
wine | asbio | White wine quality data for data mining | data.frame | 4898 | 12 |
world.co2 | asbio | World CO2 levels, by country, from 1980 to 2006 | data.frame | 27 | 16 |
world.emissions | asbio | Greenhouse gas emissions from Our World in Data | data.frame | 23708 | 16 |
world.pop | asbio | Population levels in various countries from 1980-2006 | data.frame | 27 | 14 |
kof_ts | timeseriesdb | | list | | |
adjusted_example | PredTest | Adjusted Example Dataset | data.frame | 20 | 5 |
group_cog_data | PredTest | Group Cognitive Data | data.frame | 20 | 14 |
group_data_example | PredTest | Group Data Example | data.frame | 30 | 5 |
pre_post_data_example | PredTest | Pre-Post Data Example | data.frame | 30 | 6 |
pre_post_fit | PredTest | Pre-Post Fitness Data | data.frame | 20 | 12 |
JRGdat | tsvr | Percent cover data from Jasper Ridge Biological Preserve serpentine grassland site | data.frame | 28 | 27 |
eyeshp | stampr | Hurricane Katrina eye point dataset | sf | 33 | 3 |
fire1 | stampr | Forest Fire dataset | sf | 15 | 2 |
fire2 | stampr | Forest Fire dataset | sf | 11 | 2 |
katrina | stampr | Hurricane Katrina polygons dataset | sf | 33 | 3 |
mpb | stampr | MPB dataset | sf | 711 | 4 |
bgsmtr_example_data | bgsmtr | Example Structural Neuroimaging and Genetic Data | list | | |
sp_bgsmtr_example_data | bgsmtr | Example Structural Neuroimaging and Genetic Data for Spatial Model. | list | | |
exmpl_matrix | xtranat | Data to showcase the functions in the xtranat package | matrix | 10 | |
cmis | ispd | Data from an experiment using incomplete split-plot design | data.frame | 36 | 5 |
imcs | ispd | Data from an experiment using incomplete split-plot design | data.frame | 18 | 5 |
imis | ispd | Data from an experiment using incomplete split-plot design | data.frame | 36 | 5 |
rBPS | SAFARI | Reconstructed Binary Pathology Slide | matrix | 314 | |
US_mean_center2010 | USpopcenters | Mean and Median centers of population of the US | spec_tbl_df | 1 | 3 |
US_mean_center2020 | USpopcenters | Mean and Median centers of population of the US | spec_tbl_df | 1 | 3 |
US_median_center2010 | USpopcenters | Mean and Median centers of population of the US | spec_tbl_df | 1 | 3 |
US_median_center2020 | USpopcenters | Mean and Median centers of population of the US | spec_tbl_df | 1 | 3 |
block_group2000 | USpopcenters | Centers of population of US census tracts according to the 2000 census | spec_tbl_df | 211267 | 7 |
block_group2010 | USpopcenters | Centers of population of US census block groups according to the 2010 census | spec_tbl_df | 220334 | 7 |
block_group2020 | USpopcenters | Centers of population of US census block groups according to the 2020 census | spec_tbl_df | 242335 | 7 |
county2000 | USpopcenters | Centers of population of US counties according to the 2000 census | spec_tbl_df | 3232 | 6 |
county2010 | USpopcenters | Centers of population of US counties according to the 2010 census | spec_tbl_df | 3221 | 7 |
county2020 | USpopcenters | Centers of population of US counties according to the 2020 census | spec_tbl_df | 3221 | 7 |
state2000 | USpopcenters | Centers of population of US states according to the 2000 census | spec_tbl_df | 51 | 5 |
state2010 | USpopcenters | Centers of population of US states according to the 2010 census | spec_tbl_df | 52 | 5 |
state2020 | USpopcenters | Centers of population of US states according to the 2020 census | spec_tbl_df | 52 | 5 |
tract2000 | USpopcenters | Centers of population of US census tracts according to the 2000 census | spec_tbl_df | 66304 | 6 |
tract2010 | USpopcenters | Centers of population of US census tracts according to the 2010 census | spec_tbl_df | 74002 | 6 |
tract2020 | USpopcenters | Centers of population of US census tracts according to the 2020 census | spec_tbl_df | 85395 | 6 |
analytis_77 | devRate | Analytis equation of development rate as a function of temperature. | list | | |
bayoh_03 | devRate | Bayoh and Lindsay equation of development rate as a function of temperature. | list | | |
beta_16 | devRate | Beta2 equation of development rate as a function of temperature. | list | | |
beta_95 | devRate | Beta equation of development rate as a function of temperature. | list | | |
bieri1_83 | devRate | Bieri equation 1 of development rate as a function of temperature. | list | | |
briere1_99 | devRate | Briere et al equation 1 of development rate as a function of temperature. | list | | |
briere2_99 | devRate | Briere et al equation 2 of development rate as a function of temperature. | list | | |
campbell_74 | devRate | Campbell et al. equation of development rate as a function of temperature. | list | | |
damos_08 | devRate | Simplified beta type equation of development rate as a function of temperature. | list | | |
damos_11 | devRate | Inverse second-order polynomial equation of development rate as a function of temperature. | list | | |
davidson_44 | devRate | Davidson equation of development rate as a function of temperature. | list | | |
devRateEqList | devRate | The list of all available equations of development rate as a function of temperature. | list | | |
devRateEqStartVal | devRate | Default starting values for each equation listed in the devRateEqList object. | list | | |
exTropicalMoth | devRate | Tropical moth development rate at constant temperatures. | list | | |
harcourtYee_82 | devRate | Harcourt and Yee equation of development rate as a function of temperature. | list | | |
hilbertLogan_83 | devRate | Holling type III equation of development rate as a function of temperature. | list | | |
janisch_32 | devRate | Janisch equation of development rate as a function of temperature (Analytis modification). | list | | |
kontodimas_04 | devRate | Kontodimas et al. equation of development rate as a function of temperature. | list | | |
lactin1_95 | devRate | Lactin et al. equation 1 of development rate as a function of temperature. | list | | |
lactin2_95 | devRate | Lactin et al. equation 2 of development rate as a function of temperature. | list | | |
lamb_92 | devRate | Lamb equation of development rate as a function of temperature. | list | | |
logan10_76 | devRate | Logan et al. equation 10 of development rate as a function of temperature. | list | | |
logan6_76 | devRate | Logan et al. equation 6 of development rate as a function of temperature. | list | | |
perf2_11 | devRate | Performance-2 equation of development rate as a function of temperature. | list | | |
poly2 | devRate | Second-order polynomial equation of development rate as a function of temperature. | list | | |
poly4 | devRate | Fourth-order polynomial equation of development rate as a function of temperature. | list | | |
ratkowsky_82 | devRate | Ratkowsky equation of development rate as a function of temperature (Shi modification). | list | | |
ratkowsky_83 | devRate | Ratkowsky equation of development rate as a function of temperature (Shi 2016 modification). | list | | |
regniere_12 | devRate | Regniere equation of development rate as a function of temperature. | list | | |
rootsq_82 | devRate | Root square equation of development rate as a function of temperature. | list | | |
schoolfieldHigh_81 | devRate | Schoolfield et al. equation of development rate as a function of temperature for intermediate to high temperatures only. | list | | |
schoolfieldLow_81 | devRate | Schoolfield et al. equation of development rate as a function of temperature for intermediate to low temperatures only. | list | | |
schoolfield_81 | devRate | Schoolfield et al. equation of development rate as a function of temperature. | list | | |
sharpeDeMichele_77 | devRate | Sharpe and DeMichele equation of development rate as a function of temperature. | list | | |
shi_11 | devRate | Shi equation of development rate as a function of temperature. | list | | |
stinner_74 | devRate | Stinner et al equation of development rate as a function of temperature. | list | | |
taylor_81 | devRate | Taylor equation of development rate as a function of temperature. | list | | |
wagner_88 | devRate | Hagstrum et Milliken equation of development rate as a function of temperature retrieved from Wagner 1984. | list | | |
wang_82 | devRate | Wang et al. equation of development rate as a function of temperature. | list | | |
wangengel_98 | devRate | Wang and Engel equation of development rate as a function of temperature. | list | | |
example_cocoso | cocosoR | Data example of logistic provider selection problem. | data.frame | 10 | |
BassRiverData | RGN | Hydrological data for Bass River catchment in Victoria, Australia | list | | |
dsm_5 | despair | A data frame of DSM-5 adjectives | data.frame | 150 | 1 |
family | despair | A data frame of family-related demotivational quotes | data.frame | 13 | 1 |
life | despair | A data frame of life-related demotivational quotes. | data.frame | 27 | 1 |
lit | despair | A data frame of modern and contemporary literature motivational quotes. | data.frame | 22 | 1 |
modern | despair | A data frame of modern and contemporary historical and pop-culture icons | data.frame | 40 | 1 |
psych | despair | A data frame of psychology-related quotes | data.frame | 34 | 1 |
rednecks | despair | A data frame of sweet redneck humor | data.frame | 21 | 1 |
religion | despair | A data frame of religious motivational quotes | data.frame | 45 | 1 |
science | despair | A data frame of science-related demotivational quotes | data.frame | 27 | 1 |
shake_adjectives | despair | A data frame of Shakespeare's adjectives | data.frame | 100 | 1 |
shake_animals | despair | A data frame of Shakespeare's animals | data.frame | 150 | 1 |
shake_chars | despair | A data frame of Shakespeare's characters | data.frame | 137 | 1 |
shake_clrs | despair | A data frame of Shakespeare and unusual colors | data.frame | 300 | 1 |
shake_jobs | despair | A data frame of Shakespeare's jobs | data.frame | 103 | 1 |
shake_things | despair | A data frame of Shakespeare's objects | data.frame | 150 | 1 |
stoic | despair | A data frame o stoic motivational quotes | data.frame | 32 | 1 |
tv | despair | A data frame of tv and film demotivational quotes | data.frame | 23 | 1 |
work | despair | A data frame of work-related demotivational quotes | data.frame | 31 | 1 |
seq.cnv | saasCNV | Internal Functions and Data | data.frame | 74 | 31 |
seq.data | saasCNV | Internal Functions and Data | data.frame | 28779 | 8 |
seq.segs | saasCNV | Internal Functions and Data | data.frame | 84 | 15 |
seq.segs.merge | saasCNV | Internal Functions and Data | data.frame | 74 | 15 |
snp.cnv | saasCNV | Internal Functions and Data | data.frame | 119 | 31 |
snp.cnv.anno | saasCNV | Internal Functions and Data | data.frame | 109 | 20 |
snp.cnv.refine | saasCNV | Internal Functions and Data | data.frame | 119 | 31 |
snp.cnv.refine.anno | saasCNV | Internal Functions and Data | data.frame | 109 | 20 |
snp.segs | saasCNV | Internal Functions and Data | data.frame | 137 | 15 |
snp.segs.merge | saasCNV | Internal Functions and Data | data.frame | 119 | 15 |
initbetaBinomial | icmm | Initial values for the regression coefficients used in example for running ICM/M algorithm in binary logistic model | matrix | 400 | |
initbetaCox | icmm | Initial values for the regression coefficients used in example for running ICM/M algorithm in Cox's model | matrix | 400 | |
initbetaGaussian | icmm | Initial values for the regression coefficients used in example for running ICM/M algorithm in normal linear regression model | matrix | 400 | |
linearrelation | icmm | Linear structure of predictors | data.frame | 400 | 2 |
simBinomial | icmm | Simulated data from the binary logistic regression model | data.frame | 100 | 401 |
simCox | icmm | Simulated data from Cox's regression model | data.frame | 100 | 402 |
simGaussian | icmm | Simulated data from the normal linear regression model | data.frame | 100 | 401 |
QRISK3_2017_test | QRISK3 | Test data for QRISK3 2017 algorithm - 2017 data | data.frame | 48 | 27 |
QRISK3_2019_test | QRISK3 | Test data for QRISK3 2017 algorithm - 2019 data | data.frame | 49 | 27 |
concrete_example | tidyposterior | Example Data Sets | vfold_cv | 50 | 16 |
contrast_samples | tidyposterior | Example Data Sets | posterior_diff | 10000 | 4 |
noisy_example | tidyposterior | Example Data Sets | vfold_cv | 100 | 15 |
posterior_samples | tidyposterior | Example Data Sets | posterior | 15000 | 2 |
precise_example | tidyposterior | Example Data Sets | vfold_cv | 10 | 29 |
ts_example | tidyposterior | Example Data Sets | rolling_origin | 52 | 14 |
DF1 | fermicatsR | 1DF Catalog (First D3PO Fermi catalog of gamma-ray source candidates) | tbl_df | 3106 | 48 |
FGL0 | fermicatsR | 0FGL Catalog (Fermi Large Area Telescope Bright Gamma-ray Source List) | tbl_df | 205 | 21 |
FGL1 | fermicatsR | 1FGL Catalog (Fermi Large Area Telescope First Source Catalog) | tbl_df | 1451 | 89 |
FGL2 | fermicatsR | 2FGL Catalog (Fermi Large Area Telescope Second Source Catalog) | tbl_df | 1873 | 137 |
FGL3 | fermicatsR | 3FGL Catalog (Fermi Large Area Telescope Third Source Catalog) | tbl_df | 3034 | 224 |
FHL1 | fermicatsR | 1FHL Catalog (First Fermi-LAT Catalog of Sources Above 10 GeV) | tbl_df | 514 | 39 |
FHL2 | fermicatsR | 2FHL Catalog (Second Catalog of Hard Fermi-LAT Sources) | tbl_df | 360 | 42 |
FIG1 | fermicatsR | 1FIG (First Fermi-LAT Inner Galaxy point source Catalog) | tbl_df | 48 | 31 |
LAC3_HI | fermicatsR | 3LAC_HI (Third Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope - High Galactic Latitude) | tbl_df | 1591 | 26 |
LAC3_LO | fermicatsR | 3LAC_LO (Third Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope - Low Galactic Latitude) | tbl_df | 182 | 20 |
pulsars | fermicatsR | pulsars (Public List of LAT-Detected Gamma-Ray Pulsars) | tbl_df | 205 | 8 |
longitudinal_counts | multilevelmod | Simulated longitudinal Poisson counts | tbl_df | 1000 | 4 |
msa_data | multilevelmod | Measurement systems analysis data | tbl_df | 112 | 3 |
riesby | multilevelmod | Imipramine longitudinal data | tbl_df | 250 | 7 |
avs_roster_2021 | ISAR | 2021 NHL Colorado Avalanche Roster (from NHL's API) | data.frame | 26 | 8 |
avs_roster_2022 | ISAR | 2022-23 NHL Colorado Avalanche Roster (from NHL's API) | data.frame | 24 | 8 |
avs_stats_2021 | ISAR | NHL's Colorado Avalanche Player Statistics | data.frame | 188 | 50 |
avs_stats_2022 | ISAR | NHL's Colorado Avalanche Player Statistics | data.frame | 162 | 50 |
dk_edm_col | ISAR | Draftkings Showdown Game in NHL (Edmonton Oilers versus Colorado Avalanche) | data.frame | 252 | 9 |
dk_lac_dal | ISAR | Draftkings Showdown Game (Clippers vs. Mavericks) | data.frame | 68 | 9 |
dk_mem_utah | ISAR | Draftkings Showdown Game (Grizzlies vs. Jazz) | data.frame | 68 | 9 |
dk_nyr_car | ISAR | Draftkings Showdown Game in NHL (New York Rangers versus Carolina Hurricanes) | data.frame | 252 | 9 |
epl_gk_stats_2022 | ISAR | 2021-2022 English Premier League Goalkeeper Statistics | data.frame | 42 | 32 |
epl_gk_stats_2023 | ISAR | 2022-2023 English Premier League Goalkeeper Statistics | data.frame | 39 | 35 |
epl_gk_stats_2024 | ISAR | 2023-2024 English Premier League Goalkeeper Statistics | data.frame | 40 | 35 |
epl_player_stats_2022 | ISAR | 2021-2022 English Premier League Player Statistics | data.frame | 545 | 35 |
epl_player_stats_2023 | ISAR | 2022-2023 English Premier League Player Statistics | data.frame | 569 | 38 |
epl_player_stats_2024 | ISAR | 2023-2024 English Premier League Player Statistics | data.frame | 569 | 38 |
epl_team_stats | ISAR | 2017-18 through 2021-22 English Premier League Tea, Statistics | tbl_df | 100 | 29 |
epl_team_stats_2023 | ISAR | 2022-23 English Premier League Team Statistics | data.frame | 20 | 31 |
epl_team_stats_2024 | ISAR | 2023-24 English Premier League Team Statistics | data.frame | 20 | 31 |
masters | ISAR | 2019 Masters Tournament Scores | data.frame | 87 | 9 |
nba_adv_team_2021 | ISAR | 2021 NBA Advanced Statistics (Team) for the 2020-21 Season | tbl_df | 2160 | 29 |
nba_adv_team_2023 | ISAR | NBA Advanced Statistics (Team) for the 2022-23 Season | tbl_df | 2460 | 29 |
nba_ff_team_2021 | ISAR | 2021 NBA Four Factors (Team) for the 2020-21 Season | tbl_df | 2160 | 11 |
nba_ff_team_2023 | ISAR | NBA Four Factors (Team) for the 2020-21 Season | tbl_df | 2460 | 14 |
nba_games_2021 | ISAR | 2021 NBA Game Logs for the 2020-21 Season | tbl_df | 2160 | 47 |
nba_games_2023 | ISAR | NBA Game Logs for the 2022-23 Season | tbl_df | 2460 | 29 |
nba_nuggets_shots | ISAR | 2014/5 - 2022/23 Shot Data for the NBA's Denver Nuggets | tbl_df | 62196 | 27 |
nhl_data_hockey_reference | ISAR | NHL Skater Statistics from Hockey Reference | tbl_df | 12375 | 48 |
nhl_team_stats_2021 | ISAR | 2021-2 NHL Team Statistics | data.frame | 32 | 35 |
nhl_team_stats_2022 | ISAR | 2022-3 NHL Team Statistics | data.frame | 32 | 35 |
nwsl_player_stats | ISAR | 2022 NWSL Player Statistics | data.frame | 314 | 13 |
ow_golf_rankings | ISAR | 2022 Official World Golf Rankings | data.frame | 300 | 2 |
pga_tournaments | ISAR | PGA Tournament Data | data.frame | 3676 | 34 |
hivdata | locfdr | HIV data set | numeric | | |
lfdrsim | locfdr | Simulated data set for locfdr | data.frame | 2000 | 2 |
obfuscation | clust.bin.pair | Obfuscated C code misinterpretation data | data.frame | 114 | 4 |
psychiatry | clust.bin.pair | Psychiatrist and patient disagreement data | data.frame | 29 | 7 |
thyroids | clust.bin.pair | PET and SPECT data for diagnosing hyperparathyroidism | data.frame | 21 | 6 |
bakers | bakeoff | Bakers | tbl_df | 120 | 24 |
bakers_raw | bakeoff | Bakers (raw) | tbl_df | 120 | 8 |
bakes_raw | bakeoff | Bakes (raw) | tbl_df | 548 | 6 |
challenges | bakeoff | Challenges | tbl_df | 1136 | 7 |
episodes | bakeoff | Episodes | tbl_df | 94 | 10 |
episodes_raw | bakeoff | Each episodes' challenges (raw) | tbl_df | 704 | 6 |
ratings | bakeoff | Ratings | tbl_df | 94 | 11 |
ratings_raw | bakeoff | Each episode's ratings (raw) | tbl_df | 94 | 9 |
results_raw | bakeoff | Each baker's results by episode (raw) | tbl_df | 1136 | 4 |
seasons_raw | bakeoff | Data about each season aired in the US (raw) | tbl_df | 50 | 4 |
series_raw | bakeoff | Data about each series aired in the UK (raw) | tbl_df | 10 | 11 |
spice_test_wide | bakeoff | Spice Test | tbl_df | 4 | 7 |
attitudeStartingValues | cusp | Multistability in political attitudes | numeric | | |
attitudes | cusp | Multistability in political attitudes | data.frame | 1387 | 3 |
oliva | cusp | Synthetic cusp data set | data.frame | 50 | 12 |
zeeman1 | cusp | Measurements from Zeeman's Catastrophe Machine | data.frame | 150 | 3 |
zeeman2 | cusp | Measurements from Zeeman's Catastrophe Machine | data.frame | 198 | 3 |
zeeman3 | cusp | Measurements from Zeeman's Catastrophe Machine | data.frame | 282 | 3 |
peru | geoperu | Distritos Peru | sf | 1874 | 4 |
combined_background | DysPIAData | Background gene pairs. | matrix | 682417 | 2 |
pathway_list | DysPIAData | Gene pair based pathway lists. | list | | |
TV | resample | Data sets for resampling examples | data.frame | 20 | 2 |
Verizon | resample | Data sets for resampling examples | data.frame | 1687 | 2 |
TESTIND | TSEind | A data set created by merging 1) data from a "gold standard" survey and 2) data from other surveys of the same universe. Data from the "gold standard" survey are assumed to be the survey universe's "actual" response; data from the other surveys have survey error which the functions in 'TSEind' can calculate. Data are organized by survey (columns) and survey question (rows), and their values are the aggregate, "topline" responses to the survey questions which can range from 1 to 99 (the scale used by each survey question). | data.frame | 10 | 6 |
cfd.example.data | cfdecomp | Example Data for the cfdecomp package | data.frame | 5000 | 7 |
balls | ntsDists | Balls data | matrix | 23 | 2 |
remission | ntsDists | Remission data | matrix | 128 | 2 |
daVinciSphere | cxhull | Leonardo da Vinci's 72-sided sphere | matrix | 62 | |
hexacosichoron | cxhull | Vertices of the 600-cell | matrix | 120 | |
complete | ratdat | Complete survey data. | tbl_df | 35549 | 13 |
complete_old | ratdat | Complete survey data from 1977 to 1989. | tbl_df | 16878 | 13 |
plots | ratdat | Plots data. | tbl_df | 24 | 2 |
species | ratdat | Species data. | tbl_df | 54 | 4 |
surveys | ratdat | Survey data. | tbl_df | 35549 | 9 |
leaflife | smatr | Leaf longevity and leaf mass per area for plant species from different sites | data.frame | 67 | 5 |
leafmeas | smatr | Leaf mass per area and photosynthetic rate for plant species from different sites | data.frame | 530 | 4 |
RandomVA3 | Tariff | 400 records of Sample Input | data.frame | 400 | 170 |
SampleCategory3 | Tariff | Grouping of causes in RandomVA3 | data.frame | 17 | 2 |
CanadaMite | RSE | mite incidence in moss patches of 32 locations of western Canada (Chen et al. 2015) | matrix | 412 | 2 |
HerpetologicalData | RSE | Abundance of herpetofauna in the conserved and human disturbed areas of Mexico (Suazo-Ortuno et al. 2008) | data.frame | 62 | 2 |
response | CustomerScoringMetrics | response data | list | | |
stiff | SMLoutliers | The Board Stiffness Dataset | data.frame | 30 | 4 |
AfricaPopData | micromapST | Test data for the Africa border Group | data.frame | 52 | 10 |
Educ8thData | micromapST | Education Survey of 8th Grade Proficiency in Math | data.frame | 51 | 7 |
KansPopInc | micromapST | Test data for the Kansas border Group | data.frame | 105 | 3 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 2827 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 491 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 232 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 420 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 433 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 138 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 5865 | 4 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 486 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 486 | 5 |
L2VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 64 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 2827 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 491 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 232 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 420 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 433 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 138 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 5787 | 4 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 108 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 108 | 5 |
L3VisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 64 | 5 |
LOWESSData | micromapST | Test data for US LOWESS ScatDot glyphs. | data.frame | 51 | 3 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 2827 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1142 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 232 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 420 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 433 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 138 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 5865 | 4 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 181 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 181 | 5 |
RegVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 64 | 5 |
Seer18Area | micromapST | Test data for 18 U.S. Seer Registries (of 20) for general dot, arrow, and bar glyphics. | data.frame | 18 | 11 |
SeoulPopData | micromapST | Test data for the Seoul South Korea city district border Group | data.frame | 25 | 5 |
SynTable | micromapST | This data set contains a synonym table to help translate common incorrect location id strings | data.frame | 30 | 3 |
TSdata | micromapST | Time Series Example Dataset | array | | |
UKIrelandPopData | micromapST | Test data for the UK-Ireland border Group | data.frame | 218 | 5 |
UKIrelandPopData2 | micromapST | Test data for the UK-Ireland border Group | data.frame | 52 | 5 |
UtahPopData | micromapST | Test data for the Utah state border Group | data.frame | 29 | 73 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 52 | 13 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 34 | 12 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 105 | 12 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 24 | 12 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 62 | 12 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 25 | 12 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 219 | 12 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 20 | 14 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 51 | 14 |
areaNamesAbbrsIDs | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 29 | 12 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 15 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 16 |
areaParms | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1 | 14 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 4192 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1972 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 2122 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 972 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1841 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 678 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 7858 | 4 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 265 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 486 | 5 |
areaVisBorders | micromapST | Defining different spatial areas for Linked Micromap using the micromapST package | data.frame | 1262 | 5 |
cnPopData | micromapST | Test data for the China border Group | data.frame | 34 | 2 |
detailsVariables | micromapST | Validation and Translation table for details variables | data.frame | 186 | 10 |
mdPopData | micromapST | Test data for the Maryland border Group | data.frame | 24 | 13 |
nyPopData | micromapST | Test data for the New York border Group | data.frame | 62 | 14 |
statePop2010 | micromapST | US State Population for 2010 | data.frame | 51 | 6 |
wflung00and95 | micromapST | Lung cancer mortality data for white females, 2000-4 and 1995-9 | data.frame | 51 | 12 |
wflung00and95US | micromapST | wflung 2000 to 2004 and 1995 to 1999 US data | data.frame | 1 | 12 |
wflung00cnty | micromapST | Lung cancer mortality data for white females, by county, 2000-4 | data.frame | 2577 | 6 |
wmlung5070 | micromapST | Lung cancer mortality data for white males, 1950-69 and 1970-94 | data.frame | 51 | 5 |
wmlung5070US | micromapST | U.S. lung cancer mortality data for white males, 1950-1969 and 1970-1994 | data.frame | 1 | 5 |
TDMaize | statgenGxE | Field data for a maize experiment in Tlaltizapan, Mexico | TD | | |
dropsPheno | statgenGxE | DROPS data set | data.frame | 2460 | 20 |
ardieres | POT | High Flood Flows of the Ardieres River at Beaujeu | data.frame | 33237 | 2 |
babies | smdata | Babies gaze data | data.frame | 1180 | 6 |
carsales | smdata | Car salesperson problem | data.frame | 155 | 4 |
cocaine | smdata | Sex by method of cocaine ingestion | data.frame | 7592 | 2 |
cocaineplus | smdata | Sex and race by method of cocaine ingestion | data.frame | 7592 | 8 |
dass | smdata | Depression, Anxieity, and Stress | data.frame | 166 | 3 |
dyslexic3 | smdata | Dyslexic readers data | data.frame | 44 | 3 |
email | smdata | Marital Status and Email Usage | data.frame | 3967 | 3 |
euthan | smdata | Euthanasia Scale | data.frame | 351 | 3 |
exam | smdata | Exam data | data.frame | 154 | 3 |
finance | smdata | Confidence in financial knowledge | data.frame | 4230 | 11 |
fixations | smdata | Word Color and Fixations | data.frame | 48 | 6 |
grades | smdata | Grades and marks for an undergraduate course | data.frame | 165 | 6 |
guilt1 | smdata | Study 1 judged probabilities of guilt | data.frame | 104 | 7 |
guilt3 | smdata | Study 3 judged probabilities of guilt | data.frame | 96 | 3 |
intervalbeta | smdata | Lower and upper probability estimates | data.frame | 220 | 5 |
phono | smdata | Word and non-word response data | data.frame | 16 | 3 |
rtime | smdata | Censored response time data | data.frame | 300 | 3 |
skipping | smdata | School Skipping | data.frame | 252 | 6 |
trchoice | smdata | Transportation mode choice | data.frame | 10 | 4 |
treatment | smdata | Chest Pain Treatment Preferences | data.frame | 235 | 4 |
trlong | smdata | Transportation mode choice, long format | data.frame | 31680 | 6 |
workdays | smdata | Work Days Missed | data.frame | 777 | 8 |
exclusionList_FR | inpdfr | Stop words in French. | character | | |
exclusionList_SP | inpdfr | Stop words in Spanish. | character | | |
exclusionList_UK | inpdfr | Stop words in English. | character | | |
loremIpsum | inpdfr | Lorem Ipsum text. | character | | |
wordOccuDF | inpdfr | Lorem Ipsum word occurrences. | data.frame | 161 | 6 |
breast | xhaz | Simulated clinical trial data with non comparability bias in term of individuals expected hazard | data.frame | 4978 | 9 |
ccr.mevents | xhaz | colorectum cancer data with multiple events | data.frame | 936 | 9 |
dataCancer | xhaz | Simulated data with cause death information with non comparability bias in term of individuals expected hazard | data.frame | 1000 | 8 |
rescaledData | xhaz | Simulated data with cause death information with non comparability bias in term of individuals expected hazard | data.frame | 1996 | 7 |
simuData | xhaz | Simulated data with cause death information in long term follow-up setting without non comparability bias in term of individuals expected hazard | data.frame | 2000 | 8 |
heating | mlogitBMA | Heating Dataset | data.frame | 900 | 19 |
known_Oct1 | winfapReader | Known events which happened on October 1st before 9am | data.frame | 38 | 3 |
four_days_1min.data | photobiologySun | Ground level irradiance for wavelength bands | tbl_df | 5760 | 17 |
gap.mspct | photobiologySun | Solar spectral irradiance in a tree canopy gap (measured) | source_mspct | | |
irrad_Kipp.data | photobiologySun | Ground level solar irradiance (measured) | tbl_df | 24479 | 6 |
ppfd_BF.data | photobiologySun | Ground level solar PAR photon irradiance, direct and diffuse (measured) | tbl_df | 24479 | 10 |
ppfd_LICOR.data | photobiologySun | Ground level solar PAR photon irradiance (measured) | tbl_df | 24479 | 6 |
sun_elevation.spct | photobiologySun | Ground level spectral irradiance and sun elevation | source_spct | 13940 | 6 |
sun_hourly_august.spct | photobiologySun | Ground level spectral irradiance at hourly intervals | source_spct | 15841 | 4 |
sun_hourly_june.spct | photobiologySun | Ground level spectral irradiance at hourly intervals | source_spct | 29464 | 4 |
sun_hourly_ozone.spct | photobiologySun | Ground level spectral irradiance at hourly intervals | source_spct | 16256 | 7 |
sun_may_morning.spct | photobiologySun | Ground level solar spectral irradiance (measured) | source_spct | 1421 | 2 |
sun_reference.mspct | photobiologySun | Reference solar spectra from ASTM G173 | source_mspct | | |
dat_cfa | modelbpp | A Sample Dataset Based On a Confirmatory Factor Analysis Model (For Testing) | data.frame | 200 | 6 |
dat_path_model | modelbpp | A Sample Dataset Based on a Path Model (For Testing) | data.frame | 100 | 4 |
dat_path_model_p06 | modelbpp | A Sample Dataset Based On a Complex Path Model (For Testing) | data.frame | 200 | 6 |
dat_sem | modelbpp | A Sample Dataset Based On a Structural Model (For Testing) | data.frame | 250 | 16 |
dat_serial_4 | modelbpp | A Sample Dataset Based On a Serial Mediation Model (For Testing) | data.frame | 100 | 4 |
dat_serial_4_weak | modelbpp | A Sample Dataset Based On a Serial Mediation Model With Weak Paths (For Testing) | data.frame | 100 | 4 |
sim.data | poisson.glm.mix | Simulated data set of 500 observations | matrix | 500 | |
sim.data | poisson.glm.mix | Simulated data set of 500 observations | matrix | 5000 | |
madsim_test | madsim | A microarray data sample, one colomn numerical values | data.frame | 33297 | 1 |
bkRNA18 | NormExpression | bkRNA18 | data.frame | 57955 | 18 |
bkRNA18_factors | NormExpression | bkRNA18_factors | data.frame | 18 | 13 |
scRNA663 | NormExpression | scRNA663 | character | | |
scRNA663_factors | NormExpression | scRNA663_factors | data.frame | 663 | 12 |
SimuData | EventWinRatios | A simulated semi-competing risks data set with non-terminal events and terminal events | data.frame | 3500 | 5 |
ames_new | applicable | Recent Ames Iowa Houses | spec_tbl_df | 2 | 67 |
binary_tr | applicable | Binary QSAR Data | data.frame | 4330 | 67 |
binary_unk | applicable | Binary QSAR Data | data.frame | 5 | 67 |
okc_binary_test | applicable | OkCupid Binary Predictors | tbl_df | 12938 | 61 |
okc_binary_train | applicable | OkCupid Binary Predictors | tbl_df | 38809 | 61 |
egOpioidsCTN0094 | CTNote | Opioid Use by Study Day for Example CTN-0094 Participants | tbl_df | 71 | 3 |
outcomesCTN0094 | CTNote | All Treatment Outcomes for CTN-0094 Participants | spec_tbl_df | 3560 | 64 |
timevisData | timevis | Timevis sample data | data.frame | 11 | 6 |
timevisDataGroups | timevis | Timevis sample group data | data.frame | 3 | 2 |
endosymbiont_1pop | Mondrian | Multiple infection profiles in one population | data.frame | 10 | 3 |
endosymbiont_3pop | Mondrian | Multiple infection profiles in three populations | data.frame | 30 | 6 |
gwa_sample | QCGWAS | Sample dataset for the QCGWAS package | data.frame | 10000 | 15 |
header_translations | QCGWAS | Translation table for GWAS dataset headers | data.frame | 104 | 2 |
rr | CC | Baseline Heart Rate Summaries | data.frame | 76 | 2 |
full_data | longsurr | Example data to illustrate functions | tbl_df | 10100 | 5 |
flechsig | ggsegFlechsig | flechsig atlas | brain_atlas | | |
flechsig_3d | ggsegFlechsig | flechsig atlas | ggseg3d_atlas | 4 | 4 |
classifications | rtrees | Classifications of species | tbl_df | 37033 | 3 |
taxa_supported | rtrees | Taxonomic groups supported | character | | |
desterieux | ggsegDesterieux | Desterieux cortical parcellations | brain_atlas | | |
desterieux_3d | ggsegDesterieux | Desterieux cortical parcellations | ggseg3d_atlas | 6 | 4 |
cut_off | SpatialRDD | Dataset with boundaries and polygons for the SpatialRDD vignette. | sf | 1 | 1 |
polygon_full | SpatialRDD | Dataset with boundaries and polygons for the SpatialRDD vignette. | sf | 1 | 7 |
polygon_treated | SpatialRDD | Dataset with boundaries and polygons for the SpatialRDD vignette. | sf | 1 | 7 |
my_linkedInads_data | linkedInadsR | Sample data of LinkedIn ads from the Windsor API. | data.frame | 14 | 5 |
my_tiktokads_data | tiktokadsR | Sample data of TikTok ads from the Windsor API. | data.frame | 14 | 5 |
cricsheet_codes | cricketdata | Codes used for competitions on Cricsheet | tbl_df | 39 | 2 |
player_meta | cricketdata | Meta data on players listed at ESPNCricinfo | tbl_df | 14073 | 10 |
GrunfeldGreene | systemfit | Grunfeld Data as published by Greene (2003) | data.frame | 100 | 5 |
KleinI | systemfit | Klein Model I | data.frame | 22 | 14 |
Kmenta | systemfit | Partly Artificial Data on the U. S. Economy | data.frame | 20 | 5 |
ppine | systemfit | Tree Growth Data for Ponderosa Pine | data.frame | 166 | 8 |
world_countries | geoparser | Dataset: World countries list | data.frame | 256 | 10 |
my_shopifyads_data | shopifyadsR | Sample of digital marketing data from Shopify Ads downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
small_posterior_2chains | factor.switching | Example data | list | | |
castillo2024.rgmomentum.e1 | samplrData | Data from Experiment 1 in Castillo et al. (2024) | data.frame | 5836 | 29 |
castillo2024.rgmomentum.e2 | samplrData | Data from Experiment 2 in Castillo et al. (2024) | data.frame | 28483 | 20 |
spicer2022.anchoringrepulsion.e1 | samplrData | Data from Experiment 1 in Spicer et al. (2022) | data.frame | 9600 | 11 |
spicer2022.anchoringrepulsion.e2 | samplrData | Data from Experiment 2 in Spicer et al. (2022) | data.frame | 2960 | 13 |
spicer2022.anchoringrepulsion.e2a | samplrData | Data from Experiment 2a in Spicer et al. (2022) | data.frame | 9920 | 13 |
sundh2023.meanvariance.e3 | samplrData | Data from Experiment 3 in Sundh et al. (2023) | data.frame | 12420 | 10 |
sundh2023.meanvariance.e4 | samplrData | Data from Experiment 4 in Sundh et al. (2023) | data.frame | 13320 | 7 |
zhu2020.bayesiansampler.e1 | samplrData | Data from Experiment 1 in Zhu et al. (2020) | data.frame | 7080 | 10 |
zhu2020.bayesiansampler.e2 | samplrData | Data from Experiment 2 in Zhu et al. (2020) | data.frame | 22380 | 10 |
zhu2022.coherenceaccuracy.e1 | samplrData | Data from Experiment 1 in Zhu et al. (2022) | data.frame | 82 | 23 |
zhu2022.coherenceaccuracy.e2 | samplrData | Data from Experiment 2 in Zhu et al. (2022) | data.frame | 186 | 23 |
zhu2022.structurenoise.animals | samplrData | Data from Animal Experiment in Zhu et al. (2022) | data.frame | 4967 | 7 |
zhu2022.structurenoise.time | samplrData | Data from Time Experiment in Zhu et al. (2022) | data.frame | 29822 | 6 |
object | RSSOP | An Object From Class Of creatArea | area | | |
TESTWGT | TSEwgt | A data set created by merging 1) "actual" data from a "gold standard" survey (A1, A2), and 2) data from another survey (Q1, Q2), including weights columns for that data (W1, W2). A1/Q1 and A2/Q2 are responses to the same two questions, asked to the same 10 respondents (ID), along the same 1-99 response scale. | data.frame | 10 | 7 |
b.data.bin | tLagInterim | Toy Dataset With a Binary Outcome For Illustration | data.frame | 722 | 5 |
b.data.cat | tLagInterim | Toy Dataset With a Categorical Outcome For Illustration | data.frame | 477 | 5 |
b.data.cont | tLagInterim | Toy Dataset With a Continuous Outcome For Illustration | data.frame | 245 | 5 |
t.data.bin | tLagInterim | Toy Dataset With a Binary Outcome For Illustration | data.frame | 722 | 3 |
t.data.cat | tLagInterim | Toy Dataset With a Categorical Outcome For Illustration | data.frame | 477 | 3 |
t.data.cont | tLagInterim | Toy Dataset With a Continuous Outcome For Illustration | data.frame | 245 | 6 |
x.data.bin | tLagInterim | Toy Dataset With a Binary Outcome For Illustration | data.frame | 722 | 2 |
x.data.cat | tLagInterim | Toy Dataset With a Categorical Outcome For Illustration | data.frame | 477 | 2 |
x.data.cont | tLagInterim | Toy Dataset With a Continuous Outcome For Illustration | data.frame | 245 | 2 |
calves | calibrate | Delivery of Dutch Calves | matrix | 6 | 3 |
goblets | calibrate | Size measurements of archeological goblets | data.frame | 25 | 6 |
heads | calibrate | Dimensions of heads of first and second sons for 25 families | data.frame | 25 | 4 |
linnerud | calibrate | Linnerud's exercise and body measurements | data.frame | 20 | 6 |
spaindist | calibrate | Road distances between Spanish cities | data.frame | 47 | 47 |
storks | calibrate | Frequencies of nesting storks in Denmark | data.frame | 25 | 4 |
example.data | CMFsurrogate | Example data | list | | |
marker_cont | OptimalSurrogate | Simulated data with continuous surrogate marker | data.frame | 1000 | 3 |
marker_disc | OptimalSurrogate | Simulated data with discrete surrogate marker | data.frame | 1000 | 3 |
adjectives | codename | A Data Frame of Adjectives | spec_tbl_df | 1353 | 1 |
animals | codename | A Data Frame of Animals | tbl_df | 382 | 1 |
gods | codename | A Data Frame of Gods | tbl_df | 221 | 2 |
nicka_blocks | codename | A Data Frame of NICKA Blocks | tbl_df | 626 | 4 |
nouns | codename | A Data Frame of Nouns | spec_tbl_df | 6801 | 1 |
wu_adjs | codename | A Data Frame of Adjectives from the "Wu-Tang Name Generator" | spec_tbl_df | 45 | 1 |
wu_nouns | codename | A Data Frame of Nouns from the "Wu-Tang Name Generator" | spec_tbl_df | 40 | 1 |
xkcd_colors | codename | A Data Frame of Colors | tbl_df | 948 | 1 |
Zaab | RHMS | datasets for Zaab subbasin, a subbasin in Kurdistan, Iran. | list | | |
palermo | logNormReg | Air quality in Palermo (Italy), 1997-2001 | data.frame | 1826 | 8 |
paris | logNormReg | PM2.5 and PM10 measurements in Paris in 2019 | tbl_df | 647 | 4 |
birds | rimu | Subset of the Great Backyard Bird survey | data.frame | 3046 | 13 |
ethnicity | rimu | Toy example using New Zealand level 1 ethnicity values | mr | 6 | 5 |
nzbirds | rimu | Toy example using New Zealand birds | ms | 6 | 5 |
rstudiosurvey | rimu | Subset of RStudio 2019 Community Survey | data.frame | 1838 | 52 |
usethnicity | rimu | Data from Youth Risk Behaviour Survey | data.frame | 14765 | 4 |
BioOxyDemand | MPV | Biochemical Oxygen Demand | data.frame | 14 | 2 |
Juliet | MPV | Juliet | data.frame | 28 | 9 |
Wpgtemp | MPV | Winnipeg Maximum Temperatures | data.frame | 7671 | 2 |
bp | MPV | Blood Pressure Measurements on a Single Adult Male | data.frame | 121 | 4 |
cement | MPV | Table B21 - Cement Data | data.frame | 13 | 5 |
cigbutts | MPV | Cigarette Butts | data.frame | 15 | 2 |
earthquake | MPV | Earthquakes Data | data.frame | 2178 | 4 |
fires | MPV | Micro-fires recorded in a lab setting | data.frame | 31 | 7 |
gasdata | MPV | Natural Gas Consumption in a Single-Family Residence | data.frame | 70 | 9 |
lengthguesses | MPV | Length Guesses Data | list | | |
lesions | MPV | Lesions in Rat Colons | data.frame | 396 | 7 |
motor | MPV | Motor Vibration Data | data.frame | 6 | 5 |
noisyimage | MPV | noisy image | list | | |
oldwash | MPV | oldwash | data.frame | 49 | 8 |
p11.12 | MPV | Data For Problem 11-12 | data.frame | 19 | 2 |
p11.15 | MPV | Data set for Problem 11-15 | data.frame | 9 | 2 |
p12.11 | MPV | Data Set for Problem 12-11 | data.frame | 44 | 2 |
p12.12 | MPV | Data Set for Problem 12-12 | data.frame | 18 | 3 |
p12.16 | MPV | Data Set for Problem 12-16 | data.frame | 26 | 5 |
p12.8 | MPV | Data Set for Problem 12-8 | data.frame | 14 | 2 |
p13.1 | MPV | Data Set for Problem 13-1 | data.frame | 25 | 2 |
p13.16 | MPV | Data Set for Problem 13-16 | data.frame | 16 | 5 |
p13.2 | MPV | Data Set for Problem 13-2 | data.frame | 20 | 2 |
p13.20 | MPV | Data Set for Problem 13-20 | data.frame | 30 | 2 |
p13.3 | MPV | Data Set for Problem 13-3 | data.frame | 10 | 3 |
p13.4 | MPV | Data Set for Problem 13-4 | data.frame | 11 | 3 |
p13.5 | MPV | Data Set for Problem 13-5 | data.frame | 20 | 3 |
p13.6 | MPV | Data Set for Problem 13-6 | data.frame | 15 | 3 |
p13.7 | MPV | Data Set for Problem 13-7 | data.frame | 44 | 5 |
p14.1 | MPV | Data Set for Problem 14-1 | data.frame | 15 | 3 |
p14.2 | MPV | Data Set for Problem 14-2 | data.frame | 18 | 3 |
p15.4 | MPV | Data Set for Problem 15-4 | data.frame | 40 | 4 |
p2.10 | MPV | Data Set for Problem 2-10 | data.frame | 26 | 2 |
p2.12 | MPV | Data Set for Problem 2-12 | data.frame | 12 | 2 |
p2.13 | MPV | Data Set for Problem 2-13 | data.frame | 16 | 2 |
p2.14 | MPV | Data Set for Problem 2-14 | data.frame | 8 | 2 |
p2.15 | MPV | Data Set for Problem 2-15 | data.frame | 8 | 2 |
p2.16 | MPV | Data Set for Problem 2-16 | data.frame | 33 | 2 |
p2.17 | MPV | Data Set for Problem 2-17 | data.frame | 17 | 2 |
p2.18 | MPV | Data Set for Problem 2-18 | data.frame | 21 | 3 |
p2.7 | MPV | Data Set for Problem 2-7 | data.frame | 20 | 2 |
p2.9 | MPV | Data Set for Problem 2-9 | data.frame | 25 | 2 |
p4.18 | MPV | Data Set for Problem 4-18 | data.frame | 13 | 4 |
p4.19 | MPV | Data Set for Problem 4-19 | data.frame | 14 | 4 |
p4.20 | MPV | Data Set for Problem 4-20 | data.frame | 26 | 6 |
p5.1 | MPV | Data Set for Problem 5-1 | data.frame | 8 | 2 |
p5.10 | MPV | Data Set for Problem 5-10 | data.frame | 27 | 8 |
p5.11 | MPV | Data Set for Problem 5-11 of the Third Edition of MPV | data.frame | 8 | 7 |
p5.12 | MPV | Data Set for Problem 5-12 | data.frame | 27 | 9 |
p5.13 | MPV | Data Set for Problem 5-13 | data.frame | 8 | 7 |
p5.2 | MPV | Data Set for Problem 5-2 | data.frame | 11 | 2 |
p5.21 | MPV | Data Set for Problem 5-21 | data.frame | 4 | 5 |
p5.22 | MPV | Data Set for Problem 5-22 | data.frame | 18 | 2 |
p5.23 | MPV | Data Set for Problem 5-23 | data.frame | 18 | 3 |
p5.24 | MPV | Data Set for Problem 5-24 | data.frame | 13 | 7 |
p5.3 | MPV | Data Set for Problem 5-3 | data.frame | 12 | 2 |
p5.4 | MPV | Data Set for Problem 5-4 | data.frame | 8 | 2 |
p5.5 | MPV | Data Set for Problem 5-5 | data.frame | 14 | 2 |
p7.1 | MPV | Data Set for Problem 7-1 | data.frame | 10 | 1 |
p7.11 | MPV | Data Set for Problem 7-11 | data.frame | 11 | 2 |
p7.13 | MPV | Data Set for Problem 7-13 | data.frame | 11 | 2 |
p7.15 | MPV | Data Set for Problem 7-15 | data.frame | 6 | 2 |
p7.16 | MPV | Data Set for Problem 7-16 | data.frame | 26 | 4 |
p7.17 | MPV | Data Set for Problem 7-17 | data.frame | 6 | 2 |
p7.18 | MPV | Data Set for Problem 7-18 | data.frame | 26 | 4 |
p7.19 | MPV | Data Set for Problem 7-19 | data.frame | 10 | 2 |
p7.2 | MPV | Data Set for Problem 7-2 | data.frame | 10 | 2 |
p7.20 | MPV | Data Set for Problem 7-20 | data.frame | 10 | 2 |
p7.4 | MPV | Data Set for Problem 7-4 | data.frame | 12 | 2 |
p7.6 | MPV | Data Set for Problem 7-6 | data.frame | 12 | 3 |
p8.11 | MPV | Data Set for Problem 8-11 | data.frame | 25 | 2 |
p8.16 | MPV | Data Set for Problem 8-16 | data.frame | 17 | 4 |
p8.3 | MPV | Data Set for Problem 8-3 | data.frame | 25 | 3 |
p9.10 | MPV | Data Set for Problem 9-10 | data.frame | 31 | 7 |
pathoeg | MPV | Pathological Example | data.frame | 40 | 10 |
radon | MPV | Radon Release | data.frame | 6 | 5 |
rectangles | MPV | Length Measurements on Rectangular Objects | data.frame | 51 | 4 |
seismictimings | MPV | Seismic Timing Data | data.frame | 504 | 3 |
softdrink | MPV | Softdrink Data | data.frame | 25 | 3 |
solar | MPV | Solar Data | data.frame | 29 | 6 |
stain | MPV | Stain Removal Data | data.frame | 15 | 2 |
table.b1 | MPV | Table B1 | data.frame | 28 | 10 |
table.b10 | MPV | Table B10 | data.frame | 40 | 3 |
table.b11 | MPV | Table B11 | data.frame | 38 | 7 |
table.b12 | MPV | Table B12 | data.frame | 32 | 6 |
table.b13 | MPV | Table B13 | data.frame | 40 | 7 |
table.b14 | MPV | Table B14 | data.frame | 25 | 6 |
table.b15 | MPV | Table B15 - Air Pollution and Mortality Data | data.frame | 60 | 7 |
table.b16 | MPV | Table B16 Data Set | data.frame | 38 | 6 |
table.b17 | MPV | Table B17 | data.frame | 25 | 5 |
table.b18 | MPV | Table B18 | data.frame | 16 | 9 |
table.b19 | MPV | Table B19 | data.frame | 32 | 11 |
table.b2 | MPV | Table B2 | data.frame | 29 | 6 |
table.b20 | MPV | Table B20 | data.frame | 18 | 6 |
table.b22 | MPV | Table B22 - Baseball Data | data.frame | 30 | 12 |
table.b23 | MPV | Table B23 | data.frame | 59 | 8 |
table.b24 | MPV | Table B24 - Rental Data | data.frame | 51 | 6 |
table.b25 | MPV | Table B25 Golf Data | data.frame | 50 | 6 |
table.b3 | MPV | Table B3 | data.frame | 32 | 12 |
table.b4 | MPV | Table B4 | data.frame | 24 | 10 |
table.b5 | MPV | Data Set for Table B5 | data.frame | 27 | 8 |
table.b6 | MPV | Data Set for Table B6 | data.frame | 28 | 5 |
table.b7 | MPV | Data Set for Table B7 | data.frame | 16 | 6 |
table.b8 | MPV | Table B8 | data.frame | 36 | 3 |
table.b9 | MPV | Data Set for Table B9 | data.frame | 62 | 5 |
table5.2 | MPV | Table 5.2 | data.frame | 53 | 3 |
table5.5 | MPV | Table 5.5 | data.frame | 25 | 2 |
table5.9 | MPV | Table 5.9 | data.frame | 30 | 2 |
tarimage | MPV | target image | list | | |
tree.sample | MPV | Sample of Loblolly Pine Data | nfnGroupedData | 14 | 2 |
widths | MPV | Measurements of the Widths of Book Covers | numeric | | |
windWin80 | MPV | Winnipeg Wind Speed | data.frame | 366 | 2 |
wxNWO | MPV | Weather Observations for Three Stations in Northwestern Ontario | data.frame | 10959 | 31 |
sample_data | NBBDesigns | sample data for analysis of NBB/PNBB design | data.frame | 110 | 5 |
D1.genie | approximator | Genie datasets for approximator package | matrix | 36 | 4 |
D1.toy | approximator | Toy datasets for approximator package | matrix | 20 | 3 |
betas.toy | approximator | Toy datasets for approximator package | matrix | 4 | 5 |
hpa.genie.optimal | approximator | Genie datasets for approximator package | list | | |
hpa.genie.start | approximator | Genie datasets for approximator package | list | | |
hpa.toy | approximator | Toy datasets for approximator package | list | | |
subsets.genie | approximator | Genie datasets for approximator package | list | | |
subsets.toy | approximator | Toy datasets for approximator package | list | | |
z.genie | approximator | Genie datasets for approximator package | list | | |
z.toy | approximator | Toy datasets for approximator package | list | | |
data_list | label.switching | Simulated MCMC sample and related information | list | | |
lamb | label.switching | Fetal lamb dataset | integer | | |
annot_g | rifiComparative | The result of gff3_preprocessing of gff3 file A list containing all necessary information from a gff file for adjusting_HLToInt and visualization. | list | | |
data_combined_minimal | rifiComparative | The result of joining_by_row for inp_s and inp_f example data A data frame containing the output of joining_by_row as a data frame | data.frame | 600 | 49 |
df_comb_minimal | rifiComparative | The result of joining_by_column for data_combined_minimal example data A data frame containing the output of joining_by_row as a data frame | data.frame | 300 | 18 |
df_mean_minimal | rifiComparative | The result of adjusting_HLToInt for stats_df_comb_minimal and annotation example data A data frame containing the output of adjusting_HLToInt as a data frame | data.frame | 52 | 15 |
differential_expression | rifiComparative | An example data frame from Synechosystis PCC 6803 differential probes expression obtained from limma package and only interesting variables were selected. The data frame was used entirely. | data.frame | 55508 | 4 |
fragment_int | rifiComparative | The result of fragmentation for df_comb_minimal example data A data frame containing the output of fragmentation as a data frame | data.frame | 500 | 24 |
inp_f | rifiComparative | The result of loading_fun for stats_se_cdt2 example data Two data frame containing the output of loading_fun as second element of a list. | data.frame | 500 | 49 |
inp_s | rifiComparative | The result of loading_fun for stats_se_cdt1 example data Two data frame containing the output of loading_fun as first element of a list. | data.frame | 500 | 49 |
pen_HL | rifiComparative | The result of penalties for df_comb_minimal example data. A list containing the output from penalties including the logbook and two penalty objects. | list | | |
pen_int | rifiComparative | The result of penalties for df_comb_minimal example data. A list containing the output from penalties including the logbook and two penalty objects. | list | | |
penalties_df | rifiComparative | The result of penalties for df_comb_minimal example data A data frame containing the output of penalties as a data frame | data.frame | 500 | 20 |
stats_df_comb_minimal | rifiComparative | The result of statistics for fragment_int example data A data frame containing the output of statistics as a data frame | data.frame | 500 | 26 |
stats_se_cdt1 | rifiComparative | An example SummarizedExperiment from Synechosystis PCC 6803 first condition obtained from rifi_statistics and used as input for rifiComparative | RangedSummarizedExperiment | | |
stats_se_cdt2 | rifiComparative | An example SummarizedExperiment from Synechosystis PCC 6803 second condition obtained from rifi_statistics and used as input for rifiComparative | RangedSummarizedExperiment | | |
genes | OncoScore | A list of genes | character | | |
query | OncoScore | The result of perform.web.query on genes | matrix | 5 | 2 |
query.timepoints | OncoScore | The result of perform.time.series.query on genes and timepoints | list | | |
timepoints | OncoScore | A list of timepoints | character | | |
RedWine | SurrogateRsq | Red wine quality dataset of the Portuguese "Vinho Verde" wine | data.frame | 1599 | 12 |
WhiteWine | SurrogateRsq | White wine quality dataset of the Portuguese "Vinho Verde" wine | data.frame | 4898 | 12 |
child | eha | Child mortality, Skellefteå, Sweeden 1850-1900. | data.frame | 26574 | 10 |
fert | eha | Marital fertility nineteenth century | data.frame | 12169 | 9 |
infants | eha | Infant mortality and maternal death, Sweeden 1821-1894. | data.frame | 105 | 11 |
logrye | eha | Rye prices, Scania, southern Sweden, 1801-1894. | data.frame | 94 | 2 |
male.mortality | eha | Male mortality in ages 40-60, nineteenth century | data.frame | 1208 | 6 |
mort | eha | Male mortality in ages 40-60, nineteenth century | data.frame | 1208 | 6 |
oldmort | eha | Old age mortality, Sundsvall, Sweden, 1860-1880. | data.frame | 6495 | 13 |
scania | eha | Old age mortality, Scania, southern Sweden, 1813-1894. | data.frame | 1931 | 9 |
swedeaths | eha | Swedish death data, 1969-2020. | data.frame | 10504 | 5 |
swepop | eha | Swedish population data, 1969-2020. | data.frame | 10504 | 5 |
vrc01 | vimp | Neutralization sensitivity of HIV viruses to antibody VRC01 | data.frame | 611 | 837 |
Cigar | plm | Cigarette Consumption | data.frame | 1380 | 9 |
Crime | plm | Crime in North Carolina | data.frame | 630 | 44 |
EmplUK | plm | Employment and Wages in the United Kingdom | data.frame | 1031 | 7 |
Gasoline | plm | Gasoline Consumption | data.frame | 342 | 6 |
Grunfeld | plm | Grunfeld's Investment Data | data.frame | 200 | 5 |
Hedonic | plm | Hedonic Prices of Census Tracts in the Boston Area | data.frame | 506 | 15 |
LaborSupply | plm | Wages and Hours Worked | data.frame | 5320 | 7 |
Males | plm | Wages and Education of Young Males | data.frame | 4360 | 12 |
Parity | plm | Purchasing Power Parity and other parity relationships | data.frame | 1768 | 9 |
Produc | plm | US States Production | data.frame | 816 | 11 |
RiceFarms | plm | Production of Rice in Indonesia | data.frame | 1026 | 20 |
Snmesp | plm | Employment and Wages in Spain | data.frame | 5904 | 8 |
SumHes | plm | The Penn World Table, v. 5 | data.frame | 3250 | 7 |
Wages | plm | Panel Data of Individual Wages | data.frame | 4165 | 12 |
dat.metap | metap | Example data | list | | |
met | inti | Swedish cultivar trial data | data.frame | 1069 | 8 |
potato | inti | Water use efficiency in 15 potato genotypes | data.frame | 150 | 17 |
mp_table | massProps | Example Mass Properties Table | data.frame | 1765 | 24 |
mp_table_small | massProps | Example Small Mass Properties Table | data.frame | 37 | 24 |
mp_tree | massProps | Example Mass Properties Tree | igraph | | |
mp_tree_small | massProps | Example Small Mass Properties Tree | igraph | | |
sawe_table | massProps | Mass Properties and Uncertainties Table from SAWE Paper No. 3360 | data.frame | 3 | 23 |
sawe_tree | massProps | Mass Properties and Uncertainties Tree from SAWE Paper No. 3360 | igraph | | |
test_table | massProps | Example Mass Properties Table | data.frame | 12 | 14 |
test_tree | massProps | Example Mass Properties Tree | igraph | | |
test_unc_table | massProps | Example Mass Properties and Uncertainties Table | data.frame | 12 | 24 |
AlbumSales | jmvReadWrite | Imagine that you worked for a record company and that your boss was interested in predicting album sales from advertising. | data.frame | 200 | 5 |
ToothGrowth | jmvReadWrite | The Effect of Vitamin C on Tooth Growth in Guinea Pigs | data.frame | 60 | 7 |
bfi_sample | jmvReadWrite | Twenty-five personality self-report items taken from the International Personality Item Pool | data.frame | 254 | 33 |
bfi_sample2 | jmvReadWrite | Twenty-five personality self-report items taken from the International Personality Item Pool (includes jamovi-attributes; should result in a file identical to bfi_sample2.omv under "extdata" when written with write_omv) | data.frame | 250 | 29 |
bfi_sample3 | jmvReadWrite | Twenty-five personality self-report items taken from the International Personality Item Pool (testing file for ordered factors / "Ordinal"-variables in jamovi) | data.frame | 250 | 28 |
Olink_Explore_1536 | pQTLdata | Olink/Explore 1536 panel | data.frame | 1472 | 3 |
Olink_Explore_3072 | pQTLdata | Olink/Explore 3072 panels | data.frame | 2943 | 4 |
Olink_Explore_HT | pQTLdata | Olink/Explore HT panels | data.frame | 5416 | 4 |
Olink_Target_96 | pQTLdata | Olink/Target 96 panels | data.frame | 1161 | 3 |
Olink_qPCR | pQTLdata | Olink/qPCR panels | data.frame | 1112 | 7 |
SomaScan11k | pQTLdata | SomaScan 11k | data.frame | 10776 | 5 |
SomaScan160410 | pQTLdata | Somascan panel | data.frame | 5178 | 10 |
SomaScanV4.1 | pQTLdata | SomaScan v4.1 | data.frame | 7288 | 6 |
caprion | pQTLdata | Caprion panel | grouped_df | 987 | 15 |
inf1 | pQTLdata | Olink/INF1 panel | data.frame | 92 | 14 |
scallop_inf1 | pQTLdata | Supplementary table 3 | data.frame | 180 | 19 |
seer1980 | pQTLdata | Seer 1980 panel | data.frame | 1980 | 10 |
swath_ms | pQTLdata | SWATH-MS panel | data.frame | 684 | 5 |
FakeData | vtree | Fake clinical dataset | data.frame | 46 | 18 |
FakeRCT | vtree | Fake Randomized Controlled Trial (RCT) data | data.frame | 12 | 6 |
the.matrix | vtree | The Matrix trilogy characters | tbl_df | 34 | 13 |
academy | pixarfilms | Pixar Academy awards and nominations | tbl_df | 88 | 3 |
box_office | pixarfilms | Box office reception numbers | tbl_df | 28 | 5 |
genres | pixarfilms | Genres describing Pixar films | tbl_df | 204 | 3 |
pixar_films | pixarfilms | Pixar films | tbl_df | 28 | 6 |
pixar_people | pixarfilms | People behind Pixar | tbl_df | 260 | 3 |
public_response | pixarfilms | Critical and public response | tbl_df | 28 | 8 |
co2_conc | fluxible | CO2 concentration | spec_tbl_df | 1251 | 13 |
co2_conc_missing | fluxible | CO2 concentration | spec_tbl_df | 668 | 13 |
co2_df_missing | fluxible | CO2 concentration with missing data | spec_tbl_df | 1148 | 5 |
co2_df_short | fluxible | CO2 concentration | tbl_df | 1801 | 5 |
co2_fluxes | fluxible | CO2 fluxes | spec_tbl_df | 6 | 11 |
co2_liahovden | fluxible | CO2 concentration at Liahovden | tbl_df | 89692 | 5 |
conc_poster | fluxible | CO2 concentration | tbl_df | 530 | 11 |
raw_twogases | fluxible | CO2 and CH4 concentration | tbl_df | 21681 | 4 |
record_liahovden | fluxible | Measurements meta data at Liahovden | tbl_df | 138 | 4 |
record_short | fluxible | Measurements meta data | tbl_df | 6 | 3 |
slopes0 | fluxible | Slopes for each flux | tbl_df | 1251 | 22 |
slopes0_flag | fluxible | Slopes for each flux | tbl_df | 1251 | 27 |
slopes0_temp | fluxible | Slopes for each flux | tbl_df | 1251 | 24 |
slopes0_vol | fluxible | Slopes for each flux | tbl_df | 1251 | 23 |
slopes0lin | fluxible | Slopes for each flux | tbl_df | 1251 | 19 |
slopes0lin_flag | fluxible | Slopes for each flux | tbl_df | 1251 | 22 |
slopes30 | fluxible | Slopes for each flux | tbl_df | 1251 | 22 |
slopes30_flag | fluxible | Slopes for each flux | tbl_df | 1251 | 27 |
slopes30lin | fluxible | Slopes for each flux | tbl_df | 1251 | 19 |
slopes30lin_flag | fluxible | Slopes for each flux | tbl_df | 1251 | 22 |
slopes30qua | fluxible | Slopes for each flux | tbl_df | 1251 | 24 |
slopes30qua_flag | fluxible | Slopes for each flux | tbl_df | 1251 | 26 |
slopes60 | fluxible | Slopes for each flux | tbl_df | 1251 | 22 |
slopes60lin | fluxible | Slopes for each flux | tbl_df | 1251 | 19 |
twogases_record | fluxible | Field record | tbl_df | 12 | 1 |
AAUP | LSTbook | 1984 salaries in various professional fields | spec_tbl_df | 28 | 7 |
Anthro_F | LSTbook | Anthropometric data from college-aged women | tbl_df | 184 | 19 |
Big | LSTbook | Short, simple data frames for textbook examples | data.frame | 344 | 4 |
Birdkeepers | LSTbook | Birdkeeping and Lung Cancer | data.frame | 147 | 7 |
Births2022 | LSTbook | Records on births in the US in 2022 | tbl_df | 20000 | 38 |
Boston_marathon | LSTbook | Winning times in the Boston Marathon | tbl_df | 175 | 6 |
Butterfly | LSTbook | World records in the 100 & 200 m butterfly swim | data.frame | 224 | 7 |
CRDS | LSTbook | Smoking and lung function among youths | tbl_df | 654 | 5 |
Calif_precip | LSTbook | Annual precipitation in California locations | spec_tbl_df | 30 | 6 |
Callback | LSTbook | Resume Experiment Data | data.frame | 4870 | 3 |
Clock_auction | LSTbook | Data from McClave-Sincich _Statistics_ 11/e | tbl_df | 32 | 3 |
Dowsing | LSTbook | Data from McClave-Sincich _Statistics_ 11/e | tbl_df | 26 | 4 |
Econ_outlook_poll | LSTbook | SIMULATED data from an economic outlook poll | data.frame | 10000 | 3 |
FARS | LSTbook | Annual summaries concerning motor-vehicle related fatalities in the US#' | data.frame | 23 | 13 |
Framingham | LSTbook | Data from the Framingham heart study | spec_tbl_df | 4238 | 16 |
Geography_journals | LSTbook | Data from McClave-Sincich _Statistics_ 11/e | spec_tbl_df | 28 | 5 |
Germany1933vote | LSTbook | Voting patterns in the 1933 German national election | data.frame | 681 | 7 |
Gilbert | LSTbook | Data from the trial of serial killer Kristen Gilbert | tbl_df | 1641 | 2 |
Go_vote | LSTbook | Get out the vote experiment | data.frame | 305866 | 6 |
Gradepoint | LSTbook | Sample from a college registrar's database | tbl_df | 14 | 2 |
Grades | LSTbook | Sample from a college registrar's database | data.frame | 6124 | 3 |
Hill_racing | LSTbook | Winning times in Scottish Hill races, 2005-2017 | tbl_df | 2236 | 7 |
MPG | LSTbook | Fuel economy measurements on US car models | tbl_df | 1154 | 39 |
McCredie_Kurtz | LSTbook | "Big Five" personality ratings for college first-year students | tbl_df | 191 | 15 |
Monocacy_river | LSTbook | Data on run-off from the Monocacy river at Jug Bridge, Maryland. | tbl_df | 25 | 2 |
Names_and_race | LSTbook | Resume Experiment Data | grouped_df | 36 | 2 |
Natality_2014 | LSTbook | Medical info on each birth in the US in 2014 | data.frame | 10000 | 45 |
Nats | LSTbook | Short, simple data frames for textbook examples | tbl_df | 8 | 4 |
Offspring | LSTbook | Relative sizes offspring/parent for many species | tbl_df | 3971 | 5 |
Orings | LSTbook | Space Shuttle O-Ring Failures | data.frame | 24 | 2 |
PGA_index | LSTbook | Data from McClave-Sincich _Statistics_ 11/e | spec_tbl_df | 40 | 4 |
PIDD | LSTbook | Pima Indians Diabetes Database | spec_tbl_df | 768 | 9 |
Penguins | LSTbook | Body measurements on penguins | tbl_df | 333 | 8 |
STAR | LSTbook | STAR Project Data | data.frame | 6292 | 6 |
Sessions | LSTbook | Sample from a college registrar's database | tbl_df | 1687 | 6 |
Shipping_losses | LSTbook | Shipping losses in 1941 in the Atlantic | spec_tbl_df | 36 | 4 |
Tiny | LSTbook | Short, simple data frames for textbook examples | data.frame | 8 | 4 |
UCB_applicants | LSTbook | Roster of applicants to six major departments at UC Berkeley | tbl_df | 4526 | 3 |
US_wildfires | LSTbook | Monthly tallies of wildfires in the US from 2000 to 2022 | tbl_df | 275 | 4 |
Wheat | LSTbook | Experimental data on the yield of winter wheat | tbl_df | 168 | 5 |
sim_00 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_01 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_02 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_03 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_04 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_05 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_06 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_07 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_08 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_09 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_10 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_11 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_12 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_flights | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_medical_observations | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_prob_21.1 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_satgpa | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_school1 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_school2 | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
sim_vaccine | LSTbook | Simulations for use in _Lessons in Statistical Thinking_ | list | | |
polar_data | openairmaps | Example data for polar mapping functions | tbl_df | 35040 | 13 |
traj_data | openairmaps | Example data for trajectory mapping functions | tbl_df | 5432 | 17 |
examens | summarytools | Bulletin de notes (donnees simulees) | data.frame | 30 | 8 |
exams | summarytools | Report Cards - Simulated Data | data.frame | 30 | 8 |
tabagisme | summarytools | Usage du tabac et etat de sante (donnees simulees) | data.frame | 1000 | 9 |
tobacco | summarytools | Tobacco Use and Health - Simulated Dataset | data.frame | 1000 | 9 |
actg | mlr3proba | ACTG 320 Clinical Trial Dataset | data.frame | 1151 | 16 |
gbcs | mlr3proba | German Breast Cancer Study (GBCS) Dataset | data.frame | 686 | 16 |
grace | mlr3proba | GRACE 1000 Dataset | data.frame | 1000 | 9 |
whas | mlr3proba | Worcester Heart Attack Study (WHAS) Dataset | data.frame | 481 | 14 |
data_1w_death | grafify | In vitro experiments measuring percentage cell death in three genotypes of cells. | data.frame | 15 | 3 |
data_2w_Festing | grafify | Data from two-way ANOVA with randomised block design of treatments of strains of mice. | data.frame | 16 | 4 |
data_2w_Tdeath | grafify | In vitro measurement of percentage cell death - two-way ANOVA design with repeated measures, and randomised blocks. | data.frame | 24 | 6 |
data_cholesterol | grafify | Hierarchical data from 25 subjects either treated or not at 5 hospitals - two-way ANOVA design with repeated measures. | tbl_df | 50 | 4 |
data_doubling_time | grafify | Doubling time of E.coli measured by 10 students three independent times. | tbl_df | 30 | 3 |
data_t_pdiff | grafify | Matched data from two groups where difference between them is consistent. | data.frame | 20 | 3 |
data_t_pratio | grafify | Matched data from two groups where ratio between them is consistent. | data.frame | 66 | 3 |
data_zooplankton | grafify | Time-series data on zooplankton in lake Menon. | data.frame | 1127 | 8 |
adp | oce | Sample adp Data | adp | | |
adv | oce | Sample adv Data | adv | | |
amsr | oce | Sample amsr Data (Near Nova Scotia) | amsr | | |
argo | oce | Sample argo Data | argo | | |
cm | oce | Sample cm Data | cm | | |
coastlineWorld | oce | Sample coastline Data (Global, at 1:110M scale) | coastline | | |
ctd | oce | Sample ctd Data | ctd | | |
ctdRaw | oce | Sample ctd Data, Not Trimmed of Extraneous Data | ctd | | |
echosounder | oce | Sample echosounder Data | echosounder | | |
landsat | oce | Sample landsat Data | landsat | | |
lisst | oce | Sample lisst Data | lisst | | |
lobo | oce | Sample lobo Data | lobo | | |
met | oce | Sample met Data | met | | |
ocecolors | oce | Data That Define Some Color Palettes | list | | |
rsk | oce | Sample rsk Data | rsk | | |
sealevel | oce | Sample sealevel Data (Halifax Harbour) | sealevel | | |
sealevelTuktoyaktuk | oce | Sample sealevel Data (Tuktoyaktuk) | sealevel | | |
section | oce | Sample section Data | section | | |
tidalCurrent | oce | Tidal Current Dataset | data.frame | 888 | 3 |
tidedata | oce | Tidal Constituent Information | list | | |
topoWorld | oce | Global Topographic Data (at Half-degree Resolution) | topo | | |
wind | oce | Sample Wind Data | data.frame | 31 | 3 |
xbt | oce | Sample xbt Data | xbt | | |
data_location_lookup | sonata | Referência: location_uuid | tbl_df | 635 | 7 |
data_observation_lookup | sonata | Referência: data_observation_lookup | tbl_df | 11632 | 2 |
data_type_id_lookup | sonata | Referência: data_type_id_lookup | tbl_df | 807 | 7 |
lalonde | kbal | Data from National Supported Work program and Panel Study in Income Dynamics | data.frame | 2675 | 14 |
GlobalPatterns | mia | Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. | TreeSummarizedExperiment | | |
HintikkaXOData | mia | Multiomics dataset from 40 rat samples | MultiAssayExperiment | | |
Tengeler2020 | mia | Gut microbiota profiles of 27 individuals with ADHD and healthy controls | TreeSummarizedExperiment | | |
Tito2024QMP | mia | Fecal microbiota samples from 589 patients across different colorectal cancer stages | TreeSummarizedExperiment | | |
dmn_se | mia | Twins' microbiome data from 278 individuals | SummarizedExperiment | | |
enterotype | mia | Human gut microbiome dataset from 22 subjects based on shotgun DNA sequencing | TreeSummarizedExperiment | | |
esophagus | mia | Human esophageal community from 3 individuals | TreeSummarizedExperiment | | |
peerj13075 | mia | Skin microbial profiles 58 genetically unrelated individuals | TreeSummarizedExperiment | | |
faahko_sub | xcms | LC-MS preprocessing result test data sets | XCMSnExp | | |
faahko_sub2 | xcms | LC-MS preprocessing result test data sets | XcmsExperiment | | |
xdata | xcms | LC-MS preprocessing result test data sets | XCMSnExp | | |
xmse | xcms | LC-MS preprocessing result test data sets | XcmsExperiment | | |
habsburg | verbalisr | Habsburg pedigree. | ped | | |
flow | DTSg | Daily river flows | data.table | 2169 | 2 |
background_error_rate | PlasmaMutationDetector | The package provide the SNV and INDEL PERs computed for the Ion AmpliSeq™ Colon and Lung Cancer Panel v2 from 29 controls in a table available in the data file 'background_error_rate.txt'. | data.frame | 10199 | 10 |
hotspot | PlasmaMutationDetector | The package provide a list of known hotspot positions located on the amplicons of the Ion AmpliSeq™ Colon and Lung Cancer Panel v2 as a txt file 'hotspot.txt' which contains a vector/variable -named chrpos (first row)- of chars, of the form chrN:XXXXXXXXX defining genomic positions. | data.frame | 44 | 1 |
pos_ind | PlasmaMutationDetector | The package provide the positions and ranges computed for the Ion AmpliSeq™ Colon and Lung Cancer Panel v2 as a Rdata file 'positions_ranges.rda'. | character | | |
pos_ranges | PlasmaMutationDetector | The package provide the positions and ranges computed for the Ion AmpliSeq™ Colon and Lung Cancer Panel v2 as a Rdata file 'positions_ranges.rda'. | GRanges | | |
pos_snp | PlasmaMutationDetector | The package provide the positions and ranges computed for the Ion AmpliSeq™ Colon and Lung Cancer Panel v2 as a Rdata file 'positions_ranges.rda'. | character | | |
ME_ECharmserve | soilassessment | Harmonization models for soil electrical conductivity | list | | |
ME_PHharmserve | soilassessment | Harmonization models for soil pH | list | | |
nutrient | soilassessment | Sample data of decision ranking table for mapping soil nutrient condition | matrix | 11 | 11 |
nutrindicator | soilassessment | A grid stack map of indicators for crop fertility requirements | SpatialGridDataFrame | | |
soil | soilassessment | Sample soil dataset for salinity mapping | SpatialPointsDataFrame | | |
suitabinput | soilassessment | Sample grid stack map of nutrient indicators for crop fertility requirements | SpatialGridDataFrame | | |
textureinput | soilassessment | Sample texture dataset for mapping soil texture | SpatialGridDataFrame | | |
exmplData1 | PEIMAN2 | Example dataset1 | list | | |
exmplData2 | PEIMAN2 | Example dataset 2 | tbl_df | 209 | 2 |
mod_ont | PEIMAN2 | Database of protein modifications | data.frame | 2102 | 3 |
ptmlist | PEIMAN2 | Controlled vocabulary for post-translational modifications (PTM) terms | data.frame | 686 | 5 |
df_crc | miRetrieve | Dataset of PubMed data of miRNAs in Colorectal Cancer | tbl_df | 508 | 8 |
df_mirtarbase | miRetrieve | miRTarBase version 8.0 | tbl_df | 557182 | 3 |
df_panc | miRetrieve | Dataset of PubMed data of miRNAs in Pancreatic Cancer | tbl_df | 381 | 8 |
df_test | miRetrieve | Test dataset of PubMed abstracts | tbl_df | 11 | 6 |
ngram_stopwords | miRetrieve | Stop words for n-grams | character | | |
stopwords_2gram | miRetrieve | Stop words for text mining with common PubMed 2-grams | tbl_df | 230 | 2 |
stopwords_miretrieve | miRetrieve | Stop words for text mining with miRetrieve | tbl_df | 9773 | 2 |
stopwords_pubmed | miRetrieve | Stop words for text mining from PubMed abstracts | tbl_df | 8394 | 2 |
data.apa | PerMallows | Sample of permutations APA | matrix | 5738 | |
data.order | PerMallows | Sample of permutations | data.frame | 3 | 5 |
perm.sample.med | PerMallows | Sample of permutations | matrix | 20 | |
perm.sample.small | PerMallows | Sample of permutations | matrix | 5 | 4 |
aidsCT | icensBKL | AIDS Clinical Trial ACTG 181 | data.frame | 204 | 4 |
aidsCohort | icensBKL | AIDS Cohort Study on Patients with Hemophilia | data.frame | 257 | 8 |
breastCancer | icensBKL | Cosmetic Data on Breast Cancer Patients | data.frame | 94 | 3 |
graft | icensBKL | Homograft's Survival Times | data.frame | 272 | 22 |
hiv | icensBKL | HIV Data on Hemophilia Patients | data.frame | 368 | 4 |
mastitis | icensBKL | Data on Mastitis in Dairy Cattle | data.frame | 400 | 10 |
mobile | icensBKL | Survey on Mobile Phone Purchases | data.frame | 478 | 16 |
tandmob | icensBKL | Signal Tandmobiel Data, Subsample | data.frame | 500 | 79 |
tandmobAll | icensBKL | Signal Tandmobiel Data | data.frame | 4430 | 143 |
yoghurt | icensBKL | Sensory Shelf Life of Yoghurt | data.frame | 93 | 4 |
simData.cca | iSFun | Example data for method iscca | list | | |
simData.pca | iSFun | Example data for method ispca | list | | |
simData.pls | iSFun | Example data for method ispls | list | | |
Insurance | splm | Insurance consumption across Italian provinces, 1998-2002 | data.frame | 515 | 22 |
RiceFarms | splm | Production of Rice in India | data.frame | 1026 | 21 |
itaww | splm | Spatial weights matrix - Italian provinces | matrix | 103 | 103 |
riceww | splm | Spatial weights matrix of Indonesian rice farms | matrix | 171 | |
usaww | splm | Spatial weights matrix - US states | matrix | 48 | 48 |
banknote | qcluster | Swiss Banknotes Data | data.frame | 200 | 7 |
ESTX | ufRisk | EURO STOXX 50 (ESTX) Financial Time Series Data | data.frame | 3697 | 10 |
WMT | ufRisk | Walmart Inc. (WMT) Financial Time Series Data | data.frame | 5535 | 10 |
exampleX | VDSM | exampleX | matrix | 30 | 8 |
examplef | VDSM | examplef | numeric | | |
gf_nepa17 | dendRoAnalyst | Dendrometer data of Kathmandu for 2017 with gap filled | data.frame | 8760 | 3 |
ktm_rain17 | dendRoAnalyst | Daily rainfall data of Kathmandu for 2017. | tbl_df | 365 | 2 |
nepa | dendRoAnalyst | Dendrometer data from Kathmandu | data.frame | 14543 | 3 |
nepa17 | dendRoAnalyst | Dendrometer data of Kathmandu for 2017 | data.frame | 8753 | 3 |
nepa2 | dendRoAnalyst | Dendrometer data from Kathmandu version 2 | data.frame | 14543 | 8 |
dataobs | vdar | Simulated observation data | data.frame | 200 | 3 |
dataobs_coda | vdar | Simulated observation of compositional data | data.frame | 200 | 4 |
datatrue | vdar | Simulated true data | data.frame | 200 | 3 |
datatrue_coda | vdar | Simulated true compositional data | data.frame | 200 | 4 |
uncertainties | vdar | Simulated observation uncertainties | data.frame | 200 | 3 |
uncertainties_coda | vdar | Simulated observation uncertainties of compositional data | data.frame | 200 | 4 |
bbs1980 | betapart | BBS data by state for two timeslices | matrix | 49 | 569 |
bbs2000 | betapart | BBS data by state for two timeslices | matrix | 49 | 569 |
betatest | betapart | A data set of 4 communities, 107 species and a 4D functional space | list | | |
ceram.n | betapart | Cerambycidae from Northern European Countries | data.frame | 18 | 634 |
ceram.s | betapart | Cerambycidae from Southern European Countries | data.frame | 15 | 634 |
coords.n | betapart | Spatial coordinates for Southern European Countries | data.frame | 18 | 4 |
coords.s | betapart | Spatial coordinates for Southern European Countries | data.frame | 15 | 4 |
CPP | CoMiRe | Collaborative Perinatal Project data | data.frame | 2313 | 8 |
test_adlp_component | ADLP | Test ADLP Component | adlp_component | | |
test_claims_dataset | ADLP | Claims Data in data.frame Format | data.frame | 1600 | 4 |
Vehicle2 | dawai | Vehicle Silhouettes 2 | data.frame | 846 | 5 |
ExampleBSseq | PQLseq | BSseq example dataset | list | | |
ExampleRNAseq | PQLseq | RNAseq example dataset | list | | |
favourite_numbers | favnums | Favourite Numbers based on an online poll | data.frame | 1123 | 4 |
pisaL | pisaRT | PISA Example Responses and Response Times Data (long format) | data.frame | 6000 | 5 |
pisaW | pisaRT | PISA Example Responses and Response Times Data (wide format) | data.frame | 500 | 37 |
CIC | agricolae | Data for late blight of potatoes | list | | |
Chz2006 | agricolae | Data amendment Carhuaz 2006 | list | | |
ComasOxapampa | agricolae | Data AUDPC Comas - Oxapampa | data.frame | 168 | 4 |
DC | agricolae | Data for the analysis of carolina genetic design | list | | |
Glycoalkaloids | agricolae | Data Glycoalkaloids | data.frame | 25 | 2 |
Hco2006 | agricolae | Data amendment Huanuco 2006 | list | | |
LxT | agricolae | Data Line by tester | data.frame | 92 | 4 |
RioChillon | agricolae | Data and analysis Mother and baby trials | list | | |
clay | agricolae | Data of Ralstonia population in clay soil | data.frame | 69 | 3 |
corn | agricolae | Data of corn | data.frame | 34 | 3 |
cotton | agricolae | Data of cotton | data.frame | 96 | 5 |
disease | agricolae | Data evaluation of the disease overtime | data.frame | 21 | 7 |
frijol | agricolae | Data of frijol | data.frame | 84 | 3 |
genxenv | agricolae | Data of potato yield in a different environment | data.frame | 250 | 3 |
grass | agricolae | Data for Friedman test | data.frame | 48 | 3 |
greenhouse | agricolae | Data in greenhouse | list | | |
growth | agricolae | Data growth of trees | data.frame | 30 | 3 |
haynes | agricolae | Data of AUDPC for nonparametrical stability analysis | data.frame | 16 | 9 |
heterosis | agricolae | Data of potato, Heterosis | data.frame | 324 | 11 |
huasahuasi | agricolae | Data: Rainfall thresholds as support for timing fungicide applications in the control of potato late blight in Peru | list | | |
markers | agricolae | Data of molecular markers | data.frame | 23 | 27 |
melon | agricolae | Data of yield of melon in a Latin square experiment | data.frame | 16 | 4 |
natives | agricolae | Data of native potato | data.frame | 876 | 4 |
pamCIP | agricolae | Data Potato Wild | data.frame | 43 | 107 |
paracsho | agricolae | Data of Paracsho biodiversity | data.frame | 110 | 6 |
plots | agricolae | Data for an analysis in split-plot | data.frame | 18 | 5 |
plrv | agricolae | Data clones from the PLRV population | data.frame | 504 | 6 |
potato | agricolae | Data of cutting | data.frame | 18 | 4 |
ralstonia | agricolae | Data of assessment of the population in the soil R.solanacearum | data.frame | 13 | 8 |
rice | agricolae | Data of Grain yield of rice variety IR8 | data.frame | 36 | 18 |
sinRepAmmi | agricolae | Data for AMMI without repetition | data.frame | 250 | 3 |
soil | agricolae | Data of soil analysis for 13 localities | data.frame | 13 | 23 |
sweetpotato | agricolae | Data of sweetpotato yield | data.frame | 12 | 2 |
wilt | agricolae | Data of Bacterial Wilt (AUDPC) and soil | data.frame | 13 | 15 |
yacon | agricolae | Data Yacon | data.frame | 432 | 19 |
tableNIST2010 | NISTunits | Fundamental Physical Constants | data.frame | 335 | 4 |
tableNISTfactors | NISTunits | Factors for Unit Conversion | data.frame | 452 | 4 |
tableNISTnonSI | NISTunits | Fundamental Physical Constants non-SI | data.frame | 35 | 7 |
Aph0_a | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph0_b | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph0_c | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph0_d | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph100_a | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph100_b | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph100_c | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph100_d | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph50_a | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph50_b | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph50_c | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
Aph50_d | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
NIH3T3_a | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
NIH3T3_b | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
NIH3T3_c | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
NIH3T3_d | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
tAph0_a | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph0_b | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph0_c | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph0_d | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph100_a | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph100_b | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph100_c | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph100_d | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph50_a | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph50_b | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph50_c | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tAph50_d | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
tNIH3T3_a | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
tNIH3T3_b | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
tNIH3T3_c | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
tNIH3T3_d | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
volumes_A10_vSMC | cellVolumeDist | Volume distribution data for A10 vSMC cell cultures | numeric | | |
volumes_nih3t3 | cellVolumeDist | Volume distribution data for NIH3T3 cell cultures | numeric | | |
citrus.combinedFCSSet | mineCitrus | Cytometry data set for example of Citrus data set from nolanlab/citrus | citrus.combinedFCSSet | | |
citrus.foldClustering | mineCitrus | Clustering data for example of Citrus data set from nolanlab/citrus | citrus.foldClustering | | |
citrus.foldFeatureSet | mineCitrus | Correlation data for example of Citrus data set from nolanlab/citrus | citrus.foldFeatureSet | | |
atp_2019 | welo | ATP matches in 2019 | data.frame | 2610 | 36 |
wta_2019 | welo | WTA matches in 2019 | data.frame | 2472 | 32 |
nutrient | cenGAM | Nutrient dataset | data.frame | 1200 | 3 |
citiesData | alphaOutlier | Population of the 999 largest German cities | data.frame | 999 | 1 |
daysabs | alphaOutlier | Number of absence days of students | numeric | | |
distM1 | gStream | An distance matrix constructed from L2 distance | matrix | 40 | 40 |
eegdata | eegkitdata | EEG Data from Alcoholic and Control Subjects | data.frame | 1638400 | 7 |
bmt | smcure | Bone marrow transplant study | data.frame | 91 | 3 |
e1684 | smcure | Eastern Cooperative Oncology Group (ECOG) data | data.frame | 285 | 5 |
lake_id_xref | mwlaxeref | Lake identification cross-reference table | tbl_df | 529413 | 9 |
wis_lakes | mwlaxeref | Example of lake ID data from Wisconsin | casc_wi_age_data | 10 | 4 |
hybrid_phe | predhy.GUI | Phenotypic data of hybrids | data.frame | 410 | 3 |
input_geno | predhy.GUI | Genotype in Hapmap Format | data.frame | 4979 | 359 |
input_geno1 | predhy.GUI | Genotype in Numeric Format | data.frame | 1000 | 50 |
ksl | deal | Health and social characteristics | data.frame | 1083 | 9 |
rats | deal | Weightloss of rats | data.frame | 24 | 4 |
reinis | deal | deal internal functions | data.frame | 1841 | 6 |
baker | blockmodeling | Citation data between social work journals for the 1985-86 period | matrix | 20 | 20 |
notesBorrowing | blockmodeling | The notes borrowing network between social-informatics students | matrix | 13 | |
datosabiertos | covidmx | Datos abiertos de COVID-19 | list | | |
miniimagematrix | PSSIM | Mini-image data matrice for example | list | | |
capacity | panelView | State capacity dataset | data.frame | 7186 | 8 |
simdata | panelView | A simulated dataset | data.frame | 900 | 4 |
turnout | panelView | Voter turnout data. | data.frame | 1128 | 6 |
reproai | mhsmm | Artificial insemination times for seven cows | data.frame | 12 | 2 |
reprocows | mhsmm | Reproductive data from seven dairy cows | data.frame | 13040 | 4 |
reproppa | mhsmm | Observed lengths of post-partum anoestrus for 73 dairy cows | numeric | | |
example | momentuHMM | Example dataset | list | | |
forest | momentuHMM | Example dataset | RasterLayer | | |
miExample | momentuHMM | Example dataset | list | | |
ex_data_JoBS | SE.EQ | Example dataset from Hoffelder et al. (2015) | tbl_df | 24 | 4 |
X | MultiwayRegression | Simulated multi-way data for prediction | array | | |
Y | MultiwayRegression | Simulated multi-way data for prediction | array | | |
coryphanthaA | Rramas | Transition Matrices of Three Coryphantha robbinsorum Populations | matrix | 3 | 3 |
coryphanthaB | Rramas | Transition Matrices of Three Coryphantha robbinsorum Populations | matrix | 3 | 3 |
coryphanthaC | Rramas | Transition Matrices of Three Coryphantha robbinsorum Populations | matrix | 3 | |
human_chr_locations | SCpubr | Chromosome arm locations for human genome GRCh38. | tbl_df | 48 | 4 |
commits | jqr | GitHub Commits Data | character | | |
Collins | metamisc | Collins data | data.frame | 9 | 2 |
DVTipd | metamisc | Hypothetical dataset for diagnosis of Deep Vein Thrombosis (DVT) | data.frame | 500 | 16 |
DVTmodels | metamisc | Risk prediction models for diagnosing Deep Venous Thrombosis (DVT) | litmodels | | |
Daniels | metamisc | Daniels and Hughes data | data.frame | 15 | 4 |
EuroSCORE | metamisc | Predictive performance of EuroSCORE II | data.frame | 23 | 15 |
Fibrinogen | metamisc | Meta-analysis of the association between plasma fibrinogen concentration and the risk of coronary heath disease | data.frame | 31 | 5 |
Framingham | metamisc | Predictive performance of the Framingham Risk Score in male populations | data.frame | 24 | 19 |
Kertai | metamisc | Kertai data | data.frame | 7 | 4 |
Roberts | metamisc | Roberts data | data.frame | 14 | 2 |
Scheidler | metamisc | Diagnostic accuracy data | data.frame | 44 | 7 |
Tzoulaki | metamisc | The incremental value of cardiovascular risk factors | data.frame | 100 | 15 |
Zhang | metamisc | Meta-analysis of the prognostic role of hormone receptors in endometrial cancer | data.frame | 20 | 10 |
impact | metamisc | IMPACT data | data.frame | 11022 | 11 |
FANG | tidyquant | Stock prices for the "FANG" stocks. | tbl_df | 4032 | 8 |
SpainNat | CvmortalityMult | Spain National Mortality data | CVmortalityData | 600 | 8 |
SpainRegions | CvmortalityMult | Spain Regions Mortality data | CVmortalityData | 10800 | 7 |
regions | CvmortalityMult | regions | SpainRegionsData | | |
ExampleData | mnet | Internal mnet functions | data.frame | 20000 | 5 |
dataKoval13 | mnet | Internal mnet functions | data.frame | 5716 | 14 |
EFSA_GW_interception_2014 | pfm | Subset of EFSA crop interception default values for groundwater modelling | matrix | 7 | 10 |
EFSA_washoff_2017 | pfm | Subset of EFSA crop washoff default values | matrix | 7 | 10 |
FOCUS_GW_scenarios_2012 | pfm | A very small subset of the FOCUS Groundwater scenario definitions | list | | |
FOCUS_Step_12_scenarios | pfm | Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator | list | | |
drift_data_JKI | pfm | Deposition from spray drift expressed as percent of the applied dose as published by the JKI | list | | |
drift_parameters_focus | pfm | Regression parameters for the Rautmann drift data | tbl_df | 49 | 8 |
perc_runoff_exposit | pfm | Runoff loss percentages as used in Exposit 3 | data.frame | 12 | 3 |
perc_runoff_reduction_exposit | pfm | Runoff reduction percentages as used in Exposit | list | | |
soil_scenario_data_EFSA_2015 | pfm | Properties of the predefined scenarios from the EFSA guidance from 2015 | data.frame | 6 | 10 |
soil_scenario_data_EFSA_2017 | pfm | Properties of the predefined scenarios from the EFSA guidance from 2017 | data.frame | 6 | 12 |
m4ra_hampi | m4ra | m4ra_hampi | SC | | |
data_dictionary | pacta.multi.loanbook | Data dictionary | tbl_df | 194 | 5 |
climate_tags | ColOpenData | climate_tags | list | | |
datasets_list | ColOpenData | datasets_list | list | | |
geospatial_dictionaries | ColOpenData | geospatial_dictionaries | list | | |
gtfs_duke | tidytransit | Example GTFS data | tidygtfs | | |
route_type_names | tidytransit | Dataframe of route type id's and the names of the types (e.g. "Bus") | spec_tbl_df | 136 | 2 |
Anderson | rcompanion | Hypothetical data for Alexander Anderson | matrix | 4 | 2 |
AndersonBias | rcompanion | Hypothetical data for Alexander Anderson with gender bias | data.frame | 16 | 4 |
AndersonRainBarrel | rcompanion | Hypothetical data for Alexander Anderson on rain barrel installation | matrix | 2 | 2 |
AndersonRainGarden | rcompanion | Hypothetical data for Alexander Anderson on rain garden installation | matrix | 3 | 3 |
BobBelcher | rcompanion | Hypothetical data for ratings of instructors in unreplicated CBD | data.frame | 40 | 3 |
Breakfast | rcompanion | Hypothetical data for students' breakfast habits and travel to school | table | 3 | 5 |
BrendonSmall | rcompanion | Hypothetical data for Brendon Small and company | data.frame | 45 | 6 |
BullyHill | rcompanion | Hypothetical data for proportion of students passing a certification | data.frame | 12 | 5 |
Catbus | rcompanion | Hypothetical data for Catbus and company | data.frame | 26 | 5 |
HayleySmith | rcompanion | Hypothetical data for responses about adopting lawn care practices | data.frame | 56 | 3 |
Monarchs | rcompanion | Hypothetical data for monarch butterflies in gardens | data.frame | 24 | 2 |
Nurseries | rcompanion | Data for proportion of good practices followed by plant nuseries | data.frame | 38 | 2 |
Pennsylvania18 | rcompanion | Votes for the Democratic candidate in Pennsylvania 18 in 2016 and 2018 | matrix | 2 | 2 |
Pooh | rcompanion | Hypothetical data for paired ratings of Pooh Bear | data.frame | 20 | 4 |
PoohPiglet | rcompanion | Hypothetical data for ratings of Pooh, Piglet, and Tigger | data.frame | 30 | 2 |
Religion | rcompanion | Hypothetical data for change in religion after a caucusing event | matrix | 4 | 4 |
bartlett2009 | phenopix | Bartlett 2009 raw data | data.frame | 2891 | 9 |
bartlett2009.filtered | phenopix | Bartlett 2009 dataset filtered | zoo | | |
bartlett2009.fitted | phenopix | Bartlett 2009 dataset with computed fitting and uncertainty estimation | list | | |
bartlett2009.processed | phenopix | Bartlett 2009 dataset processed by greenExplore function | phenopix | | |
cars2017 | tigerData | Fuel Economy Data on Cars in 2017 | data.frame | 1103 | 7 |
deathpenalty | tigerData | Race and the Death Penalty | data.frame | 326 | 3 |
diabetes | tigerData | Diabetes Risk | data.frame | 9096 | 26 |
donation | tigerData | Zen Center Donations | spec_tbl_df | 66 | 5 |
event | tigerData | Zen Center Events | tbl_df | 15 | 7 |
eventParticipation | tigerData | Zen Center Participants | tbl_df | 107 | 5 |
firesetting | tigerData | Fire-setting Among Teenagers | data.frame | 975 | 7 |
haliotis | tigerData | Age of Sea Snails? | data.frame | 2923 | 9 |
lgbtplus | tigerData | Homonegativity in Greek Life | data.frame | 75 | 22 |
nhanes18 | tigerData | National Health and Nutrition Examination Survey | tbl_df | 9842 | 18 |
parkExp | tigerData | Territoriality: The Experiment | data.frame | 156 | 12 |
parking | tigerData | Territoriality in Parking | data.frame | 237 | 12 |
participant | tigerData | Zen Center Participants | tbl_df | 11 | 11 |
payphones | tigerData | Territoriality in Pay-Phones | data.frame | 56 | 3 |
reviews | tigerData | Amazon.com Book Reviews | tbl_df | 243269 | 5 |
shrooms | tigerData | Can You Eat This Mushroom? | data.frame | 5891 | 23 |
uniformsNFL | tigerData | Malevolence of NFL Uniforms and Penalty Yardage | data.frame | 28 | 3 |
whately_2015 | tigerData | Weather data from Macleish Field Stations | tbl_df | 52560 | 8 |
school | collegeScorecard | College Scorecard: School Data | tbl_df | 11300 | 25 |
scorecard | collegeScorecard | College Scorecard: Scorecard Data | tbl_df | 183306 | 24 |
fwa_collection_name | fwatlasbc | Collection Name | spec_tbl_df | 12 | 2 |
fwa_stream_name | fwatlasbc | Stream Name | tbl_df | 11606 | 2 |
fifa_nl | manydist | FIFA 21 Player Data - Dutch League | data.frame | 408 | 24 |
res_example | octad | Differential expression example for HCC vs adjacent liver tissue computed in diffExp() function | data.frame | 963 | 18 |
sRGES_example | octad | Data of computed example sRGEs for HCC vs liver adjacent tissues on octad.small dataset | tbl_df | 12442 | 6 |
BBSColinusVirginianus | PointedSDMs | Dataset of Colinus Virginianus obtained from the North American Breeding Bird survey across Alabama state. | sf | 140 | 4 |
Koala | PointedSDMs | Dataset of Eucalyptus globulus (common name: blue gum) sightings collected across the Koala conservation reserve on Phillip island (Australia) between 1993 and 2004. Two marks are considered from this dataset: "koala" which describes the number of koala visits to each tree, and "food" which is some index of the palatability of the leaves. | list | | |
SetophagaData | PointedSDMs | List of all data objects used for the Setophaga vignette. | list | | |
SolitaryTinamou | PointedSDMs | List of all data objects used for the solitary tinamou vignette. | list | | |
. | clifford | Class "dot" | dot | | |
carcassubmission | singleRcapture | The British farm carcass submissions data | data.frame | 1858 | 4 |
farmsubmission | singleRcapture | The British farm submissions data | data.frame | 12036 | 4 |
netherlandsimmigrant | singleRcapture | Immigration data in the Netherlands | data.frame | 1880 | 5 |
ab | fishbc | Government of Alberta Fish Codes | tbl_df | 85 | 3 |
cdc | fishbc | BC Conservation Data Centre Fish Codes | spec_tbl_df | 550 | 45 |
freshwaterfish | fishbc | BC Fish Data | tbl_df | 161 | 17 |
adams_bt_03 | ypr | Adams Lake Bull Trout Population Parameters (2003) | ypr_population | | |
chilliwack_bt_05 | ypr | Chilliwack Lake Bull Trout Populations Parameters (2005) | ypr_populations | | |
kootenay_bt_13 | ypr | Kootenay Lake Bull Trout Population Parameters (2013) | ypr_population | | |
kootenay_rb | ypr | Kootenay Lake Rainbow Trout Population Parameters | ypr_population | | |
kootenay_rb_13 | ypr | Kootenay Lake Rainbow Trout Population Parameters (2013) | ypr_population | | |
quesnel_bt | ypr | Quesnel Lake Bull Trout Population Parameters | ypr_population | | |
quesnel_lt | ypr | Quesnel Lake Lake Trout Population Parameters | ypr_population | | |
quesnel_rb | ypr | Quesnel Lake Rainbow Trout Population Parameters | ypr_population | | |
capture | klexdatr | Fish Capture Data | tbl_df | 245 | 9 |
deployment | klexdatr | Receiver Deployment Data | tbl_df | 197 | 4 |
detection | klexdatr | Acoustic Detection Data | tbl_df | 313522 | 4 |
recapture | klexdatr | Fish Recapture Data | tbl_df | 54 | 8 |
section | klexdatr | Section Data | sf | 33 | 4 |
station | klexdatr | Station Data | sf | 28 | 3 |
polymod_uk | finalsize | Example POLYMOD social contact data for the U.K. | list | | |
landscape | shar | Example landscape (random cluster neutral landscape model). | PackedSpatRaster | | |
species_a | shar | Species a | ppp | | |
species_b | shar | Species b | ppp | | |
americas_annual_data | denguedatahub | Dengue and severe dengue cases and deaths for subregions of the Americas | tbl_df | 899134 | 5 |
cdc_casesby_week | denguedatahub | All dengue cases by week in US states and territories, 2010 - 2023 | spec_tbl_df | 742 | 4 |
cdc_dengue_agesex | denguedatahub | All dengue cases by age group and sex in US states and territories, 2010 - 2023 | spec_tbl_df | 140 | 5 |
cdc_dengue_casesbyjurisdiction | denguedatahub | All dengue cases by jurisdiction of residence in US states and territories, 2010 - 2023 | spec_tbl_df | 616 | 6 |
cdc_dengue_countyyear | denguedatahub | All dengue cases by county of residence in US states and territories, 2010 - 2023 | spec_tbl_df | 3900 | 7 |
cdc_local_dengue_cases | denguedatahub | Locally acquired dengue cases by year, 2010 - 2023 | spec_tbl_df | 14 | 3 |
cdc_travel_associated_dengue_cases | denguedatahub | Travel associated dengue cases by year, 2010 - 2023 | spec_tbl_df | 14 | 3 |
cdc_usa_dengue_infection | denguedatahub | Annual number of dengue fever infections in the USA | tbl_df | 9039 | 6 |
china_annual_data | denguedatahub | Dengue related data in China | spec_tbl_df | 16 | 5 |
india_annual_data | denguedatahub | DENGUE/DHF situation in India since 2017 | tbl_df | 432 | 5 |
korea_dengue | denguedatahub | Imported dengue cases in Korea | tbl_df | 33 | 7 |
level_of_risk | denguedatahub | Level of Dengue risk around the world | tbl_df | 293 | 4 |
philippines_daily_data | denguedatahub | Daily number of dengue fever infections in Philippines | tbl_df | 32701 | 5 |
singapore_weekly_data | denguedatahub | Weekly number of dengue fever infections in Sri Lanka | tbl_df | 272 | 3 |
sl_annual | denguedatahub | Annual reported dengue cases in Sri Lanka | spec_tbl_df | 35 | 3 |
sl_dengue_serotype | denguedatahub | Identification of dengue serotypes circulating in Sri Lanka | spec_tbl_df | 13 | 2 |
sl_province_districts | denguedatahub | Provinces and Districts of Sri Lanka | tbl_df | 26 | 2 |
sl_sites | denguedatahub | Type and proportion of breeding habitats positive for Aedes aegypti mosquitoes, across provinces in Sri Lanka, 2017. | spec_tbl_df | 9 | 7 |
srilanka_weekly_data | denguedatahub | Weekly number of dengue fever infections in Sri Lanka | tbl_df | 24323 | 6 |
taiwan_dengue | denguedatahub | Indigenous and imported dengue cases in Taiwan, 1987-2023. | tbl_df | 37 | 3 |
world_annual | denguedatahub | Annual number of dengue fever infections around the world | data.frame | 2773284 | 10 |
pdo | rpdo | Pacific Decadal Oscillation Index | tbl_df | 1425 | 3 |
envData | DRviaSPCN | An environment variable which includes some example data | environment | | |
shoveler | nmixgof | Northern shoveler data | list | | |
london_area | zonebuilder | Region representing London in projected coordinate system | sfc_POLYGON | | |
london_area_lonlat | zonebuilder | Region representing London in projected coordinate system | sfc_POLYGON | | |
london_cent | zonebuilder | Region representing London in projected coordinate system | sfc_POINT | | |
london_cent_lonlat | zonebuilder | Region representing London in projected coordinate system | sfc_POINT | | |
zb_100_triangular_numbers | zonebuilder | The first 100 triangular numbers | integer | | |
d.glass | ModTools | Measurements of Forensic Glass Fragments | data.frame | 214 | 10 |
d.pima | ModTools | Diabetes survey on Pima Indians | data.frame | 768 | 9 |
d.pima2 | ModTools | Diabetes survey on Pima Indians | data.frame | 768 | 9 |
rws_data | readwritesqlite | Example Data | tbl_df | 3 | 6 |
jfk | carelesswhisper | Audio sample for testing | audioSample | | |
Asym | stagedtrees | Asym dataset | data.frame | 1000 | 4 |
PhDArticles | stagedtrees | PhD Students Publications | data.frame | 915 | 6 |
Pokemon | stagedtrees | Pokemon Go Users | data.frame | 999 | 5 |
covid_patients | stagedtrees | Trajectories of hospitalized SARS-CoV-2 patients | data.frame | 10000 | 4 |
falls_long | stagedtrees | Falls Intervention Dataset | data.frame | 50000 | 6 |
trajectories | stagedtrees | Hospital trajectories | data.frame | 10000 | 5 |
bulltrout | kootlake | Bull Trout Counts | tbl_df | 17 | 4 |
fish | kootlake | Rainbow Trout data | spec_tbl_df | 1952 | 12 |
fishery | kootlake | Kootenay Lake Fishery | tbl_df | 30 | 3 |
fishery_catch | kootlake | Fishery Catch Counts | tbl_df | 144 | 7 |
gerrard | kootlake | Gerrard Escapement | tbl_df | 62 | 4 |
kokanee | kootlake | Kokanee Escapement | tbl_df | 54 | 6 |
dat1 | r2redux | Phenotypes and 10 sets of PGSs | data.frame | 1000 | 11 |
dat2 | r2redux | Phenotypes and 2 sets of PGSs | data.frame | 1000 | 3 |
mrm_plus2015 | matrixset | Table S1 and S2 of MRMPlus Paper in 'matrixset' Format | matrixset | | |
student_results | matrixset | Fake Final Exam Results of School Students Before and After Remedial Courses | matrixset | | |
bbourecruit_a | bboudata | Sample Data for Population A | tbl_df | 696 | 9 |
bbourecruit_b | bboudata | Sample Data for Population B | tbl_df | 481 | 9 |
bbourecruit_c | bboudata | Sample Data for Population C | tbl_df | 134 | 9 |
bbourecruit_sim1 | bboudata | Simulated Data for Scenario 1 | tbl_df | 701 | 9 |
bbourecruit_sim2 | bboudata | Simulated Data for Scenario 2 | tbl_df | 2441 | 9 |
bbourecruit_sim3 | bboudata | Simulated Data for Scenario 3 | tbl_df | 1466 | 9 |
bbourecruit_sim4 | bboudata | Simulated Data for Scenario 4 | tbl_df | 138 | 9 |
bbousurv_a | bboudata | Sample Data for Population A | tbl_df | 364 | 6 |
bbousurv_b | bboudata | Sample Data for Population B | tbl_df | 206 | 6 |
bbousurv_c | bboudata | Sample Data for Population C | tbl_df | 122 | 6 |
bbousurv_sim1 | bboudata | Simulated Data for Scenario 1 | tbl_df | 240 | 6 |
bbousurv_sim2 | bboudata | Simulated Data for Scenario 2 | tbl_df | 240 | 6 |
bbousurv_sim3 | bboudata | Simulated Data for Scenario 3 | tbl_df | 240 | 6 |
bbousurv_sim4 | bboudata | Simulated Data for Scenario 4 | tbl_df | 240 | 6 |
NorwegianFrequencies | Familias | A list of markers with allele names and frequencies | list | | |
OrthG_Hs_Ch | CACIMAR | Orthologs genes database for homo sapiens and zebrafish | data.frame | 16754 | 5 |
OrthG_Hs_Mm | CACIMAR | Orthologs genes database for homo sapiens and mus musculus | data.frame | 16754 | 5 |
OrthG_Hs_Zf | CACIMAR | Orthologs genes database for homo sapiens and zebrafish | data.frame | 12017 | 5 |
OrthG_Mm_Ch | CACIMAR | Orthologs genes database for mus musculus and chicken | data.frame | 62661 | 5 |
OrthG_Mm_Zf | CACIMAR | Orthologs genes database for mus musculus and zebrafish | data.frame | 65631 | 5 |
OrthG_Zf_Ch | CACIMAR | Orthologs genes database for mus zebrafish and chicken | data.frame | 38394 | 5 |
Tranfac201803_Ch_MotifTFsF | CACIMAR | | data.frame | 4597 | 5 |
Tranfac201803_Hs_MotifTFsF | CACIMAR | | data.frame | 5725 | 5 |
Tranfac201803_Mm_MotifTFsF | CACIMAR | | data.frame | 5012 | 5 |
Tranfac201803_Zf_MotifTFsF | CACIMAR | | data.frame | 3835 | 5 |
ZfscRNA_genes | CACIMAR | | data.frame | 32266 | 5 |
ch_zf_csct_revised | CACIMAR | | data.frame | 9 | 15 |
mm_ch_csct_revised | CACIMAR | | data.frame | 17 | 9 |
mm_zf_csct_revised | CACIMAR | | data.frame | 17 | 15 |
actg019 | BayesPPD | AIDS Clinical Trial ACTG019 (1990). | data.frame | 404 | 4 |
actg036 | BayesPPD | AIDS Clinical Trial ACTG036 (1991). | data.frame | 183 | 5 |
dataset.test | fscaret | Example testing data set | data.frame | 7 | 30 |
dataset.train | fscaret | Example training data set | data.frame | 61 | 30 |
funcClassPred | fscaret | Classification methods used. | character | | |
funcRegPred | fscaret | All regression methods used | character | | |
requiredPackages | fscaret | requiredPackages | character | | |
mcmc_data_example | mcmcdata | Example mcmc data Object | mcmc_data | | |
flob_obj | flobr | A flob Object | flob | | |
expected_ind | mindthegap | Full list of expected indicators | tbl_df | 62 | 5 |
indicator_map | mindthegap | Mapping of EDMS Indicator Names to those used in OHA | tbl_df | 21 | 3 |
pepfar | mindthegap | PEPFAR countries + ISO codes | tbl_df | 55 | 2 |
req_cols | mindthegap | EMDS Extract Required Columns | character | | |
borealis_simulated | sspm | Simulated biomass data | tbl_df | 1800 | 8 |
catch_simulated | sspm | Simulated catch data | tbl_df | 2020 | 7 |
predator_simulated | sspm | Simulated predator data | tbl_df | 10200 | 7 |
sfa_boundaries | sspm | SFA boundaries data | sf | 4 | 3 |
waterways | geomtextpath | A simple features data frame of three Scottish waterways | sf | 29 | 3 |
simDat | reReg | Simulated dataset for demonstration | data.frame | 874 | 7 |
nhefs_weights | tidysmd | NHEFS with various propensity score weights | tbl_df | 1566 | 14 |
all_column_names | logib | Column names | list | | |
datalist_example | logib | Example datalist | data.frame | 285 | 23 |
dataset | LeadSense | JSON list sample session file | list | | |
Current | TroublemakeR | A PackedSpatRaster of 4 species with its projected distribution for current conditions | PackedSpatRaster | | |
CurrentLanduse | TroublemakeR | A PackedSpatRaster of the current landuse | PackedSpatRaster | | |
Species | TroublemakeR | A list of 4 species with its projected distribution for 4 landuses | list | | |
Species_Landuse | TroublemakeR | A list of 4 species with its projected distribution for 4 landuses | list | | |
occupations | promptr | Occupations | tbl_df | 3948 | 2 |
occupations_examples | promptr | Labelled Occupations | tbl_df | 9 | 2 |
scotus_tweets | promptr | Tweets About The Supreme Court of the United States | tbl_df | 945 | 6 |
scotus_tweets_examples | promptr | Labelled Example Tweets About The Supreme Court of the United States | tbl_df | 12 | 4 |
analyte | curtisquadata | Analyte Data | tbl_df | 54 | 3 |
analytesample | curtisquadata | Analyte Sample Data | tbl_df | 48 | 3 |
analytevalue | curtisquadata | Analyte Value Data | tbl_df | 2412 | 3 |
benthiccount | curtisquadata | Benthic Count Data | tbl_df | 450 | 5 |
benthicsample | curtisquadata | Benthic Sample Data | tbl_df | 18 | 3 |
biosite | curtisquadata | Biosite Data | tbl_df | 6 | 3 |
creek | curtisquadata | Creek Data | tbl_df | 2 | 1 |
effish | curtisquadata | Electrofishing Fish | tbl_df | 277 | 7 |
efsite | curtisquadata | Electrofishing Site Data | tbl_df | 8 | 6 |
efspecies | curtisquadata | Electrofishing Species | tbl_df | 2 | 2 |
efvisit | curtisquadata | Electrofishing Visit | tbl_df | 24 | 6 |
periphyton | curtisquadata | Periphyton Data | tbl_df | 178 | 5 |
taxon | curtisquadata | Taxon Data | tbl_df | 28 | 2 |
temperature | curtisquadata | Temperature Data | tbl_df | 105546 | 3 |
tempsite | curtisquadata | Temperature Site Data | tbl_df | 4 | 3 |
bluebug | bauw | "Blue bug" Woodpile Counts | data.frame | 27 | 21 |
burnet | bauw | Six-spot burnet moth counts | data.frame | 665 | 4 |
fritillary | bauw | Fritillary butterfly abundance data | data.frame | 665 | 4 |
hm | bauw | House martin annual counts | data.frame | 20 | 2 |
leisleri | bauw | Leisler's bats survival data | data.frame | 181 | 19 |
orchids | bauw | Showy lady's slipper capture-recapture data | data.frame | 250 | 11 |
owls | bauw | Long-eared owl detections | data.frame | 40 | 11 |
p610 | bauw | Point count number 610 data | data.frame | 146 | 9 |
peregrine | bauw | Peregrine falcon breeding population data | data.frame | 40 | 4 |
pinna | bauw | Pen shell detection data | data.frame | 143 | 3 |
tits | bauw | Coal tits breeding survey data | data.frame | 235 | 31 |
GE600_se | ISLET | ISLET deconvolution example raw input data | SummarizedExperiment | | |
GE600age_se | ISLET | ISLET example datasets for slope variable testing in csDE | SummarizedExperiment | | |
CCLEsmall | PharmacoGx | Cancer Cell Line Encyclopedia (CCLE) Example PharmacoSet | PharmacoSet | | |
CMAPsmall | PharmacoGx | Connectivity Map Example PharmacoSet | PharmacoSet | | |
GDSCsmall | PharmacoGx | Genomics of Drug Sensitivity in Cancer Example PharmacoSet | PharmacoSet | | |
HDAC_genes | PharmacoGx | HDAC Gene Signature | data.frame | 14 | 2 |
data_GSE17054 | BLMA | Gene expression dataset GSE17054 from Majeti et al. | data.frame | 4738 | 9 |
data_GSE33223 | BLMA | Gene expression dataset GSE33223 from Bacher et al. | data.frame | 4114 | 30 |
data_GSE42140 | BLMA | The gene expression dataset GSE42140 obtained from Gene Expression Omnibus | data.frame | 4114 | 31 |
data_GSE57194 | BLMA | Gene expression dataset GSE57194 from Abdul-Nabi et al. | data.frame | 4114 | 12 |
group_GSE17054 | BLMA | Gene expression dataset GSE17054 from Majeti et al. | character | | |
group_GSE33223 | BLMA | Gene expression dataset GSE33223 from Bacher et al. | character | | |
group_GSE42140 | BLMA | The gene expression dataset GSE42140 obtained from Gene Expression Omnibus | character | | |
group_GSE57194 | BLMA | Gene expression dataset GSE57194 from Abdul-Nabi et al. | character | | |
immigration | FactorHet | Small dataset on immigration preferences | data.frame | 1000 | 10 |
hg19.GoNLsnps | omicsPrint | Dataframe with overlaps GoNL variants and 450K probes | DFrame | | |
hm450.manifest.pop.GoNL | omicsPrint | HM450 population-specific probe-masking recommendations | GRanges | | |
epheno | phenoTest | epheno object. | epheno | | |
eset | phenoTest | Example data. | ExpressionSet | | |
eset.genelevel | phenoTest | Example data. | ExpressionSet | | |
OntargetM | DeepTarget | An object containing a small part of the data from the Cancer Dependency Map (depmap.org) to demonstrate in DeepTarget pipeline | list | | |
Yosemite | topoDistance | Spatial data for western fence lizards in Yosemite | RasterBrick | | |
ENB | HTT | Energy efficiency dataset | data.frame | 768 | 10 |
ck_dat_hc92 | cellKey | A real-world data set on persons | data.table | 820000 | 6 |
testdata | cellKey | A real-world data set on household income and expenditures | data.frame | 4580 | 15 |
rndnames | radiant.design | 100 random names | tbl_df | 100 | 2 |
affinity.spline.coefs | gcrma | Spline coefficients for estimation of affinity from probe sequence | numeric | | |
weatherCodes | worldmet | Codes for weather types | tbl_df | 100 | 2 |
spec_matrix_example | spectrolab | Example spectral dataset | matrix | 50 | 2102 |
data_sim_mirt | lvmcomp | Simulated dataset for multivariate item response theory model. | list | | |
data_sim_pcirt | lvmcomp | Simulated dataset for generalized partial credit model. | list | | |
enrichr_metadata | geneset | Datasets go_org contains GO species information | data.frame | 276 | 5 |
ensOrg_name | geneset | Datasets go_org contains GO species information | data.frame | 317 | 4 |
go_org | geneset | Datasets go_org contains GO species information | data.frame | 143 | 2 |
kegg_org | geneset | Datasets go_org contains GO species information | tbl_df | 8213 | 3 |
mesh_metadata | geneset | Datasets go_org contains GO species information | grouped_df | 125 | 3 |
mesh_org | geneset | Datasets go_org contains GO species information | tbl_df | 71 | 3 |
msigdb_org | geneset | Datasets go_org contains GO species information | data.frame | 20 | 2 |
org2cate | geneset | Datasets go_org contains GO species information | data.frame | 8426 | 2 |
reactome_org | geneset | Datasets go_org contains GO species information | data.frame | 11 | 2 |
wiki_org | geneset | Datasets go_org contains GO species information | data.frame | 16 | 2 |
AIPulmonaryNodules_df | OncoDataSets | AI for Assessment of Indeterminate Pulmonary Nodules | data.table | 200 | 2 |
AflatoxinLiverCancer_df | OncoDataSets | Aflatoxin Dosage and Liver Cancer in Lab Animals | data.frame | 6 | 3 |
AlcoholIntakeCancer_df | OncoDataSets | Alcohol Intake and Colorectal Cancer Data | data.frame | 48 | 7 |
BRCA1BreastCancer_df | OncoDataSets | Cumulative Risk of Women Breast Cancer BRCA1 Mutation | data.frame | 11 | 2 |
BRCA1OvarianCancer_df | OncoDataSets | Cumulative Risk of Women Ovarian Cancer BRCA1 Mutation | data.frame | 63 | 2 |
BRCA2BreastCancer_df | OncoDataSets | Cumulative Risk of Women Breast Cancer BRCA2 Mutation | data.frame | 11 | 2 |
BRCA2OvarianCancer_df | OncoDataSets | Cumulative Risk of Women Ovarian Cancer BRCA2 Mutation | data.frame | 63 | 2 |
BladderCancer_df | OncoDataSets | Bladder Cancer Recurrences | data.frame | 340 | 7 |
BloodStorageProstate_df | OncoDataSets | Effects of Blood Storage on Prostate Cancer Study | data.frame | 316 | 20 |
BrainCancerCases_df | OncoDataSets | New Mexico Brain Cancer Cases Data | data.frame | 1175 | 5 |
BrainCancerGeo_df | OncoDataSets | New Mexico Brain Cancer Geography Data | data.frame | 32 | 3 |
BreastCancerWI_df | OncoDataSets | Breast Cancer Wisconsin (Diagnostic) | data.frame | 569 | 31 |
CA19PancreaticCancer_df | OncoDataSets | Diagnosis of Pancreatic Cancer with CA19-9 Biomarker | data.table | 22 | 5 |
CASP8BreastCancer_df | OncoDataSets | CASP8 Polymorphism and Breast Cancer Risk | data.frame | 4 | 7 |
CancerSmokeCity_array | OncoDataSets | Lung Cancer by Smoking Status and City | array | | |
Carcinoma_p53_df | OncoDataSets | Mutant p53 Gene and Squamous Cell Carcinoma | data.frame | 6 | 5 |
CervicalCancer_df | OncoDataSets | Cervical Cancer Screening with Smartphones | data.table | 181 | 10 |
ChildCancer_df | OncoDataSets | Childhood Cancer Data from North Portugal | data.frame | 406 | 8 |
ColonCancerChemo_df | OncoDataSets | Chemotherapy for Stage B/C Colon Cancer | data.frame | 1858 | 16 |
ColorectalMiRNAs_tbl_df | OncoDataSets | PubMed Data of miRNAs in Colorectal Cancer | tbl_df | 508 | 8 |
EndometrialCancer_df | OncoDataSets | Histology Grade and Risk Factors for Endometrial Cancer | data.frame | 79 | 4 |
HeadNeckCarcinoma_df | OncoDataSets | Head and Neck Squamous-Cell Carcinoma Treatment | data.frame | 65 | 5 |
ICGCLiver_df | OncoDataSets | ICGC Liver Cancer Data from Japan | data.frame | 232 | 6 |
LeukemiaLymphomaCases_df | OncoDataSets | North Humberside Leukemia and Lymphoma Cases | data.frame | 191 | 2 |
LeukemiaLymphomaControl_df | OncoDataSets | North Humberside Leukemia and Lymphoma Control Cases | data.frame | 191 | 2 |
LeukemiaLymphomaGeo_df | OncoDataSets | North Humberside Leukemia and Lymphoma Geographic Data | data.frame | 191 | 3 |
LeukemiaRemission_df | OncoDataSets | Impact of 6-MP on Acute Leukemia Remission Duration | data.table | 42 | 5 |
LeukemiaSurvival_df | OncoDataSets | Leukemia Remission Survival Times Placebo-Controlled RCT | data.frame | 42 | 5 |
LungCancerETS_df | OncoDataSets | Passive Smoking's Lung Cancer Threat in Women | escalc | 37 | 11 |
LungNodulesDetected_df | OncoDataSets | Incidental or Screen-Detected Lung Nodules | data.table | 999 | 8 |
MaleMiceCancer_df | OncoDataSets | Mouse Cancer Data | data.frame | 181 | 4 |
Melanoma_df | OncoDataSets | Survival from Malignant Melanoma | data.frame | 205 | 7 |
MiceDeathRadiation_df | OncoDataSets | Mice Deaths from Radiation | data.frame | 177 | 4 |
NCCTGLungCancer_df | OncoDataSets | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
NodalProstate_df | OncoDataSets | Nodal Involvement in Prostate Cancer | data.frame | 53 | 7 |
OvarianCancer_df | OncoDataSets | Ovarian Cancer Survival Data | data.frame | 26 | 6 |
PSAProstateCancer_df | OncoDataSets | Factors associated with prostate specific antigen | data.frame | 97 | 9 |
PancreaticMiRNAs_tbl_df | OncoDataSets | PubMed Data of miRNAs in Pancreatic Cancer | tbl_df | 381 | 8 |
ProstateMethylation_df | OncoDataSets | DNA Methylation Data from Patients Prostate Cancer | data.frame | 5067 | 9 |
ProstateSurgery_df | OncoDataSets | Prostate Cancer Surgery Study | data.frame | 97 | 9 |
ProstateSurvival_df | OncoDataSets | Prostate Cancer Survival Data | data.frame | 14294 | 5 |
RadiationEffects_df | OncoDataSets | Radiation Dose Effects on Chromosomal Abnormality | data.frame | 27 | 4 |
RotterdamBreastCancer_df | OncoDataSets | Rotterdam Breast Cancer Data | data.frame | 2982 | 15 |
SkinCancerChemo_df | OncoDataSets | Simulated Data from Skin Cancer Chemoprevention Trial | data.frame | 894 | 7 |
SmallCellLung_tbl_df | OncoDataSets | Small Cell Lung Cancer Data | tbl_df | 121 | 3 |
SmokingLungCancer_df | OncoDataSets | Years of Smoking and Lung Cancer Deaths in Men | data.frame | 63 | 4 |
SuspectedCancer_df | OncoDataSets | Suspected Cancer (SCAN) Pathway | data.frame | 750 | 8 |
UKLungCancerDeaths_df | OncoDataSets | Lung Cancer Deaths among UK Physicians | data.frame | 63 | 4 |
USCancerStats_df | OncoDataSets | US Cancer Incidence, Mortality, and Survival Changes | data.frame | 20 | 4 |
USMortalityCancer_df | OncoDataSets | US Mortality Rates by Cause (Cancer) and Gender | data.frame | 40 | 5 |
USRegionalMortality_df | OncoDataSets | US Region Mortality Rates by Cause (Cancer) and Gender | data.frame | 400 | 6 |
VALungCancer_list | OncoDataSets | VA Lung Cancer Data Set | list | | |
VinylideneLiverCancer_df | OncoDataSets | Effect of Vinylidene Fluoride on Liver Cancer | data.frame | 40 | 4 |
WBreastCancer_tbl_df | OncoDataSets | Women with Breast Cancer Study | tbl_df | 1207 | 9 |
cancer_in_dogs_tbl_df | OncoDataSets | Cancer in Dogs and Exposure to 2,4-D Herbicide | tbl_df | 1436 | 2 |
canada.cities | maps | Database of Canadian cities | data.frame | 916 | 6 |
county.fips | maps | FIPS county codes for US County Map | data.frame | 3085 | 2 |
countyMapEnv | maps | United States County Map | character | | |
franceMapEnv | maps | France Map | character | | |
iso3166 | maps | ISO 3166 country codes (2 or 3 letters) and sovereignty. | data.frame | 269 | 5 |
italyMapEnv | maps | Italy Map | character | | |
lakesMapEnv | maps | World lakes database | character | | |
nzMapEnv | maps | New Zealand Basic Map | character | | |
ozone | maps | Sample datasets | data.frame | 41 | 3 |
state.carto.center | maps | United States State Population Cartogram Map | list | | |
state.cartoMapEnv | maps | United States State Population Cartogram Map | character | | |
state.fips | maps | FIPS state codes for US 48 State Map | data.frame | 63 | 6 |
state.vbm.center | maps | United States State Visibility Base Map | list | | |
state.vbmMapEnv | maps | United States State Visibility Base Map | character | | |
stateMapEnv | maps | United States State Boundaries Map | character | | |
unemp | maps | Sample datasets | data.frame | 3218 | 3 |
us.cities | maps | Database of US cities | data.frame | 1005 | 6 |
usaMapEnv | maps | United States Coast Map | character | | |
votes.repub | maps | Sample datasets | matrix | 50 | 31 |
world.cities | maps | Database of world cities | data.frame | 43645 | 6 |
world2MapEnv | maps | Pacific Centric Low resolution World Map | character | | |
worldMapEnv | maps | Low (mid) resolution World Map | character | | |
gregorius | genetics | Probability of Observing All Alleles with a Given Frequency in a Sample of a Specified Size. | data.frame | 16 | 4 |
gene_chromosome_data | idiffomix | Data containing chromosome information and the genes located on them. | data.frame | 20 | 2 |
gene_expression_data | idiffomix | Gene expression data for patients suffering from breast cancer | data.frame | 20 | 9 |
methylation_data | idiffomix | Methylation array data for patients suffering from breast cancer | data.frame | 205 | 10 |
segDups | RSVSim | Segmental duplications | GRanges | | |
weightsMechanisms | RSVSim | Weights for SV formation mechanisms | data.frame | 5 | 5 |
weightsRepeats | RSVSim | Weights for repeat region bias | data.frame | 7 | 5 |
slb1_meteo | LWFBrook90R | Meteorological Data from the Solling Beech and Spruce experimental site | data.frame | 19724 | 9 |
slb1_prec2013_hh | LWFBrook90R | Hourly precipitation data from Solling Beech experimental site 'SLB1' for year 2013 | data.frame | 8760 | 2 |
slb1_soil | LWFBrook90R | Soil profile data from the Solling Beech experimental site 'SLB1' | data.frame | 21 | 10 |
slb1_standprop | LWFBrook90R | Annual stand properties of the Solling Beech experimental site 'SLB1' | data.frame | 49 | 7 |
diff_exp_example1 | STRINGdb | example of microarray data (data processed from GEO GSE9008) | data.frame | 20861 | 3 |
interactions_example | STRINGdb | example of a protein-protein interactions sorted data frame | data.frame | 70000 | 3 |
example_annotation | PersomicsArray | Example Annotation Data for PersomicsArray Package | data.frame | 2 | 2 |
example_plate | PersomicsArray | Example Plate Image for PersomicsArray Package | array | | |
datExpr1 | MODA | datExpr1 | matrix | 20 | 500 |
datExpr2 | MODA | datExpr2 | matrix | 25 | 500 |
DSM_GoodsMatrix | wordspace | A Scored Co-occurrence Matrix of Nouns Denoting Goods (wordspace) | matrix | 240 | 4 |
DSM_HieroglyphsMatrix | wordspace | A Small Co-occurrence Matrix (wordspace) | matrix | 7 | 6 |
DSM_SingularValues | wordspace | Typical Singular Values of a Term-Context Matrix (wordspace) | numeric | | |
DSM_TermContext | wordspace | Example of a Term-Context Co-occurrence Matrix (wordspace) | dsm | | |
DSM_TermContextMatrix | wordspace | Example of a Term-Context Co-occurrence Matrix (wordspace) | dgCMatrix | | |
DSM_TermTerm | wordspace | Example of a Term-Term Co-occurrence Matrix (wordspace) | dsm | | |
DSM_TermTermMatrix | wordspace | Example of a Term-Term Co-occurrence Matrix (wordspace) | matrix | 7 | 7 |
DSM_Vectors | wordspace | Pre-Compiled DSM Vectors for Selected Words (wordspace) | matrix | 1677 | 50 |
DSM_VerbNounTriples_BNC | wordspace | Verb-Noun Co-occurrence Frequencies from British National Corpus (wordspace) | data.frame | 250117 | 5 |
ESSLLI08_Nouns | wordspace | Noun Clustering Task from ESSLLI 2008 (wordspace) | data.frame | 44 | 5 |
RG65 | wordspace | Similarity Ratings for 65 Noun Pairs (wordspace) | data.frame | 65 | 3 |
SemCorWSD | wordspace | SemCor Word Sense Disambiguation Task (wordspace) | data.frame | 647 | 8 |
WordSim353 | wordspace | Similarity Ratings for 351 Noun Pairs (wordspace) | data.frame | 351 | 6 |
pso | calm | Psoriasis RNA-seq dataset | data.frame | 18151 | 3 |
baro | bridgr | Swiss Economic Indicators | tbl_df | 228 | 2 |
fcurve | bridgr | Swiss Economic Indicators | tbl_df | 4875 | 2 |
gdp | bridgr | Swiss Economic Indicators | tbl_df | 76 | 2 |
wea | bridgr | Swiss Economic Indicators | tbl_df | 939 | 2 |
NIR | chemometrics | NIR data | list | | |
PAC | chemometrics | GC retention indices | list | | |
Phenyl | chemometrics | Phenyl data set | data.frame | 600 | 659 |
ash | chemometrics | ash data | data.frame | 99 | 17 |
cereal | chemometrics | Data from cereals | list | | |
glass | chemometrics | glass vessels data | matrix | 180 | 13 |
glass.grp | chemometrics | glass types of the glass data | numeric | | |
hyptis | chemometrics | Hyptis data set | data.frame | 30 | 9 |
hce_scenario_a | maraca | Example HCE scenario A. | data.frame | 1000 | 6 |
hce_scenario_b | maraca | Example HCE scenario B. | data.frame | 1000 | 6 |
hce_scenario_c | maraca | Example HCE scenario C. | data.frame | 1000 | 6 |
hce_scenario_d | maraca | Example HCE scenario D. | data.frame | 1000 | 6 |
hce_scenario_kccq3 | maraca | Example HCE scenario KCCQ3. | data.frame | 5000 | 8 |
GIFTrData | GIFTr | Questions Data with asterisk and multiple answer mcq | data.frame | 11 | 9 |
GIFTrData_2 | GIFTr | Questions Data without asterisk | data.frame | 11 | 9 |
OrEcoLevel3 | micromap | Example Dataset: Oregon Level 3 Ecoregion Shapefile | SpatialPolygonsDataFrame | | |
USstates | micromap | Example Dataset: U.S. States Polygons | SpatialPolygonsDataFrame | | |
WSA3 | micromap | Example Dataset: Major U.S. EcoRegions | SpatialPolygonsDataFrame | | |
edPov | micromap | Example Dataset: Education and Poverty Levels | data.frame | 51 | 5 |
lungMort | micromap | Example Dataset: Lung Cancer Mortality | data.frame | 51 | 14 |
statesFlatfile | micromap | Example Dataset: A Table of State Polygons | data.frame | 434 | 4 |
vegCov | micromap | Example Dataset: Vegetation Coverage Percentages | data.frame | 12 | 13 |
WaggaWagga | sequenceR | Wagga-Wagga dataset | data.frame | 79 | 3 |
bell | sequenceR | Bell sample | soundSample | | |
globalT | sequenceR | Global Temperature Anomalies dataset | data.frame | 172 | 2 |
hiHat | sequenceR | Hi-hat sample | soundSample | | |
hiHat2 | sequenceR | Hi-hat sample 2 | soundSample | | |
hiHat_o | sequenceR | Open Hi-hat sample | soundSample | | |
kick | sequenceR | Kick sample | soundSample | | |
kick2 | sequenceR | Kick sample2 | soundSample | | |
mini909 | sequenceR | TR-909 minimalistic drumkit | instrument | | |
ride | sequenceR | Ride sample | soundSample | | |
snare | sequenceR | Snare sample | soundSample | | |
snare2 | sequenceR | Snare sample 2 | soundSample | | |
df_hospitals_ortho | mactivate | Orthopedic Device Sales | data.frame | 4703 | 15 |
Col12 | ChemoSpecUtils | Color in ChemoSpec and ChemoSpec2D | character | | |
Col7 | ChemoSpecUtils | Color in ChemoSpec and ChemoSpec2D | character | | |
Col8 | ChemoSpecUtils | Color in ChemoSpec and ChemoSpec2D | character | | |
Sym12 | ChemoSpecUtils | Color in ChemoSpec and ChemoSpec2D | numeric | | |
Sym8 | ChemoSpecUtils | Color in ChemoSpec and ChemoSpec2D | numeric | | |
breastcancer | OneR | Breast Cancer Wisconsin Original Data Set | data.frame | 699 | 10 |
bolsafam | randomMachines | Bolsa Família Dataset | data.frame | 5564 | 11 |
ionosphere | randomMachines | Ionosphere Dataset | spec_tbl_df | 351 | 35 |
whosale | randomMachines | Wholesale Dataset | spec_tbl_df | 440 | 8 |
IJ_ORIG | ROptimus | Tutorial 5 Genomic Contact Data | data.frame | 734 | 2 |
ex.m.fun | ROptimus | Tutorial 5 m() function | function | | |
ex.r.fun | ROptimus | Tutorial 5 r() function | function | | |
ex.u.fun | ROptimus | Tutorial 5 u() function | function | | |
mscdata | seas | Meteorological Service of Canada sample climate data | data.frame | 26358 | 10 |
mybiotypes | NOISeq | Marioni's dataset | character | | |
mychroms | NOISeq | Marioni's dataset | data.frame | 5088 | 3 |
mycounts | NOISeq | Marioni's dataset | data.frame | 5088 | 10 |
mydata | NOISeq | Example of objects used and created by the NOISeq package | ExpressionSet | | |
myfactors | NOISeq | Marioni's dataset | data.frame | 10 | 2 |
mygc | NOISeq | Marioni's dataset | numeric | | |
mylength | NOISeq | Marioni's dataset | numeric | | |
mynoiseq | NOISeq | Example of objects used and created by the NOISeq package | Output | | |
survo.sounds | survo.audio | Sounds for Survo R | list | | |
data_example_landpred | landpred | Hypothetical data to be used in examples. | data.frame | 4868 | 5 |
mobility | extRC | Social mobility data | matrix | 5 | 5 |
pokemon | safar6 | Data for Encounters with Wild Pokemon | data.frame | 10 | 10 |
kms | ivx | KMS Monthly data | tbl_df | 1033 | 13 |
kms_quarterly | ivx | KMS Quarterly data | tbl_df | 345 | 13 |
monthly | ivx | Monthly dataset of KMS | tbl_df | 1033 | 13 |
quarterly | ivx | Quarterly dataset of KMS | tbl_df | 345 | 13 |
ylpc | ivx | YLPC Quarterly data | spec_tbl_df | 174 | 12 |
cov_d | RMLPCA | Covariance matrix for mlpca_d model | matrix | 20 | |
cov_e | RMLPCA | Covariance matrices for mlpca_e model | array | | |
data_clean | RMLPCA | Error free data for all examples. | matrix | 300 | |
data_clean_e | RMLPCA | Error free data for all examples. | matrix | 30 | |
data_cleaned_mlpca_b | RMLPCA | Cleaned dataset after applied MLPCA B used for tests only | matrix | 300 | |
data_cleaned_mlpca_c | RMLPCA | Cleaned dataset after applied MLPCA C used for tests only | matrix | 300 | |
data_cleaned_mlpca_d | RMLPCA | Cleaned dataset after applied MLPCA D used for tests only | matrix | 300 | |
data_cleaned_mlpca_e | RMLPCA | Cleaned dataset after applied MLPCA E used for tests only | matrix | 30 | |
data_error_b | RMLPCA | Errors generated for mlpca_b model | matrix | 300 | |
data_error_c | RMLPCA | Errors generated for mlpca_c model | matrix | 300 | |
data_error_d | RMLPCA | Errors generated for mlpca_d model | matrix | 300 | |
data_error_e | RMLPCA | Errors generated for mlpca_e model | matrix | 30 | |
sds_b | RMLPCA | Standard deviations for mlpca_b model | matrix | 300 | |
sds_c | RMLPCA | Standard deviations for mlpca_c model | matrix | 300 | |
climatezones | kgc | Koppen-Geiger Climate Zones reference table. | data.frame | 92416 | 3 |
kgcities | kgc | Koppen Geiger climates for selected cities reference table. | data.frame | 88 | 5 |
kmz | kgc | High resolution (100s) Koppen Geiger climate zones. | numeric | | |
zipcodes | kgc | Zip Code reference table. | data.frame | 33144 | 3 |
dataEx | marqLevAlg | Simulated dataset | data.frame | 2429 | 6 |
past_epop | wcde | Past population sizes for all countries by education | tbl_df | 840126 | 7 |
wic_col4 | wcde | Colours used in Wittgenstein Centre for Demography and Human Capital Data Explorer | character | | |
wic_col6 | wcde | Colours used in Wittgenstein Centre for Demography and Human Capital Data Explorer | character | | |
wic_col8 | wcde | Colours used in Wittgenstein Centre for Demography and Human Capital Data Explorer | character | | |
wic_indicators | wcde | Indicators used in the Wittgenstein Centre Human Capital Data Explorer | tbl_df | 37 | 12 |
wic_locations | wcde | Locations used in the Wittgenstein Centre Human Capital Data Explorer | tbl_df | 232 | 8 |
wic_scenarios | wcde | Scenarios used in the Wittgenstein Centre Human Capital Data Explorer | tbl_df | 9 | 6 |
continent_colors | gapminder | Gapminder color schemes. | character | | |
country_codes | gapminder | Country codes | tbl_df | 187 | 3 |
country_colors | gapminder | Gapminder color schemes. | character | | |
gapminder | gapminder | Gapminder data | tbl_df | 1704 | 6 |
gapminder_unfiltered | gapminder | Gapminder data, unfiltered. | tbl_df | 3313 | 6 |
geese | diverse | Geese dataset | matrix | 4 | 11 |
pantheon | diverse | Pantheon dataset | data.frame | 119 | 3 |
scidat | diverse | Scidat dataset | matrix | 10 | 27 |
sample_matrix | xts | Sample Data Matrix For xts Example and Unit Testing | matrix | 180 | 4 |
MaungaWhau | maxlike | Fake data for the Maunga Whau volcano | list | | |
carw.data | maxlike | The Carolina Wren data used by Royle et al. (2012) | list | | |
bioassay | BioRssay | Example bioassay data set | list | | |
style | tablexlsx | An R list that contains the styles of each element for formatting data frames in excel files | list | | |
rice_qtl | haplotyper | Real experimental data | data.frame | 326 | 38 |
sim_qtl | haplotyper | simple QTL simulated | data.frame | 5 | 8 |
nyweather | rlist | New York hourly weather data | list | | |
timeseries_data_example | EKMCMC | Product concentration of 101 observed time with different initial conditions | matrix | 101 | 8 |
gause_1931_AmN_f01 | gauseR | Growth of population of the flour beetle Tribolium confusum in 16 and 64 grams of flour | data.frame | 18 | 6 |
gause_1931_AmN_f02 | gauseR | The influence of quantity of food on the asymptotic population of Triboliurn confusum. | data.frame | 4 | 5 |
gause_1931_AmN_f03 | gauseR | The influence of temperature on the asymptotic population of Moina macrocopa. | data.frame | 3 | 5 |
gause_1932_QR_t05 | gauseR | The influence of temperature on the growth of the yeast Saccharomyces cerevisiae | data.frame | 102 | 7 |
gause_1934_book_app_t01 | gauseR | Raw data on the abundances and volumes of Saccharomyces cerevisiae and Schizosaccharomyces kephir | data.frame | 60 | 10 |
gause_1934_book_app_t02 | gauseR | Alcohol production of Saccharomyces cerevisiae and Schizosaccharomyces kephir | data.frame | 28 | 7 |
gause_1934_book_app_t03 | gauseR | Raw data of Paramecium caudatum and Paramecium aurelia grown in Monoculture and Mixture | data.frame | 104 | 6 |
gause_1934_book_app_t04 | gauseR | Raw data of Paramecium caudatum and Paramecium aurelia grown on different media | data.frame | 68 | 7 |
gause_1934_book_app_t05 | gauseR | Raw data of Stylonychia pustulata in monoculture and mixture with and Paramecium aurelia and P. caudatum | data.frame | 575 | 8 |
gause_1934_book_f04 | gauseR | Growth of Paramecium caudatum | data.frame | 8 | 5 |
gause_1934_book_f09 | gauseR | Growth of Saccharomyces cerevisiae | data.frame | 9 | 5 |
gause_1934_book_f10 | gauseR | Growth of Saccharomyces cerevisiae with medium change | data.frame | 29 | 6 |
gause_1934_book_f11 | gauseR | Gause Yeast Data | data.frame | 11 | 7 |
gause_1934_book_f12 | gauseR | Growth of Saccharomyces cerevisiae with additional alcohol | data.frame | 6 | 5 |
gause_1934_book_f13 | gauseR | Growth of Saccharomyces cerevisiae and Schizosaccaromyces kephir in mixed population | data.frame | 47 | 5 |
gause_1934_book_f14 | gauseR | Growth of Saccharomyces cerevisiae in mixed population | data.frame | 32 | 6 |
gause_1934_book_f15 | gauseR | Growth of Schizosaccaromyces kephir in mixed population - anaerobic | data.frame | 24 | 6 |
gause_1934_book_f16 | gauseR | Growth of Schizosaccaromyces kephir and Saccharomyces cerevisiae in mixed population -aerobic | data.frame | 27 | 6 |
gause_1934_book_f18 | gauseR | Growth of Paramecium caudatum and Stylonychia mytilis in Mixture | data.frame | 28 | 6 |
gause_1934_book_f19 | gauseR | Growth of Paramecium caudatum and Stylonychia mytilis in Mixture with Wild bacteria | data.frame | 17 | 6 |
gause_1934_book_f21 | gauseR | Growth of Paramecium caudatum and Paramecium aurelia in Monoculture | data.frame | 87 | 6 |
gause_1934_book_f22 | gauseR | Paramecium competition experiment | data.frame | 72 | 8 |
gause_1934_book_f23 | gauseR | Growth of Paramecium caudatum and Paramecium aurelia with wild bacteria | data.frame | 61 | 6 |
gause_1934_book_f24 | gauseR | Growth of Paramecium caudatum and Paramecium aurelia in Mixture on Half Loop Medium | data.frame | 56 | 6 |
gause_1934_book_f25 | gauseR | Growth of Paramecium caudatum and Paramecium aurelia in Mixture on One Loop Medium | data.frame | 57 | 6 |
gause_1934_book_f26 | gauseR | Growth of Stylorzychia pustulata in Monoculture and in Mixture | data.frame | 104 | 6 |
gause_1934_book_f28 | gauseR | Elementary interaction between Didinium nasutum and Paramecium caudatum | data.frame | 12 | 6 |
gause_1934_book_f29 | gauseR | Paramecium/Didinium predator-prey experiment | data.frame | 62 | 7 |
gause_1934_book_f30 | gauseR | The elementary interaction between Didinium nasutum and Paramecium caudatum | data.frame | 16 | 6 |
gause_1934_book_f31 | gauseR | The interaction between Didinium nasutum and Paramecium caudatum on oat medium | data.frame | 12 | 6 |
gause_1934_book_f32 | gauseR | Didinium/Paramecium predator/prey experiment | data.frame | 17 | 8 |
gause_1934_book_f39.1 | gauseR | The interaction between Paramecium bursaria and Schizosaccharomyces pombe | data.frame | 36 | 5 |
gause_1934_science_f01 | gauseR | Didinium/Paramecium predator/prey experiment | data.frame | 17 | 8 |
gause_1934_science_f02_03 | gauseR | Paramecium competition experiment | data.frame | 63 | 8 |
gause_1936_AnE_f01 | gauseR | Interaction between predators (Cheyletus eruditus) and prey (Aleuiroglyphus agilis) on millet, wheat flour and a mixture of these substances. | data.frame | 34 | 6 |
gause_1936_AnE_f03.1 | gauseR | Interaction between predators (Cheyletus eruditus) and prey (Aleuroglyphus agilis) with occasional immigration | data.frame | 22 | 6 |
gause_1936_AnE_f03.3a | gauseR | Interaction between predators (Cheyletus eruditus) and prey (Aleuroglyphus agilis) with artifical immigration | data.frame | 24 | 6 |
gause_1936_AnE_f03.3b | gauseR | Interaction between predators (Cheyletus eruditus) and prey (Aleuroglyphus agilis) with artifical immigration | data.frame | 26 | 6 |
gause_1936_AnE_t02 | gauseR | Interaction between predators (Cheyletus eruditus) and prey (Aleuroglyphus agilis)-Raw data | data.frame | 191 | 12 |
gause_1936_AnE_t03 | gauseR | Interaction between predators (Paramecium bursaria) and prey (Saccharomyces exiguus) | data.frame | 266 | 6 |
huffaker_1963 | gauseR | Huffaker Mite Data | data.frame | 168 | 6 |
mclaren_1994_f03 | gauseR | Wolf, Moose, and Fir dynamics from Isle Royale | data.frame | 140 | 7 |
tobit.list | superdiag | Data: A 'mcmc.list' from a Tobit model | mcmc.list | | |
patternsExample | MPFE | patternsExample | data.frame | 21 | 3 |
dat1 | R2ROC | Raw phenotypes and 2 sets of discovery PGSs | data.frame | 10000 | 3 |
dat2 | R2ROC | Pre-adjusted phenotypes and 2 sets of discovery PGSs | data.frame | 10000 | 3 |
skcm.list | crso | Example data set derived from TCGA skin cutaneous melanoma (SKCM) data. | list | | |
city_data_UK | spatPomp | City data in the United Kingdom | data.frame | 40 | 4 |
he10coordinates | spatPomp | City data in the United Kingdom | data.frame | 20 | 3 |
he10demography | spatPomp | Demographic data for 20 towns in the United Kingdom | data.frame | 501 | 4 |
he10measles | spatPomp | Measles in the United Kingdom | data.frame | 21920 | 3 |
he10mle | spatPomp | Measles in the United Kingdom: MLE from He et al (2010) | data.frame | 20 | 19 |
measlesUK | spatPomp | Measles in the United Kingdom | data.frame | 21880 | 5 |
column_structure_draft_mlb | baseballr | *Column structure of the MLB Draft data* | tbl_df | | 72 |
statcast_impute | baseballr | *Statcast Label Imputation* | data.frame | 44 | 4 |
stats_api_live_empty_df | baseballr | *Column structure of MLB Stats Live Game API data frame* | tbl_df | | 131 |
teams_lu_table | baseballr | *A Team Lookup Table* | data.frame | 797 | 31 |
SVMcurve | ZetaSuite | The SVM curve lines in Zeta-plot. | data.frame | 24 | 4 |
ZseqList | ZetaSuite | The bin size for Zeta calculation. | data.frame | 11 | 2 |
countMat | ZetaSuite | Subsampled data from in-house HTS2 screening for global splicing regulators. | data.frame | 1609 | 100 |
countMatSC | ZetaSuite | The cell x gene matrix from single-cell RNA-seq. | data.frame | 1090 | 10000 |
negGene | ZetaSuite | Input negative file. | data.frame | 510 | 1 |
nonExpGene | ZetaSuite | Input internal negative control file. | data.frame | 722 | 1 |
posGene | ZetaSuite | Input positive file. | data.frame | 299 | 1 |
Comadre | Rcompadre | Subsamples of the COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database for testing and examples | CompadreDB | | |
Compadre | Rcompadre | Subsamples of the COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database for testing and examples | CompadreDB | | |
CompadreLegacy | Rcompadre | Subsample of a legacy version of the COMPADRE Plant Matrix Database for testing and examples | list | | |
ClinicalTrial.AE | swimplot | Clinical Trial: Adverse events | data.frame | 11 | 6 |
ClinicalTrial.Arm | swimplot | Clinical Trial: Treatment | data.frame | 53 | 6 |
ClinicalTrial.Response | swimplot | Clinical Trial: Response | data.frame | 36 | 7 |
ClinicalTrial.Stage | swimplot | Clinical Trial: Stage | data.frame | 36 | 2 |
spain.1900 | lar | Spain_1900_(JMTL-Sept2011).xlsx (MS Excel file) | data.frame | 823 | 52 |
directed_dummy_net | motifr | Two-level directed network dummy example | igraph | | |
dummy_net | motifr | Three-level network dummy example | network | | |
large_directed_dummy_net | motifr | Large two-level directed network dummy example | network | | |
ml_net | motifr | Two-level network example (wetlands management) | network | | |
tidygraph_dummy_net | motifr | Two-level tidygraph network example | tbl_graph | | |
swamptrees | pcds | Tree Species in a Swamp Forest | data.frame | 734 | 4 |
encoded_string_api_4 | osrmr | encoded_string_api_4: An encoded route to illustrate the 'osrmr::decode_geom()' function. After decoding all points on the route are available as wgs84 coordinates. Decoding varies on the API version of OSRMR. This version is decoded using API v4. | character | | |
encoded_string_api_5 | osrmr | encoded_string_api_5: An encoded route to illustrate the 'osrmr::decode_geom()' function. After decoding all points on the route are available as wgs84 coordinates. Decoding varies on the API version of OSRMR. This version is decoded using API v5. | character | | |
ERSPC | casebase | Data on the men in the European Randomized Study of Prostate Cancer Screening | data.frame | 159893 | 3 |
bmtcrr | casebase | Data on transplant patients | data.frame | 177 | 7 |
brcancer | casebase | German Breast Cancer Study Group 2 | data.frame | 686 | 12 |
eprchd | casebase | Estrogen plus Progestin and the Risk of Coronary Heart Disease (eprchd) | data.frame | 16608 | 3 |
simdat | casebase | Simulated data under Weibull model with Time-Dependent Treatment Effect | data.frame | 2000 | 4 |
support | casebase | Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT) | data.frame | 9104 | 34 |
demo_ped | PolyHaplotyper | pedigree | data.frame | 661 | 4 |
demo_snpdos | PolyHaplotyper | dosages of SNP alleles | data.frame | 30 | 663 |
phFS | PolyHaplotyper | members of FS families | list | | |
phblocks | PolyHaplotyper | List of markers per haploblock | list | | |
phdos | PolyHaplotyper | dosages of SNP alleles | matrix | 8 | 631 |
phpar | PolyHaplotyper | parents of FS families | matrix | 4 | |
phped | PolyHaplotyper | pedigree | data.frame | 661 | 3 |
phresults | PolyHaplotyper | haplotyping results | list | | |
Miller2015 | CTD | Miller et al. (2015) | data.frame | 1204 | 202 |
Thistlethwaite2020 | CTD | Thistlethwaite et al. (2020) | data.frame | 1364 | 553 |
Wangler2017 | CTD | Wangler et al. (2017) | data.frame | 683 | 29 |
cohorts_coded | CTD | Disease cohorts with coded identifiers | list | | |
Evans | robusTest | Evans County dataset | data.frame | 609 | 9 |
trial | tidycmprsk | Results from a simulated study of two chemotherapy agents | tbl_df | 200 | 9 |
Attributes | MullerPlot | Attributes of OTUs | matrix | 8 | 3 |
PopulationDataList | MullerPlot | Population/Abundance data of OTUs over time for "list" method | data.frame | 382 | 3 |
PopulationDataTable | MullerPlot | Population/Abundance data of OTUs over time for "table" method | matrix | 8 | 101 |
FuranMale | corr2D | FT-Raman spectra of furan maleimide based self-healing polymer | matrix | 6 | 145 |
glosses | glossr | Examples of glosses | tbl_df | 5 | 6 |
malayrootwords | malaytextr | Data of Malay root words | tbl_df | 4310 | 2 |
malaysia_politic_sentiment | malaytextr | Malaysia Politic Tweets Sentiment Dataset (Positive, Negative or Neutral) | spec_tbl_df | 71 | 3 |
malaystopwords | malaytextr | Data of Malay stop words | spec_tbl_df | 512 | 1 |
normalized | malaytextr | Data of Malay normalized words | spec_tbl_df | 153 | 2 |
sentiment_general | malaytextr | Data of Sentiment Words (Positive or Negative) | spec_tbl_df | 1428 | 2 |
lung.pvclust | scaleboot | Clustering of 73 Lung Tumors | pvclust | | |
lung.sb | scaleboot | Clustering of 73 Lung Tumors | scalebootv | | |
lung73.pvclust | scaleboot | Clustering of 73 Lung Tumors | pvclust | | |
lung73.sb | scaleboot | Clustering of 73 Lung Tumors | scalebootv | | |
mam105.ass | scaleboot | Mammal Phylogenetic Analysis for 15 trees | list | | |
mam105.aux | scaleboot | Mammal Phylogenetic Analysis for 15 trees | list | | |
mam105.mt | scaleboot | Mammal Phylogenetic Analysis for 15 trees | matrix | 3414 | 105 |
mam105.relltest | scaleboot | Mammal Phylogenetic Analysis for 15 trees | relltest | | |
mam15.ass | scaleboot | Mammal Phylogenetic Analysis for 15 trees | list | | |
mam15.aux | scaleboot | Mammal Phylogenetic Analysis for 15 trees | list | | |
mam15.mt | scaleboot | Mammal Phylogenetic Analysis for 15 trees | matrix | 3414 | 15 |
mam15.relltest | scaleboot | Mammal Phylogenetic Analysis for 15 trees | relltest | | |
mam26.ass | scaleboot | Mammal Phylogenetic Analysis for 15 trees | list | | |
mam26.aux | scaleboot | Mammal Phylogenetic Analysis for 15 trees | list | | |
mam26.mt | scaleboot | Mammal Phylogenetic Analysis for 15 trees | matrix | 3414 | 26 |
edge | HGraph | edge file to R | matrix | 3 | 3 |
FR_Genotype | PolyPatEx | Example genotype allele dataset | data.frame | 139 | 32 |
GF_Phenotype | PolyPatEx | Example phenotype allele dataset | data.frame | 70 | 45 |
kidney | SimSeq | Kidney Renal Clear Cell Carcinoma [KIRC] RNA-Seq data | list | | |
weather | MarketMatching | Weather dataset | tbl_df | 6935 | 3 |
soldat | ada | Solubility Data | data.frame | 5631 | 73 |
fungusTreeNetwork | sbm | fungus-tree interaction network | list | | |
multipartiteEcologicalNetwork | sbm | Ecological multipartite interaction network | list | | |
war | sbm | War data set | list | | |
S | sparseR | Data sets | Surv | 442 | 2 |
Z | sparseR | Data sets | data.frame | 442 | 6 |
cleveland | sparseR | Data sets | data.frame | 303 | 14 |
hungarian | sparseR | Data sets | data.frame | 294 | 14 |
irlcs_radon_syn | sparseR | Data sets | data.frame | 1027 | 16 |
switzerland | sparseR | Data sets | data.frame | 123 | 14 |
va | sparseR | Data sets | data.frame | 200 | 14 |
SalmonSurvCUI | MARSS | Salmon Survival Indices | data.frame | 42 | 3 |
graywhales | MARSS | Population Data Sets | matrix | 39 | 2 |
grouse | MARSS | Population Data Sets | matrix | 30 | 2 |
harborSeal | MARSS | Harbor Seal Population Count Data (Log counts) | matrix | 30 | 13 |
harborSealWA | MARSS | Harbor Seal Population Count Data (Log counts) | matrix | 22 | 6 |
isleRoyal | MARSS | Isle Royale Wolf and Moose Data | matrix | 53 | 15 |
ivesDataByWeek | MARSS | Plankton Data Sets | matrix | 269 | 9 |
ivesDataLP | MARSS | Plankton Data Sets | matrix | 91 | 8 |
kestrel | MARSS | Population Data Sets | matrix | 40 | 4 |
lakeWAplanktonRaw | MARSS | Plankton Data Sets | matrix | 396 | 20 |
lakeWAplanktonTrans | MARSS | Plankton Data Sets | matrix | 396 | 20 |
loggerhead | MARSS | Loggerhead Turtle Tracking Data | data.frame | 291 | 6 |
loggerheadNoisy | MARSS | Loggerhead Turtle Tracking Data | data.frame | 557 | 6 |
okanaganRedds | MARSS | Population Data Sets | matrix | 53 | 3 |
prairiechicken | MARSS | Population Data Sets | matrix | 50 | 2 |
redstart | MARSS | Population Data Sets | matrix | 30 | 2 |
rockfish | MARSS | Population Data Sets | matrix | 54 | 10 |
wilddogs | MARSS | Population Data Sets | matrix | 22 | 2 |
icd10chapters | ukbtools | International Classification of Diseases Revision 10 (ICD-10) chapters | data.frame | 21 | 3 |
icd10codes | ukbtools | International Classification of Diseases Revision 10 (ICD-10) codes | data.frame | 18761 | 2 |
icd9chapters | ukbtools | International Classification of Diseases Revision 9 (ICD-9) chapters | data.frame | 19 | 3 |
icd9codes | ukbtools | International Classification of Diseases Revision 9 (ICD-9) codes | data.frame | 13679 | 2 |
ukbcentre | ukbtools | UKB assessment centre | data.frame | 27 | 2 |
Elec.demand | bsamGP | Electricity demand data | data.frame | 288 | 7 |
London.Mortality | bsamGP | Daily Moratlity in London | data.frame | 5113 | 7 |
cadmium | bsamGP | Cadmium dose-response meta data | data.frame | 190 | 5 |
plasma | bsamGP | A Data Set for Plasma Levels of Retinol and Beta-Carotene | data.frame | 314 | 14 |
traffic | bsamGP | Monthly traffic accidents data | data.frame | 108 | 6 |
wage.union | bsamGP | Wage-Union data | data.frame | 534 | 11 |
otu_data_preproc | mikropml | Mini OTU abundance dataset - preprocessed | list | | |
otu_mini_bin | mikropml | Mini OTU abundance dataset | data.frame | 200 | 11 |
otu_mini_bin_results_glmnet | mikropml | Results from running the pipeline with L2 logistic regression on 'otu_mini_bin' with feature importance and grouping | list | | |
otu_mini_bin_results_rf | mikropml | Results from running the pipeline with random forest on 'otu_mini_bin' | list | | |
otu_mini_bin_results_rpart2 | mikropml | Results from running the pipeline with rpart2 on 'otu_mini_bin' | list | | |
otu_mini_bin_results_svmRadial | mikropml | Results from running the pipeline with svmRadial on 'otu_mini_bin' | list | | |
otu_mini_bin_results_xgbTree | mikropml | Results from running the pipeline with xbgTree on 'otu_mini_bin' | list | | |
otu_mini_cont_results_glmnet | mikropml | Results from running the pipeline with glmnet on 'otu_mini_bin' with 'Otu00001' as the outcome | list | | |
otu_mini_cont_results_nocv | mikropml | Results from running the pipeline with glmnet on 'otu_mini_bin' with 'Otu00001' as the outcome column, using a custom train control scheme that does not perform cross-validation | list | | |
otu_mini_cv | mikropml | Cross validation on 'train_data_mini' with grouped features. | list | | |
otu_mini_multi | mikropml | Mini OTU abundance dataset with 3 categorical variables | data.frame | 490 | 11 |
otu_mini_multi_group | mikropml | Groups for otu_mini_multi | character | | |
otu_mini_multi_results_glmnet | mikropml | Results from running the pipeline with glmnet on 'otu_mini_multi' for multiclass outcomes | list | | |
otu_small | mikropml | Small OTU abundance dataset | data.frame | 200 | 61 |
ufrj_bio_0122 | biblioverlap | UFRJ-affiliated documents from biological sciences disciplines (January 2022) | list | | |
APEX_LCQ | aLFQ | Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. | data.frame | 7230 | 2 |
APEX_ORBI | aLFQ | Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. | data.frame | 14610 | 2 |
LUDWIG_SRM | aLFQ | Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry.. | data.frame | 1166 | 8 |
UPS2_LFQ | aLFQ | Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. | data.frame | 217 | 7 |
UPS2_SC | aLFQ | Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. | data.frame | 236 | 7 |
UPS2_SRM | aLFQ | Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. | data.frame | 854 | 8 |
VWdat | VWPre | This is a sample eye-tracking dataset included in the package | grouped_df | 188909 | 20 |
CD_FINNGEN | topr | Finngen r7 Crohn‘s disease (K11_CROHNS) | data.frame | 32303 | 8 |
CD_UKBB | topr | UKBB Crohns disease (ICD 10 code K50) | data.frame | 21717 | 8 |
R2_CD_UKBB | topr | Example dataset including the R2 column for the locuszoom plot function | data.frame | 328 | 5 |
UC_UKBB | topr | UKBB Ulcerative colitis (ICD 10 code K51) | data.frame | 45012 | 8 |
artificialeg | mplot | Artificial example | data.frame | 50 | 10 |
bodyfat | mplot | Body fat data set | data.frame | 128 | 15 |
diabetes | mplot | Blood and other measurements in diabetics | data.frame | 442 | 11 |
fev | mplot | Forced Expiratory Volume | data.frame | 654 | 5 |
wallabies | mplot | Rock-wallabies data set | data.frame | 200 | 8 |
antigenicity | AccelStab | Antigenicity Accelerated Stability Data | data.frame | 50 | 5 |
potency | AccelStab | Potency Accelerated Stability Data | data.frame | 78 | 3 |
impulsivity | ERP | Event-Related Potentials data from a study conducted by Shen et al. (2014) to investigate neural correlates of impulsive behavior. | data.frame | 144 | 505 |
fam_dat | wcox | Simulated time-to-event data. | data.frame | 1069 | 5 |
demo_gate_data | tidygate | Demo gate data | tbl_df | 26 | 3 |
gate_list | tidygate | Example gate_list | list | | |
tidygate_data | tidygate | Example data set | spec_tbl_df | 2240 | 8 |
X | HDBRR | Durum Wheat X | matrix | 338 | |
pheno | HDBRR | Durum Wheat | data.frame | 1336 | 6 |
Cross.Parker.Consulting.net.info | tnet | Intra-organisational networks | data.frame | 877 | 3 |
Cross.Parker.Consulting.net.value | tnet | Intra-organisational networks | data.frame | 855 | 3 |
Cross.Parker.Consulting.node.gender | tnet | Intra-organisational networks | integer | | |
Cross.Parker.Consulting.node.location | tnet | Intra-organisational networks | integer | | |
Cross.Parker.Consulting.node.orglevel | tnet | Intra-organisational networks | integer | | |
Cross.Parker.Consulting.node.region | tnet | Intra-organisational networks | integer | | |
Cross.Parker.Manufacturing.net.aware | tnet | Intra-organisational networks | data.frame | 2326 | 3 |
Cross.Parker.Manufacturing.net.info | tnet | Intra-organisational networks | data.frame | 2228 | 3 |
Cross.Parker.Manufacturing.node.location | tnet | Intra-organisational networks | integer | | |
Cross.Parker.Manufacturing.node.orglevel | tnet | Intra-organisational networks | integer | | |
Cross.Parker.Manufacturing.node.tenure | tnet | Intra-organisational networks | integer | | |
Davis.Southern.women.1mode.Cooccurance | tnet | Davis' Southern Women network | data.frame | 278 | 3 |
Davis.Southern.women.1mode.Newman | tnet | Davis' Southern Women network | data.frame | 278 | 3 |
Davis.Southern.women.2mode | tnet | Davis' Southern Women network | data.frame | 89 | 2 |
Freemans.EIES.net.1.n48 | tnet | Freeman's EIES network data | data.frame | 695 | 3 |
Freemans.EIES.net.2.n48 | tnet | Freeman's EIES network data | data.frame | 830 | 3 |
Freemans.EIES.net.3.n32 | tnet | Freeman's EIES network data | data.frame | 440 | 3 |
Freemans.EIES.node.Citations.n32 | tnet | Freeman's EIES network data | integer | | |
Freemans.EIES.node.Discipline.n32 | tnet | Freeman's EIES network data | integer | | |
Freemans.EIES.node.Name.n32 | tnet | Freeman's EIES network data | character | | |
Newman.Condmat.95.99.net.1mode.wNewman | tnet | Newman's condmat 95-99 network (two-mode structure) | data.frame | 95188 | 3 |
Newman.Condmat.95.99.net.2mode | tnet | Newman's condmat 95-99 network (two-mode structure) | data.frame | 58595 | 2 |
OnlineSocialNetwork.n1899.lnet | tnet | Facebook-like Online Social Network | data.frame | 61734 | 4 |
OnlineSocialNetwork.n1899.net | tnet | Facebook-like Online Social Network | data.frame | 20296 | 3 |
USairport.n500.net | tnet | The network among the 500 busiest US commercial airports. | data.frame | 5960 | 3 |
celegans.n306.net | tnet | The neural network of the Caenorhabditis elegans worm (c.elegans) | data.frame | 2345 | 3 |
simdat | factorial2x2 | Simulated 2x2 factorial trial data | matrix | 100 | 10 |
simdata | factorial2x2 | Simulated 2x2 factorial trial data | matrix | 4600 | 10 |
simdataSub | factorial2x2 | Simulated 2x2 factorial trial data | matrix | 100 | 10 |
BayesTraitsMods | caper | Datasets used for benchmarking caper | data.frame | 10 | 9 |
BritishBirds.data | caper | Conservation status of British birds (Thomas 2008) | data.frame | 181 | 26 |
BritishBirds.tree | caper | Conservation status of British birds (Thomas 2008) | phylo | | |
CAIC.BrDi1057 | caper | Datasets used for benchmarking caper | data.frame | 38 | 10 |
CAIC.BrDi1157 | caper | Datasets used for benchmarking caper | data.frame | 50 | 10 |
CAIC.BrDi813 | caper | Datasets used for benchmarking caper | data.frame | 40 | 10 |
CAIC.BrDi913 | caper | Datasets used for benchmarking caper | data.frame | 57 | 10 |
CAIC.BrPl1057 | caper | Datasets used for benchmarking caper | data.frame | 38 | 10 |
CAIC.BrPl1157 | caper | Datasets used for benchmarking caper | data.frame | 50 | 10 |
CAIC.BrPl813 | caper | Datasets used for benchmarking caper | data.frame | 39 | 10 |
CAIC.BrPl913 | caper | Datasets used for benchmarking caper | data.frame | 56 | 10 |
CAIC.CrDi213 | caper | Datasets used for benchmarking caper | data.frame | 199 | 10 |
CAIC.CrDi657 | caper | Datasets used for benchmarking caper | data.frame | 172 | 10 |
CAIC.CrPl213 | caper | Datasets used for benchmarking caper | data.frame | 193 | 10 |
CAIC.CrPl413 | caper | Datasets used for benchmarking caper | data.frame | 193 | 10 |
CAIC.CrPl657 | caper | Datasets used for benchmarking caper | data.frame | 168 | 10 |
FuscoDiSpp | caper | Datasets used for benchmarking caper | list | | |
FuscoDiTax | caper | Datasets used for benchmarking caper | list | | |
FuscoPolySpp | caper | Datasets used for benchmarking caper | list | | |
FuscoPolyTax | caper | Datasets used for benchmarking caper | list | | |
MacroCAIC.DiSpp23 | caper | Datasets used for benchmarking caper | data.frame | 132 | 13 |
MacroCAIC.DiSpp67 | caper | Datasets used for benchmarking caper | data.frame | 125 | 13 |
MacroCAIC.DiTax23 | caper | Datasets used for benchmarking caper | data.frame | 199 | 13 |
MacroCAIC.DiTax67 | caper | Datasets used for benchmarking caper | data.frame | 178 | 13 |
MacroCAIC.PolySpp23 | caper | Datasets used for benchmarking caper | data.frame | 125 | 13 |
MacroCAIC.PolySpp67 | caper | Datasets used for benchmarking caper | data.frame | 119 | 13 |
MacroCAIC.PolyTax23 | caper | Datasets used for benchmarking caper | data.frame | 188 | 13 |
MacroCAIC.PolyTax67 | caper | Datasets used for benchmarking caper | data.frame | 168 | 13 |
MeSA.I | caper | Datasets used for benchmarking caper | data.frame | 105 | 5 |
benchData | caper | Datasets used for benchmarking caper | data.frame | 200 | 14 |
benchTreeDicho | caper | Datasets used for benchmarking caper | phylo | | |
benchTreePoly | caper | Datasets used for benchmarking caper | phylo | | |
carnivora.data | caper | Example dataset for the caper package | data.frame | 270 | 12 |
carnivora.tree | caper | Example dataset for the caper package | phylo | | |
chiroptera.data | caper | Example dataset for the caper package | data.frame | 925 | 12 |
chiroptera.tree | caper | Example dataset for the caper package | phylo | | |
fuscoBirdData | caper | Example dataset for Fusco imbalance calculations | data.frame | 137 | 2 |
fuscoBirdTree | caper | Example dataset for Fusco imbalance calculations | phylo | | |
marsupialia.data | caper | Example dataset for the caper package | data.frame | 272 | 12 |
marsupialia.tree | caper | Example dataset for the caper package | phylo | | |
perissodactyla.data | caper | Example dataset for the CAIC package | data.frame | 13 | 5 |
perissodactyla.tree | caper | Example dataset for the CAIC package | phylo | | |
primates.data | caper | Example dataset for the caper package | data.frame | 233 | 12 |
primates.tree | caper | Example dataset for the caper package | phylo | | |
shorebird.data | caper | Example dataset for the caper package | data.frame | 71 | 6 |
shorebird.tree | caper | Example dataset for the caper package | phylo | | |
syrphidaeRich | caper | The syrphidae dataset of Katzourakis et al. 2001 | data.frame | 204 | 2 |
syrphidaeTree | caper | The syrphidae dataset of Katzourakis et al. 2001 | phylo | | |
testData | caper | Datasets used for benchmarking caper | data.frame | 64 | 3 |
testTree | caper | Datasets used for benchmarking caper | phylo | | |
ionosphere_data | DCEM | Ionosphere data: A dataset of 351 radar readings | data.frame | 351 | 35 |
index | pcev | Methylation values around BLK gene | numeric | | |
methylation | pcev | Methylation values around BLK gene | matrix | 40 | 5986 |
methylation2 | pcev | Methylation values around BLK gene | matrix | 1000 | 40 |
pheno | pcev | Methylation values around BLK gene | integer | | |
pheno2 | pcev | Methylation values around BLK gene | character | | |
position | pcev | Methylation values around BLK gene | data.table | 5986 | 2 |
position2 | pcev | Methylation values around BLK gene | integer | | |
ecodata | NonParRolCor | Ecological data set to exemplify the use of the functions contained in _NonParRolCor_ | matrix | 240 | 4 |
ecodata2 | NonParRolCor | Environmental data set to exemplify the use of the functions contained in _NonParRolCor_ | matrix | 237 | 3 |
syntheticdata | NonParRolCor | Synthetic data set to exemplify the use of the functions contained in _NonParRolCor_ | matrix | 500 | 3 |
alnumx | lsa | Regular expression for removal of non-alphanumeric characters (saving special characters) | character | | |
corpus_essays | lsa | Corpora (Essay Scoring) | textmatrix | 1056 | 26 |
corpus_scores | lsa | Corpora (Essay Scoring) | data.frame | 26 | 1 |
corpus_training | lsa | Corpora (Essay Scoring) | textmatrix | 1056 | 74 |
specialchars | lsa | List of special character html entities and their character replacement | list | | |
stopwords_ar | lsa | Stopwordlists in German, English, Dutch, French, Polish, and Arab | character | | |
stopwords_de | lsa | Stopwordlists in German, English, Dutch, French, Polish, and Arab | character | | |
stopwords_en | lsa | Stopwordlists in German, English, Dutch, French, Polish, and Arab | character | | |
stopwords_fr | lsa | Stopwordlists in German, English, Dutch, French, Polish, and Arab | character | | |
stopwords_nl | lsa | Stopwordlists in German, English, Dutch, French, Polish, and Arab | character | | |
stopwords_pl | lsa | Stopwordlists in German, English, Dutch, French, Polish, and Arab | character | | |
CO2 | hpackedbubble | Carbon emissions around the world (2014) | tbl_df | 116 | 3 |
errors | kisopenapi | Errors | data.frame | 105 | 2 |
kosdaq_master_columns | kisopenapi | kospi and kosdaq master columns | data.frame | 64 | 2 |
kospi_master_columns | kisopenapi | kospi and kosdaq master columns | data.frame | 70 | 2 |
aims_aluminium_marine | ssddata | Species Sensitivity Data for aluminium_marine | tbl_df | 20 | 9 |
aims_data | ssddata | Species Sensitivity Data provided by AIMS | tbl_df | 40 | 11 |
aims_gallium_marine | ssddata | Species Sensitivity Data for gallium_marine | tbl_df | 6 | 9 |
aims_molybdenum_marine | ssddata | Species Sensitivity Data for molybdenum_marine | tbl_df | 14 | 9 |
anon_a | ssddata | Anonymous Species Sensitivity Data anon_a | tbl_df | 18 | 2 |
anon_b | ssddata | Anonymous Species Sensitivity Data anon_b | tbl_df | 10 | 2 |
anon_c | ssddata | Anonymous Species Sensitivity Data anon_c | tbl_df | 16 | 2 |
anon_d | ssddata | Anonymous Species Sensitivity Data anon_d | tbl_df | 12 | 2 |
anon_data | ssddata | Anonymous Species Sensitivity Data | tbl_df | 73 | 2 |
anon_e | ssddata | Anonymous Species Sensitivity Data anon_e | tbl_df | 17 | 2 |
anzg_data | ssddata | ANZG Species Sensitivity Data | tbl_df | 21 | 12 |
anzg_metolachlor_fresh | ssddata | Species Sensitivity Data for metolachlor_fresh | tbl_df | 21 | 10 |
ccme_boron | ssddata | CCME Species Sensitivity Data for ccme_boron | tbl_df | 28 | 5 |
ccme_cadmium | ssddata | CCME Species Sensitivity Data for ccme_cadmium | tbl_df | 36 | 5 |
ccme_chloride | ssddata | CCME Species Sensitivity Data for ccme_chloride | tbl_df | 28 | 5 |
ccme_data | ssddata | CCME Species Sensitivity Data | tbl_df | 144 | 5 |
ccme_endosulfan | ssddata | CCME Species Sensitivity Data for ccme_endosulfan | tbl_df | 12 | 5 |
ccme_glyphosate | ssddata | CCME Species Sensitivity Data for ccme_glyphosate | tbl_df | 18 | 5 |
ccme_silver | ssddata | CCME Species Sensitivity Data for ccme_silver | tbl_df | 9 | 5 |
ccme_uranium | ssddata | CCME Species Sensitivity Data for ccme_uranium | tbl_df | 13 | 5 |
csiro_chlorine_marine | ssddata | Species Sensitivity Data for chlorine_marine | tbl_df | 30 | 2 |
csiro_cobalt_marine | ssddata | Species Sensitivity Data for cobalt_marine | tbl_df | 14 | 7 |
csiro_data | ssddata | Species Sensitivity Data provided by CSIRO | tbl_df | 91 | 11 |
csiro_lead_marine | ssddata | Species Sensitivity Data for lead_marine | tbl_df | 16 | 7 |
csiro_nickel_fresh | ssddata | Species Sensitivity Data for nickel_fresh | tbl_df | 31 | 6 |
ssd_fits | ssddata | Species Sensitivity Distribution Fit Data | tbl_df | 249 | 12 |
Y | MultiVarSel | This is a metabolomic dataset from 30 copals samples of trees coming from Africa | matrix | 30 | 1019 |
group | MultiVarSel | This is a qualitative variable indicating the type of tree each row of Y is. | factor | | |
metab | MultiVarSel | This is a dataset containing the abundance of 199 metabolites from 9 seeds samples just after germination. The temperature of seed maturation vary between the different seeds. | data.frame | 9 | 200 |
prot | MultiVarSel | This is a dataset containing the abundance of 724 proteins from 9 seeds samples just after germination. The temperature of seed maturation vary between the different seeds. | data.frame | 9 | 725 |
HGMND_SimuData | HGMND | An example of simulated data for HGMND | list | | |
constant | obcost | Default Input of Relative Risk | tbl_df | 9 | 2 |
full_data | obcost | Necessary Raw Data for Generating New Tables With User Input | list | | |
obesity_cost_disease | obcost | Relevant Data for Obesity, Cost, and Diseases | data.frame | 10350 | 8 |
obesity_cost_full | obcost | Relevant Data for Obesity, Cost, and Diseases | data.frame | 1150 | 16 |
obesity_cost_national_summary | obcost | National summary cost calculations in a given year | data.frame | 23 | 9 |
ames_geo | AmesHousing | Raw Ames Housing Data | tbl_df | 2932 | 3 |
ames_new | AmesHousing | Raw Ames Housing Data | spec_tbl_df | 2 | 82 |
ames_raw | AmesHousing | Raw Ames Housing Data | tbl_df | 2930 | 82 |
ames_school_districts_sf | AmesHousing | Ames Public Schools | sf | 5 | 3 |
ames_schools_geo | AmesHousing | Ames Public Schools | tbl_df | 8 | 3 |
hood_levels | AmesHousing | Raw Ames Housing Data | character | | |
Koren.16S | mixOmics | 16S microbiome atherosclerosis study | list | | |
breast.TCGA | mixOmics | Breast Cancer multi omics data from TCGA | list | | |
breast.tumors | mixOmics | Human Breast Tumors Data | list | | |
diverse.16S | mixOmics | 16S microbiome data: most diverse bodysites from HMP | list | | |
linnerud | mixOmics | Linnerud Dataset | list | | |
liver.toxicity | mixOmics | Liver Toxicity Data | list | | |
multidrug | mixOmics | Multidrug Resistence Data | list | | |
nutrimouse | mixOmics | Nutrimouse Dataset | list | | |
srbct | mixOmics | Small version of the small round blue cell tumors of childhood data | list | | |
stemcells | mixOmics | Human Stem Cells Data | list | | |
vac18 | mixOmics | Vaccine study Data | list | | |
vac18.simulated | mixOmics | Simulated data based on the vac18 study for multilevel analysis | list | | |
yeast | mixOmics | Yeast metabolomic study | list | | |
DEM | hypsoLoop | Digital Elevation Model (DEM) of Yanze Watershed | stars | | |
lulcYanze | hypsoLoop | Land Use - Land Cover map of Yanze | stars | | |
watersheds | hypsoLoop | Yanze sub-catchments boundaries | sf | 3 | 9 |
watersheds_df | hypsoLoop | Yanze sub-catchments table | data.frame | 3 | 8 |
Quant_13B_problem_chcemat | TestGardener | Test data for 24 math calculation questions from the SweSAT data. | matrix | 1000 | |
Quant_13B_problem_dataList | TestGardener | List of objects essential for an analysis of the abbreviated SweSAT Quantitative multiple choice test. | list | | |
Quant_13B_problem_infoList | TestGardener | Arclength or information parameter list for 24 items from the quantitative SweSAT subtest. | list | | |
Quant_13B_problem_key | TestGardener | Option information for the short form of the SweSAT Quantitative test. | integer | | |
Quant_13B_problem_parmList | TestGardener | Parameter list for 24 items from the quantitative SweSAT subtest. | list | | |
Arthritis | depigner | Data for example | data.frame | 84 | 5 |
correspondence | cansim | The correspondence file for old to new StatCan table numbers is included in the package | tbl_df | 4807 | 2 |
calibrationSample | EBMAforecast | Sample data Insurgency Predictions | matrix | 696 | 4 |
presidentialForecast | EBMAforecast | Sample data Presidential Election | data.frame | 15 | 7 |
testSample | EBMAforecast | Sample data Insurgency Predictions | matrix | 348 | 4 |
rats | OLStrajr | Rat Weight Data from HLM manual | data.frame | 10 | 7 |
robins | OLStrajr | Ratio of robin males to females in Walker Creek and Knobs Flat, Eglinton Valley | data.frame | 2 | 6 |
AJR | hdm | AJR data set | data.frame | 64 | 11 |
BLP | hdm | BLP data set | list | | |
EminentDomain | hdm | Eminent Domain data set | list | | |
GrowthData | hdm | Growth data set | data.frame | 90 | 63 |
cps2012 | hdm | cps2012 data set | data.frame | 29217 | 23 |
pension | hdm | Pension 401(k) data set | data.frame | 9915 | 44 |
IllRivValleyCty | seawaveQ | Water-quality data for 05586100 Illinois River at Valley City, Ill. | data.frame | 168 | 20 |
cqwMoRivOmaha | seawaveQ | Continuously monitored (daily) data for 06610000 Missouri River at Omaha, Neb. | data.frame | 2922 | 8 |
examplecavdat | seawaveQ | Example continuous ancillary variable data. | data.frame | 2893 | 6 |
examplecavmat | seawaveQ | Example continuous ancillary variable matrix. | matrix | 115 | 2 |
examplecdatsub | seawaveQ | Example water-quality data. | data.frame | 115 | 8 |
examplecentmp | seawaveQ | Example logical vector. | logical | | |
exampleclog | seawaveQ | Example of logarithmically transformed concentration data. | numeric | | |
exampleqwcols | seawaveQ | Example data indicators. | character | | |
examplestpars | seawaveQ | Example matrix for internal use. | matrix | 2 | |
exampletndlin | seawaveQ | Example numeric vector used internally. | numeric | | |
exampletndlinpr | seawaveQ | Example numeric vector used internally. | numeric | | |
exampletseas | seawaveQ | Example numeric vector used internally. | numeric | | |
exampletseaspr | seawaveQ | Example numeric vector used internally. | numeric | | |
exampletyr | seawaveQ | Example numeric vector used internally. | numeric | | |
exampletyrpr | seawaveQ | Example numeric vector used internally. | numeric | | |
qwMoRivOmaha | seawaveQ | Water-quality data for 06610000 Missouri River at Omaha, Nebr. | data.frame | 115 | 19 |
dat_cond_logreg | pooling | Dataset for Examples in cond_logreg | list | | |
dat_p_gdfa | pooling | Dataset for Examples in p_gdfa | list | | |
dat_p_linreg_yerrors | pooling | Dataset for Examples in p_linreg_yerrors | list | | |
dat_p_ndfa | pooling | Dataset for Examples in p_ndfa | list | | |
pdat1 | pooling | Dataset for Examples in p_dfa_xerrors and p_logreg_xerrors | data.frame | 4999 | 5 |
pdat2 | pooling | Dataset for Examples in p_dfa_xerrors2 and p_logreg_xerrors2 | list | | |
simdata | pooling | Dataset for a Paper Under Review | list | | |
covid1 | AEenrich | Covid Vaccine Adverse Event Data | tbl_df | 12500 | 5 |
covid2 | AEenrich | Covid Vaccine Adverse Event Data | tbl_df | 2656 | 6 |
group | AEenrich | Group Structure Data | tbl_df | 35339 | 2 |
heights | brolgar | World Height Data | tbl_ts | 1490 | 4 |
pisa | brolgar | Student data from 2000-2018 PISA OECD data | tbl_df | 433 | 11 |
wages | brolgar | Wages data from National Longitudinal Survey of Youth (NLSY) | tbl_ts | 6402 | 9 |
HIPC_Stanford_1228_1A | CytOpT | HIPC_Stanford data | data.frame | 31342 | 7 |
HIPC_Stanford_1228_1A_labels | CytOpT | HIPC_Stanford data | factor | | |
HIPC_Stanford_1369_1A | CytOpT | HIPC_Stanford data | data.frame | 33992 | 7 |
HIPC_Stanford_1369_1A_labels | CytOpT | HIPC_Stanford data | factor | | |
liv_lsoa | smile | Liverpool Lower Super Output Area. | sf | 298 | 8 |
liv_msoa | smile | Liverpool Middle Super Output Area. | sf | 61 | 5 |
nl_ct | smile | Nova Lima census tracts | sf | 113 | 15 |
welldata | changepoint.influence | Welllog data | numeric | | |
bulldozer | firebehavioR | Bitmap of bulldozer Bitmap image of bulldozer | rastergrob | | |
coForest | firebehavioR | Colorado dry forest inventory summary. | data.frame | 14 | 10 |
fboTable | firebehavioR | Fire Behavior Officer's table | list | | |
fireChartData | firebehavioR | Template data for fire characteristics chart | data.frame | 9180 | 3 |
firefighter | firebehavioR | Bitmap of firefighter Bitmap image of bulldozer | rastergrob | | |
fuelModels | firebehavioR | Surface fuel models. | data.frame | 60 | 18 |
fuelMoisture | firebehavioR | Modified Scott & Burgan (2005) moisture scenarios. | data.frame | 16 | 7 |
kbdiTable | firebehavioR | KBDI Lookup table | data.frame | 1680 | 7 |
plume | firebehavioR | Bitmap of fire plume Bitmap image of fire plume | rastergrob | | |
rrRAWS | firebehavioR | Rampart Range RAWS meteorological data | data.frame | 4392 | 5 |
tree | firebehavioR | Bitmap of burning tree Bitmap image of burning tree | rastergrob | | |
Dat | Frames2 | Joint sample database | data.frame | 2402 | 8 |
DatA | Frames2 | Database of household expenses for frame A | data.frame | 105 | 11 |
DatB | Frames2 | Database of household expenses for frame B | data.frame | 135 | 10 |
DatMA | Frames2 | Database of students' program choice for frame A | data.frame | 180 | 10 |
DatMB | Frames2 | Database of students' program choice for frame B | data.frame | 232 | 10 |
DatPopM | Frames2 | Database of auxiliary information for the whole population of students | data.frame | 10000 | 4 |
PiklA | Frames2 | Matrix of inclusion probabilities for units selected in sample from frame A | matrix | 105 | |
PiklB | Frames2 | Matrix of inclusion probabilities for units selected in sample from frame B | matrix | 135 | |
sat_band | satres | Final part of the name and extension of the satellite band files | character | | |
sat_rest | satres | Final part of the name and extension of satellite rasters that are not bands | character | | |
sat_rest_msk | satres | Mask of name of satellite rasters that are not bands | character | | |
US13wTB | BLCOP | Risk free rate of return | matrix | 71 | 1 |
monthlyReturns | BLCOP | Monthly equity returns | matrix | 71 | 6 |
sp500Returns | BLCOP | S&P500 Returns | matrix | 71 | 1 |
ts | OBL | Ten (10) simulated univaariate time series data. | ts | | |
example_data | TANDEM | A small artificial data set | list | | |
sampledata | lchemix | The example data is meant to represent one dataset for Scenario II in simulation study, which is explored in Section S3 of Supplementary Materials in the paper. The 'sampledata' file contains 378 rows and 83 variables. | matrix | 378 | 83 |
apes | shapes | Great ape data | list | | |
brains | shapes | Brain landmark data | list | | |
cortical | shapes | Cortical surface data | list | | |
digit3.dat | shapes | Digit 3 data | array | | |
dna.dat | shapes | DNA data | array | | |
gels | shapes | Electrophoresis gel data | array | | |
gorf.dat | shapes | Female gorilla data | array | | |
gorm.dat | shapes | Male gorilla data | array | | |
humanmove | shapes | Human movement data | array | | |
macaques | shapes | Male and Female macaque data | list | | |
macf.dat | shapes | Female macaque data | array | | |
macm.dat | shapes | Male macaque data | array | | |
mice | shapes | T2 mouse vertabrae data | list | | |
nsa | shapes | Internal function(s) | array | | |
panf.dat | shapes | Female chimpanzee data | array | | |
panm.dat | shapes | Male chimpanzee data | array | | |
pongof.dat | shapes | Female orang utan data | array | | |
pongom.dat | shapes | Male orang utan data | array | | |
protein | shapes | Internal function(s) | array | | |
qcet2.dat | shapes | Control T2 mouse vertabrae data | array | | |
qlet2.dat | shapes | Large T2 mouse vertabrae data | array | | |
qset2.dat | shapes | Small T2 mouse vertabrae data | array | | |
rats | shapes | Rat skulls data | list | | |
sand | shapes | Sand particle outline data | list | | |
schizophrenia | shapes | Bookstein's schizophrenia data | list | | |
schizophrenia.dat | shapes | Bookstein's schizophrenia data | array | | |
shells | shapes | Microfossil shell data | list | | |
sooty | shapes | Sooty mangabey data | array | | |
sooty.dat | shapes | Internal function(s) | array | | |
steroids | shapes | Steroid data | list | | |
Bisdata1 | BisRNA | Synthetic sample 1 of RNA bisulfite sequencing | data.frame | 335 | 4 |
Bisdata2 | BisRNA | Synthetic sample 2 of RNA bisulfite sequencing | data.frame | 345 | 4 |
Bisdata3 | BisRNA | Synthetic sample 3 of RNA bisulfite sequencing | data.frame | 330 | 4 |
advantage_details | survivoR | Advantage Details | tbl_df | 378 | 9 |
advantage_movement | survivoR | Advantage Movement | tbl_df | 857 | 15 |
auction_details | survivoR | Survivor Auction Details | tbl_df | 261 | 19 |
boot_mapping | survivoR | Boot mapping | tbl_df | 14743 | 14 |
castaway_details | survivoR | Castaway details | tbl_df | 1142 | 21 |
castaway_scores | survivoR | Castaway scores | tbl_df | 857 | 31 |
castaways | survivoR | Castaways | tbl_df | 1393 | 27 |
challenge_description | survivoR | Challenge Description | tbl_df | 1831 | 46 |
challenge_results | survivoR | Challenge Results | tbl_df | 20749 | 20 |
challenge_summary | survivoR | Challenge Summary | tbl_df | 51540 | 14 |
confessionals | survivoR | Confessionals | tbl_df | 13125 | 11 |
episodes | survivoR | Episodes | tbl_df | 1173 | 14 |
jury_votes | survivoR | Jury votes | tbl_df | 1540 | 9 |
screen_time | survivoR | Screen Time | grouped_df | 150 | 4 |
season_palettes | survivoR | Season palettes | tbl_df | 355 | 5 |
season_summary | survivoR | Season summary | tbl_df | 72 | 26 |
survivor_auction | survivoR | Survivor Auction | tbl_df | 290 | 12 |
tribe_colours | survivoR | Tribe colours | tbl_df | 267 | 7 |
tribe_mapping | survivoR | Tribe mapping | tbl_df | 14553 | 10 |
viewers | survivoR | Viewers | tbl_df | 1173 | 13 |
vote_history | survivoR | Vote history | tbl_df | 8999 | 24 |
GeneSets | GSEMA | GSEMA synthetic data | list | | |
objectMApathSim | GSEMA | GSEMA synthetic data | list | | |
study1Ex | GSEMA | GSEMA synthetic data | matrix | 18434 | 30 |
study1Pheno | GSEMA | GSEMA synthetic data | data.frame | 30 | 1 |
study2Ex | GSEMA | GSEMA synthetic data | matrix | 18417 | 20 |
study2Pheno | GSEMA | GSEMA synthetic data | data.frame | 20 | 1 |
horse_mesh | stelfi | Example Delaunay triangulation | fm_mesh_2d | | |
horse_sf | stelfi | Example 'sf' 'POLYGON' | sfc_POLYGON | | |
iraq_terrorism | stelfi | Terrorism in Iraq, 2013 - 2017 | sf | 4208 | 17 |
marked | stelfi | Example marked point pattern data set | data.frame | 159 | 5 |
multi_hawkes | stelfi | Example multivariate Hawkes dataset | data.frame | 213 | 2 |
nz_earthquakes | stelfi | Earthquakes in Canterbury, NZ, 2010 - 2014 | sf | 3824 | 4 |
nz_murders | stelfi | Murders of NZ, 2004 - 2019 | sf | 967 | 12 |
retweets_niwa | stelfi | Retweets of NIWA's viral leopard seal Tweet | POSIXct | | |
sasquatch | stelfi | Sasquatch (bigfoot) sightings in the USA, 2000 - 2005 | sf | 972 | 28 |
uk_serial | stelfi | Serial killers of the UK, 1828 - 2015 | data.frame | 62 | 8 |
xyt | stelfi | Self-exciting point pattern | stppp | | |
mask | TCIU | mask | array | | |
mask_dict | TCIU | mask_dict | data.frame | 56 | 2 |
mask_label | TCIU | mask_label | array | | |
phase1_pval | TCIU | phase1_pval | numeric | | |
phase2_pval | TCIU | phase2_pval | array | | |
phase3_pval | TCIU | phase3_pval | array | | |
sample_save | TCIU | sample_save | list | | |
nsw | DRDID | National Supported Work Demonstration dataset | data.frame | 19204 | 14 |
nsw_long | DRDID | National Supported Work Demonstration dataset, in long format | data.frame | 38408 | 15 |
sim_rc | DRDID | Simulated repeated cross-section data | data.frame | 1000 | 8 |
DREAM | ePCR | FIMM-UTU DREAM winning implementation of an ensemble of Penalized Cox Regression models for mCPRC research (ePCR) | PEP | | |
TYKS | ePCR | ePCR model fitted to the Turku University Hospital cohorts (all features) | PEP | | |
TYKS_reduced | ePCR | ePCR model fitted to the Turku University Hospital cohorts (features derived from text mining only) | PEP | | |
xMEDISIMU | ePCR | TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital | data.frame | 150 | 101 |
xTEXTSIMU | ePCR | TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital | data.frame | 500 | 101 |
yMEDISIMU | ePCR | TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital | data.frame | 150 | 3 |
yTEXTSIMU | ePCR | TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital | data.frame | 500 | 3 |
dataQualiN | bnpa | A qualitative data set to test functions | data.frame | 500 | 8 |
dataQuantC | bnpa | A quantiative data set to test functions | data.frame | 500 | 7 |
flood | QuantileNPCI | The flood rate of Feature River and Blackstone River. | data.frame | 96 | 3 |
Event | coda4microbiome | data_survival | numeric | | |
Event_time | coda4microbiome | data_survival | numeric | | |
MSM_HIV | coda4microbiome | HIV | factor | | |
metadata | coda4microbiome | ecam_filtered | data.frame | 873 | 12 |
taxanames | coda4microbiome | ecam_filtered | character | | |
x | coda4microbiome | data_survival | data.frame | 150 | 48 |
x_Crohn | coda4microbiome | Crohn | data.frame | 975 | 48 |
x_HIV | coda4microbiome | HIV | data.frame | 155 | 60 |
x_ecam | coda4microbiome | ecam_filtered | matrix | 873 | 37 |
x_sCD14 | coda4microbiome | sCD14 | matrix | 151 | 60 |
y_Crohn | coda4microbiome | Crohn | factor | | |
y_HIV | coda4microbiome | HIV | factor | | |
y_sCD14 | coda4microbiome | sCD14 | numeric | | |
chistes | TextMiningGUI | chistes | data.frame | 2419 | 5 |
jockes | TextMiningGUI | jockes | data.frame | 1500 | 2 |
example_data | INSPECTumours | Tumour volume data over time for in-vivo studies | data.frame | 1462 | 6 |
myoglobin | protag | Simulated MALDI-TOF data of equine myoglobin, labeled (with dimethylation) and control | tbl_df | 192 | 8 |
Ericacsubfams | MonoPhy | Example dataset for the package MonoPhy. | data.frame | 77 | 3 |
Ericactree | MonoPhy | Example dataset for the package MonoPhy. | phylo | | |
Ericactribes | MonoPhy | Example dataset for the package MonoPhy. | data.frame | 77 | 2 |
funs_examples | foodwebWrapper | foodwebWrapper data sets | character | | |
x_colorMap | foodwebWrapper | foodwebWrapper data sets | character | | |
x_examples | foodwebWrapper | foodwebWrapper data sets | foodweb | | |
x_f | foodwebWrapper | foodwebWrapper data sets | character | | |
x_funs | foodwebWrapper | foodwebWrapper data sets | character | | |
x_m | foodwebWrapper | foodwebWrapper data sets | matrix | 33 | 33 |
x_m2 | foodwebWrapper | foodwebWrapper data sets | matrix | 33 | 33 |
x_m3 | foodwebWrapper | foodwebWrapper data sets | matrix | 33 | 33 |
x_m4 | foodwebWrapper | foodwebWrapper data sets | matrix | 33 | |
x_m5 | foodwebWrapper | foodwebWrapper data sets | matrix | 16 | |
x_packages | foodwebWrapper | foodwebWrapper data sets | character | | |
x_v | foodwebWrapper | foodwebWrapper data sets | character | | |
x_v2 | foodwebWrapper | foodwebWrapper data sets | list | | |
x_where | foodwebWrapper | foodwebWrapper data sets | character | | |
x_x | foodwebWrapper | foodwebWrapper data sets | foodweb | | |
x_x2 | foodwebWrapper | foodwebWrapper data sets | character | | |
x_x3 | foodwebWrapper | foodwebWrapper data sets | character | | |
x_y | foodwebWrapper | foodwebWrapper data sets | character | | |
statepop | mapindia | Indian Population (census and projections) by states | spec_tbl_df | 36 | 8 |
wb_2011 | mapindia | West Bengal population, sex-ratio, and literacy data (2011) | spec_tbl_df | 23 | 8 |
topgear | crmReg | Top Gear car data | data.frame | 245 | 11 |
diabetes | quickReg | A hypothetical dataset | data.frame | 1000 | 14 |
hivdata | fdrDiscreteNull | HIV data | data.frame | 118 | 2 |
listerdata | fdrDiscreteNull | Methylation data for Arabidopsis thaliana | matrix | 3525 | 2 |
fluxMeas | gasfluxes | Data from chamber N2O flux measurements. | data.table | 5300 | 5 |
data_low_vision | mnreadR | MNREAD data collected in subjects with low vision. | data.frame | 437 | 7 |
data_normal_vision | mnreadR | MNREAD data collected in subjects with normal vision. | data.frame | 684 | 6 |
vitaminA | noncomplyR | Vitamin A Randomized Trial Data Set | data.frame | 23682 | 3 |
intensity_PXD000022 | imputeLCMD | Dataset PXD000022 from ProteomeXchange. | data.frame | 660 | 7 |
intensity_PXD000052 | imputeLCMD | Dataset PXD000052 from ProteomeXchange. | data.frame | 1991 | 17 |
intensity_PXD000438 | imputeLCMD | Dataset PXD000438 from ProteomeXchange. | data.frame | 3709 | 13 |
intensity_PXD000501 | imputeLCMD | Dataset PXD000501 from ProteomeXchange. | data.frame | 7363 | 19 |
armaxsim | sysid | Data simulated from an ARMAX model | idframe | | |
arxsim | sysid | Data simulated from an ARX model | idframe | | |
bjsim | sysid | Data simulated from an BJ model | idframe | | |
cstr | sysid | Continuous stirred tank reactor data (idframe) | idframe | | |
cstrData | sysid | Continuous stirred tank reactor data (data.frame) | data.frame | 7500 | 3 |
cstr_mis | sysid | Continuous stirred tank reactor data with missing values | idframe | | |
frd | sysid | Frequency response data | idfrd | | |
oesim | sysid | Data simulated from an OE model | idframe | | |
adhd | DTRlearn2 | A 2-stage SMART data of children with ADHD | data.frame | 150 | 11 |
P | ClimClass | Precipitation | data.frame | 19358 | 39 |
Tm | ClimClass | Mean daily temperature | data.frame | 19358 | 15 |
Tn | ClimClass | Minimum daily temperature | data.frame | 19358 | 15 |
Tx | ClimClass | Maximum daily temperature | data.frame | 19358 | 15 |
W_balance | ClimClass | Water balance | list | | |
arid_ind_tables | ClimClass | Aridity index | list | | |
clima_81_10 | ClimClass | Climatic normals of precipitation and temperatures | list | | |
coeff_rad | ClimClass | Radiative energy coefficients | numeric | | |
continental_ind_tables | ClimClass | Continentality/oceanicity indices | list | | |
coord_elev | ClimClass | Geographical position for each meteorological station | data.frame | 40 | 4 |
lista_cli | ClimClass | Dataset of meteorological measures | list | | |
quantiles | ClimClass | Monthly quantiles of the meteorological variables | list | | |
thornt_lst | ClimClass | Input for the Thornthwaite function | list | | |
coExp | dlmtree | Randomly sampled exposure from Colorado counties | matrix | 10000 | |
exposureCov | dlmtree | Exposure covariance structure | matrix | 185 | 185 |
pm25Exposures | dlmtree | PM2.5 Exposure data | data.table | 12383 | 42 |
zinbCo | dlmtree | Time-series exposure data for ZINB simulated data | tbl_df | 35904 | 163 |
MyStopWords | CINE | My StopWords | character | | |
df_CINE | CINE | Peru: International Standard Classification of Education | tbl_df | 8357 | 8 |
df_LEMMA | CINE | Lemma of the Education Program | tbl_df | 2184 | 2 |
loanData | logiBin | Simulated default data of 100 customers | data.frame | 100 | 6 |
ACTG181 | CopulaCenR | ACTG181 | data.frame | 408 | 6 |
AREDS | CopulaCenR | AREDS | data.frame | 1258 | 8 |
DRS | CopulaCenR | DRS | data.frame | 394 | 7 |
Kidney | CopulaCenR | Kidney | data.frame | 76 | 7 |
data_scmprisk | CopulaCenR | data_scmprisk | data.frame | 150 | 10 |
data_scmprisk_rc | CopulaCenR | data_scmprisk_rc | data.frame | 150 | 7 |
data_sim_RC | CopulaCenR | data_sim_RC | data.frame | 500 | 5 |
data_sim_ic | CopulaCenR | data_sim_ic | data.frame | 500 | 7 |
data_sim_multi_rec | CopulaCenR | data_sim_multi_rec | list | | |
data_sim_rec | CopulaCenR | data_sim_rec | data.frame | 500 | 6 |
data_sim_scmprisk_vs | CopulaCenR | data_sim_scmprisk_vs | data.frame | 1000 | 10 |
liverpool | cdrcR | Liverpool LSOA boundaries | sf | 297 | 7 |
xbio | plotMCMC | MCMC Results for Biomass | data.frame | 1000 | 34 |
xpar | plotMCMC | MCMC Results for Model Parameters | data.frame | 1000 | 8 |
xpro | plotMCMC | MCMC Results for Future Projections | data.frame | 1000 | 4 |
xrec | plotMCMC | MCMC Results for Recruitment | data.frame | 1000 | 33 |
edge_df | neatmaps | Edge List Data Frame | data.frame | 10 | 10 |
network_attr_df | neatmaps | Network Attributes Data | data.frame | 10 | 4 |
node_attr_df | neatmaps | Node Attribute Data | data.frame | 10 | 25 |
market | IndexConstruction | Market capitalization data for Cryptocurrencies. | xts | 1091 | 406 |
price | IndexConstruction | Pricing data for Cryptocurrencies. | xts | 1091 | 406 |
vol | IndexConstruction | Volume data for Cryptocurrencies. | xts | 1091 | 406 |
municipalities | jackstrap | Dataset of Municipalities of Bahia state in Brazil | data.frame | 489 | 5 |
remittance_by_province | sfDR | Percentage distribution by provinces of remittances received by the Dominican Republic | tbl_df | 15 | 2 |
df | MicroNiche | Data frame for package MicroNiche | data.frame | 60 | 41 |
diabetes | maclogp | Diabetes data | data.frame | 442 | 11 |
gamesdata | ocp | This is data to be included in the package | data.frame | 16 | 11 |
gamesdatacounts | ocp | This is data to be included in the package | data.frame | 16 | 2 |
BIN | numOSL | BIN data | loadBIN | | |
EDdata | numOSL | Equivalent dose values | list | | |
SARdata | numOSL | Data sets used for SAR equivalent dose calculation | data.frame | 840 | 5 |
Signaldata | numOSL | Decay curves datasets | list | | |
cervical | MVPBT | Scheidler et al. (1997)'s cervical cancer data | data.frame | 44 | 8 |
evian_binary_bim | evian | Example map data for evian_binary_raw. | data.frame | 30 | 6 |
evian_binary_raw | evian | Example dataset with a binary outcome. | data.frame | 250 | 39 |
evian_linear_bim | evian | Example map data for evian_linear_raw. | data.frame | 10 | 6 |
evian_linear_raw | evian | Example dataset with a quantative outcome. | data.frame | 781 | 19 |
MEPS14 | glmMisrep | MEPS 2014 Full Year Consolidated Data File | data.frame | 13301 | 7 |
LETTER | evtclass | Database of character image features. | data.frame | 20000 | 17 |
bcos2 | survivalMPL | Breast Cosmesis Data | data.frame | 94 | 3 |
simul.data | DIFboost | Simulated data set | data.frame | 100 | 13 |
atsdr_tsca_ld50_a | chem.databases | Collection of ATSDR, NCI, and TSCA Chemical Databases Combined Focused on ATSDR and TSCA Data | data.table | 69557 | 4 |
atsdr_tsca_ld50_b | chem.databases | Collection of ATSDR, NCI, and TSCA Chemical Databases Combined Focused on NCI Data | data.table | 80081 | 4 |
chem_wiki | chem.databases | CompTox Chemicals Dashboard From Wikipedia | data.table | 19239 | 9 |
ohiovowels | vowels | ohiovowels: Sample data for vowels.R | data.frame | 447 | 9 |
tempEng | ArDec | Time series of monthly temperature values | ts | | |
movies | MLPUGS | FiveThirtyEight's Movie Scores | data.frame | 146 | 9 |
movies_test | MLPUGS | FiveThirtyEight's Movie Scores | data.frame | 59 | 7 |
movies_train | MLPUGS | FiveThirtyEight's Movie Scores | data.frame | 87 | 7 |
simulasi | ddp | Simulation data | data.frame | 5 | 218 |
compounds | rIntervalTree | Compounds with mass ranges | data.frame | 23 | 3 |
cn.mat | MVisAGe | DNA copy number data from 98 head and neck squamous cell carcinoma (HNSC) patients | matrix | 2719 | 100 |
exp.mat | MVisAGe | Gene expression data from 100 head and neck squamous cell carcinoma (HNSC) patients | matrix | 2161 | 100 |
gene.annot | MVisAGe | Gene annotation data (hg38) | matrix | 26417 | 3 |
sample.annot | MVisAGe | Sample annotation data | matrix | 279 | 3 |
gExprs.p53 | GANPAdata | Gene expression data for the P53 dataset | list | | |
gNET | GANPAdata | gNET: A comprehensive gene functional association network | array | | |
gsets.msigdb.pnas | GANPAdata | Functional Gene Sets Used in GSEA PNAS Publication | list | | |
msp.groups | GANPAdata | A List of Human Multi-subunit Proteins | list | | |
dropsMap | statgenGWAS | DROPS data sets | data.frame | 41722 | 5 |
dropsMarkers | statgenGWAS | DROPS data sets | data.frame | 246 | 41723 |
dropsPheno | statgenGWAS | DROPS data sets | data.frame | 2460 | 20 |
DNase | MANCIE | The demo dataset for the 'MANCIE' package | data.frame | 3000 | 61 |
ann_DNase | MANCIE | The demo dataset for the 'MANCIE' package | data.frame | 3000 | 6 |
ann_exp | MANCIE | The demo dataset for the 'MANCIE' package | data.frame | 395 | 5 |
exp | MANCIE | The demo dataset for the 'MANCIE' package | matrix | 395 | 61 |
guaguas | guaguas | Nombres de bebés (guaguas) | tbl_df | 858782 | 5 |
guaguas_frecuentes | guaguas | Nombres de bebés (guaguas) | tbl_df | 86366 | 5 |
fishMR | rMR | Gnathonemus Respirometry Trial Data | data.frame | 64239 | 6 |
CD4 | gamlss.data | The CD4 Count Data files for GAMLSS | data.frame | 609 | 2 |
InfMort | gamlss.data | Infant Mortality Data | data.frame | 399 | 11 |
LGAclaims | gamlss.data | The LGA Claims Data files for GAMLSS | data.frame | 176 | 11 |
Leukemia | gamlss.data | The Leukemia data | data.frame | 1988 | 4 |
Mums | gamlss.data | Mothers encouragement data | data.frame | 871 | 7 |
VictimsOfCrime | gamlss.data | Reported victims of crime data | data.frame | 10590 | 2 |
abdom | gamlss.data | Abdominal Circumference Data | data.frame | 610 | 2 |
acidity | gamlss.data | The Acidity Data files for GAMLSS | data.frame | 155 | 1 |
aep | gamlss.data | The Hospital Stay Data | data.frame | 1383 | 8 |
aids | gamlss.data | Aids Cases in England and Wales | data.frame | 45 | 3 |
aircond | gamlss.data | Air-conditioning data | numeric | | |
alveolar | gamlss.data | The Alveolar Data files for GAMLSS | data.frame | 23 | 2 |
brownfat | gamlss.data | The brown fat data set | data.frame | 4842 | 14 |
bush2000 | gamlss.data | The Bush 2000 election data | data.frame | 51 | 10 |
cable | gamlss.data | The cable data set | data.frame | 283 | 6 |
computer | gamlss.data | The Computer Failure Data files for GAMLSS | data.frame | 128 | 1 |
cysts | gamlss.data | Data for count data | data.frame | 12 | 2 |
db | gamlss.data | Head Circumference of Dutch Boys | data.frame | 7040 | 2 |
dbbmi | gamlss.data | BMI of Dutch Boys | data.frame | 7294 | 2 |
dbhh | gamlss.data | Head circumference and height of Dutch Boys | data.frame | 6885 | 3 |
eu15 | gamlss.data | GDP of 15 EU counties from 1960 to 2009 | data.frame | 50 | 5 |
fabric | gamlss.data | The Fabric Data | data.frame | 32 | 3 |
film30 | gamlss.data | Film revenue data for the 1930's | data.frame | 969 | 3 |
film90 | gamlss.data | Film revenue data for the 1990's | data.frame | 4031 | 4 |
glass | gamlss.data | The Glass Data files for GAMLSS | data.frame | 63 | 1 |
glasses | gamlss.data | Reading Glasses Data | data.frame | 1016 | 3 |
grip | gamlss.data | The hand grip strength data | data.frame | 3766 | 2 |
hodges | gamlss.data | Hodges data | data.frame | 341 | 6 |
hodges1 | gamlss.data | Hodges data | data.frame | 45 | 4 |
lice | gamlss.data | Data files for GAMLSS | data.frame | 71 | 2 |
lungFunction | gamlss.data | The lung function data | data.frame | 3164 | 3 |
margolin | gamlss.data | The Margolin Data files for GAMLSS | data.frame | 18 | 2 |
meta | gamlss.data | A Meta Analysis on Smoking Cessation | data.frame | 54 | 6 |
mvi | gamlss.data | The motor vehicle insurance data | data.frame | 2000 | 11 |
mviBig | gamlss.data | The motor vehicle insurance data | data.frame | 67143 | 11 |
oil | gamlss.data | The oil price data | data.frame | 1000 | 25 |
parzen | gamlss.data | The Parzen Data File for GAMLSS | data.frame | 63 | 1 |
plasma | gamlss.data | The plasma data set | data.frame | 315 | 14 |
polio | gamlss.data | Poliomyelitis cases in US | ts | | |
rent | gamlss.data | Rent data | data.frame | 1969 | 9 |
rent99 | gamlss.data | Munich rent data of 1999 | data.frame | 3082 | 9 |
rent99.polys | gamlss.data | The boundaries file for Munich rent data from the 1999 survey. | list | | |
respInf | gamlss.data | Respiratory Infection in Indonesian Children. | data.frame | 1200 | 14 |
sleep | gamlss.data | Data on sleep | data.frame | 106 | 9 |
species | gamlss.data | The Fish Species Data files for GAMLSS | data.frame | 70 | 2 |
stylo | gamlss.data | The Stylometric Data files for GAMLSS | data.frame | 64 | 2 |
tensile | gamlss.data | The Tensile Data files for GAMLSS | data.frame | 30 | 1 |
tidal | gamlss.data | The tidal data set | data.frame | 90 | 3 |
trd | gamlss.data | Tokyo Rainfall Data | numeric | | |
tse | gamlss.data | The Turkish stock exchange index | data.frame | 2868 | 6 |
ultra | gamlss.data | Ultrasound data | data.frame | 1038 | 8 |
usair | gamlss.data | US air pollution data set | data.frame | 41 | 7 |
vas5 | gamlss.data | Visual analog scale (VAS) data | data.frame | 364 | 3 |
data_sim | cquad | Simulated dataset | data.frame | 5000 | 5 |
Warszawa | adr | Dzielnice Warszawy | data.frame | 18 | 13 |
donations | adr | donations: wpłaty na cele charytatywne w Holandii w 1993 | data.frame | 2000 | 17 |
ncn | adr | Statystyki składania wniosków do NCN | data.frame | 2979 | 14 |
pgss | adr | Polskie Generalne Sondaże Społeczne | data.frame | 16234 | 73 |
pgss2 | adr | Polskie Generalne Sondaże Społeczne | data.frame | 16234 | 73 |
polpan | adr | POLPAN: zachowania wyborcze | data.frame | 6671 | 22 |
uczniowie_atrybuty | adr | Dane o uczniach pewnej klasy podstawowej | data.frame | 26 | 4 |
uczniowie_relacje | adr | Dane o uczniach pewnej klasy podstawowej | data.frame | 254 | 3 |
wybory2015_1 | adr | Wybory Prezydenta RP 2015 | data.frame | 27817 | 38 |
wybory2015_2 | adr | Wybory Prezydenta RP 2015 | data.frame | 27817 | 25 |
auto_mpg | ascentTraining | Auto MPG Data Set | spec_tbl_df | 398 | 10 |
bbc_articles | ascentTraining | BBC articles data | spec_tbl_df | 201571 | 2 |
bbc_articles_full | ascentTraining | Full BBC Articles data | spec_tbl_df | 927 | 2 |
bbc_business_123 | ascentTraining | BBC Business article data | spec_tbl_df | 107 | 2 |
bbc_politics_123 | ascentTraining | BBC Politics article data | spec_tbl_df | 86 | 2 |
body_image | ascentTraining | Body image dataset | spec_tbl_df | 246 | 8 |
book_sections | ascentTraining | Gutenberg Project books dataset | spec_tbl_df | 108657 | 2 |
boston | ascentTraining | Boston housing dataset | spec_tbl_df | 506 | 15 |
breast_cancer | ascentTraining | Wisconsin Diagnostic Breast Cancer (WDBC) | spec_tbl_df | 569 | 22 |
breast_cancer_clean_features | ascentTraining | Wisconsin Breast Cancer Database | list | | |
breast_cancer_clean_target | ascentTraining | Wisconsin Breast Cancer Database | list | | |
carriers | ascentTraining | Carrier data | spec_tbl_df | 1491 | 2 |
commute | ascentTraining | R For Data Science tidytuesday commute dataset | spec_tbl_df | 3496 | 9 |
demoData | ascentTraining | Demographics data | data.frame | 33 | 7 |
demo_data | ascentTraining | Demographics data | data.frame | 33 | 7 |
dowJonesData | ascentTraining | Dow Jones Index Data | data.frame | 252 | 7 |
dow_jones_data | ascentTraining | Dow Jones Index Data | data.frame | 252 | 7 |
drugs | ascentTraining | Repeated Measures Drug data | data.frame | 20 | 3 |
emaxData | ascentTraining | Data that can be used to fit or plot Emax models | data.frame | 64 | 6 |
emax_data | ascentTraining | Data that can be used to fit or plot Emax models | data.frame | 64 | 6 |
messyData | ascentTraining | Messy clinical trial data | data.frame | 33 | 7 |
messy_data | ascentTraining | Messy clinical trial data | data.frame | 33 | 7 |
missingPk | ascentTraining | Clinical trial data | data.frame | 165 | 4 |
missing_pk | ascentTraining | Clinical trial data | data.frame | 165 | 4 |
pkData | ascentTraining | Typical PK data | data.frame | 165 | 4 |
pk_data | ascentTraining | Typical PK data | data.frame | 165 | 4 |
policyData | ascentTraining | Insurance Policy Data | data.frame | 926 | 13 |
policy_data | ascentTraining | Insurance Policy Data | data.frame | 926 | 13 |
qtpk2 | ascentTraining | Typical PK data | data.frame | 2061 | 8 |
runData | ascentTraining | An example of NONMEM run data | data.frame | 73 | 10 |
run_data | ascentTraining | An example of NONMEM run data | data.frame | 73 | 10 |
students | ascentTraining | Students simulated data | spec_tbl_df | 146 | 15 |
tubeData | ascentTraining | London Tube Performance data | data.frame | 1050 | 9 |
tube_data | ascentTraining | London Tube Performance data | data.frame | 1050 | 9 |
x_iris | ascentTraining | Iris predictors data for Species classification | list | | |
xpData | ascentTraining | Typical NONMEM data | data.frame | 1061 | 23 |
xp_data | ascentTraining | Typical NONMEM data | data.frame | 1061 | 23 |
y_iris | ascentTraining | Iris class data for Species classification | list | | |
cropdatape | cropdatape | Annual agricultural production data of Peru | tbl_df | 15080 | 9 |
cattle50K | CMplot | Genotyped by Bovine50K chip | data.frame | 42551 | 6 |
pig60K | CMplot | Genotyped by pig 60k chip | data.frame | 44580 | 6 |
sampleData | WtTopsis | A example of multiple-criteria decision making data. | data.frame | 21 | 11 |
UCI.BCD.Wisconsin | simpleNeural | Breast Cancer Wisconsin (Diagnostic) Data Set | data.frame | 569 | 32 |
UCI.ISOLET.ABC | simpleNeural | ISOLET Data Set (ABC) | data.frame | 900 | 618 |
UCI.transfusion | simpleNeural | Blood Transfusion Service Center Data Set | data.frame | 748 | 5 |
longitudinalMetabolomics | MetaboDynamics | A simulated data set of longitudinal concentration tables of metabolites. | SummarizedExperiment | | |
metabolite_modules | MetaboDynamics | KEGG Query Results of experimental metabolites | spec_tbl_df | 348 | 8 |
modules_compounds | MetaboDynamics | Background KEGG Query Results Of Functional Modules | data.frame | 3242 | 6 |
BIFP | ercv | EEMBC AutoBench suite (Benchmark 3) | integer | | |
EURUSD | ercv | Euro/Dollar daily exchange rates | data.frame | 6575 | 1 |
FFT | ercv | EEMBC AutoBench suite (Benchmark 2) | integer | | |
MA | ercv | EEMBC AutoBench suite (Benchmark 4) | integer | | |
bilbao | ercv | Bilbao waves data set | numeric | | |
iFFT | ercv | EEMBC AutoBench suite (Benchmark 1) | integer | | |
countData | SurfR | countData | matrix | 2500 | 4 |
enrichedList | SurfR | enrichedList | list | | |
ind_deg | SurfR | ind_deg | list | | |
metadata | SurfR | metadata | data.frame | 4 | 3 |
oil.2012.10.01 | RND | West Texas Intermediate Crude Oil Options on 2013-10-01 | data.frame | 332 | 7 |
sp500.2013.04.19 | RND | S&P 500 Index Options on 2013-04-19 | data.frame | 171 | 19 |
sp500.2013.06.24 | RND | S&P 500 Index Options on 2013-06-24 | data.frame | 173 | 9 |
vix.2013.06.25 | RND | VIX Options on 2013-06-25 | data.frame | 35 | 13 |
Ohio | osDesign | Ohio lung cancer data | data.frame | 12 | 5 |
infants | osDesign | Infant mortality data from North Carolina | data.frame | 235272 | 9 |
DNaseI_RoadMapSingle | coMET | Data sets | AnnotationTrack | | |
DupTrack | coMET | Data sets | AnnotationTrack | | |
DupTrack | coMET | Data sets | AnnotationTrack | | |
OtherRegulatoryRegionsTrackAll | coMET | Data sets | AnnotationTrack | | |
OtherRegulatoryRegionsTrackSingle | coMET | Data sets | AnnotationTrack | | |
PancreasIG | coMET | Data sets | BiomartGeneRegionTrack | | |
PancreasIGtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
PancreasimprintedIG | coMET | Data sets | AnnotationTrack | | |
PancreasimprintedIGtrack | coMET | Data sets | AnnotationTrack | | |
RegulatoryEvidenceBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
RegulatoryEvidenceBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
RegulatoryEvidenceBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
RegulatoryFeaturesBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
RegulatoryFeaturesBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
RegulatoryFeaturesBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
RegulatorySegmentsBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
RegulatorySegmentsBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
RegulatorySegmentsBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
StomachIG | coMET | Data sets | AnnotationTrack | | |
StomachIGtrack | coMET | Data sets | AnnotationTrack | | |
allIG | coMET | Data sets | BiomartGeneRegionTrack | | |
allIGtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
allimprintedIG | coMET | Data sets | BiomartGeneRegionTrack | | |
allimprintedIGtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
bindMotifsBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackDouble | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
bindMotifsBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
chipTFtrack | coMET | Data sets | AnnotationTrack | | |
chipTFtrack | coMET | Data sets | AnnotationTrack | | |
chromHMM_RoadMapAll | coMET | Data sets | AnnotationTrack | | |
chromHMM_RoadMapAllE063 | coMET | Data sets | AnnotationTrack | | |
chromHMM_RoadMapMultiple | coMET | Data sets | AnnotationTrack | | |
chromHMM_RoadMapSingle | coMET | Data sets | AnnotationTrack | | |
chromatinHMMRoadMapAll | coMET | Data sets | AnnotationTrack | | |
chromatinHMMRoadMapMultiple | coMET | Data sets | AnnotationTrack | | |
chromatinHMMRoadMapSingle | coMET | Data sets | AnnotationTrack | | |
chromhmmNoPattern | coMET | Data sets | list | | |
chromhmmPattern | coMET | Data sets | list | | |
chromhmmtrackone | coMET | Data sets | AnnotationTrack | | |
clinCNV | coMET | Data sets | AnnotationTrack | | |
clinVariant | coMET | Data sets | AnnotationTrack | | |
coreilVariant | coMET | Data sets | AnnotationTrack | | |
coreilVariant | coMET | Data sets | AnnotationTrack | | |
cosmicVariant | coMET | Data sets | AnnotationTrack | | |
cpgIstrack | coMET | Data sets | AnnotationTrack | | |
cpgIstrack | coMET | Data sets | AnnotationTrack | | |
dgfootprints_RoadMapSingle | coMET | Data sets | AnnotationTrack | | |
dnasetrack | coMET | Data sets | AnnotationTrack | | |
dyaRM | coMET | Data sets | AnnotationTrack | | |
dyaRMtrack | coMET | Data sets | AnnotationTrack | | |
eGTexTrackSNP | coMET | Data sets | AnnotationTrack | | |
eGTexTrackall | coMET | Data sets | AnnotationTrack | | |
eQTLTrackAll | coMET | Data sets | AnnotationTrack | | |
eQTLTrackAll | coMET | Data sets | AnnotationTrack | | |
eQTLTrackMultiple | coMET | Data sets | AnnotationTrack | | |
eQTLTrackMultiple | coMET | Data sets | AnnotationTrack | | |
eQTLTrackSingle | coMET | Data sets | AnnotationTrack | | |
eQTLTrackSingle | coMET | Data sets | AnnotationTrack | | |
enhFANTOMtrack | coMET | Data sets | AnnotationTrack | | |
enhRM | coMET | Data sets | AnnotationTrack | | |
enhRMtrack | coMET | Data sets | AnnotationTrack | | |
gadtrack | coMET | Data sets | AnnotationTrack | | |
gctrack | coMET | Data sets | DataTrack | | |
gctrack | coMET | Data sets | DataTrack | | |
geneNameEnsembl | coMET | Data sets | data.frame | 3 | 2 |
geneNameEnsembl | coMET | Data sets | data.frame | 5 | 2 |
geneRtrack | coMET | Data sets | AnnotationTrack | | |
geneRtrack | coMET | Data sets | AnnotationTrack | | |
genesUcsctrack | coMET | Data sets | GeneRegionTrack | | |
genesUcsctrack | coMET | Data sets | GeneRegionTrack | | |
genesUcsctrack | coMET | Data sets | GeneRegionTrack | | |
genetrack | coMET | Data sets | BiomartGeneRegionTrack | | |
genetrack | coMET | Data sets | BiomartGeneRegionTrack | | |
genetrack | coMET | Data sets | BiomartGeneRegionTrack | | |
genetrack | coMET | Data sets | BiomartGeneRegionTrack | | |
grtrack | coMET | Data sets | GeneRegionTrack | | |
grtrack | coMET | Data sets | GeneRegionTrack | | |
gwastrack | coMET | Data sets | AnnotationTrack | | |
histonalltrack | coMET | Data sets | list | | |
histoneonetrack | coMET | Data sets | AnnotationTrack | | |
imprintedGenesGTEx | coMET | Data sets | data.frame | 969 | 11 |
interestgenesENSMBLtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
interestgenesENSMBLtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
interesttransENSMBLtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
interesttransENSMBLtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
iscatrack | coMET | Data sets | AnnotationTrack | | |
iscatrack | coMET | Data sets | AnnotationTrack | | |
matrix_HiC_Rao | coMET | Data sets | matrix | 5 | 5 |
metQTLTrackAll | coMET | Data sets | AnnotationTrack | | |
metQTLTrackMultiple | coMET | Data sets | AnnotationTrack | | |
metQTLTrackSingle | coMET | Data sets | AnnotationTrack | | |
metQTLTrackSingle | coMET | Data sets | AnnotationTrack | | |
miRNATargetRegionsBiomartTrack | coMET | Data sets | AnnotationTrack | | |
miRNATargetRegionsBiomartTrack | coMET | Data sets | AnnotationTrack | | |
otherRegulatoryRegionsTrackAll | coMET | Data sets | AnnotationTrack | | |
otherRegulatoryRegionsTrackSingle | coMET | Data sets | AnnotationTrack | | |
promRMtrack | coMET | Data sets | AnnotationTrack | | |
promRMtrackE063 | coMET | Data sets | AnnotationTrack | | |
psiGTex | coMET | Data sets | AnnotationTrack | | |
psiGTexTrackSNP | coMET | Data sets | AnnotationTrack | | |
psiGTexTrackall | coMET | Data sets | AnnotationTrack | | |
regulationENSEMBLtrack | coMET | Data sets | AnnotationTrack | | |
regulatoryEvidenceBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
regulatoryEvidenceBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
regulatoryEvidenceBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
regulatoryFeaturesBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
regulatoryFeaturesBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
regulatoryFeaturesBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
regulatorySegmentsBiomartTrackAll | coMET | Data sets | AnnotationTrack | | |
regulatorySegmentsBiomartTrackMultiple | coMET | Data sets | AnnotationTrack | | |
regulatorySegmentsBiomartTrackSingle | coMET | Data sets | AnnotationTrack | | |
rmtrack | coMET | Data sets | AnnotationTrack | | |
snpUCSCtrack | coMET | Data sets | AnnotationTrack | | |
snptrack | coMET | Data sets | AnnotationTrack | | |
snptrack | coMET | Data sets | AnnotationTrack | | |
snptrack | coMET | Data sets | AnnotationTrack | | |
strutrack | coMET | Data sets | AnnotationTrack | | |
strutrack | coMET | Data sets | AnnotationTrack | | |
tfbsFANTOMtrack | coMET | Data sets | AnnotationTrack | | |
transENSMBLtrack | coMET | Data sets | BiomartGeneRegionTrack | | |
xenogenestrack | coMET | Data sets | GeneRegionTrack | | |
xenogenestrack | coMET | Data sets | GeneRegionTrack | | |
C19dNUTSdata | C19dNUTS | Regional cumulative COVID-19 deaths | tbl_df | 1143 | 10 |
AGSTimes | ntsDatasets | The Analgesic Datasets | matrix | 14 | 2 |
CRPK | ntsDatasets | Coronavirus Reproduction Rate Data for Pakistan | data.frame | 79 | 2 |
ChildMortalitySA | ntsDatasets | Childhood Mortality Rates in Saudi Arabia (1995-2020) | data.frame | 26 | 2 |
ICUcovidPK | ntsDatasets | The Daily ICU Occupancy dataset | data.frame | 20 | 4 |
MRcovidNL | ntsDatasets | COVID-19 Mortality Rate Data for the Netherlands | matrix | 30 | 2 |
NOx | ntsDatasets | Nitrogen Oxides Emissions Data | matrix | 29 | 2 |
PopVillUSA | ntsDatasets | Population Villages USA data | data.frame | 17 | 2 |
alloy | ntsDatasets | Alloy melting data | data.frame | 18 | 2 |
balls | ntsDatasets | Balls data | data.frame | 23 | 2 |
batteries | ntsDatasets | Lifetime of batteries data | data.frame | 23 | 2 |
debt | ntsDatasets | Public Debt Data | matrix | 23 | 2 |
dioxins | ntsDatasets | Average Daily Ingestion of Dioxins Data | data.frame | 17 | 2 |
failure | ntsDatasets | Failur time data | data.frame | 30 | 2 |
remission | ntsDatasets | Remission data | data.frame | 128 | 2 |
tempLahor | ntsDatasets | Monthly Low and High Temperatures in Lahore, Pakistan (2016–2020) | data.frame | 60 | 2 |
item5fr | pln | 5-item Test Data Set | data.frame | 67 | 6 |
item9cat5 | pln | 9 Item Test Data Set | data.frame | 500 | 9 |
Interview | mhurdle | Interview | data.frame | 1000 | 31 |
cityDIST | bayMDS | Airline distances between cities | matrix | 30 | |
BuiltInEnsembles | SynExtend | Pretrained EvoWeaver Ensemble Models | list | | |
Endosymbionts_GeneCalls | SynExtend | Example genecalls | list | | |
Endosymbionts_LinkedFeatures | SynExtend | Example synteny links | LinkedPairs | 4 | 4 |
Endosymbionts_Pairs01 | SynExtend | Example predicted pairs | data.frame | 3691 | 15 |
Endosymbionts_Pairs02 | SynExtend | Example predicted pairs | data.frame | 3691 | 16 |
Endosymbionts_Pairs03 | SynExtend | Example predicted pairs | data.frame | 2574 | 16 |
Endosymbionts_Sets | SynExtend | A list of disjoint sets. | list | | |
Endosymbionts_Synteny | SynExtend | A synteny object | Synteny | 4 | 4 |
ExampleStreptomycesData | SynExtend | Example EvoWeaver Input Data from _Streptomyces_ Species | list | | |
Generic | SynExtend | Model for predicting PID based on k-mer statistics | glm | | |
boosting_predictions_oob | probably | Boosted regression trees predictions | tbl_df | 2000 | 3 |
boosting_predictions_test | probably | Boosted regression trees predictions | tbl_df | 500 | 2 |
segment_logistic | probably | Image segmentation predictions | tbl_df | 1010 | 3 |
segment_naive_bayes | probably | Image segmentation predictions | tbl_df | 1010 | 3 |
species_probs | probably | Predictions on animal species | tbl_df | 110 | 4 |
facts | roundhouse | Chuck Norris facts | data.frame | 574 | 1 |
Fischbein_wt | umx | Weight data across time. | data.frame | 6 | 6 |
GFF | umx | Twin data: General Family Functioning, divorce, and well-being. | data.frame | 3185 | 47 |
docData | umx | Twin data for Direction of causation modelling | data.frame | 1400 | 13 |
iqdat | umx | Twin data: IQ measured longitudinally across 4 ages. | data.frame | 562 | 9 |
us_skinfold_data | umx | Anthropometric data on twins | data.frame | 395 | 20 |
class | matlib | Class Data Set | data.frame | 15 | 4 |
coffee | matlib | Data on Coffee, Stress and Heart Damage | data.frame | 20 | 4 |
therapy | matlib | Therapy Data | data.frame | 10 | 4 |
workers | matlib | Workers Data | data.frame | 10 | 4 |
KA_bloom | chillR | Cherry bloom data for Klein-Altendorf, Germany | data.frame | 25 | 2 |
KA_weather | chillR | Weather data for Klein-Altendorf, Germany | data.frame | 4534 | 5 |
Winters_hours_gaps | chillR | Hourly temperature data sample | data.frame | 6074 | 6 |
california_stations | chillR | Weather stations in California | data.frame | 284 | 6 |
simulation.1 | htetree | A Simulated Dataset | data.frame | 500 | 12 |
pollen.equiv | neotoma | A table to convert the pollen taxa identified by investigators to standardized lists. | data.frame | 9030 | 8 |
taxon.list | neotoma | Neotoma taxon list | data.frame | 37075 | 14 |
anes | blocs | Sample of 2020 ANES cumulative data file | tbl_df | 46838 | 12 |
KRAS.Mutations | iPAC | KRAS.Mutations | matrix | 149 | 188 |
PIK3CA.Mutations | iPAC | PIK3CA.Mutations | matrix | 96 | 1068 |
orthgenes | SCBN | A real dataset of orthologous genes between the different species. | data.frame | 27821 | 9 |
sim_data | SCBN | A simulation dataset of orthologous genes between the different species. | data.frame | 4143 | 4 |
ais | quantileDA | Australian Institute of Sport data | data.frame | 202 | 13 |
edf0s | ShapeSelectForest | A 21 by 7 Matrix Storing Edf0 Vectors | matrix | 21 | |
ymat | ShapeSelectForest | Response Variable Matrix | matrix | 26 | 36 |
coordinate_example | inborutils | Example 'data.frame' with coordinates | data.frame | 52 | 3 |
rain_knmi_2012 | inborutils | Example 'data.frame' with 'KNMI' downloaded data | data.frame | 1536 | 9 |
species_example | inborutils | Example 'data.frame' with species name column | data.frame | 3 | 3 |
GrowthChart | Rearrangement | Age and Height of White Males | data.frame | 533 | 3 |
PedsQLMFS | OmegaG | PedsQL Multidimensional Fatigue Scale Factor Structure | list | | |
oews2020 | oews2020 | Occupational Employment and Wage Statistics, May 2020 | data.frame | 205346 | 26 |
bw | nplplot | Internal nplplot.multi variables | logical | | |
bw | nplplot | Internal nplplot.multi variables | logical | | |
cex.axis | nplplot | Internal nplplot.multi variables | numeric | | |
cex.axis | nplplot | Internal nplplot.multi variables | numeric | | |
cex.legend | nplplot | Internal nplplot.multi variables | numeric | | |
cex.legend | nplplot | Internal nplplot.multi variables | numeric | | |
lgndtxt | nplplot | Internal nplplot.multi variables | character | | |
lgndtxt | nplplot | Internal nplplot.multi variables | character | | |
lods1 | nplplot | LOD score table for chromosome 1 | data.frame | 100 | 5 |
lods2 | nplplot | LOD score table for chromosome 2 | data.frame | 87 | 5 |
ltypes | nplplot | Internal nplplot.multi variables | numeric | | |
ltypes | nplplot | Internal nplplot.multi variables | numeric | | |
my.colors | nplplot | Internal nplplot.multi variables | character | | |
my.colors | nplplot | Internal nplplot.multi variables | character | | |
na.rm | nplplot | Internal nplplot.multi variables | logical | | |
na.rm | nplplot | Internal nplplot.multi variables | logical | | |
ptypes | nplplot | Internal nplplot.multi variables | numeric | | |
ptypes | nplplot | Internal nplplot.multi variables | numeric | | |
tcl | nplplot | Internal nplplot.multi variables | numeric | | |
tcl | nplplot | Internal nplplot.multi variables | numeric | | |
title | nplplot | Internal nplplot.multi variables | character | | |
title | nplplot | Internal nplplot.multi variables | character | | |
yfix | nplplot | Internal nplplot.multi variables | logical | | |
yfix | nplplot | Internal nplplot.multi variables | logical | | |
ylabel | nplplot | Internal nplplot.multi variables | character | | |
ylabel | nplplot | Internal nplplot.multi variables | character | | |
yline | nplplot | Internal nplplot.multi variables | numeric | | |
yline | nplplot | Internal nplplot.multi variables | numeric | | |
ymax | nplplot | Internal nplplot.multi variables | numeric | | |
ymax | nplplot | Internal nplplot.multi variables | numeric | | |
ymin | nplplot | Internal nplplot.multi variables | numeric | | |
ymin | nplplot | Internal nplplot.multi variables | numeric | | |
TMAX | springpheno | Daily High Temperature data | matrix | 366 | 25 |
TMIN | springpheno | Daily Low Temperature data | matrix | 366 | 25 |
YEAR | springpheno | Years of the Data | integer | | |
lat | springpheno | Latitude | numeric | | |
key | fsia | Key of Items | data.frame | 1 | 40 |
questionnaire | fsia | Questionnaire Responses | data.frame | 5 | 14 |
test | fsia | Test Responses | data.frame | 5 | 44 |
weights | fsia | Weights of Items | data.frame | 1 | 40 |
weights_multiple | fsia | Weights of each Response of the Items | data.frame | 4 | 41 |
DLBCL | ipred | Diffuse Large B-Cell Lymphoma | data.frame | 40 | 15 |
GlaucomaMVF | ipred | Glaucoma Database | data.frame | 170 | 67 |
Smoking | ipred | Smoking Styles | data.frame | 55 | 9 |
dystrophy | ipred | Detection of muscular dystrophy carriers. | data.frame | 209 | 10 |
weathr | cercospoRa | Weather station data | data.table | 8016 | 15 |
Distributions | FloraIberica | Taxa distributions | data.frame | 1824549 | 6 |
IberianPeninsula | FloraIberica | Iberian Peninsula contour | sfc_MULTIPOLYGON | | |
Taxa | FloraIberica | Plant taxa | spec_tbl_df | 6456 | 11 |
black | epimdr2 | | data.frame | 9 | 6 |
burnett | epimdr2 | | data.frame | 22 | 7 |
ccs | epimdr2 | | data.frame | 954 | 9 |
chabaudi | epimdr2 | | data.frame | 1300 | 11 |
cholera | epimdr2 | | data.frame | 600 | 4 |
cspring | epimdr2 | | list | | |
dalziel | epimdr2 | | data.frame | 44720 | 10 |
ebola | epimdr2 | | data.frame | 103 | 4 |
euthamia | epimdr2 | | data.frame | 360 | 8 |
ferrari | epimdr2 | | data.frame | 15 | 7 |
filipendula | epimdr2 | | data.frame | 162 | 4 |
fiv | epimdr2 | | data.frame | 238 | 18 |
flu | epimdr2 | | data.frame | 14 | 2 |
gonnet | epimdr2 | | matrix | 89 | |
gypsymoth | epimdr2 | | list | | |
icelandflu | epimdr2 | | data.frame | 360 | 3 |
litter | epimdr2 | | data.frame | 494 | 8 |
m4494 | epimdr2 | | list | | |
magono | epimdr2 | | data.frame | 422 | 4 |
meas | epimdr2 | | data.frame | 546 | 5 |
niamey | epimdr2 | | data.frame | 31 | 13 |
niamey_daily | epimdr2 | | data.frame | 10937 | 1 |
pagiard | epimdr2 | | data.frame | 448 | 3 |
paili | epimdr2 | | data.frame | 1404 | 3 |
palymes | epimdr2 | | data.frame | 448 | 3 |
pameasle | epimdr2 | | data.frame | 2195 | 3 |
pdv | epimdr2 | | list | | |
pertcop | epimdr2 | | data.frame | 1982 | 8 |
peru | epimdr2 | | data.frame | 95 | 4 |
polymod | epimdr2 | | data.frame | 900 | 3 |
rabbit | epimdr2 | | data.frame | 42 | 3 |
rabies | epimdr2 | | data.frame | 208 | 12 |
silene | epimdr2 | | data.frame | 876 | 5 |
tydiphtheria | epimdr2 | | data.frame | 1774 | 4 |
tymeasles | epimdr2 | | data.frame | 1774 | 4 |
tyscarlet | epimdr2 | | data.frame | 1774 | 4 |
tywhooping | epimdr2 | | data.frame | 1200 | 5 |
us | epimdr2 | | data.frame | 20 | 4 |
usflu | epimdr2 | | data.frame | 49 | 7 |
variants | epimdr2 | | data.frame | 48 | 7 |
waller | epimdr2 | | data.frame | 168 | 3 |
tad1 | nonmemica | A NONMEM-like Dataset | data.frame | 51 | 10 |
gpx_segment | gpx3d | A Workout Route Segment | sf | 501 | 6 |
gzma | tidyrgeoda | Guangzhou Metropolitan Social Space Quality Score Data | sf | 118 | 33 |
original | reldist | Permanent wage growth in two cohorts of the NLS | data.frame | 1834 | 3 |
precipitation | reldist | Annual Precipitation in US Cities | numeric | | |
recent | reldist | Permanent wage growth in two cohorts of the NLS | data.frame | 2103 | 3 |
CHAIN | mi | Subset of variables from the CHAIN project | data.frame | 532 | 7 |
nlsyV | mi | National Longitudinal Survey of Youth Extract | data.frame | 400 | 7 |
Hordaland_data | graphPAF | Simulated case control dataset for 5000 cases (individuals with chronic cough) and 5000 controls | data.frame | 10000 | 4 |
stroke_reduced | graphPAF | Simulated case control dataset for 6856 stroke cases and 6856 stroke controls | data.frame | 13712 | 19 |
wilcox1973 | qualvar | Results of the 1968 US presidential election | data.frame | 52 | 4 |
SnP500List | PortRisk | List of S&P500 Stocks in 2013 | data.frame | 500 | 1 |
SnP500Returns | PortRisk | Daily Returns of S&P500 Stocks in 2013 | zoo | 252 | 500 |
kibler | rrcovHD | 1985 Auto Imports Database | data.frame | 195 | 14 |
kibler.orig | rrcovHD | 1985 Auto Imports Database | data.frame | 205 | 26 |
breslow.dat | robust | Breslow Data | data.frame | 59 | 12 |
leuk.dat | robust | Leuk Data | data.frame | 33 | 3 |
mallows.dat | robust | Mallows Data | data.frame | 70 | 4 |
stack.dat | robust | Brownlee's Stack-Loss Data | data.frame | 21 | 4 |
woodmod.dat | robust | Modified Wood Data | data.frame | 20 | 5 |
bench_100 | rolltalk | Benchmark Data for Width of 100 | data.frame | 10 | 12 |
bench_1000 | rolltalk | Benchmark Data for Width of 1,000 | data.frame | 10 | 12 |
vfit | nutriNetwork | Baseline data from VFIT study | data.frame | 207 | 29 |
Industry_10 | RiskPortfolios | Industry Portfolios | matrix | 252 | 10 |
ChanningHouse | asaur | Channing House Data | data.frame | 457 | 5 |
ashkenazi | asaur | ashkenazi | data.frame | 3920 | 4 |
gastricXelox | asaur | gasticXelox | data.frame | 48 | 2 |
hepatoCellular | asaur | hepatoCellular | data.frame | 227 | 48 |
pancreatic | asaur | pancreatic | data.frame | 41 | 4 |
pancreatic2 | asaur | pancreatic2 | data.frame | 41 | 4 |
pharmacoSmoking | asaur | pharmacoSmoking | data.frame | 125 | 14 |
prostateSurvival | asaur | prostateSurvival | data.frame | 14294 | 5 |
mathTest | equateMultiple | Math Test Data | list | | |
dplace | glottospace | This is an internally stored version of D-PLACE data. Use glottoget("dplace", download = TRUE) to download the latest version | sf | 1988 | 2384 |
glottolog | glottospace | This is an internally stored version of glottolog data. Use glottoget("glottolog", download = TRUE) to download the latest version | data.frame | 26285 | 13 |
grambank | glottospace | This is an internally stored version of Grambank data. Use glottoget("grambank", download = TRUE) to download the latest version | data.frame | 2467 | 207 |
phoible_raw | glottospace | This is an internally stored version of raw PHOIBLE data. Use glottoget("phoible_raw", download = TRUE) to download the latest version | data.frame | 3020 | 3192 |
wals | glottospace | WALS data | sf | 2627 | 208 |
worldpol | glottospace | This is an internally stored version of political boundaries of the world (obtained from rnaturalearth). | sf | 265 | 7 |
acre | eHOF | Vegetation plots from arable fields in North-Eastern Germany | veg | 150 | 227 |
acre.env | eHOF | Vegetation plots from arable fields in North-Eastern Germany | data.frame | 150 | 10 |
mtf | eHOF | Species Data and Altitude from Mt. Field, Tasmania | data.frame | 167 | 5 |
mtf.env | eHOF | Species Data and Altitude from Mt. Field, Tasmania | data.frame | 167 | 1 |
pbmc3k | pbmc3k | PBMC 3k | dgCMatrix | | |
pbmc3k.sce.logcounts | pbmc3k | Log-Normalized PBMC 3k: 'SingleCellExperiment' Edition | dgCMatrix | | |
pbmc3k.seurat.counts | pbmc3k | Raw PBMC 3k Counts: 'Seurat' Edition | dgCMatrix | | |
pbmc3k.seurat.norm | pbmc3k | Log-Normalized PBMC 3k: 'Seurat' Edition | dgCMatrix | | |
GSPFF | funspace | Aboveground traits from the global spectrum of plant form and function (complete data) | data.frame | 2630 | 6 |
GSPFF_missing | funspace | Aboveground traits from the global spectrum of plant form and function (incomplete data) | data.frame | 10746 | 6 |
GSPFF_missing_tax | funspace | Taxonomic information for plants from the global spectrum of plant form and function (incomplete data) | data.frame | 10746 | 3 |
GSPFF_tax | funspace | Taxonomic information for plants from the global spectrum of plant form and function (complete data) | data.frame | 2630 | 3 |
phylo | funspace | Phylogeny for species from the global spectrum of plant form and function (incomplete data) | phylo | | |
resource_lookups | trapinch | PokéAPI Endpoints and Resources Lookup | list | | |
fleiss_data | tidyrates | Fleiss data | tbl_df | 60 | 4 |
seer_std_pop | tidyrates | Standard population reference table | tbl_df | 21 | 2 |
selvin_data_1940 | tidyrates | Selvin data, 1940 | tbl_df | 22 | 3 |
selvin_data_1960 | tidyrates | Selvin data, 1960 | tbl_df | 22 | 3 |
who_std_pop | tidyrates | Standard population reference table | tbl_df | 21 | 2 |
df_mxstate_2020 | MexBrewer | Mexican 2020 states dataset | data.frame | 32 | 11 |
mx_estados | MexBrewer | Mexican states. | sf | 32 | 4 |
nesting_prof | profr | Sample profiling datasets | profr | 11 | 7 |
reshape_prof | profr | Sample profiling datasets | profr | 169 | 7 |
NURE | ramps | Dataset of USGS NURE Uranium Measurements | data.frame | 298 | 9 |
NURE.grid | ramps | Dataset of USGS NURE Uranium Measurements | data.frame | 239 | 3 |
simGrid | ramps | Dataset of Simulated Measurements from JSS Publication | data.frame | 391 | 4 |
simIowa | ramps | Dataset of Simulated Measurements from JSS Publication | data.frame | 699 | 7 |
datasets | Crosstabs.Loglinear | datasets | list | | |
Auto | ISLR2 | Auto Data Set | data.frame | 392 | 9 |
Bikeshare | ISLR2 | Bike sharing data | data.frame | 8645 | 15 |
Boston | ISLR2 | Boston Data | data.frame | 506 | 13 |
BrainCancer | ISLR2 | Brain Cancer Data | data.frame | 88 | 8 |
Caravan | ISLR2 | The Insurance Company (TIC) Benchmark | data.frame | 5822 | 86 |
Carseats | ISLR2 | Sales of Child Car Seats | data.frame | 400 | 11 |
College | ISLR2 | U.S. News and World Report's College Data | data.frame | 777 | 18 |
Credit | ISLR2 | Credit Card Balance Data | data.frame | 400 | 11 |
Default | ISLR2 | Credit Card Default Data | data.frame | 10000 | 4 |
Fund | ISLR2 | Fund Manager Data | data.frame | 50 | 2000 |
Hitters | ISLR2 | Baseball Data | data.frame | 322 | 20 |
Khan | ISLR2 | Khan Gene Data | list | | |
NCI60 | ISLR2 | NCI 60 Data | list | | |
NYSE | ISLR2 | New York Stock Exchange Data | data.frame | 6051 | 6 |
OJ | ISLR2 | Orange Juice Data | data.frame | 1070 | 18 |
Portfolio | ISLR2 | Portfolio Data | data.frame | 100 | 2 |
Publication | ISLR2 | Time-to-Publication Data | data.frame | 244 | 9 |
Smarket | ISLR2 | S&P Stock Market Data | data.frame | 1250 | 9 |
Wage | ISLR2 | Mid-Atlantic Wage Data | data.frame | 3000 | 11 |
Weekly | ISLR2 | Weekly S&P Stock Market Data | data.frame | 1089 | 9 |
cerrado_2classes | sits | Samples of classes Cerrado and Pasture | sits | 746 | 7 |
point_mt_6bands | sits | A time series sample with data from 2000 to 2016 | sits | 1 | 7 |
samples_l8_rondonia_2bands | sits | Samples of Amazon tropical forest biome for deforestation analysis | sits | 160 | 7 |
samples_modis_ndvi | sits | Samples of nine classes for the state of Mato Grosso | sits | 1218 | 7 |
adjacency_matrix | GDILM.SEIRS | Hypothetical Datasets | matrix | 5 | |
data | GDILM.SEIRS | Hypothetical Datasets | data.frame | 100 | 12 |
r2q_pal | r2q | Color palette for R2Q Plots | data.frame | 6 | 3 |
dat | edl | Simulated learning data. | data.frame | 36 | 5 |
CRD1 | TukeyC | Completely Randomized Design (CRD) | list | | |
CRD2 | TukeyC | Completely Randomized Design ('CRD') | list | | |
FE | TukeyC | Factorial Experiment (FE) | list | | |
LSD | TukeyC | Latin Squares Design (LSD) | list | | |
RCBD | TukeyC | Randomized Complete Block Design (RCBD) | list | | |
SPE | TukeyC | Split-plot Experiment (SPE) | list | | |
SPET | TukeyC | Split-plot Experiment in Time (SPET) | list | | |
SSPE | TukeyC | Split-split-plot Experiment (SSPE) | list | | |
sorghum | TukeyC | Completely Randomized Design (CRD) | list | | |
InstInnovation | sandwich | Innovation and Institutional Ownership | data.frame | 6208 | 25 |
Investment | sandwich | US Investment Data | mts | 20 | 7 |
PetersenCL | sandwich | Petersen's Simulated Data for Assessing Clustered Standard Errors | data.frame | 5000 | 4 |
PublicSchools | sandwich | US Expenditures for Public Schools | data.frame | 51 | 2 |
canada | autoimage | Provincial and territorial boundaries of Canada, 2001 | map | | |
copoly | autoimage | Colorado state border | map | | |
ilat | autoimage | Interpolated maximum daily surface air temperatures on a regular grid. | numeric | | |
ilon | autoimage | Interpolated maximum daily surface air temperatures on a regular grid. | numeric | | |
itasmax | autoimage | Interpolated maximum daily surface air temperatures on a regular grid. | array | | |
lat | autoimage | Maximum daily surface air temperatures on a grid. | matrix | 140 | |
lon | autoimage | Maximum daily surface air temperatures on a grid. | matrix | 140 | |
tasmax | autoimage | Maximum daily surface air temperatures on a grid. | array | | |
rna | AutoPipe | rna egene expression of 48 meningiomas | data.frame | 200 | 48 |
colorado | ExceedanceTools | Colorado precipitation data | list | | |
sdata | ExceedanceTools | Synthetic data | data.frame | 100 | 3 |
comm_a | lirrr | Example community data | data.frame | 15 | 15 |
comm_b | lirrr | #' Example community data | data.frame | 15 | 9 |
tree | lirrr | #' Example phylogeny | phylo | | |
alkanes | enpls | Methylalkanes Retention Index Dataset | list | | |
logd1k | enpls | logD7.4 Data for 1,000 Compounds | list | | |
test_image | fastpng | Test images in various R formats | list | | |
blockbusters | ggstream | Worldwide Blockbusters 2019-1977 | tbl_df | 157 | 3 |
iw | ggdal | inlandwaters polygon dataset | sf | 6 | 3 |
UNlocations | wpp2015 | United Nations Table of Locations | data.frame | 274 | 19 |
e0F | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 241 | 16 |
e0F_supplemental | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Fproj | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 241 | 19 |
e0Fproj80l | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Fproj80u | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Fproj95l | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Fproj95u | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0M | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 241 | 16 |
e0M_supplemental | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Mproj | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 241 | 19 |
e0Mproj80l | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Mproj80u | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Mproj95l | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
e0Mproj95u | wpp2015 | United Nations Time Series of Life Expectancy | data.frame | 201 | 19 |
migration | wpp2015 | Dataset on Migration | data.frame | 241 | 32 |
mxF | wpp2015 | Age-specific Mortality Data | data.frame | 5450 | 33 |
mxM | wpp2015 | Age-specific Mortality Data | data.frame | 5450 | 33 |
percentASFR | wpp2015 | Datasets on Age-specific Distribution of Fertility Rates | data.frame | 1687 | 33 |
pop | wpp2015 | Estimates and Projections of Population Counts | data.frame | 241 | 16 |
popF | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 17 |
popFprojHigh | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popFprojLow | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popFprojMed | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popM | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 17 |
popMprojHigh | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popMprojLow | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popMprojMed | wpp2015 | Estimates and Projections of Population Counts | data.frame | 5061 | 20 |
popproj | wpp2015 | Estimates and Projections of Population Counts | data.frame | 241 | 19 |
popproj80l | wpp2015 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popproj80u | wpp2015 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popproj95l | wpp2015 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popproj95u | wpp2015 | Estimates and Projections of Population Counts | data.frame | 273 | 20 |
popprojHigh | wpp2015 | Estimates and Projections of Population Counts | data.frame | 241 | 19 |
popprojLow | wpp2015 | Estimates and Projections of Population Counts | data.frame | 241 | 19 |
sexRatio | wpp2015 | Sex Ratio at Birth | data.frame | 241 | 32 |
tfr | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 16 |
tfr_supplemental | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 103 | 45 |
tfrproj80l | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrproj80u | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrproj95l | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrproj95u | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 19 |
tfrprojHigh | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 19 |
tfrprojLow | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 19 |
tfrprojMed | wpp2015 | United Nations Time Series of Total Fertility Rate | data.frame | 241 | 19 |
compas1 | dsld | Criminal Offenders Screened in Florida | data.frame | 5855 | 15 |
lak | dsld | Labor Market Discrimination | data.frame | 447 | 63 |
mortgageSE | dsld | Mortgage Denial | data.frame | 2380 | 16 |
svcensus | dsld | Silicon Valley programmers and engineers data | data.frame | 20090 | 6 |
demo.knowns | TRAMPR | Demonstration Knowns Database | TRAMPknowns | | |
demo.samples | TRAMPR | Demonstration Samples Database | TRAMPsamples | | |
ansur | psyntur | Anthropometric data from US Army Personnel | spec_tbl_df | 6068 | 9 |
faithfulfaces | psyntur | Faithfulness from a Photo? | tbl_df | 170 | 7 |
jobsatisfaction | psyntur | Job Satisfaction Data for Two-Way ANOVA | tbl_df | 58 | 4 |
pairedsleep | psyntur | Paired sleep data | tbl_df | 10 | 3 |
schizophrenia | psyntur | Age of Onset of Schizophrenia Data | tbl_df | 251 | 2 |
selfesteem | psyntur | Self-Esteem Score Data for One-way Repeated Measures ANOVA | tbl_df | 10 | 4 |
selfesteem2 | psyntur | Self Esteem Score Data for Two-way Repeated Measures ANOVA | tbl_df | 24 | 5 |
selfesteem2_long | psyntur | Self Esteem Score Data for Two-way Repeated Measures ANOVA: Long format | tbl_df | 72 | 4 |
test_psychometrics | psyntur | Psychometrics raw data from testing or demo purposes | tbl_df | 44 | 30 |
vizverb | psyntur | Visual versus Verbal Perception and Responses | tbl_df | 80 | 4 |
Auto | ISLR | Auto Data Set | data.frame | 392 | 9 |
Caravan | ISLR | The Insurance Company (TIC) Benchmark | data.frame | 5822 | 86 |
Carseats | ISLR | Sales of Child Car Seats | data.frame | 400 | 11 |
College | ISLR | U.S. News and World Report's College Data | data.frame | 777 | 18 |
Credit | ISLR | Credit Card Balance Data | data.frame | 400 | 12 |
Default | ISLR | Credit Card Default Data | data.frame | 10000 | 4 |
Hitters | ISLR | Baseball Data | data.frame | 322 | 20 |
Khan | ISLR | Khan Gene Data | list | | |
NCI60 | ISLR | NCI 60 Data | list | | |
OJ | ISLR | Orange Juice Data | data.frame | 1070 | 18 |
Portfolio | ISLR | Portfolio Data | data.frame | 100 | 2 |
Smarket | ISLR | S&P Stock Market Data | data.frame | 1250 | 9 |
Wage | ISLR | Mid-Atlantic Wage Data | data.frame | 3000 | 11 |
Weekly | ISLR | Weekly S&P Stock Market Data | data.frame | 1089 | 9 |
data | gatoRs | Downloaded data from gators_download() for Galax urceolata with default settings and 'limit' set to 5: data <- gators_download(synonyms.list = c("Galax urceolata", "Galax aphylla"), limit = 5) | data.frame | 17 | 23 |
eeg | VARDetect | EEG signal data | data.frame | 4063 | 20 |
weekly | VARDetect | weekly stock price data | matrix | 824 | 20 |
data.mb01 | mdmb | Example Datasets for 'mdmb' Package | list | | |
data.mb02 | mdmb | Example Datasets for 'mdmb' Package | list | | |
data.mb03 | mdmb | Example Datasets for 'mdmb' Package | data.frame | 74 | 3 |
data.mb04 | mdmb | Example Datasets for 'mdmb' Package | data.frame | 500 | 4 |
data.mb05 | mdmb | Example Datasets for 'mdmb' Package | data.frame | 5001 | 13 |
exampleHiCDOCDataSet | HiCDOC | Example HiCDOCDataSet. | HiCDOCDataSet | | |
exampleHiCDOCDataSetProcessed | HiCDOC | Example HiCDOCDataSet, filtered, normalized and with compartements detected. | HiCDOCDataSet | | |
lalonde | MatchIt | Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999). | data.frame | 614 | 9 |
GDT_NAMES | FIESTAutils | Reference tables - gdal data types. | character | | |
kindcd3old | FIESTAutils | Reference table - List of RMRS plots that have fallen out of inventory because they were not found or they were in the wrong place. | data.frame | 38 | 8 |
ref_codes | FIESTAutils | Reference tables - Code definitions. | data.frame | 745 | 7 |
ref_cond | FIESTAutils | Reference table - Metadata for cond default variables output from DBgetPlots() | data.frame | 97 | 3 |
ref_conversion | FIESTAutils | Reference table - for conversion factors. | data.frame | 7 | 6 |
ref_diacl2in | FIESTAutils | Reference table - diameter 2-inch class codes (DIA). | data.frame | 40 | 3 |
ref_domain | FIESTAutils | Reference table - for generating tables. | data.frame | 32 | 3 |
ref_estimators | FIESTAutils | Reference table - FIESTA estimators. | data.frame | 8 | 9 |
ref_estvar | FIESTAutils | Reference table - for generating estimates | data.frame | 178 | 11 |
ref_evaltyp | FIESTAutils | Reference table - for generating tables. | data.frame | 14 | 3 |
ref_plt | FIESTAutils | Reference table - Metadata for plt default variables output from DBgetPlots() | data.frame | 59 | 3 |
ref_popType | FIESTAutils | Reference table - popType codes. | data.frame | 15 | 2 |
ref_shp | FIESTAutils | Reference table - Metadata for shp_* default variables output from DBgetPlots() | data.frame | 63 | 4 |
ref_species | FIESTAutils | Reference table - Code definitions. | data.frame | 2677 | 20 |
ref_statecd | FIESTAutils | Reference table - state codes (STATECD). | data.frame | 59 | 7 |
ref_titles | FIESTAutils | Reference table - Variable titles. | data.frame | 70 | 2 |
ref_tree | FIESTAutils | Reference table - Metadata for tree default variables output from DBgetPlots() | data.frame | 117 | 3 |
ref_units | FIESTAutils | Reference table - for variable units. | data.frame | 47 | 5 |
stunitco | FIESTAutils | SpatialPolygonsDataFrame with FIA state, unit, county codes and names | sf | 3233 | 8 |
bike_accidents | spNetwork | Road accidents including a bicyle in Montreal in 2016 | sf | 347 | 4 |
main_network_mtl | spNetwork | Primary road network of Montreal | sf | 16188 | 2 |
mtl_libraries | spNetwork | Libraries of Montreal | sf | 55 | 3 |
mtl_network | spNetwork | Road network of Montreal | sf | 2945 | 2 |
mtl_theatres | spNetwork | Theatres of Montreal | sf | 54 | 3 |
small_mtl_network | spNetwork | Smaller subset road network of Montreal | sf | 1244 | 3 |
k05ck2 | SurfRough | basic kernels | list | | |
k1c | SurfRough | basic kernels | list | | |
k1ck2 | SurfRough | basic kernels | list | | |
k2c | SurfRough | basic kernels | list | | |
k2ck2 | SurfRough | basic kernels | list | | |
k4c | SurfRough | basic kernels | list | | |
k6c | SurfRough | basic kernels | list | | |
k8c | SurfRough | basic kernels | list | | |
DiaHealth | gaawr2 | DiaHealth | data.frame | 5437 | 15 |
diabetes | gaawr2 | Diabetes Dataset | data.frame | 1000 | 14 |
sample_ohlc_data | ichimoku | Sample OHLC Price Data | data.frame | 256 | 6 |
german_elections | CooRTweet | German 2021 election campaign | tbl_df | 218971 | 7 |
russian_coord_tweets | CooRTweet | Pro-Government Russian Tweet Dataset | data.frame | 35125 | 4 |
data.calibrate | GGIR | Example output from g.calibrate | list | | |
data.getmeta | GGIR | Example output from g.getmeta | list | | |
data.inspectfile | GGIR | Example output from g.inspectfile | list | | |
data.metalong | GGIR | Metalong object as part of part 1 milestone data | list | | |
data.ts | GGIR | Time series data.frame stored by part 5 | data.frame | 10011 | 12 |
glass | ore | Multilingual sample text | character | | |
covican | REDCapDM | Subset of COVICAN's Database | list | | |
error_df_bivar | FieldSimR | Plot errors - Example data frame | data.frame | 700 | 6 |
gv_df_unstr | FieldSimR | Genetic values - Example data frame | data.frame | 700 | 5 |
abalone | SLOPE | Abalone | list | | |
bodyfat | SLOPE | Bodyfat | list | | |
heart | SLOPE | Heart disease | list | | |
student | SLOPE | Student performance | list | | |
wine | SLOPE | Wine cultivars | list | | |
enron.sample | idiolect | Enron sample | corpus | | |
dat.aloe2013 | metadat | Studies on the Association Between Supervision Quality and Various Outcomes in Social, Mental Health, and Child Welfare Workers | data.frame | 5 | 5 |
dat.anand1999 | metadat | Studies on the Effectiveness of Oral Anticoagulants in Patients with Coronary Artery Disease | data.frame | 34 | 9 |
dat.assink2016 | metadat | Studies on the Association between Recidivism and Mental Health | escalc | 100 | 8 |
dat.axfors2021 | metadat | Mortality Outcomes with Hydroxychloroquine and Chloroquine in COVID-19 from an International Collaborative Meta-Analysis of Randomized Trials | data.frame | 33 | 13 |
dat.bakdash2021 | metadat | Dataset on Situation Awareness and Task Performance Associations | data.frame | 678 | 12 |
dat.baker2009 | metadat | Studies on Pharmacologic Treatments for Chronic Obstructive Pulmonary Disease | data.frame | 94 | 6 |
dat.bangertdrowns2004 | metadat | Studies on the Effectiveness of Writing-to-Learn Interventions | escalc | 48 | 16 |
dat.bartos2023 | metadat | Results of 350,757 Coin Flips to Examine Same-Side Bias | data.frame | 48 | 7 |
dat.baskerville2012 | metadat | Studies on the Effectiveness of Practice Facilitation Interventions | data.frame | 23 | 18 |
dat.bassler2004 | metadat | Studies on Ketotifen Alone or as Additional Medication for Long-Term Control of Asthma and Wheeze in Children | data.frame | 10 | 6 |
dat.bcg | metadat | Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis | data.frame | 13 | 9 |
dat.begg1989 | metadat | Studies on Bone-Marrow Transplantation versus Chemotherapy for the Treatment of Leukemia | escalc | 20 | 6 |
dat.berkey1998 | metadat | Studies on Treatments for Periodontal Disease | escalc | 10 | 9 |
dat.besson2016 | metadat | Dataset on How Maternal Diet Impacts Copying Styles in Rodents | data.frame | 473 | 67 |
dat.bonett2010 | metadat | Studies on the Reliability of the CES-D Scale | data.frame | 9 | 6 |
dat.bornmann2007 | metadat | Studies on Gender Differences in Grant and Fellowship Awards | data.frame | 66 | 13 |
dat.bourassa1996 | metadat | Studies on the Association between Handedness and Eye-Dominance | data.frame | 96 | 14 |
dat.cannon2006 | metadat | Studies on the Effectiveness of Intensive Versus Moderate Statin Therapy for Preventing Coronary Death or Myocardial Infarction | data.frame | 4 | 16 |
dat.cohen1981 | metadat | Studies on the Relationship between Course Instructor Ratings and Student Achievement | data.frame | 20 | 5 |
dat.colditz1994 | metadat | Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis | data.frame | 13 | 9 |
dat.collins1985a | metadat | Studies on the Treatment of Upper Gastrointestinal Bleeding by a Histamine H2 Antagonist | data.frame | 27 | 14 |
dat.collins1985b | metadat | Studies on the Effects of Diuretics in Pregnancy | data.frame | 9 | 16 |
dat.craft2003 | metadat | Studies on the Relationship between the Competitive State Anxiety Inventory-2 and Sport Performance | data.frame | 60 | 6 |
dat.crede2010 | metadat | Studies on the Relationship between Class Attendance and Grades in College Students | data.frame | 97 | 8 |
dat.crisafulli2020 | metadat | Duchenne Muscular Dystrophy (DMD) Prevalence Data | data.frame | 26 | 7 |
dat.curtin2002 | metadat | Studies on Potassium Supplementation to Reduce Diastolic Blood Pressure | data.frame | 21 | 6 |
dat.curtis1998 | metadat | Studies on the Effects of Elevated CO2 Levels on Woody Plant Mass | data.frame | 102 | 20 |
dat.dagostino1998 | metadat | Studies on the Effectiveness of Antihistamines in Reducing Symptoms of the Common Cold | data.frame | 72 | 16 |
dat.damico2009 | metadat | Studies on Topical plus Systemic Antibiotics to Prevent Respiratory Tract Infections | data.frame | 16 | 8 |
dat.debruin2009 | metadat | Studies on Standard Care Quality and HAART-Adherence | data.frame | 13 | 10 |
dat.dogliotti2014 | metadat | Studies on Antithrombotic Treatments to Prevent Strokes | data.frame | 44 | 5 |
dat.dong2013 | metadat | Studies on Safety of Inhaled Medications for Chronic Obstructive Pulmonary Disease | data.frame | 99 | 4 |
dat.dorn2007 | metadat | Studies on Complementary and Alternative Medicine for Irritable Bowel Syndrome | data.frame | 19 | 12 |
dat.dumouchel1994 | metadat | Nitrogen dioxide data set | data.frame | 9 | 7 |
dat.egger2001 | metadat | Studies on the Effectiveness of Intravenous Magnesium in Acute Myocardial Infarction | data.frame | 16 | 7 |
dat.fine1993 | metadat | Studies on Radiation Therapy with or without Adjuvant Chemotherapy in Patients with Malignant Gliomas | data.frame | 17 | 11 |
dat.franchini2012 | metadat | Studies on Dopamine Agonists to Reduce "Off-Time" in Patients with Advanced Parkinson Disease | data.frame | 7 | 13 |
dat.frank2008 | metadat | Studies on the Association Between the CASP8 -652 6N del Promoter Polymorphism and Breast Cancer Risk | data.frame | 4 | 7 |
dat.furukawa2003 | metadat | Studies on Low Dosage Tricyclic Antidepressants for the Treatment of Depression | data.frame | 17 | 7 |
dat.gibson2002 | metadat | Studies on the Effectiveness of Self-Management Education and Regular Medical Review for Adults with Asthma | data.frame | 15 | 13 |
dat.graves2010 | metadat | Studies on the Effectiveness of Injected Cholera Vaccines | data.frame | 17 | 5 |
dat.gurusamy2011 | metadat | Studies on Interventions to Reduce Mortality after Liver Transplantation | data.frame | 29 | 4 |
dat.hackshaw1998 | metadat | Studies on the Risk of Lung Cancer in Women Exposed to Environmental Tobacco Smoke | escalc | 37 | 11 |
dat.hahn2001 | metadat | Studies on the Effectiveness of Different Rehydration Solutions for the Prevention of Unscheduled Intravenous Infusion in Children with Diarrhoea | data.frame | 12 | 5 |
dat.hannum2020 | metadat | Studies Comparing Objective and Subjective Olfactory Loss in COVID-19 Patients | data.frame | 35 | 11 |
dat.hart1999 | metadat | Studies on the Effectiveness of Warfarin for Preventing Strokes | data.frame | 6 | 12 |
dat.hartmannboyce2018 | metadat | Studies on the Effectiveness of Nicotine Replacement Therapy for Smoking Cessation | data.frame | 136 | 6 |
dat.hasselblad1998 | metadat | Studies on the Effectiveness of Counseling for Smoking Cessation | data.frame | 50 | 7 |
dat.hine1989 | metadat | Studies on Prophylactic Use of Lidocaine After a Heart Attack | data.frame | 6 | 6 |
dat.ishak2007 | metadat | Studies on Deep-Brain Stimulation in Patients with Parkinson's disease | escalc | 46 | 11 |
dat.kalaian1996 | metadat | Studies on the Effectiveness of Coaching for the SAT | data.frame | 67 | 12 |
dat.kearon1998 | metadat | Studies on the Accuracy of Venous Ultrasonography for the Diagnosis of Deep Venous Thrombosis | data.frame | 34 | 8 |
dat.knapp2017 | metadat | Studies on Differences in Planning Performance in Schizophrenia Patients versus Healthy Controls | data.frame | 66 | 14 |
dat.konstantopoulos2011 | metadat | Studies on the Effects of Modified School Calendars on Student Achievement | escalc | 56 | 6 |
dat.landenberger2005 | metadat | Studies on the Effectiveness of CBT for Reducing Recidivism | data.frame | 58 | 32 |
dat.laopaiboon2015 | metadat | Studies on the Effectiveness of Azithromycin for Treating Lower Respiratory Tract Infections | data.frame | 15 | 11 |
dat.lau1992 | metadat | Studies on Intravenous Streptokinase for Acute Myocardial Infarction | data.frame | 33 | 6 |
dat.lee2004 | metadat | Studies on Acupoint P6 Stimulation for Preventing Nausea | data.frame | 16 | 7 |
dat.lehmann2018 | metadat | The Effect of Red on Perceived Attractiveness | data.frame | 81 | 49 |
dat.li2007 | metadat | Studies on the Effectiveness of Intravenous Magnesium in Acute Myocardial Infarction | data.frame | 22 | 7 |
dat.lim2014 | metadat | Studies on the Association Between Maternal Size, Offspring Size, and Number of Offsprings | list | | |
dat.linde2005 | metadat | Studies on the Effectiveness of St. John's Wort for Treating Depression | data.frame | 26 | 17 |
dat.linde2015 | metadat | Studies on Classes of Antidepressants for the Primary Care Setting | data.frame | 66 | 24 |
dat.linde2016 | metadat | Studies on Antidepressants for the Primary Care Setting | data.frame | 124 | 5 |
dat.lopez2019 | metadat | Studies on the Effectiveness of CBT for Depression | data.frame | 172 | 23 |
dat.maire2019 | metadat | Studies on Temporal Trends in Fish Community Structures in French Rivers | list | | |
dat.mccurdy2020 | metadat | Studies on the Generation Effect | escalc | 1653 | 26 |
dat.mcdaniel1994 | metadat | Studies on the Validity of Employment Interviews | data.frame | 160 | 5 |
dat.michael2013 | metadat | The Non-Persuasive Power of a Brain Image | data.frame | 12 | 13 |
dat.molloy2014 | metadat | Studies on the Relationship between Conscientiousness and Medication Adherence | data.frame | 16 | 10 |
dat.moura2021 | metadat | Studies on Assortative Mating | list | | |
dat.nakagawa2007 | metadat | Assessing the Function of House Sparrows' Bib Size Using a Flexible Meta-Analysis Method | data.frame | 15 | 4 |
dat.nielweise2007 | metadat | Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections | data.frame | 18 | 7 |
dat.nielweise2008 | metadat | Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections | data.frame | 9 | 7 |
dat.normand1999 | metadat | Studies on the Length of Hospital Stay of Stroke Patients | data.frame | 9 | 8 |
dat.obrien2003 | metadat | Studies on the Relationship Between BMI and Risk of Preeclampsia | data.frame | 43 | 13 |
dat.pagliaro1992 | metadat | Studies on the Effectiveness of Nonsurgical Treatments in Cirrhosis | data.frame | 54 | 4 |
dat.pignon2000 | metadat | Studies on the Effectiveness of Locoregional Treatment plus Chemotherapy for Head and Neck Squamous-Cell Carcinoma | data.frame | 65 | 5 |
dat.pritz1997 | metadat | Studies on the Effectiveness of Hyperdynamic Therapy for Treating Cerebral Vasospasm | data.frame | 14 | 4 |
dat.raudenbush1985 | metadat | Studies on Assessing the Effects of Teacher Expectations on Pupil IQ | escalc | 19 | 10 |
dat.riley2003 | metadat | Studies on MYC-N as a Prognostic Marker for Neuroblastoma | escalc | 98 | 5 |
dat.roever2022 | metadat | Irinotecan / S-1 Toxicity Dataset | data.frame | 37 | 5 |
dat.senn2013 | metadat | Studies on the Effectiveness of Glucose-Lowering Agents | data.frame | 53 | 6 |
dat.spooner2002 | metadat | Studies on Nedocromil Sodium for Preventing Exercise-Induced Bronchoconstriction | data.frame | 17 | 9 |
dat.stowe2010 | metadat | Studies on Adjuvant Treatments to Levodopa Therapy for Parkinson disease | data.frame | 29 | 14 |
dat.tannersmith2016 | metadat | Studies on the Relationship between School Motivation and Criminal Behavior | data.frame | 113 | 8 |
dat.ursino2021 | metadat | Sorafenib Toxicity Dataset | data.frame | 49 | 5 |
dat.vanhowe1999 | metadat | Studies on the Association between Circumcision and HIV Infection | data.frame | 33 | 6 |
dat.viechtbauer2021 | metadat | Studies to Illustrate Model Checking Methods | data.frame | 20 | 6 |
dat.white2020 | metadat | Studies on the Relationship between Sexual Signal Expression and Individual Quality | data.frame | 186 | 15 |
dat.woods2010 | metadat | Studies on Treatments for Chronic Obstructive Pulmonary Disease | data.frame | 8 | 4 |
dat.yusuf1985 | metadat | Studies of Beta Blockers During and After Myocardial Infarction | data.frame | 130 | 7 |
dt_banks | pedquant | dataset of bank stocks in sse | data.frame | 3645 | 11 |
dt_ssec | pedquant | dataset of shanghai composite index | data.frame | 1215 | 11 |
grid2ip.geno | poolr | Results from testing the association between depressive symptoms and 23 SNPs in the GRID2IP gene | data.frame | 886 | 23 |
grid2ip.ld | poolr | Results from testing the association between depressive symptoms and 23 SNPs in the GRID2IP gene | matrix | 23 | 23 |
grid2ip.p | poolr | Results from testing the association between depressive symptoms and 23 SNPs in the GRID2IP gene | numeric | | |
grid2ip.pheno | poolr | Results from testing the association between depressive symptoms and 23 SNPs in the GRID2IP gene | numeric | | |
mvnlookup | poolr | Lookup Table for the mvnconv() Function | data.frame | 1991 | 9 |
flankingRegions | STRMPS | Flanking regions | tbl_df | 28 | 10 |
genotypeList | STRMPS | Genotype list | genotypeIdentifiedList | | |
identifiedSTRs | STRMPS | Identified STR regions | extractedReadsList | | |
noiseList | STRMPS | Noise list | noiseIdentifiedList | | |
stringCoverageGenotypeList | STRMPS | Combined string coverage and genotype information | stringCoverageGenotypeList | | |
stringCoverageList | STRMPS | Aggregated string coverage. | stringCoverageList | | |
carni19 | adephylo | Phylogeny and quantative trait of carnivora | list | | |
carni70 | adephylo | Phylogeny and quantitative traits of carnivora | list | | |
lizards | adephylo | Phylogeny and quantitative traits of lizards | list | | |
maples | adephylo | Phylogeny and quantitative traits of flowers | list | | |
mjrochet | adephylo | Phylogeny and quantitative traits of teleos fishes | list | | |
palm | adephylo | Phylogenetic and quantitative traits of amazonian palm trees | list | | |
procella | adephylo | Phylogeny and quantitative traits of birds | list | | |
tithonia | adephylo | Phylogeny and quantitative traits of flowers | list | | |
ungulates | adephylo | Phylogeny and quantitative traits of ungulates. | list | | |
sosialFiktiv | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 5244 | 7 |
z1 | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 32 | 3 |
z1micro | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 596 | 3 |
z1w | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 8 | 5 |
z2 | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 44 | 5 |
z2w | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 11 | 7 |
z3 | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 432 | 7 |
z3w | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 36 | 15 |
z3wb | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 36 | 15 |
lump.hg19 | RnBeads | LUMP Support | list | | |
lump.hg38 | RnBeads | LUMP Support (hg38) | list | | |
cds_scerevisiae | doubletrouble | Coding sequences (CDS) of S. cerevisiae | DNAStringSet | | |
diamond_inter | doubletrouble | Interspecies DIAMOND output for yeast species | list | | |
diamond_intra | doubletrouble | Intraspecies DIAMOND output for S. cerevisiae | list | | |
fungi_kaks | doubletrouble | Duplicate pairs and Ka, Ks, and Ka/Ks values for fungi species | list | | |
gmax_ks | doubletrouble | Duplicate pairs and Ks values for Glycine max | data.frame | 68463 | 4 |
yeast_annot | doubletrouble | Genome annotation of the yeast species S. cerevisiae and C. glabrata | CompressedGRangesList | | |
yeast_seq | doubletrouble | Protein sequences of the yeast species S. cerevisiae and C. glabrata | list | | |
life_tables | R4GoodPersonalFinances | HMD life tables | tbl_df | 51948 | 6 |
emilia | tipsae | Poverty in Emilia-Romagna (Italy) Health Districts | data.frame | 190 | 8 |
emilia_cs | tipsae | Poverty in Emilia-Romagna (Italy) Health Districts in 2016 | data.frame | 38 | 8 |
emilia_shp | tipsae | Shapefile of Emilia-Romagna (Italy) Health Districts | SpatialPolygonsDataFrame | | |
data24 | imanr | Data to show the functioning of imanr functions | tbl_df | 24 | 61 |
data31 | imanr | Data to show the functioning of imanr functions | tbl_df | 31 | 61 |
arrabida | BAT | Sample data of spiders in Arrabida (Portugal) | data.frame | 320 | 338 |
functree | BAT | Functional tree for 338 species of spiders | hclust | | |
geres | BAT | Sample data of spiders in Geres (Portugal) | data.frame | 320 | 338 |
guadiana | BAT | Sample data of spiders in Guadiana (Portugal) | data.frame | 192 | 338 |
phylotree | BAT | Taxonomic tree for 338 species of spiders (surrogate for phylogeny) | hclust | | |
smoke | ggrcs | A data on age and smoking rates. | data.frame | 995 | 5 |
Gaudinski_2001 | ISRaD | Gaudinski 2001 example dataset | list | | |
Graven_2017 | ISRaD | Graven dataset for delta-delta calculation | data.frame | 166 | 5 |
Hua_2021 | ISRaD | Hua 2021 dataset for delta-delta calculation | data.frame | 79 | 3 |
future14C | ISRaD | Future atmospheric 14C dataset for delta-delta calculation | data.frame | 6 | 3 |
open.question | Xplortext | Open.question (data) | data.frame | 300 | 10 |
carstats | CornerstoneR | Data from carstats | data.table | 406 | 9 |
rundata | CornerstoneR | Voltage of 26 Different Runs | data.table | 7826 | 3 |
titanic | CornerstoneR | Survival of Passengers on the Titanic | data.table | 2201 | 4 |
FarmData | wflo | Data set for wind farm layout optimization. | list | | |
hds | cenROC | NASA Hypobaric Decompression Sickness Marker Data | data.frame | 238 | 3 |
mayo | cenROC | Mayo Marker Data | data.frame | 312 | 4 |
HKdata | VGAMextra | Air pollution and hospital admissions due to respiratory and cardiovascular causes, Hong Kong. | data.frame | 1090 | 15 |
ap.mx | VGAMextra | Air pollution Data, Mexico City. | data.frame | 547 | 5 |
b4 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
b5 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
b6 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
b7 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
batsch1 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 16 |
batsch2 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 16 |
batsch3 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 16 |
batsch4 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 16 |
batsch5 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 16 |
boggy | qpcR | The (published) datasets implemented in qpcR | data.frame | 40 | 13 |
cm3 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
competimer | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 148 |
dil4reps94 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 376 |
dyemelt | qpcR | The (published) datasets implemented in qpcR | data.frame | 165 | 6 |
expGrowth | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
expSDM | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
guescini1 | qpcR | The (published) datasets implemented in qpcR | data.frame | 50 | 85 |
guescini2 | qpcR | The (published) datasets implemented in qpcR | data.frame | 50 | 61 |
htPCR | qpcR | The (published) datasets implemented in qpcR | matrix | 35 | 8859 |
karlen1 | qpcR | The (published) datasets implemented in qpcR | data.frame | 40 | 81 |
karlen2 | qpcR | The (published) datasets implemented in qpcR | data.frame | 40 | 97 |
karlen3 | qpcR | The (published) datasets implemented in qpcR | data.frame | 40 | 97 |
l4 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
l5 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
l6 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
l7 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
lievens1 | qpcR | The (published) datasets implemented in qpcR | data.frame | 60 | 91 |
lievens2 | qpcR | The (published) datasets implemented in qpcR | data.frame | 60 | 91 |
lievens3 | qpcR | The (published) datasets implemented in qpcR | data.frame | 60 | 91 |
lin2 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
linexp | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
mak2 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
mak2i | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
mak3 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
mak3i | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
reps | qpcR | The (published) datasets implemented in qpcR | data.frame | 49 | 29 |
reps2 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 31 |
reps3 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 22 |
reps384 | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 380 |
rutledge | qpcR | The (published) datasets implemented in qpcR | data.frame | 45 | 121 |
spl3 | qpcR | The nonlinear/mechanistic models implemented in qpcR | list | | |
testdat | qpcR | The (published) datasets implemented in qpcR | data.frame | 49 | 25 |
vermeulen1 | qpcR | The (published) datasets implemented in qpcR | matrix | 50 | 1281 |
vermeulen2 | qpcR | The (published) datasets implemented in qpcR | data.frame | 50 | 961 |
aegis | refreg | A Estrada Glycation and Inflammation Study (AEGIS) Data | data.frame | 1516 | 7 |
pollution | refreg | SO2 and Nox concentrations | data.frame | 4325 | 13 |
pollution_episode | refreg | SO2 and Nox concentrations during a pollution episode | data.frame | 288 | 13 |
leukaemia | mixcure | Data from leukaemia patients with bone marrow transplants | data.frame | 91 | 3 |
USconsum | meboot | Consumption and Disposable Income Data (Annual 1948-1998) | mts | 51 | 2 |
USfygt | meboot | Long-term Treasury Bond Rates and Deficit Data Set (Annual 1948-200) | mts | 53 | 7 |
ullwan | meboot | Data about Some of the S&P 500 Stock Prices | pdata.frame | 469 | 6 |
GeneRepeat | augSIMEX | Example data for univariate error-prone covariates in repeated measurements case | list | | |
GeneUni | augSIMEX | Example of genetic data for univariate error-prone covariates | list | | |
ToyMult | augSIMEX | Toy example data for multivariate error-prone covariates | list | | |
ToyRepeat | augSIMEX | Toy example data for univariate error-prone covariates in repeated measurements case | list | | |
ToyUni | augSIMEX | Toy example data for univariate error-prone covariates | list | | |
Velib | funLBM | The Velib data set. | list | | |
dots | sft | RT and and Accuracy from a Simple Detection Task | data.frame | 57600 | 6 |
fqa_citations | fqadata | Citations for Floristic Quality Assessment Databases | data.frame | 44 | 4 |
fqa_db | fqadata | Regional Floristic Quality Assessment Databases | data.frame | 129059 | 13 |
ctern | placer | Caspian terns plastic debris in Senegal. | data.frame | 529 | 8 |
SW.phruta | phruta | Scholl and Wiens (2016) phylogenies | multiPhylo | | |
dataset | HUM | simulated data | data.frame | 300 | 7 |
sim | HUM | desease data | data.frame | 92 | 13 |
bcos | thregI | Breast Cosmesis Data | data.frame | 94 | 4 |
hdsd | thregI | NASAs Hypobaric Decompression Sickness Data | data.frame | 238 | 7 |
CorrectiveTermsDepthFromPressure | sonar | Corrective terms to be added for obtaining depth from pressure | data.frame | 13 | 5 |
CorrectiveTermsPressureFromDepth | sonar | Corrective terms to be subtracted for obtaining pressure from depth | data.frame | 14 | 5 |
MolecularRelaxationAttenuationCoeficient | sonar | Molecular relaxation attenuation coeficient (alpha) | data.frame | 3 | 11 |
SpeedAlgorithmParameterRanges | sonar | Data on Speed of Sound Algorithm Parameter Ranges | data.frame | 10 | 10 |
boco | rbmn | Body Composition Variables and Covariables | data.frame | 100 | 13 |
screen8 | htestClust | Example data for informative cluster size | data.frame | 2224 | 12 |
sample_data | PHclust | Sample of sparse microbiome count data | matrix | 100 | |
vf.cigts | vfprogression | Combined Visual Field Series for General Progression Method | data.frame | 20 | 54 |
vf.plr.nouri.2012 | vfprogression | Combined Visual Field Series for General Progression Method | data.frame | 20 | 54 |
vf.schell2014 | vfprogression | Combined Visual Field Series for General Progression Method | data.frame | 20 | 2 |
vf.vfi | vfprogression | Combined Visual Field Series for General Progression Method | data.frame | 20 | 3 |
vfseries | vfprogression | Combined Visual Field Series for General Progression Method | data.frame | 20 | 278 |
Bank_Branch_Operating_Efficiency | MultiplierDEA | Data: Bank Branch Operating Efficiency data | tbl_df | 17 | 8 |
BenchMark_Tests_And_Microcomputer | MultiplierDEA | Data: Relationship between benchmark tests and Microcomputer price data | tbl_df | 22 | 9 |
Data_City | MultiplierDEA | Data: City data | tbl_df | 15 | 8 |
Departments_Of_Accounting | MultiplierDEA | Data: UK University Departments Of Accounting Efficiency data. | tbl_df | 20 | 11 |
Evaluation_Educational_Program | MultiplierDEA | Data: Educational program data | tbl_df | 22 | 9 |
Evaluations_Of_NonProfitOrganizations | MultiplierDEA | Data: Evaluation of Non-Profit organizations data | tbl_df | 16 | 7 |
Japanese_Companies | MultiplierDEA | Data: Japanese Companies data. | tbl_df | 20 | 6 |
Metropolitan_And_London_Rates_Departments | MultiplierDEA | Data: Metropolitan and London rates departments data | tbl_df | 62 | 6 |
incid2010 | LARisk | Cancer incidence table of Korea 2010 | data.frame | 1919 | 4 |
incid2018 | LARisk | Cancer incidence table of Korea 2018 | data.frame | 1919 | 4 |
life2010 | LARisk | Lifetime table of Korea 2010 | data.frame | 101 | 3 |
life2018 | LARisk | Lifetime table of Korea 2018 | data.frame | 101 | 3 |
nuclear | LARisk | Simulated data of organ radiation exposure dose | data.frame | 100 | 11 |
organ | LARisk | Simulated data of organ radiation exposure dose | data.frame | 971 | 11 |
Treeht | THREC | Tree data from the long-term forest experiments in Sweden | data.frame | 334 | 9 |
broad | THREC | Tree data from the long-term forest experiments in Sweden | data.frame | 117 | 9 |
pdf_data | DeBoinR | Simulated PDF data. | data.frame | 399 | 200 |
x_grid | DeBoinR | Simulated PDF data. | numeric | | |
BarLevData | RRTCS | Randomized Response Survey on industrial company income | data.frame | 370 | 4 |
ChaudhuriChristofidesData | RRTCS | Randomized Response Survey on agricultural subsidies | data.frame | 100 | 3 |
ChaudhuriChristofidesDatapij | RRTCS | Matrix of the second-order inclusion probabilities | matrix | 100 | |
ChristofidesData | RRTCS | Randomized Response Survey on eating disorders | data.frame | 150 | 3 |
DevoreData | RRTCS | Randomized Response Survey on instant messaging | data.frame | 240 | 4 |
DianaPerri1Data | RRTCS | Randomized Response Survey on defrauded taxes | data.frame | 150 | 3 |
DianaPerri2Data | RRTCS | Randomized Response Survey of a simulated population | data.frame | 1000 | 3 |
EichhornHayreData | RRTCS | Randomized Response Survey on family income | data.frame | 150 | 4 |
ErikssonData | RRTCS | Randomized Response Survey on student cheating | data.frame | 102 | 4 |
ForcedResponseData | RRTCS | Randomized Response Survey of a simulated population | data.frame | 1000 | 3 |
ForcedResponseDataSt | RRTCS | Randomized Response Survey on infertility | data.frame | 442 | 4 |
HorvitzData | RRTCS | Randomized Response Survey on student bullying | data.frame | 411 | 3 |
HorvitzDataRealSurvey | RRTCS | Randomized Response Survey on a sensitive questions | data.frame | 710 | 6 |
HorvitzDataStCl | RRTCS | Randomized Response Survey on infidelity | data.frame | 365 | 5 |
HorvitzUBData | RRTCS | Randomized Response Survey on drugs use | data.frame | 188 | 5 |
KukData | RRTCS | Randomized Response Survey on excessive sexual activity | data.frame | 200 | 3 |
MangatSinghData | RRTCS | Randomized Response Survey on cannabis use | data.frame | 240 | 4 |
MangatSinghSinghData | RRTCS | Randomized Response Survey on internet betting | data.frame | 802 | 5 |
MangatSinghSinghUBData | RRTCS | Randomized Response Survey on overuse of the internet | data.frame | 500 | 4 |
SahaData | RRTCS | Randomized Response Survey on spending on alcohol | data.frame | 100 | 3 |
SinghJoarderData | RRTCS | Randomized Response Survey on compulsive spending | data.frame | 170 | 3 |
SoberanisCruzData | RRTCS | Randomized Response Survey on speeding | data.frame | 290 | 4 |
WarnerData | RRTCS | Randomized Response Survey on alcohol abuse | data.frame | 125 | 3 |
asthma | condGEE | Asthma recurrence in children | data.frame | 1037 | 7 |
GDP.Per.head.dist.1995 | asymmetry.measures | annual Gross Domestic Product (GDP) per head across 15 European Union (EU) countries | numeric | | |
GDP.Per.head.dist.2005 | asymmetry.measures | annual Gross Domestic Product (GDP) per head across 15 European Union (EU) countries | numeric | | |
DMCE | CircOutlier | The simulated 10% and 5% points of the distribution of DMCE. | data.frame | 141 | 200 |
wind | CircOutlier | Wind Direction | numeric | | |
wind2 | CircOutlier | Wind Direction | matrix | 129 | 2 |
wagepan_mgt | DIDmultiplegt | wagepan_mgt | data.frame | 4360 | 5 |
foo | mcmc | Simulated logistic regression data. | data.frame | 100 | 4 |
logit | mcmc | Simulated logistic regression data. | data.frame | 100 | 5 |
impulse | Rivivc | PK profile after drug intravenous administration | data.frame | 19 | 2 |
input | Rivivc | In vivo absorption of the drug | data.frame | 19 | 2 |
resp | Rivivc | PK profile after drug oral administration | data.frame | 19 | 2 |
Topoisomerase_II_Inhibitors | FRCC | Example data sets | data.frame | 15 | 60 |
microRNA | FRCC | Example data sets | data.frame | 365 | 60 |
soilspec | FRCC | Example data sets | data.frame | 45 | 16 |
example.data | JGL | Toy two-class gene expression dataset. | list | | |
const | robfilter | Correction factors to achieve unbiasedness of the Qn scale estimator | data.frame | 96 | 2 |
const.Q | robfilter | Correction factors to achieve unbiasedness of the regression-free Q scale estimator | matrix | 300 | 7 |
critvals | robfilter | Critical Values for the RM Goodness of Fit Test | matrix | 600 | 61 |
dfs | robfilter | Degrees of freedom for the SCARM test statistic. | data.frame | 20 | 20 |
multi.ts | robfilter | Generated Multivariate Time Series | matrix | 500 | |
sizecorrection | robfilter | Bias correction factors for the robust scale estimators MAD, Sn, Qn, and LSH | data.frame | 31 | 4 |
timecorrection | robfilter | Correction factors for the scale estimation of the filtering procedure proposed by Fried (2004). | data.frame | 250 | 16 |
var.n | robfilter | Variance of the Repeated Median slope estimator. | data.frame | 300 | 7 |
Arosa | assist | Monthly Mean Ozone Thickness in Arosa of Switzerland | data.frame | 518 | 3 |
Stratford | assist | Daily maximum temperatures in Stratford | data.frame | 73 | 2 |
TXtemp | assist | Texas Historical Climate Data | data.frame | 17280 | 6 |
USAtemp | assist | Average Winter temperature in the United States | data.frame | 1214 | 3 |
acid | assist | Lake Acidity Study | data.frame | 112 | 4 |
bond | assist | Treasury and GE bonds | data.frame | 1234 | 5 |
canadaTemp | assist | Monthly Mean Temperatures | data.frame | 420 | 3 |
chickenpox | assist | Chickenpox in New York City | data.frame | 498 | 3 |
climate | assist | Winter Average Temperatures | data.frame | 690 | 5 |
dog | assist | Coronary Ainus Potassium Concentrations | data.frame | 252 | 4 |
horm.cort | assist | Hormone Measurements of Cortisol | data.frame | 425 | 4 |
paramecium | assist | Growth of paramecium caudatum population | data.frame | 25 | 2 |
seizure | assist | IEEG segments from a seizure patient | data.frame | 60000 | 3 |
star | assist | Magnitude of the Mira Variable R Hydrae | data.frame | 1086 | 2 |
ultrasound | assist | Ultrasound imaging of the tongue shape | data.frame | 1215 | 4 |
wesdr | assist | Wisconsin Epidemiological Study of Diabetic Retinopathy | data.frame | 669 | 5 |
SimData | ClickClustCont | Simulated Data | list | | |
mMSNBC | ClickClustCont | Revised MSNBC Data | list | | |
infilt | HydroMe | Water infiltration characteristics data | data.frame | 1105 | 6 |
isric | HydroMe | Water Retention World Dataset from ISRIC | data.frame | 320 | 6 |
cents_sf | stplanr | Spatial points representing home locations | sf | 8 | 5 |
destinations_sf | stplanr | Example destinations data | sf | 87 | 7 |
flow | stplanr | Data frame of commuter flows | data.frame | 49 | 15 |
flow_dests | stplanr | Data frame of invented commuter flows with destinations in a different layer than the origins | data.frame | 49 | 15 |
flowlines_sf | stplanr | Spatial lines dataset of commuter flows | sf | 42 | 16 |
od_data_lines | stplanr | Example of desire line representations of origin-destination data from UK Census | sf | 64 | 19 |
od_data_routes | stplanr | Example segment-level route data | sf | 750 | 11 |
od_data_sample | stplanr | Example of origin-destination data from UK Census | tbl_df | 64 | 18 |
osm_net_example | stplanr | Example of OpenStreetMap road network | sf | 71 | 6 |
rnet_cycleway_intersection | stplanr | Example of cycleway intersection data showing problems for SpatialLinesNetwork objects | sf | 2 | 28 |
rnet_overpass | stplanr | Example of overpass data showing problems for SpatialLinesNetwork objects | sf | 8 | 28 |
rnet_roundabout | stplanr | Example of roundabout data showing problems for SpatialLinesNetwork objects | sf | 9 | 28 |
route_network_sf | stplanr | Spatial lines dataset representing a route network | sf | 80 | 2 |
route_network_small | stplanr | Spatial lines dataset representing a small route network | sf | 8 | 2 |
routes_fast_sf | stplanr | Spatial lines dataset of commuter flows on the travel network | sf | 42 | 17 |
routes_slow_sf | stplanr | Spatial lines dataset of commuter flows on the travel network | sf | 42 | 17 |
zones_sf | stplanr | Spatial polygons of home locations for flow analysis. | sf | 8 | 5 |
aotus | phylotaR | aotus | Phylota | | |
birds | phylotaR | birds | TreeMan | | |
bromeliads | phylotaR | bromeliads | Phylota | | |
cycads | phylotaR | cycads | Phylota | | |
dragonflies | phylotaR | dragonflies | Phylota | | |
mammals | phylotaR | mammals | TreeMan | | |
plants | phylotaR | plants | TreeMan | | |
sturgeons | phylotaR | sturgeons | Phylota | | |
tardigrades | phylotaR | tardigrades | Phylota | | |
tinamous | phylotaR | tinamous | Phylota | | |
yeasts | phylotaR | yeasts | Phylota | | |
ex.dem.navarre | rsat | A Digital Elevation Model (DEM) of the region of Navarre (Spain) | RasterStack | | |
ex.madrid | rsat | A polygon with the border of Madrid (Spain) | sf | 1 | 2 |
ex.manhattan | rsat | A polygon with the border of Manhattan (USA) | sf | 1 | 5 |
ex.navarre | rsat | A polygon with the border of Navarre (Spain) | sf | 1 | 2 |
ex.ndvi.navarre | rsat | A time series of NDVI in Navarre (Spain) | RasterBrick | | |
bike_test_data | bikedata | Test data for all 6 cities | list | | |
lo_stns | bikedata | Docking stations for London, U.K. | data.frame | 786 | 4 |
aohi | CoordinateCleaner | Artificial Hotspot Occurrence Inventory | tbl_df | 309 | 11 |
buffland | CoordinateCleaner | Global Coastlines buffered by 1 degree | PackedSpatVector | | |
buffsea | CoordinateCleaner | Global Coastlines buffered by -1 degree | PackedSpatVector | | |
countryref | CoordinateCleaner | Country Centroids and Country Capitals | data.frame | 5305 | 13 |
institutions | CoordinateCleaner | Global Locations of Biodiversity Institutions | tbl_df | 11601 | 12 |
pbdb_example | CoordinateCleaner | Example data from the Paleobiologydatabase | data.frame | 5000 | 36 |
CNO | eph | Categorias de las 4 dimensiones del Clasificador Nacional de Ocupaciones 2001. | tbl_df | 63 | 4 |
adulto_equivalente | eph | Tabla de valores de adulto equivalente segun sexo y edad | data.frame | 222 | 3 |
caes | eph | Categorias del Clasificador de Actividades Economicas para encuestas Sociodemograficas | tbl_df | 414 | 9 |
canastas_reg_example | eph | Canastas Basicas Alimentarias y Canastas Basicas Totales segun region y trimestre | tbl_df | 12 | 5 |
centroides_aglomerados | eph | Tabla de centroides de los aglomerados | tbl_df | 32 | 4 |
diccionario_aglomerados | eph | Diccionario de aglomerados segun diseno de registro de la EPH | data.frame | 32 | 2 |
diccionario_regiones | eph | Diccionario de regiones segun diseno de registro de la EPH | data.frame | 6 | 2 |
errores_muestrales | eph | Tabla con los errores muestrales para estimaciones de poblacion | tbl_df | 1687 | 6 |
toybase_hogar_2016_04 | eph | Seleccion aleatoria de casos de la base 2016 trimestre 4 para la base hogar | data.frame | 2000 | 88 |
toybase_individual_2016_03 | eph | Seleccion aleatoria de casos de la base 2016 trimestre 3 para la base individual | data.frame | 2000 | 177 |
toybase_individual_2016_04 | eph | Seleccion aleatoria de casos de la base 2016 trimestre 4 para la base individual | data.frame | 2000 | 177 |
grdcLTMMD | hddtools | Data set: The grdcLTMMD look-up table | data.frame | 6 | 4 |
available_assets | unifir | Vector of assets unifir can download and import | character | | |
dct_filmies | dwctaxon | Taxonomic data of filmy ferns | tbl_df | 2451 | 5 |
dct_terms | dwctaxon | Darwin Core Taxon terms | tbl_df | 47 | 2 |
mobydick | tokenizers | The text of Moby Dick | character | | |
greece_borders | hydroscoper | Greek borders | tbl_df | 18474 | 6 |
stations | hydroscoper | stations | tbl_df | 2322 | 9 |
timeseries | hydroscoper | timeseries | tbl_df | 10804 | 8 |
dat_bin | visdat | A small toy dataset of binary data with missings. | tbl_df | 100 | 3 |
typical_data | visdat | A small toy dataset of imaginary people | tbl_df | 5000 | 9 |
typical_data_large | visdat | A small toy dataset of imaginary people | tbl_df | 300 | 49 |
demo_route_clean | birdsize | Cleaned data for a hypothetical Breeding Bird Survey route. | data.frame | 27 | 15 |
demo_route_raw | birdsize | Raw data for a hypothetical Breeding Bird Survey route. | data.frame | 1160 | 15 |
known_species | birdsize | List of species known to 'birdsize' | tbl_df | 443 | 3 |
nontarget_species | birdsize | Which AOUs correspond to nontarget species. | data.frame | 263 | 4 |
raw_masses | birdsize | Records of mean and standard deviation body masses | data.frame | 928 | 19 |
sd_table | birdsize | Species-level means for the mean and standard deviation of body size for species in the North American Breeding Bird Survey. | data.frame | 443 | 7 |
toy_aou_community | birdsize | Toy data frame of abundances and AOUs (for vignettes) | tbl_df | 5 | 2 |
toy_size_community | birdsize | Toy data frame of abundances and species mean sizes (for vignettes) | tbl_df | 5 | 3 |
toy_species_name_community | birdsize | Toy data frame of abundances and species names (for vignettes) | tbl_df | 5 | 2 |
unidentified_species | birdsize | Table of AOUs corresponding to unidentified species. | data.frame | 72 | 4 |
breast_cancer | QuadratiK | Breast Cancer Wisconsin (Diagnostic) | data.frame | 569 | 31 |
wine | QuadratiK | Wine data set | data.frame | 178 | 14 |
wireless | QuadratiK | Wireless Indoor Localization | data.frame | 2000 | 8 |
lbh1 | ohun | Long-billed hermit recording | Wave | | |
lbh2 | ohun | Long-billed hermit recording | Wave | | |
lbh_reference | ohun | Example data frame of a selection table including all sound events of interests | selection_table | 19 | 6 |
lgas_nigeria | naijR | Local Government Areas of Nigeria | data.frame | 774 | 3 |
states_nigeria | naijR | States of Nigeria | data.frame | 37 | 3 |
cosinor_mixed | GLMMcosinor | cosinor_mixed dataset for cosinor modeling examples. | data.frame | 150 | 3 |
vitamind | GLMMcosinor | Vitamin D dataset for cosinor modeling examples. | data.frame | 200 | 3 |
meve | FedData | The boundary of Mesa Verde National Park | sfc_POLYGON | | |
all_nuts_codes | nuts | List of all NUTS codes and classifications | tbl_df | 8896 | 3 |
cross_walks | nuts | Conversion table provided by the Joint Research Center of the European Commission | tbl_df | 47340 | 11 |
manure | nuts | Manure storage facilities by NUTS 3 regions from Eurostat (aei_fm_ms) | tbl_df | 17151 | 4 |
patents | nuts | Patent applications to the EPO by priority year by NUTS 3 regions (pat_ep_rtot) | tbl_df | 103075 | 4 |
fq_attachments | ruODK | A tibble of submission attachments. | tbl_df | 5 | 2 |
fq_data | ruODK | Parsed submission data for an ODK Central form. | tbl_df | 1 | 41 |
fq_data_strata | ruODK | Parsed submission data for a subgroup of an ODK Central form. | tbl_df | 2 | 50 |
fq_data_taxa | ruODK | Parsed submission data for a subgroup of an ODK Central form. | tbl_df | 2 | 49 |
fq_form_detail | ruODK | A tibble of form metadata. | tbl_df | 1 | 12 |
fq_form_list | ruODK | A tibble of forms. | tbl_df | 15 | 30 |
fq_form_schema | ruODK | JSON form schema for an ODK Central form. | tbl_df | 33 | 6 |
fq_form_xml | ruODK | A nested list of a form definition. | xml_document | | |
fq_meta | ruODK | OData metadata document for an ODK Central form. | list | | |
fq_project_detail | ruODK | A tibble of project metadata. | tbl_df | 1 | 9 |
fq_project_list | ruODK | A tibble of project metadata. | tbl_df | 3 | 14 |
fq_raw | ruODK | OData submission data for an ODK Central form. | list | | |
fq_raw_strata | ruODK | OData submission data for a subgroup of an ODK Central form. | list | | |
fq_raw_taxa | ruODK | OData submission data for a subgroup of an ODK Central form. | list | | |
fq_submission_list | ruODK | A tibble of submission metadata. | tbl_df | 1 | 14 |
fq_submissions | ruODK | A nested list of submission data. | list | | |
fq_svc | ruODK | OData service document for an ODK Central form. | tbl_df | 3 | 3 |
fq_zip_data | ruODK | A tibble of the main data table of records from a test form. | tbl_df | 1 | 38 |
fq_zip_strata | ruODK | A tibble of a repeated sub-group of records from a test form. | tbl_df | 2 | 47 |
fq_zip_taxa | ruODK | A tibble of a repeated sub-group of records from a test form. | tbl_df | 2 | 49 |
fs_v7 | ruODK | The parsed XML form_schema of a form from ODK Central v0.6. | tbl_df | 12 | 3 |
fs_v7_raw | ruODK | The unparsed XML form_schema of a form from ODK Central v0.6 as nested list. | list | | |
geo_fs | ruODK | The form_schema of a form containing geofields in GeoJSON. | tbl_df | 19 | 6 |
geo_gj | ruODK | The parsed submissions of a form containing geofields in GeoJSON. | tbl_df | 1 | 58 |
geo_gj88 | ruODK | The parsed submissions of a form containing geofields in GeoJSON with trailing empty coordinates present. | tbl_df | 1 | 51 |
geo_gj_raw | ruODK | The unparsed submissions of a form containing geofields in GeoJSON. | list | | |
geo_wkt | ruODK | The parsed submissions of a form containing geofields in WKT. | tbl_df | 1 | 55 |
geo_wkt88 | ruODK | The parsed submissions of a form containing geofields in WKT with trailing empty coordinates present. | tbl_df | 1 | 48 |
geo_wkt_raw | ruODK | The unparsed submissions of a form containing geofields in WKT. | list | | |
cyclestreets_route | slopes | A journey from CycleStreets.net | sf | 18 | 15 |
dem_lisbon_raster | slopes | Elevation in central Lisbon, Portugal | RasterLayer | | |
lisbon_road_network | slopes | Road segments in Lisbon | sf | 271 | 8 |
lisbon_road_segment | slopes | A road segment in Lisbon, Portugal | sf | 1 | 8 |
lisbon_road_segment_3d | slopes | A road segment in Lisbon, Portugal | sf | 1 | 8 |
lisbon_road_segment_xyz_mapbox | slopes | A road segment in Lisbon, Portugal | sf | 1 | 8 |
lisbon_route | slopes | A route composed of a single linestring in Lisbon, Portugal | sf | 1 | 4 |
lisbon_route_3d | slopes | A route composed of a single linestring in Lisbon, Portugal | sf | 1 | 4 |
lisbon_route_xyz_mapbox | slopes | A route composed of a single linestring in Lisbon, Portugal | sf | 1 | 4 |
magnolia_xy | slopes | Road segments in Magnolia, Seattle | sf | 433 | 4 |
nl_distinct | nlrx | Wolf Sheep model sample data: simdesign distinct | nl | | |
nl_eFast | nlrx | Wolf Sheep model sample data: simdesign eFast | nl | | |
nl_ff | nlrx | Wolf Sheep model sample data: simdesign ff | nl | | |
nl_gensa | nlrx | Wolf Sheep model sample data: gensa | nl | | |
nl_lhs | nlrx | Wolf Sheep model sample data: simdesign lhs | nl | | |
nl_morris | nlrx | Wolf Sheep model sample data: simdesign morris | nl | | |
nl_simple | nlrx | Wolf Sheep model sample data: simdesign simple | nl | | |
nl_sobol | nlrx | Wolf Sheep model sample data: simdesign sobol | nl | | |
nl_sobol2007 | nlrx | Wolf Sheep model sample data: simdesign sobol2007 | nl | | |
nl_soboljansen | nlrx | Wolf Sheep model sample data: simdesign soboljansen | nl | | |
nl_spatial | nlrx | Wolf Sheep model sample data: spatial | nl | | |
myOccCiteObject | occCite | Results of an occCite search for *Protea cynaroides* | occCiteData | | |
exampledates | datefixR | Example dataset of dates in different formats | data.frame | 7 | 3 |
accidents_sample | stats19 | Sample of stats19 data (2022 collisions) | tbl_df | 3 | 37 |
accidents_sample_raw | stats19 | Sample of stats19 data (2022 collisions) | tbl_df | 3 | 37 |
casualties_sample | stats19 | Sample of stats19 data (2022 casualties) | tbl_df | 3 | 21 |
casualties_sample_raw | stats19 | Sample of stats19 data (2022 casualties) | tbl_df | 3 | 16 |
file_names | stats19 | stats19 file names for easy access | list | | |
file_names_old | stats19 | stats19 file names for easy access | list | | |
police_boundaries | stats19 | Police force boundaries in England (2016) | sf | 43 | 3 |
schema_original | stats19 | Schema for stats19 data (UKDS) | data.frame | 1060 | 4 |
stats19_schema | stats19 | Stats19 schema and variables | tbl_df | 1785 | 6 |
stats19_variables | stats19 | Stats19 schema and variables | grouped_df | 128 | 4 |
vehicles_sample | stats19 | Sample of stats19 data (2022 vehicles) | tbl_df | 3 | 34 |
vehicles_sample_raw | stats19 | Sample of stats19 data (2022 vehicles) | tbl_df | 3 | 23 |
json_example_drive | opentripplanner | Example JSON for driving | character | | |
json_example_long_drive | opentripplanner | Example JSON for driving long distance | character | | |
json_example_transit | opentripplanner | Example JSON for transit | character | | |
MetroFull | skynet | Metro (Full) Data | data.frame | 5802 | 6 |
MetroLookup | skynet | Metro Data | tbl_df | 5782 | 4 |
OD_Sample | skynet | Sample OD data | data.frame | 4000 | 19 |
aircraft_type | skynet | Aircraft type data | spec_tbl_df | 422 | 2 |
airportCode | skynet | Airport Data - clean | data.table | 6435 | 5 |
airportCodeFull | skynet | Airport Data - full | data.table | 6435 | 9 |
airportMaster | skynet | Airport Data - master | data.table | 13555 | 28 |
carriers | skynet | Carrier data | data.table | 1882 | 5 |
gutenberg_authors | gutenbergr | Metadata about Project Gutenberg authors | tbl_df | 25514 | 7 |
gutenberg_languages | gutenbergr | Metadata about Project Gutenberg languages | tbl_df | 74457 | 3 |
gutenberg_metadata | gutenbergr | Gutenberg metadata about each work | tbl_df | 77649 | 8 |
gutenberg_subjects | gutenbergr | Gutenberg metadata about the subject of each work | tbl_df | 249409 | 3 |
sample_books | gutenbergr | Sample Book Downloads | tbl_df | 9579 | 4 |
country_codes | comtradr | Country codes | data.frame | 565 | 9 |
ct_pretty_cols | comtradr | ct_pretty_cols | data.frame | 50 | 2 |
Easplist | taxlist | List of vascular plants from East Africa | taxlist | | |
measles | iheatmapr | measles | matrix | 59 | 72 |
ots_commodities | tradestatistics | OTS Commodities | data.table | 5302 | 4 |
ots_commodities_short | tradestatistics | OTS Commodities Short | data.table | 1225 | 2 |
ots_countries | tradestatistics | OTS Countries | data.table | 275 | 5 |
ots_countries_colors | tradestatistics | OTS Countries Colors | data.table | 275 | 3 |
ots_gdp_deflator | tradestatistics | GDP Deflator | data.table | 8010 | 4 |
ots_sections | tradestatistics | OTS Sections | data.table | 22 | 2 |
ots_sections_colors | tradestatistics | OTS Sections Colors | data.table | 22 | 2 |
ots_tables | tradestatistics | OTS Tables | data.table | 12 | 3 |
ea_wbids | cde | Details of name and index of all sites/catchments. | data.frame | 5237 | 9 |
XenaData | UCSCXenaTools | Xena Hub Information | tbl_df | 2486 | 17 |
nivel_educacional_biobio | censo2017 | Poblacion por Nivel Educacional en la Region del Bio Bio | grouped_df | 860 | 4 |
micro_mctq | mctq | A fictional muMCTQ dataset | tbl_df | 50 | 19 |
shift_mctq | mctq | A fictional MCTQ Shift dataset | tbl_df | 50 | 135 |
std_mctq | mctq | A fictional standard MCTQ dataset | tbl_df | 50 | 39 |
abvd | lingtypology | ABVD's Language identifiers | tbl_df | 1468 | 2 |
autotyp | lingtypology | AUTOTYP's Language identifiers | data.frame | 1342 | 3 |
bantu | lingtypology | BANTU's Language identifiers | data.frame | 430 | 2 |
circassian | lingtypology | Circassian villages in Russia | spec_tbl_df | 158 | 6 |
countries | lingtypology | Catalogue of countries | spec_tbl_df | 189 | 5 |
eurasianphonology | lingtypology | Eurasianphonology data | tbl_df | 19825 | 19 |
glottolog | lingtypology | Catalogue of languages of the world | data.frame | 26953 | 10 |
iso_639 | lingtypology | ISO 639-3 is a set of codes that defines three-letter identifiers for all known human languages. | spec_tbl_df | 183 | 5 |
oto_mangueanIC | lingtypology | Oto-Manguean Inflectional Class Database Language identifiers | tbl_df | 20 | 2 |
phoible | lingtypology | Phoible glottolog - language correspondencies | spec_tbl_df | 2185 | 2 |
phonological_profiles | lingtypology | Number of consonants and presence of ejectives | data.frame | 19 | 7 |
providers | lingtypology | Providers | list | | |
soundcomparisons | lingtypology | SOUNDCOMPARISONS's Language identifiers | data.frame | 556 | 3 |
uralex | lingtypology | UraLex's Language identifiers | data.frame | 27 | 3 |
wals | lingtypology | WALS's Language identifiers | spec_tbl_df | 2678 | 2 |
catholic_dioceses | historydata | Roman Catholic dioceses in the United States, Canada, and Mexico | tbl_df | 425 | 6 |
dijon_prices | historydata | Wholesale Market Prices in Dijon, France 1568-1630 | tbl_df | 1110 | 6 |
dijon_prices_wide | historydata | Wholesale Market Prices in Dijon, France 1568-1630 | tbl_df | 19 | 65 |
early_colleges | historydata | Early colleges in the United States | tbl_df | 65 | 6 |
judges_appointments | historydata | Federal judges in the United States of America | tbl_df | 4202 | 15 |
judges_people | historydata | Federal judges in the United States of America | tbl_df | 3532 | 13 |
methodists | historydata | Methodist Minutes of the Annual Conferences, 1786-1834 | tbl_df | 20241 | 11 |
minneapolisfed_cpi | historydata | Consumer Price Index, 1800-2024 | tbl_df | 225 | 3 |
naval_promotions | historydata | Promotions of U.S. Navy officers, 1798-1849 | tbl_df | 5705 | 5 |
paulist_missions | historydata | Records of missions held by the Paulist Fathers, 1851-1893 | tbl_df | 841 | 17 |
presbyterians | historydata | Presbyterians Statistics Through One Hundred Years | tbl_df | 133 | 37 |
quasi_war | historydata | Naval encounters during the Quasi War between France and the United States of America | tbl_df | 198 | 16 |
sarna | historydata | Population estimates of American Jews | tbl_df | 92 | 3 |
tudors | historydata | Tudor dynasty | tbl_df | 35 | 3 |
us_cities_pop | historydata | United States Historical City Populations, 1790-2010 | tbl_df | 62224 | 17 |
us_military_strengths | historydata | US Military Strengths | tbl_df | 50 | 27 |
us_national_population | historydata | Population of the United States, 1790-2010 | tbl_df | 23 | 2 |
us_state_populations | historydata | Populations of US states and territories, 1790-2010 | tbl_df | 983 | 4 |
ecpe | GDINA | Examination for the Certificate of Proficiency in English (ECPE) data | list | | |
frac20 | GDINA | Tatsuoka's fraction subtraction data | list | | |
sim10GDINA | GDINA | Simulated data (10 items, G-DINA model) | list | | |
sim10MCDINA | GDINA | Simulated data (10 items, MC-DINA model) | list | | |
sim10MCDINA2 | GDINA | Simulated data (10 items, MC-DINA model) | list | | |
sim20seqGDINA | GDINA | Simulated data (20 items, sequential G-DINA model) | list | | |
sim21seqDINA | GDINA | Simulated data (21 items, sequential DINA model) | list | | |
sim30DINA | GDINA | Simulated data (30 items, DINA model) | list | | |
sim30GDINA | GDINA | Simulated data (30 items, G-DINA model) | list | | |
sim30pGDINA | GDINA | Simulated data (30 items, polytomous G-DINA model) | list | | |
Engel95 | np | 1995 British Family Expenditure Survey | data.frame | 1655 | 10 |
Italy | np | Italian GDP Panel | data.frame | 1008 | 2 |
bw | np | Cross Country Growth Panel | rbandwidth | | |
bw.all | np | Cross-Sectional Data on Wages | rbandwidth | | |
bw.subset | np | Cross-Sectional Data on Wages | rbandwidth | | |
cps71 | np | Canadian High School Graduate Earnings | data.frame | 205 | 2 |
oecdpanel | np | Cross Country Growth Panel | data.frame | 616 | 7 |
wage1 | np | Cross-Sectional Data on Wages | data.frame | 526 | 24 |
RING_Visser_2021 | dendroNetwork | Roman tree-ring site chronologies | rwl | 1184 | 247 |
hol_rom | dendroNetwork | Roman tree-ring site chronologies from Hollstein | rwl | 1053 | 52 |
available_locales_df | charlatan | Available locales | data.frame | 52 | 4 |
available_providers | charlatan | Available Providers | character | | |
db_info_table | c14bazAAR | Database lookup table | tbl_df | 30 | 4 |
example_c14_date_list | c14bazAAR | Example c14_date_list | c14_date_list | 9 | 19 |
npis | npi | Sample results from the NPI Registry | npi_results | 10 | 11 |
tapajos | chirps | Tapajos National Forest | sfc_POLYGON | | |
bbbike_zones | osmextract | An sf object of geographical zones taken from bbbike.org | sf | 237 | 6 |
geofabrik_zones | osmextract | An sf object of geographical zones taken from geofabrik.de | sf | 476 | 9 |
openstreetmap_fr_zones | osmextract | An sf object of geographical zones taken from download.openstreetmap.fr | sf | 1187 | 7 |
test_zones | osmextract | An sf object of geographical zones taken from download.openstreetmap.fr | sf | 2 | 7 |
ex_survey | saros | ex_survey: Mockup dataset of a survey. | tbl_df | 300 | 32 |
HR | DALEX | Human Resources Data | data.frame | 7847 | 6 |
HRTest | DALEX | Human Resources Data | data.frame | 7897 | 6 |
HR_test | DALEX | Human Resources Data | data.frame | 7897 | 6 |
apartments | DALEX | Apartments Data | data.frame | 1000 | 6 |
apartmentsTest | DALEX | Apartments Data | data.frame | 9000 | 6 |
apartments_test | DALEX | Apartments Data | data.frame | 9000 | 6 |
covid_spring | DALEX | Data for early COVID mortality | data.frame | 10000 | 12 |
covid_summer | DALEX | Data for early COVID mortality | data.frame | 10000 | 12 |
dragons | DALEX | Dragon Data | data.frame | 2000 | 8 |
dragons_test | DALEX | Dragon Data | data.frame | 1000 | 8 |
fifa | DALEX | FIFA 20 preprocessed data | data.frame | 5000 | 42 |
happiness_test | DALEX | World Happiness Report data | data.frame | 156 | 7 |
happiness_train | DALEX | World Happiness Report data | data.frame | 625 | 7 |
titanic | DALEX | Passengers and Crew on the RMS Titanic Data | data.frame | 2207 | 9 |
titanic_imputed | DALEX | Passengers and Crew on the RMS Titanic Data | data.frame | 2207 | 8 |
Bushisms | ds4psy | Data: Bushisms | character | | |
Trumpisms | ds4psy | Data: Trumpisms | character | | |
countries | ds4psy | Data: Names of countries | character | | |
data_1 | ds4psy | Data import data_1. | spec_tbl_df | 100 | 4 |
data_2 | ds4psy | Data import data_2. | spec_tbl_df | 100 | 4 |
data_t1 | ds4psy | Data table data_t1. | spec_tbl_df | 20 | 4 |
data_t1_de | ds4psy | Data import data_t1_de. | spec_tbl_df | 20 | 4 |
data_t1_tab | ds4psy | Data import data_t1_tab. | spec_tbl_df | 20 | 4 |
data_t2 | ds4psy | Data table data_t2. | spec_tbl_df | 20 | 4 |
data_t3 | ds4psy | Data table data_t3. | spec_tbl_df | 20 | 4 |
data_t4 | ds4psy | Data table data_t4. | spec_tbl_df | 20 | 4 |
dt_10 | ds4psy | Data from 10 Danish people | spec_tbl_df | 10 | 7 |
exp_num_dt | ds4psy | Data from an experiment with numeracy and date-time variables | tbl_df | 1000 | 15 |
exp_wide | ds4psy | Data exp_wide. | spec_tbl_df | 10 | 7 |
falsePosPsy_all | ds4psy | Data: False Positive Psychology | spec_tbl_df | 78 | 19 |
fame | ds4psy | Data: fame | tbl_df | 67 | 4 |
flowery | ds4psy | Data: Flowery phrases | character | | |
fruits | ds4psy | Data: Names of fruits | character | | |
outliers | ds4psy | Outlier data. | spec_tbl_df | 1000 | 3 |
pi_100k | ds4psy | Data: 100k digits of pi. | character | | |
posPsy_AHI_CESD | ds4psy | Positive Psychology: AHI CESD data | spec_tbl_df | 992 | 50 |
posPsy_long | ds4psy | Positive Psychology: AHI CESD corrected data (in long format) | spec_tbl_df | 990 | 50 |
posPsy_p_info | ds4psy | Positive Psychology: Participant data | spec_tbl_df | 295 | 6 |
posPsy_wide | ds4psy | Positive Psychology: All corrected data (in wide format) | spec_tbl_df | 295 | 294 |
t3 | ds4psy | Data: t3 | spec_tbl_df | 10 | 4 |
t4 | ds4psy | Data: t4 | spec_tbl_df | 10 | 4 |
t_1 | ds4psy | Data: t_1 | spec_tbl_df | 8 | 9 |
t_2 | ds4psy | Data: t_2 | spec_tbl_df | 8 | 5 |
t_3 | ds4psy | Data: t_3 | spec_tbl_df | 16 | 6 |
t_4 | ds4psy | Data: t_4 | spec_tbl_df | 16 | 8 |
table6 | ds4psy | Data: table6 | spec_tbl_df | 6 | 2 |
table7 | ds4psy | Data: table7 | spec_tbl_df | 6 | 1 |
table8 | ds4psy | Data: table8 | spec_tbl_df | 3 | 5 |
table9 | ds4psy | Data table9. | xtabs | | |
tb | ds4psy | Data table tb. | spec_tbl_df | 100 | 5 |
fieldsdf | patentsview | Fields data frame | data.frame | 384 | 5 |
fewlevels_en | gendercoder | fewlevels_en | character | | |
manylevels_en | gendercoder | manylevels_en | character | | |
sample | gendercoder | sample | data.frame | 7756 | 1 |
example_matrix | psborrow2 | Example data matrix | matrix | 500 | 11 |
example_surv | psborrow2 | Simulated Survival Data | data.frame | 600 | 8 |
visium_row_col | SpatialFeatureExperiment | Row and columns of Visium barcodes on the slide | data.frame | 4992 | 3 |
Huggins89.t1 | VGAM | Table 1 of Huggins (1989) | data.frame | 20 | 21 |
Huggins89table1 | VGAM | Table 1 of Huggins (1989) | data.frame | 20 | 21 |
V1 | VGAM | V1 Flying-Bombs Hits in London | data.frame | 6 | 2 |
V2 | VGAM | V2 Missile Hits in London | data.frame | 4 | 2 |
alclevels | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
alcoff | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
auuc | VGAM | Auckland University Undergraduate Counts Data | data.frame | 4 | 5 |
backPain | VGAM | Data on Back Pain Prognosis, from Anderson (1984) | data.frame | 101 | 4 |
backPain2 | VGAM | Data on Back Pain Prognosis, from Anderson (1984) | data.frame | 101 | 4 |
beggs | VGAM | Bacon and Eggs Data | data.frame | 5 | 5 |
bmi.nz | VGAM | Body Mass Index of New Zealand Adults Data | data.frame | 700 | 2 |
budworm | VGAM | Western Spuce Budworm | data.frame | 12 | 9 |
car.all | VGAM | Undocumented and Internally Used Functions and Classes | data.frame | 111 | 36 |
cfibrosis | VGAM | Cystic Fibrosis Data | data.frame | 24 | 4 |
chest.nz | VGAM | Chest Pain in NZ Adults Data | data.frame | 73 | 5 |
chinese.nz | VGAM | Chinese Population in New Zealand 1867-2001 Data | data.frame | 27 | 4 |
coalminers | VGAM | Breathlessness and Wheeze Amongst Coalminers Data | data.frame | 9 | 5 |
corbet | VGAM | Corbet's Butterfly Data | data.frame | 24 | 2 |
crashbc | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
crashf | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
crashi | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
crashmc | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
crashp | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
crashtr | VGAM | Crashes on New Zealand Roads in 2009 | data.frame | 24 | 7 |
deermice | VGAM | Captures of Peromyscus maniculatus (Also Known as Deer Mice). | data.frame | 38 | 9 |
ducklings | VGAM | Relative Frequencies of Serum Proteins in White Pekin Ducklings | data.frame | 23 | 3 |
enzyme | VGAM | Enzyme Data | data.frame | 12 | 2 |
finney44 | VGAM | Toxicity trial for insects | data.frame | 6 | 3 |
flourbeetle | VGAM | Mortality of Flour Beetles from Carbon Disulphide | data.frame | 8 | 4 |
gew | VGAM | General Electric and Westinghouse Data | data.frame | 20 | 7 |
grain.us | VGAM | Grain Prices Data in USA | data.frame | 142 | 4 |
hormone | VGAM | Hormone Assay Data | data.frame | 85 | 2 |
hspider | VGAM | Hunting Spider Data | data.frame | 28 | 18 |
hunua | VGAM | Hunua Ranges Data | data.frame | 392 | 18 |
lakeO | VGAM | Annual catches on Lake Otamangakau from October 1974 to October 1989 | data.frame | 15 | 5 |
leukemia | VGAM | Acute Myelogenous Leukemia Survival Data | data.frame | 23 | 3 |
lirat | VGAM | Low-iron Rat Teratology Data | data.frame | 58 | 4 |
lpossums | VGAM | Leadbeater's Possums | data.frame | 10 | 2 |
machinists | VGAM | Machinists Accidents | data.frame | 8 | 2 |
marital.nz | VGAM | New Zealand Marital Data | data.frame | 6053 | 3 |
melbmaxtemp | VGAM | Melbourne Daily Maximum Temperatures | numeric | | |
olym08 | VGAM | 2008 and 2012 Summer Olympic Final Medal Count Data | data.frame | 87 | 6 |
olym12 | VGAM | 2008 and 2012 Summer Olympic Final Medal Count Data | data.frame | 85 | 6 |
oxtemp | VGAM | Oxford Temperature Data | data.frame | 80 | 2 |
pneumo | VGAM | Pneumoconiosis in Coalminers Data | data.frame | 8 | 4 |
prats | VGAM | Pregnant Rats Toxological Experiment Data | data.frame | 32 | 3 |
prinia | VGAM | Yellow-bellied Prinia | data.frame | 151 | 23 |
ruge | VGAM | Rutherford-Geiger Polonium Data | data.frame | 15 | 2 |
toxop | VGAM | Toxoplasmosis Data | data.frame | 34 | 4 |
ucberk | VGAM | University California Berkeley Graduate Admissions | data.frame | 6 | 5 |
venice | VGAM | Venice Maximum Sea Levels Data | data.frame | 51 | 11 |
venice90 | VGAM | Venice Maximum Sea Levels Data | data.frame | 455 | 7 |
waitakere | VGAM | Waitakere Ranges Data | data.frame | 579 | 18 |
wine | VGAM | Bitterness in Wine Data | data.frame | 4 | 7 |
acacia | canaper | _Acacia_ example data | list | | |
biod_example | canaper | Biodiverse example data | list | | |
biod_results | canaper | Output from Biodiverse | tbl_df | 127 | 7 |
cpr_endem_cols | canaper | CVD-friendly color palette for plotting results of CANAPE | character | | |
cpr_endem_cols_2 | canaper | CVD-friendly color palette for plotting results of CANAPE, version 2 | character | | |
cpr_endem_cols_3 | canaper | CVD-friendly color palette for plotting results of CANAPE, version 3 | character | | |
cpr_endem_cols_4 | canaper | CVD-friendly color palette for plotting results of CANAPE, version 4 | character | | |
cpr_signif_cols | canaper | CVD-friendly color palette for plotting results of randomization test | character | | |
cpr_signif_cols_2 | canaper | CVD-friendly color palette for plotting results of randomization test, version 2 | character | | |
mishler_endem_cols | canaper | Original color palette for plotting results of CANAPE | character | | |
mishler_signif_cols | canaper | Original color palette for plotting results of CANAPE | character | | |
phylocom | canaper | Phylocom example data | list | | |
ppo_filters | rppo | List of filters to use with 'ppo_data'. | list | | |
cat_ls | grainchanger | Example categorical raster (fine_dat) | RasterLayer | | |
cont_ls | grainchanger | Example continuous raster (fine_dat) | RasterLayer | | |
g_sf | grainchanger | Example grid (coarse_dat) | sf | 25 | 1 |
poly_sf | grainchanger | Example polygon (coarse_dat) | sf | 110 | 1 |
anolis | treedata.table | Anole data | list | | |
eml | EML | eml | list | | |
air_pressure | wateRinfo | Air pressure data of January 1st, 2017 | tbl_df | 765 | 13 |
liedekerke | wateRinfo | Soil moisture data of Liedekerke, January 2017 | tbl_df | 23816 | 9 |
sp500 | VARcpDetectOnline | S&P 500 Daily Log Returns and Corresponding Dates | list | | |
endpoints | epair | Endpoints available in the EPA API | character | | |
service.names | epair | Names of services offered by the EPA API | data.frame | 2 | 13 |
services | epair | Services offered by the EPA API | list | | |
variable.types | epair | Variable parameter names to use | list | | |
variables | epair | Variables used for querying in EPA API | data.frame | 3 | 18 |
middleearth | outcomerate | middleearth Dataset | tbl_df | 1691 | 9 |
providers | oai | Metadata providers data.frame. | data.frame | 2751 | 3 |
tableinfo | exoplanets | Table Information | tbl_df | 546 | 13 |
tree_scaling | timbeR | Tree scaling example data | tbl_df | 136 | 5 |
charlson | coder | Classcodes for Charlson comorbidity based on ICD-codes | classcodes | 17 | 14 |
cps | coder | Classcodes for the comorbidity-polypharmacy score (CPS) based on ICD-10 codes | classcodes | 2 | 3 |
elixhauser | coder | Classcodes for Elixhauser based on ICD-codes | classcodes | 31 | 13 |
ex_atc | coder | Example data for random ATC codes | tbl_df | 10000 | 3 |
ex_icd10 | coder | Example data for random codes assigned to random people | tbl_df | 2376 | 4 |
ex_people | coder | Example data for random people | tbl_df | 100 | 2 |
hip_ae | coder | Classcodes for adverse events after knee and hip arthroplasty | classcodes | 7 | 5 |
hip_ae_hailer | coder | Classcodes for infection and dislocation after hip arthroplasty | classcodes | 2 | 3 |
knee_ae | coder | Classcodes for adverse events after knee and hip arthroplasty | classcodes | 7 | 4 |
rxriskv | coder | Classcodes for RxRisk V based on ATC codes | classcodes | 46 | 6 |
absent_list | treestartr | bears | character | | |
bears | treestartr | bears | data.frame | 22 | 2 |
mrca_df | treestartr | bears | data.frame | 5 | 2 |
tax_frame | treestartr | bears | data.frame | 22 | 2 |
tree | treestartr | bears | phylo | | |
fake_DVR | tacmagic | Fake DVR data for vignette and package testing | data.frame | 50 | 4 |
davis1974 | chlorpromazineR | Chlorpromazine equivalent key from Davis 1974 data | list | | |
gardner2010 | chlorpromazineR | Chlorpromazine equivalent key from Gardner et al. 2010 data | list | | |
gardner2010_withsai | chlorpromazineR | Chlorpromazine equivalent key from Gardner et al. 2010 data | list | | |
leucht2016 | chlorpromazineR | Chlorpromazine equivalent key from Leucht et al. 2016 data | list | | |
leucht2020 | chlorpromazineR | Antipsychotic equivalent key from Leucht et al. 2020 data | list | | |
woods2003 | chlorpromazineR | Chlorpromazine equivalent key from Woods 2003 data | list | | |
Gr21a.Gr63a | DoOR.data | Gr21a.Gr63a | data.frame | 693 | 10 |
Ir31a | DoOR.data | Ir31a | data.frame | 693 | 6 |
Ir41a | DoOR.data | Ir41a | data.frame | 693 | 6 |
Ir64a.DC4 | DoOR.data | Ir64a.DC4 | data.frame | 693 | 6 |
Ir64a.DP1m | DoOR.data | Ir64a.DP1m | data.frame | 693 | 6 |
Ir75a | DoOR.data | Ir75a | data.frame | 693 | 7 |
Ir75d | DoOR.data | Ir75d | data.frame | 693 | 6 |
Ir76a | DoOR.data | Ir76a | data.frame | 693 | 6 |
Ir84a | DoOR.data | Ir84a | data.frame | 693 | 7 |
Ir92a | DoOR.data | Ir92a | data.frame | 693 | 6 |
ORs | DoOR.data | ORs | data.frame | 78 | 1 |
Or10a | DoOR.data | Or10a | data.frame | 693 | 15 |
Or13a | DoOR.data | Or13a | data.frame | 693 | 12 |
Or19a | DoOR.data | Or19a | data.frame | 693 | 9 |
Or1a | DoOR.data | Or1a | data.frame | 693 | 6 |
Or22a | DoOR.data | Or22a | data.frame | 693 | 19 |
Or22b | DoOR.data | Or22b | data.frame | 693 | 6 |
Or22c | DoOR.data | Or22c | data.frame | 693 | 7 |
Or23a | DoOR.data | Or23a | data.frame | 693 | 7 |
Or24a | DoOR.data | Or24a | data.frame | 693 | 7 |
Or2a | DoOR.data | Or2a | data.frame | 693 | 9 |
Or30a | DoOR.data | Or30a | data.frame | 693 | 8 |
Or33a | DoOR.data | Or33a | data.frame | 693 | 6 |
Or33b | DoOR.data | Or33b | data.frame | 693 | 9 |
Or33c | DoOR.data | Or33c | data.frame | 693 | 6 |
Or35a | DoOR.data | Or35a | data.frame | 693 | 9 |
Or42a | DoOR.data | Or42a | data.frame | 693 | 11 |
Or42b | DoOR.data | Or42b | data.frame | 693 | 14 |
Or43a | DoOR.data | Or43a | data.frame | 693 | 7 |
Or43b | DoOR.data | Or43b | data.frame | 693 | 10 |
Or45a | DoOR.data | Or45a | data.frame | 693 | 8 |
Or45b | DoOR.data | Or45b | data.frame | 693 | 8 |
Or46a | DoOR.data | Or46a | data.frame | 693 | 8 |
Or47a | DoOR.data | Or47a | data.frame | 693 | 11 |
Or47b | DoOR.data | Or47b | data.frame | 693 | 11 |
Or49a | DoOR.data | Or49a | data.frame | 693 | 8 |
Or49b | DoOR.data | Or49b | data.frame | 693 | 11 |
Or59a | DoOR.data | Or59a | data.frame | 693 | 8 |
Or59b | DoOR.data | Or59b | data.frame | 693 | 15 |
Or59c | DoOR.data | Or59c | data.frame | 693 | 9 |
Or65a | DoOR.data | Or65a | data.frame | 693 | 9 |
Or67a | DoOR.data | Or67a | data.frame | 693 | 8 |
Or67b | DoOR.data | Or67b | data.frame | 693 | 9 |
Or67c | DoOR.data | Or67c | data.frame | 693 | 10 |
Or67d | DoOR.data | Or67d | data.frame | 693 | 7 |
Or69a | DoOR.data | Or69a | data.frame | 693 | 6 |
Or71a | DoOR.data | Or71a | data.frame | 693 | 9 |
Or74a | DoOR.data | Or74a | data.frame | 693 | 8 |
Or7a | DoOR.data | Or7a | data.frame | 693 | 16 |
Or82a | DoOR.data | Or82a | data.frame | 693 | 14 |
Or83c | DoOR.data | Or83c | data.frame | 693 | 6 |
Or85a | DoOR.data | Or85a | data.frame | 693 | 7 |
Or85b | DoOR.data | Or85b | data.frame | 693 | 13 |
Or85c | DoOR.data | Or85c | data.frame | 693 | 8 |
Or85d | DoOR.data | Or85d | data.frame | 693 | 8 |
Or85e | DoOR.data | Or85e | data.frame | 693 | 6 |
Or85f | DoOR.data | Or85f | data.frame | 693 | 7 |
Or88a | DoOR.data | Or88a | data.frame | 693 | 8 |
Or92a | DoOR.data | Or92a | data.frame | 693 | 11 |
Or94a | DoOR.data | Or94a | data.frame | 693 | 9 |
Or94b | DoOR.data | Or94b | data.frame | 693 | 9 |
Or98a | DoOR.data | Or98a | data.frame | 693 | 11 |
Or9a | DoOR.data | Or9a | data.frame | 693 | 8 |
ab2B | DoOR.data | ab2B | data.frame | 693 | 12 |
ab4B | DoOR.data | ab4B | data.frame | 693 | 10 |
ab5B | DoOR.data | ab5B | data.frame | 693 | 9 |
ac1 | DoOR.data | ac1 | data.frame | 693 | 6 |
ac1A | DoOR.data | ac1A | data.frame | 693 | 7 |
ac1B | DoOR.data | ac1B | data.frame | 693 | 6 |
ac1BC | DoOR.data | ac1BC | data.frame | 693 | 6 |
ac2 | DoOR.data | ac2 | data.frame | 693 | 6 |
ac2A | DoOR.data | ac2A | data.frame | 693 | 7 |
ac2B | DoOR.data | ac2B | data.frame | 693 | 6 |
ac2BC | DoOR.data | ac2BC | data.frame | 693 | 6 |
ac3A | DoOR.data | ac3A | data.frame | 693 | 8 |
ac3B | DoOR.data | ac3B | data.frame | 693 | 9 |
ac3_noOr35a | DoOR.data | ac3_noOr35a | data.frame | 693 | 6 |
ac4 | DoOR.data | ac4 | data.frame | 693 | 8 |
door_AL_map | DoOR.data | door_AL_map | list | | |
door_data_format | DoOR.data | door_data_format | data.frame | 693 | 5 |
door_dataset_info | DoOR.data | door_dataset_info | data.frame | 42 | 15 |
door_excluded_data | DoOR.data | door_excluded_data | data.frame | 78 | 2 |
door_glo_dist | DoOR.data | door_glo_dist | data.frame | 49 | 49 |
door_global_normalization_weights | DoOR.data | door_global_normalization_weights | data.frame | 78 | 42 |
door_mappings | DoOR.data | door_mappings | data.frame | 96 | 20 |
door_response_matrix | DoOR.data | door_response_matrix | data.frame | 693 | 78 |
door_response_matrix_non_normalized | DoOR.data | door_response_matrix_non_normalized | data.frame | 693 | 78 |
door_response_range | DoOR.data | door_response_range | data.frame | 42 | 4 |
odor | DoOR.data | odor | data.frame | 693 | 22 |
pb2A | DoOR.data | pb2A | data.frame | 693 | 8 |
cc.genes.cyclone | chevreulProcess | Cyclone cell cycle pairs by symbol | list | | |
ensembl_version | chevreulProcess | Ensembl version used for build | character | | |
grch38 | chevreulProcess | Human annotation data | tbl_df | 76062 | 9 |
grch38_tx2gene | chevreulProcess | Human transcripts to genes | tbl_df | 277081 | 2 |
human_to_mouse_homologs | chevreulProcess | Gene Homologs Between Human and Mouse | data.frame | 23188 | 2 |
small_example_dataset | chevreulProcess | Small example SingleCellExperiment | SingleCellExperiment | | |
tiny_sce | chevreulProcess | Tiny example SingleCellExperiment | SingleCellExperiment | | |
grid | CVN | Data for a grid of graphs (3 x 3) | list | | |
example_compounds | eiR | Example Compounds | character | | |
MTMM_data | BifactorIndicesCalculator | Simulated data for multi-trait multi-method | data.frame | 500 | 27 |
SRS_data | BifactorIndicesCalculator | Response Data to the SRS-22r | data.frame | 500 | 20 |
SKAT.example | SKAT | Example data for SKAT | list | | |
SKAT.example.ChrX | SKAT | Example data for SKAT | list | | |
SKAT.fam.example | SKAT | Example data for SKAT_NULL_emmaX | list | | |
SKAT.haplotypes | SKAT | Haplotype dataset for power calculation | list | | |
SKATBinary.example | SKAT | Example data for SKAT | list | | |
SBB | recmap | jss2711 data | recmapGA | | |
Switzerland | recmap | jss2711 data | recmapGA | | |
UK | recmap | jss2711 data | recmapGA | | |
cmp_GA_GRASP | recmap | jss2711 data | list | | |
mbb_check | recmap | jss2711 data | data.frame | 11400 | 6 |
phyllostomid | mvMORPH | Phylogeny and trait data for a sample of Phyllostomid bats | list | | |
aatemp | faraway | Annual mean temperatures in Ann Arbor, Michigan | data.frame | 115 | 2 |
abrasion | faraway | Wear on materials according to type, run and position | data.frame | 16 | 4 |
aflatoxin | faraway | aflatoxin dosage and liver cancer in lab animals | data.frame | 6 | 3 |
africa | faraway | miltary coups and politics in sub-Saharan Africa | data.frame | 47 | 9 |
airpass | faraway | Airline passengers | data.frame | 144 | 2 |
alfalfa | faraway | Effects of seed inoculum, irrigation and shade on alfalfa yield | data.frame | 25 | 4 |
amlxray | faraway | Match pair study for AML and Xray link | data.frame | 238 | 11 |
anaesthetic | faraway | Time in minutes to eye opening after reversal of anaesthetic. | data.frame | 80 | 2 |
babyfood | faraway | Respiratory disease rates of babies fed in different ways | data.frame | 6 | 4 |
beetle | faraway | Beetles exposed to fumigant | data.frame | 10 | 3 |
bliss | faraway | Insect mortality due to insecticide | data.frame | 5 | 3 |
breaking | faraway | Breaking strength of materials | data.frame | 16 | 4 |
broccoli | faraway | Broccoli weight variation | data.frame | 36 | 4 |
butterfat | faraway | Butterfat content of milk by breed | data.frame | 100 | 3 |
cathedral | faraway | Cathedral nave heights and lengths in England | data.frame | 25 | 3 |
cheddar | faraway | Taste of Cheddar cheese | data.frame | 30 | 4 |
chicago | faraway | Chicago insurance redlining | data.frame | 47 | 7 |
chiczip | faraway | Chicago zip codes north-south | character | | |
chmiss | faraway | Chicago insurance redlining | data.frame | 47 | 6 |
choccake | faraway | Chocolate cake experiment with split plot design | data.frame | 270 | 4 |
chredlin | faraway | Chicago insurance redlining | data.frame | 47 | 7 |
clot | faraway | Blood clotting times | data.frame | 18 | 3 |
cmob | faraway | Social class mobility from 1971 to 1981 in the UK | data.frame | 36 | 3 |
cns | faraway | Malformations of the central nervous system | data.frame | 16 | 7 |
coagulation | faraway | Blood coagulation times by diet | data.frame | 24 | 2 |
composite | faraway | Strength of a thermoplastic composite depending on two factors | data.frame | 9 | 3 |
cornnit | faraway | Corn yields from nitrogen application | data.frame | 44 | 2 |
corrosion | faraway | Corrosion loss in Cu-Ni alloys | data.frame | 13 | 2 |
cpd | faraway | Projected and actual sales of 20 consumer products | data.frame | 20 | 2 |
crawl | faraway | Crawling babies by month | data.frame | 12 | 4 |
ctsib | faraway | Effects of surface and vision on balance. | data.frame | 480 | 8 |
death | faraway | Death penalty in Florida 1977 | data.frame | 8 | 4 |
debt | faraway | psychology of debt | data.frame | 464 | 13 |
denim | faraway | Denim wastage by supplier | data.frame | 95 | 2 |
diabetes | faraway | Diabetes and obesity, cardiovascular risk factors | data.frame | 403 | 19 |
dicentric | faraway | Radiation dose effects on chromosomal abnormality | data.frame | 27 | 4 |
divusa | faraway | Divorce in the USA 1920-1996 | data.frame | 77 | 7 |
drugpsy | faraway | Choice of drug treatment for psychiatry patients | data.frame | 10 | 3 |
dvisits | faraway | Doctor visits in Australia | data.frame | 5190 | 19 |
eco | faraway | Ecological regression example | data.frame | 51 | 4 |
eggprod | faraway | Treatment and block effects on egg production | data.frame | 12 | 3 |
eggs | faraway | Nested data on lab testing of eggs | data.frame | 48 | 4 |
epilepsy | faraway | Epileptic seizures in clinical trial of drug | data.frame | 295 | 6 |
esdcomp | faraway | Complaints about emergency room doctors | data.frame | 44 | 6 |
exa | faraway | Simulated non-parametric regression data | data.frame | 256 | 3 |
exb | faraway | Simulated non-parametric regression data | list | | |
eyegrade | faraway | grading of eye pairs for distance vision | data.frame | 16 | 3 |
fat | faraway | Percentage of Body Fat and Body Measurements in Men | data.frame | 252 | 18 |
femsmoke | faraway | Mortality due to smoking according age group in women | data.frame | 28 | 4 |
fortune | faraway | Billionaires' wealth and age | data.frame | 232 | 3 |
fpe | faraway | 1981 French Presidential Election | data.frame | 24 | 14 |
fruitfly | faraway | Longevity of fruitflies depending on sexual activity and thorax length | data.frame | 124 | 3 |
gala | faraway | Species diversity on the Galapagos Islands | data.frame | 30 | 7 |
galamiss | faraway | Species diversity on the Galapagos Islands | data.frame | 30 | 7 |
gammaray | faraway | Xray decay from a gamma ray burst | data.frame | 63 | 3 |
gavote | faraway | Undercounted votes in Georgia in 2000 presidential election | data.frame | 159 | 10 |
globwarm | faraway | Northern Hemisphere temperatures and climate proxies in the last millenia | data.frame | 1001 | 10 |
haireye | faraway | Hair and eye color | data.frame | 16 | 3 |
happy | faraway | MBA students experience love, sex, work and happiness | data.frame | 39 | 5 |
hemoglobin | faraway | Treatment of insulin dependent diabetic children | data.frame | 16 | 5 |
hips | faraway | Ankylosing Spondylitis | data.frame | 78 | 7 |
hormone | faraway | Hormone concentrations in gay and straight men | data.frame | 26 | 3 |
hprice | faraway | Housing prices in US cities 86-94 | data.frame | 324 | 8 |
hsb | faraway | Career choice of high school students | data.frame | 200 | 11 |
infmort | faraway | Infant mortality according to income and region | data.frame | 105 | 4 |
insulgas | faraway | Effects of insulation on gas consumption | data.frame | 44 | 3 |
irrigation | faraway | Irrigation methods in an agricultural field trial | data.frame | 16 | 4 |
jsp | faraway | Junior School Project | data.frame | 3236 | 9 |
kanga | faraway | Kangaroo skull measurements | data.frame | 148 | 20 |
lawn | faraway | Cut-off times of lawnmowers | data.frame | 24 | 4 |
leafblotch | faraway | Leaf blotch on barley | data.frame | 90 | 3 |
leafburn | faraway | Data on the burning time of samples of tobacco leaves | data.frame | 30 | 4 |
mammalsleep | faraway | Sleep in Mammals: Ecological and Constitutional Correlates | data.frame | 62 | 10 |
manilius | faraway | Mayer's 1750 data on the Manilius crater on the moon | data.frame | 27 | 4 |
mba | faraway | MBA students experience love, sex, work and happiness | data.frame | 39 | 5 |
meatspec | faraway | Meat spectrometry to determine fat content | data.frame | 215 | 101 |
melanoma | faraway | Melanoma by type and location | data.frame | 12 | 3 |
motorins | faraway | Third party motor insurance claims in Sweden in 1977 | data.frame | 1797 | 8 |
neighbor | faraway | Questionnaire study of neighborly help | data.frame | 181 | 8 |
nels88 | faraway | National Education Longitudinal Study of 1988 | data.frame | 260 | 5 |
nepali | faraway | Nepali child heath study | data.frame | 1000 | 9 |
nes96 | faraway | US 1996 national election study | data.frame | 944 | 10 |
newhamp | faraway | New Hampshire Democratic Party Primary 2008 | data.frame | 276 | 12 |
oatvar | faraway | Yields of oat varieties planted in blocks | data.frame | 40 | 3 |
odor | faraway | Odor of chemical by production settings | data.frame | 15 | 4 |
ohio | faraway | Ohio Children Wheeze Status | data.frame | 2148 | 4 |
orings | faraway | Spache Shuttle Challenger O-rings | data.frame | 23 | 2 |
ozone | faraway | Ozone in LA in 1976 | data.frame | 330 | 10 |
parstum | faraway | Marijuana and parent alcohol and drug use | data.frame | 9 | 3 |
peanut | faraway | Carbon dioxide effects on peanut oil extraction | data.frame | 16 | 6 |
penicillin | faraway | Penicillin yield by block and treatment | data.frame | 20 | 3 |
phbirths | faraway | Birth weights in Philadelphia | data.frame | 1115 | 5 |
pima | faraway | Diabetes survey on Pima Indians | data.frame | 768 | 9 |
pipeline | faraway | NIST data on ultrasonic measurements of defects in the Alaska pipeline | data.frame | 107 | 3 |
pneumo | faraway | Pneumonoconiosis in coal miners | data.frame | 24 | 3 |
potuse | faraway | Marijuana usage by youth | data.frame | 486 | 7 |
prostate | faraway | Prostate cancer surgery | data.frame | 97 | 9 |
psid | faraway | Panel Study of Income Dynamics subset | data.frame | 1661 | 6 |
pulp | faraway | Brightness of paper pulp depending on shift operator | data.frame | 20 | 2 |
punting | faraway | Leg strength and punting | data.frame | 13 | 7 |
pvc | faraway | Production of PVC by operator and resin railcar | data.frame | 48 | 3 |
pyrimidines | faraway | Activity in pyrimidines | data.frame | 74 | 27 |
rabbit | faraway | Rabbit weight gain by diet and litter | data.frame | 30 | 3 |
ratdrink | faraway | Rat growth weights affected by additives | data.frame | 135 | 4 |
rats | faraway | Effect of toxic agents on rats | data.frame | 48 | 3 |
resceram | faraway | Shape and plate effects on current noise in resistors | data.frame | 12 | 3 |
salmonella | faraway | Salmonella reverse mutagenicity assay | data.frame | 18 | 2 |
sat | faraway | School expenditure and test scores from USA in 1994-95 | data.frame | 50 | 7 |
savings | faraway | Savings rates in 50 countries | data.frame | 50 | 5 |
seatpos | faraway | Car seat position depending driver size | data.frame | 38 | 9 |
seeds | faraway | Germination of seeds depending on moisture and covering | data.frame | 48 | 3 |
semicond | faraway | Semiconductor split-plot experiment | data.frame | 48 | 5 |
sexab | faraway | Post traumatic stress disorder in abused adult females | data.frame | 76 | 3 |
sexfun | faraway | Marital sex ratings | data.frame | 16 | 3 |
snail | faraway | Snail production | data.frame | 24 | 3 |
solder | faraway | Solder skips in printing circuit boards | data.frame | 900 | 6 |
solv | faraway | Block design task testing child ability | data.frame | 24 | 3 |
sono | faraway | Sonoluminescence | data.frame | 16 | 8 |
soybean | faraway | Germination failures for soybean seeds | data.frame | 25 | 3 |
spector | faraway | Teaching methods in Economics | data.frame | 32 | 4 |
speedo | faraway | Speedometer cable shrinkage | data.frame | 16 | 16 |
star | faraway | Star temperatures and light intensites | data.frame | 47 | 3 |
stat500 | faraway | Marks in a statistics class | data.frame | 55 | 4 |
stepping | faraway | Stepping and effect on heart rate | data.frame | 30 | 6 |
strongx | faraway | Strong interaction experiment data | data.frame | 10 | 4 |
suicide | faraway | Suicide method data from the UK | data.frame | 36 | 4 |
teengamb | faraway | Study of teenage gambling in Britain | data.frame | 47 | 5 |
toenail | faraway | Toenail infection treatment study | data.frame | 1908 | 5 |
troutegg | faraway | Survival of trout eggs depending on time and location | data.frame | 20 | 4 |
truck | faraway | Truck leaf spring experiment | data.frame | 48 | 6 |
turtle | faraway | Incubation temperature and the sex of turtles | data.frame | 15 | 3 |
tvdoctor | faraway | Life, TVs and Doctors | data.frame | 38 | 3 |
twins | faraway | Twin IQs from Burt | data.frame | 27 | 3 |
uncviet | faraway | UNC student opinions about the Vietnam War | data.frame | 40 | 4 |
uswages | faraway | Weekly wages of US male workers in 1988 | data.frame | 2000 | 10 |
vision | faraway | Acuity of vision in response to light flash | data.frame | 56 | 4 |
wafer | faraway | resitivity of wafer in semiconductor experiment | data.frame | 16 | 5 |
wavesolder | faraway | Defects in a wave soldering process | data.frame | 16 | 10 |
wbca | faraway | Wisconsin breast cancer database | data.frame | 681 | 10 |
wcgs | faraway | Western Collaborative Group Study | data.frame | 3154 | 13 |
weldstrength | faraway | welding strength DOE | data.frame | 16 | 10 |
wfat | faraway | Percentage of Body Fat and Body Measurements in Women | data.frame | 184 | 19 |
wheat | faraway | Insect damage to wheat by variety | data.frame | 13 | 2 |
worldcup | faraway | Data on players from the 2010 World Cup | data.frame | 595 | 7 |
knownBL | TRADER | Release data | data.frame | 27 | 4 |
relData1 | TRADER | Release data | data.frame | 142 | 15 |
relData2 | TRADER | Release data | data.frame | 217 | 192 |
relMissPith | TRADER | Release data | data.frame | 15 | 2 |
bushfire | parody | satellite data on bushfire scars | matrix | 38 | |
tcost | parody | Data on milk transportation costs, from Johnson and Wichern, Applied Multivariate Statistical Analysis, 3rd edition | matrix | 36 | 3 |
examples | IsoplotR | Example datasets for testing 'IsoplotR' | list | | |
challenges | bushtucker | IACGMOOH celebrity challenges data | tbl_df | 135 | 7 |
contestants | bushtucker | IACGMOOH contestants data | tbl_df | 282 | 12 |
ratings | bushtucker | IACGMOOH ratings data | tbl_df | 478 | 6 |
results | bushtucker | IACGMOOH voting results data | tbl_df | 2719 | 7 |
seasons | bushtucker | IACGMOOH seasons data | tbl_df | 24 | 11 |
trials | bushtucker | IACGMOOH bushtucker trials data | tbl_df | 534 | 9 |
bats | IPMbook | Data for greater horseshoe bats from Switzerland, 1989-2017 | list | | |
bear | IPMbook | Data for black bears in Louisiana, USA, 2007-2012 | list | | |
catbird | IPMbook | Data for gray catbird from New England/Mid-Atlantic region, USA, 1992-2008 | list | | |
cormorant | IPMbook | Data from three Danish cormorant breeding colonies, 1991-2004 | list | | |
elk | IPMbook | Data from an elk population in USA, 1988-1993 | list | | |
grouse | IPMbook | Data for black grouse from Italy, 1997-2016 | list | | |
hoopoe | IPMbook | Data for hoopoe from Switzerland, 2002-2017 | list | | |
kestrel | IPMbook | Data for kestrels from Switzerland, 2002-2016 | list | | |
peregrine | IPMbook | Data for peregrine falcons from the Jura Mountains, 1965-2007 | list | | |
redbacked | IPMbook | Data for red-backed shrike from Germany, 1971-2006 | list | | |
stork | IPMbook | Data on marked white storks in Germany, 1986-2001 | matrix | 691 | 16 |
swallow | IPMbook | Data for barn swallows in Switzerland, 1997-2003 | list | | |
woodchat10 | IPMbook | Simulated data for woodchat shrike for use in chapter 10 | list | | |
woodchat11 | IPMbook | Data for woodchat shrike from Germany, 1964-1992 | list | | |
woodchat5 | IPMbook | Simulated data for woodchat shrike for use in chapter 5 | list | | |
woodchat6 | IPMbook | Simulated data for woodchat shrike for use in section 6.3 | list | | |
woodchat64 | IPMbook | Simulated data for woodchat shrike for use in section 6.4 | list | | |
woodchat66 | IPMbook | Simulated data for woodchat shrike for use in section 6.6 | list | | |
woodchat7 | IPMbook | Simulated data for woodchat shrike for use in chapter 7 | list | | |
wryneck | IPMbook | Data from wrynecks in Switzerland, 2002-2006 | data.frame | 181 | 5 |
ex_taxmap | metacoder | An example taxmap object | Taxmap | | |
hmp_otus | metacoder | A HMP subset | tbl_df | 1000 | 52 |
hmp_samples | metacoder | Sample information for HMP subset | tbl_df | 50 | 3 |
ranks_ref | metacoder | Lookup-table for IDs of taxonomic ranks | data.frame | 34 | 2 |
actuary_salaries | ExamPAData | DW Simpson actuarial salary data | spec_tbl_df | 138 | 6 |
apartment_apps | ExamPAData | Apartment Apps | data.frame | 1430 | 41 |
auto_claim | ExamPAData | Automotive claims | spec_tbl_df | 10296 | 29 |
bank_loans | ExamPAData | Bank Loans | spec_tbl_df | 41188 | 21 |
bike_sharing_demand | ExamPAData | Bike sharing demand | data.frame | 17376 | 10 |
boston | ExamPAData | Boston | spec_tbl_df | 506 | 14 |
customer_phone_calls | ExamPAData | Customer Phone Calls | spec_tbl_df | 10000 | 14 |
customer_value | ExamPAData | Customer Value | spec_tbl_df | 48842 | 8 |
exam_pa_titanic | ExamPAData | Exam PA Titanic | spec_tbl_df | 906 | 11 |
health_insurance | ExamPAData | Health insurance | spec_tbl_df | 1338 | 7 |
june_pa | ExamPAData | June_pa | spec_tbl_df | 23137 | 14 |
patient_length_of_stay | ExamPAData | Patient Length of Stay | spec_tbl_df | 10000 | 13 |
patient_num_labs | ExamPAData | Patient Number of Labs | spec_tbl_df | 10000 | 14 |
pedestrian_activity | ExamPAData | Pedestrian activity | data.frame | 11373 | 7 |
readmission | ExamPAData | Readmission | spec_tbl_df | 66782 | 9 |
student_success | ExamPAData | Student Success | spec_tbl_df | 585 | 33 |
travel_insurance | ExamPAData | Travel insurance data | data.frame | 10000 | 7 |
travel_spending | ExamPAData | Travel spending data | data.frame | 4884 | 11 |
af_colour_palettes | afcharts | Analysis Function colour palettes | list | | |
af_colour_values | afcharts | Analysis Function colour names and hex codes | character | | |
Z_t_con | WINS | Covariate history in the control group. | data.frame | 424 | 4 |
Z_t_trt | WINS | Covariate history in the treatment group. | data.frame | 796 | 4 |
data_binary | WINS | An example with three binary endpoints. | data.frame | 250 | 5 |
data_continuous | WINS | An example with three continuous endpoints. | data.frame | 250 | 5 |
data_mix | WINS | An example with a mixture of endpoint types. | data.frame | 400 | 6 |
data_mix_stratum | WINS | An example with a mixture of endpoint types with three strata. | data.frame | 400 | 7 |
data_tte | WINS | An example with three TTE endpoints. | data.frame | 400 | 8 |
agricultural_chargeoff_rates_by_quarter | IIS | Agricultural Chargeoff Rates by Quarter | data.frame | 100 | 3 |
airline_arrivals | IIS | Airline Arrivals | data.frame | 13 | 13 |
american_league_salary_2014 | IIS | American League Salary 2014 | data.frame | 447 | 3 |
arion_subfuscus | IIS | Arion Subfuscus | data.frame | 10 | 2 |
average_HDL_levels | IIS | Average HDL Levels | numeric | | |
beer_head | IIS | Beer Head | list | | |
body_temperature_and_heart_rate | IIS | Body Temperature and Heart Rate | data.frame | 130 | 3 |
chargeoff_rates | IIS | Chargeoff Rates | data.frame | 100 | 9 |
college_rankings_2012 | IIS | College Rankings 2012 | data.frame | 7793 | 9 |
delinquency_rates | IIS | Delinquency Rates | data.frame | 100 | 9 |
desimipramine | IIS | Desimipramine | data.frame | 12 | 2 |
diamonds_carats_color_cost | IIS | Diamonds Carats Color Cost | data.frame | 308 | 5 |
emissions | IIS | Emissions | data.frame | 25 | 5 |
engineering_drawing_hours | IIS | Engineering Drawing Hours | numeric | | |
female_amerindians | IIS | Female Amerindians | numeric | | |
fmr_white_tailed_deer | IIS | FMR White-Tailed Deer | list | | |
gender_roles | IIS | Gender Roles | data.frame | 7 | 2 |
goggled_green_turtles | IIS | Goggled Green Turtles | data.frame | 18 | 2 |
health_care_by_affiliation | IIS | Health Care by Affiliation | data.frame | 2 | 2 |
homes_prices | IIS | Homes Prices | data.frame | 100 | 6 |
house_lot_sizes | IIS | House Lot Sizes | data.frame | 100 | 8 |
infant_walking | IIS | Infant Walking | data.frame | 6 | 2 |
interstitial_lengths | IIS | Interstitial Lengths | data.frame | 12 | 2 |
kentucky_derby_2012 | IIS | Kentucky Derby 2012 | data.frame | 23 | 6 |
meniscal_repairs_load_at_failure | IIS | Meniscal Repairs Load at Failure | list | | |
mother_smoking_age | IIS | Mother Smoking Age | data.frame | 45 | 4 |
mother_smoking_education | IIS | Mother Smoking Education | data.frame | 40 | 4 |
mother_smoking_education_1989_1993 | IIS | Mother Smoking Education 1989-1993 | data.frame | 25 | 3 |
mother_smoking_education_2010 | IIS | Mother Smoking Education 2010 | data.frame | 8 | 2 |
motor_vehicle_death_rate_2012 | IIS | Motor Vehicle Death Rate 2012 | data.frame | 50 | 3 |
movie_facts | IIS | Movie Facts | data.frame | 100 | 6 |
national_league_salary_2014 | IIS | National League Salary 2014 | data.frame | 437 | 3 |
nba_2015_2016 | IIS | NBA 2015-2016 | data.frame | 30 | 19 |
osu_math_salaries_2015 | IIS | OSU Math Salaries 2015 | numeric | | |
pennies_age | IIS | Pennies' Age | numeric | | |
percentage_hatched_eggs | IIS | Percentage Hatched Eggs | data.frame | 9 | 2 |
pew_science_survey_data_by_age_group | IIS | Pew Science Survey Data By Age Group | matrix | 5 | 4 |
pew_science_survey_data_by_party | IIS | Pew Science Survey Data By Party | matrix | 3 | 4 |
pines_1997 | IIS | Pines 1997 | data.frame | 1000 | 15 |
pmn_migration | IIS | PMN Migration | numeric | | |
population_estimates_2015 | IIS | Population Estimates 2015 | data.frame | 50 | 17 |
presidential_election_polls | IIS | Presidential Election Polls | data.frame | 46 | 5 |
proportion_for_profit_hospitals | IIS | Proportion For-Profit Hospitals | data.frame | 20 | 3 |
q2_q4_agricultural_chargeoff_rates | IIS | Q2/Q4 Agricultural Chargeoff Rates | data.frame | 50 | 3 |
reading_habits_2011 | IIS | Reading Habits 2011 | data.frame | 2986 | 7 |
school_report_cards_2014 | IIS | School Report Cards 2014 | data.frame | 484 | 8 |
sheep_weight | IIS | Sheep Weight | data.frame | 20 | 2 |
state_cdi | IIS | State CDI | data.frame | 16 | 3 |
state_poverty_levels_2013 | IIS | State Poverty Levels 2013 | data.frame | 50 | 3 |
tiaa_cref | IIS | TIAA CREF | data.frame | 302 | 8 |
traffic_accidents | IIS | Traffic Accident Data | data.frame | 9 | 2 |
weekly_salaries | IIS | Weekly Salaries | data.frame | 44 | 6 |
weight_of_Euros | IIS | Weight of Euros | data.frame | 2000 | 3 |
performances | keys.lid | Performance results for LIDs | tbl_df | 575 | 16 |
diffFactors | ZIprop | diffFactors | data.table | 483 | 32 |
equineDiffFactors | ZIprop | equineDiffFactors | data.table | 2256 | 8 |
example_data | ZIprop | Zero-inflated proportions dataset | data.table | 440 | 23 |
clc_codes | clc | CLC Codes | character | | |
ExampleData | ClusterVAR | Datasets included in the ClusterVAR package | matrix | 18000 | 8 |
SyntheticData | ClusterVAR | Datasets included in the ClusterVAR package | data.frame | 12998 | 10 |
seal | ctmcmove | Data for one foraging trip by a male northern fur seal (NFS). | list | | |
gcdata | aghq | Globular Clusters data for Milky Way mass estimation | tbl_df | 70 | 25 |
gcdatalist | aghq | Transformed Globular Clusters data | list | | |
data_AirPassengers | rAmCharts | Air passengers for example | data.frame | 144 | 7 |
data_bar | rAmCharts | Random data for plotting bar chart examples | data.frame | 12 | 3 |
data_candleStick1 | rAmCharts | Random data for plotting candlestick chart examples | data.frame | 13 | 5 |
data_candleStick2 | rAmCharts | Random data for plotting candlestick chart examples | data.frame | 12 | 5 |
data_fbar | rAmCharts | Random data for plotting floating bar chart examples | data.frame | 12 | 4 |
data_funnel | rAmCharts | Random data for plotting funnel chart examples | data.frame | 7 | 2 |
data_gantt | rAmCharts | Random data for plotting gantt chart examples | data.frame | 5 | 4 |
data_gbar | rAmCharts | Random data for plotting bar chart examples | data.frame | 5 | 5 |
data_gdp | rAmCharts | 10 Richest Countries in the World by 2015 GDP | data.table | 10 | 2 |
data_mekko | rAmCharts | Random data for plotting mekko chart examples | data.frame | 1000 | 2 |
data_pie | rAmCharts | Random data for plotting pie chart examples | data.frame | 5 | 2 |
data_radar | rAmCharts | Random data for plotting radar chart examples | data.frame | 5 | 4 |
data_stock1 | rAmCharts | Random data for example | list | | |
data_stock_2 | rAmCharts | Random data for example | data.frame | 1416 | 3 |
data_stock_3 | rAmCharts | Random data for example | list | | |
data_waterfall | rAmCharts | Random data for plotting candlestick chart examples | data.frame | 15 | 3 |
data_wind | rAmCharts | Random data for plotting wind chart examples | data.frame | 8 | 3 |
NamePhone | bcscr | Names and Phone Numbers | data.frame | 50 | 2 |
fuel | bcscr | Speed and Fuel Efficiency (British Ford Escort) | data.frame | 15 | 2 |
m111survey | bcscr | MAT 111 Survey | data.frame | 71 | 12 |
railtrail | bcscr | Volume of Users of a Rail Trail | data.frame | 90 | 9 |
nickname | sdtmchecks | Nickname for package version | character | | |
sdtmchecksmeta | sdtmchecks | Metadata for sdtmchecks | tbl_df | 109 | 12 |
Data_Erodibility | SoilConservation | Erodibility dataset. | data.frame | 34 | 6 |
Data_Rainfall_minutes | SoilConservation | Rainfall dataset. | data.frame | 22032 | 3 |
Data_Rainfall_month | SoilConservation | Rainfall dataset. | data.frame | 11 | 13 |
Data_SoilLoss | SoilConservation | Water erosion dataset. | data.frame | 80 | 6 |
nas1982 | betaMC | 1982 National Academy of Sciences Doctoral Programs Data | data.frame | 46 | 7 |
nas1982 | betaDelta | 1982 National Academy of Sciences Doctoral Programs Data | data.frame | 46 | 7 |
air | lovecraftr | The text of H.P. Lovecraft's "Cool air" | character | | |
alchemist | lovecraftr | The text of H.P. Lovecraft's "The Alchemist" | character | | |
azathoth | lovecraftr | The text of H.P. Lovecraft's "Azathoth" | character | | |
beast_cave | lovecraftr | The text of H.P. Lovecraft's "The Beast in the Cave" | character | | |
book | lovecraftr | The text of H.P. Lovecraft's "The Book" | character | | |
call_of_cthulhu | lovecraftr | The text of H.P. Lovecraft's "The Call of Cthulhu" | character | | |
cats | lovecraftr | The text of H.P. Lovecraft's "The Cats of Ulthar" | character | | |
celephais | lovecraftr | The text of H.P. Lovecraft's "Celephaïs" | character | | |
charles_dexter | lovecraftr | The text of H.P. Lovecraft's "The Case of Charles Dexter Ward" | character | | |
city | lovecraftr | The text of H.P. Lovecraft's "The Nameless City" | character | | |
colour_space | lovecraftr | The text of H.P. Lovecraft's "The Colour Out of Space" | character | | |
dagon | lovecraftr | The text of H.P. Lovecraft's "Dagon" | character | | |
descendant | lovecraftr | The text of H.P. Lovecraft's "The Descendant" | character | | |
doom | lovecraftr | The text of H.P. Lovecraft's "The Doom That Came to Sarnath" | character | | |
door_step | lovecraftr | The text of H.P. Lovecraft's "The thing on the door-step" | character | | |
dunwich_horror | lovecraftr | The text of H.P. Lovecraft's "The Dunwich Horror" | character | | |
erich_zann | lovecraftr | The text of H.P. Lovecraft's "The Music of Erich Zann" | character | | |
festival | lovecraftr | The text of H.P. Lovecraft's "The festival" | character | | |
haunter_dark | lovecraftr | The text of H.P. Lovecraft's "The haunter of the dark" | character | | |
he | lovecraftr | The text of H.P. Lovecraft's "He" | character | | |
hound | lovecraftr | The text of H.P. Lovecraft's "The Hound" | character | | |
iranon | lovecraftr | The text of H.P. Lovecraft's "The quest of Iranon" | character | | |
key | lovecraftr | The text of H.P. Lovecraft's "The silver key" | character | | |
lurking | lovecraftr | The text of H.P. Lovecraft's "The lurking fear" | character | | |
mountain_madness | lovecraftr | The text of H.P. Lovecraft's "At the Mountains of Madness" | character | | |
outsider | lovecraftr | The text of H.P. Lovecraft's "The Outsider" | character | | |
reanimator | lovecraftr | The text of H.P. Lovecraft's "Herbert West—Reanimator" | character | | |
red_hook | lovecraftr | The text of H.P. Lovecraft's "The Horror at Red Hook" | character | | |
shadow_innsmouth | lovecraftr | The text of H.P. Lovecraft's "The Shadow Over Innsmouth" | character | | |
shadow_time | lovecraftr | The text of H.P. Lovecraft's "The Shadow out of Time" | character | | |
shunned_house | lovecraftr | The text of H.P. Lovecraft's "The Shunned House" | character | | |
temple | lovecraftr | The text of H.P. Lovecraft's "The Temple" | character | | |
united_amateur | lovecraftr | The text of H.P. Lovecraft's "Writings in the United Amateur" | character | | |
unknown_kadath | lovecraftr | The text of H.P. Lovecraft's "The Dream-Quest of Unknown Kadath" | character | | |
wall_sleep | lovecraftr | The text of H.P. Lovecraft's "Beyond the Wall of Sleep" | character | | |
witch_house | lovecraftr | The text of H.P. Lovecraft's "The Dreams in the Witch House" | character | | |
focusd1 | lemna | A Lemna scenario using FOCUS D1 Ditch environmental conditions | lemna_scenario | | |
focusd2 | lemna | A Lemna scenario using FOCUS D2 Ditch environmental conditions | lemna_scenario | | |
focusr3 | lemna | A Lemna scenario using FOCUS R3 Stream environmental conditions | lemna_scenario | | |
hommen212 | lemna | Observed frond numbers reported by Hommen _et al._ (2015) | tbl_df | 25 | 3 |
metsulfuron | lemna | A Lemna scenario fitted to metsulfuron-methyl effect data | lemna_scenario | | |
schmitt77 | lemna | Observed frond numbers reported by Schmitt _et al._ (2013) | tbl_df | 49 | 3 |
blanden | markovchain | Mobility between income quartiles | table | 4 | 4 |
craigsendi | markovchain | CD4 cells counts on HIV Infects between zero and six month | table | 3 | 3 |
holson | markovchain | Holson data set | data.frame | 1000 | 12 |
kullback | markovchain | Example from Kullback and Kupperman Tests for Contingency Tables | list | | |
preproglucacon | markovchain | Preprogluccacon DNA protein bases sequences | data.frame | 1572 | 2 |
rain | markovchain | Alofi island daily rainfall | data.frame | 1096 | 2 |
sales | markovchain | Sales Demand Sequences | matrix | 269 | 5 |
tm_abs | markovchain | Single Year Corporate Credit Rating Transititions | matrix | 8 | 8 |
lod_data_ex | lodr | Simulated data with covariates subject to limits of detection | data.frame | 100 | 4 |
midsummer | kgrams | A Midsummer Night's Dream | character | | |
much_ado | kgrams | Much Ado About Nothing | character | | |
g_forex | clustAnalytics | Forex correlation network | igraph | | |
simulate_nbfar | nbfar | Simulated data for NBFAR | list | | |
Ecoli.expr | sand | E. coli gene expression levels | matrix | 40 | 153 |
aidsblog | sand | AIDS blog citation network | igraph | | |
calldata | sand | Austrian phone call network data | data.frame | 992 | 7 |
delaydata | sand | Internet packet probes data | data.frame | 9567 | 3 |
elist.lazega | sand | Lazega lawyers network data | data.frame | 115 | 2 |
fblog | sand | Network of French political blogs | igraph | | |
g.bip | sand | A toy bipartite network | igraph | | |
hc | sand | Hospital encounter network data | data.frame | 32424 | 5 |
host.locs | sand | Internet packet probes data | character | | |
lazega | sand | Lazega lawyers network data | igraph | | |
ppi.CC | sand | Yeast protein interaction network | igraph | | |
regDB.adj | sand | E. coli gene expression levels | matrix | 153 | 153 |
strike | sand | Michael's strike network | igraph | | |
v.attr.lazega | sand | Lazega lawyers network data | data.frame | 36 | 9 |
advertisement | rbw | Data on Political Advertisement and Campaign Contributions in US Presidential Elections | data.frame | 16265 | 15 |
campaign_long | rbw | Long-format Data on Negative Campaign Advertising in US Senate and Gubernatorial Elections | data.frame | 565 | 19 |
campaign_wide | rbw | Wide-format Data on Negative Campaign Advertising in US Senate and Gubernatorial Elections | data.frame | 113 | 32 |
peace | rbw | Data on Public Support for War in a Sample of US Respondents | tbl_df | 1273 | 17 |
pact_mpox_priority | pactr | Pandemic PACT Mpox Priorities | tbl_df | 23 | 4 |
pact_research_category | pactr | Pandemic PACT Research Categories | tbl_df | 70 | 4 |
who_country_info | pactr | World Health Organization (WHO) Country Information | tbl_df | 194 | 5 |
nas1982 | betaNB | 1982 National Academy of Sciences Doctoral Programs Data | data.frame | 46 | 7 |
sample_dickson1981 | AquaEnv | sample\_dickson1981 | matrix | 51 | |
sample_dickson2007 | AquaEnv | sample\_dickson2007 | data.frame | 21 | 2 |
Transport | mclogit | Choice of Means of Transport | data.frame | 30 | 7 |
electors | mclogit | Class, Party Position, and Electoral Choice | data.frame | 450 | 7 |
Rdata | remss | Simulation data for Genetic association models for X-chromosome SNPS | data.frame | 504 | 10 |
airline_delay | usdata | Airline Delays for December 2019 and 2020. | tbl_df | 3351 | 21 |
county | usdata | United States Counties | tbl_df | 3142 | 15 |
county_2019 | usdata | American Community Survey 2019 | data.frame | 3142 | 95 |
county_complete | usdata | United States Counties | data.frame | 3142 | 188 |
fatal_police_shootings | usdata | Fatal Police Shootings data. | tbl_df | 6421 | 12 |
gerrymander | usdata | Gerrymander | tbl_df | 435 | 12 |
govrace10 | usdata | Election results for 2010 Governor races in the U.S. | tbl_df | 37 | 23 |
houserace10 | usdata | Election results for the 2010 U.S. House of Represenatives races | tbl_df | 435 | 24 |
pierce_county_house_sales | usdata | Pierce County House Sales Data for 2020 | data.frame | 16814 | 19 |
pop_age_2019 | usdata | Population Age 2019 Data. | spec_tbl_df | 4386 | 5 |
pop_race_2019 | usdata | Population Race 2019 Data. | spec_tbl_df | 408 | 6 |
prez_pwr | usdata | Presidential Power. | tbl_df | 365 | 3 |
prrace08 | usdata | Election results for the 2008 U.S. Presidential race | tbl_df | 51 | 7 |
senaterace10 | usdata | Election results for the 2010 U.S. Senate races | tbl_df | 38 | 23 |
state_stats | usdata | State-level data | tbl_df | 51 | 24 |
urban_owner | usdata | Summary of many state-level variables | tbl_df | 52 | 28 |
urban_rural_pop | usdata | State summary info | tbl_df | 51 | 5 |
us_crime_rates | usdata | US Crime Rates | spec_tbl_df | 60 | 12 |
us_temp | usdata | US Temperature Data | tbl_df | 10118 | 9 |
us_time_survey | usdata | American Time Survey 2009 - 2019 | tbl_df | 11 | 8 |
vote_nsa | usdata | Predicting who would vote for NSA Mass Surveillance | tbl_df | 434 | 5 |
voter_count | usdata | US Voter Turnout Data. | spec_tbl_df | 936 | 7 |
SimX | MatrixMixtures | Simulated Data | list | | |
rsd | REPTILE | REPTILE sample data (rsd) | list | | |
CMAPSS | CMAPSS | CMAPSS data set | list | | |
btsdemo | panelaggregation | Randomly Generated Panel Dataset | data.table | 27000 | 13 |
resX | reservoir | Reservoir X inflow time series and reservoir detail | list | | |
data51 | datastat | Data set number 1 for descriptive statistics | data.frame | 10 | 1 |
data52 | datastat | Data set number 2 for descriptive statistics | data.frame | 5 | 2 |
data53 | datastat | Data set number 3 for descriptive statistics | data.frame | 10 | 1 |
data54 | datastat | Data set number 4 for descriptive statistics | data.frame | 5 | 3 |
data81 | datastat | Data set for statistical hypothesis testing | data.frame | 10 | 2 |
data82 | datastat | Data set for the paired t-test | data.frame | 15 | 2 |
data91 | datastat | Data set for simple linear regression model | data.frame | 9 | 2 |
data92 | datastat | Data set for multiple linear regression model | data.frame | 12 | 3 |
betablockers | nspmix | Beta-blockers Data | matrix | 44 | 4 |
betablockers | nspmix | Beta-blockers Data | matrix | 44 | 4 |
brca | nspmix | Z-values of BRCA Data | numeric | | |
lungcancer | nspmix | Lung Cancer Data | matrix | 28 | 4 |
thai | nspmix | Illness Spells and Frequencies of Thai Preschool Children | data.frame | 24 | 2 |
toxo | nspmix | Toxoplasmosis Data | matrix | 34 | 4 |
inflation_data | hdflex | Quarterly U.S. Inflation Dataset (Total CPI) | matrix | 245 | 462 |
mutag | graphkernels | The mutag dataset | list | | |
Mustangs | mosaic | Mustang Prices | data.frame | 25 | 3 |
Sleep | mosaic | Sleep and Memory | data.frame | 24 | 2 |
HR_data | breakDown | Why are our best and most experienced employees leaving prematurely? | data.frame | 14999 | 10 |
wine | breakDown | White Wine Quality Data | data.frame | 4898 | 12 |
cancer_pathways | kernscr | 70 pathways from MSigDB c2CP | list | | |
downs.mi | dsrTest | Downs' syndrome cases and of total live births by maternal age and birth order, Michigan, 1950-1964. | data.frame | 30 | 5 |
sim_data_sce | SCIntRuler | My Example Dataset | SingleCellExperiment | | |
sim_result | SCIntRuler | My Example Dataset | list | | |
fly | ggmosaic | Flying Etiquette Survey Data | spec_tbl_df | 1040 | 27 |
happy | ggmosaic | Data related to happiness from the general social survey. | data.frame | 64814 | 12 |
titanic | ggmosaic | Passengers and crew on board the Titanic | data.frame | 2201 | 4 |
barleyMQM | statgenMPP | Pre-computed MQM output barley | QTLMPP | | |
barleyPheno | statgenMPP | Phenotypic data for awn length in barley | data.frame | 916 | 3 |
maizeMQM | statgenMPP | Pre-computed MQM output maize | QTLMPP | | |
maizeSQM | statgenMPP | Pre-computed SQM output maize | QTLMPP | | |
rawCCD | sitepickR | Common Core of Data (CCD) data for California schools (2017-18). | data.frame | 1890 | 12 |
airports | airportr | Table of airport detail data | tbl_df | 7698 | 17 |
data | bhm | dataset | matrix | 300 | 4 |
mortality | actLifer | A sample mortality data | tbl_df | 85 | 3 |
mortality2 | actLifer | A sample mortality data | tbl_df | 85 | 3 |
mortality3 | actLifer | A sample mortality data | tbl_df | 170 | 4 |
AIDSBlogs | MBCbook | The AIDSBlogs data set | network | | |
Advice | MBCbook | The Advice data set from Lazega (2001) | network | | |
Coworker | MBCbook | The Coworker data set from Lazega (2001) | network | | |
Friend | MBCbook | The Friend data set from Lazega (2001) | network | | |
NIR | MBCbook | The chemometrics near-infrared (NIR) data set | data.frame | 202 | 2801 |
PoliticalBlogs | MBCbook | The political blog data set | network | | |
UScongress | MBCbook | The US congress vote data set | data.frame | 434 | 17 |
amazonFineFoods | MBCbook | The Amazon Fine Foods data set | dgCMatrix | | |
credit | MBCbook | The Credit data set | data.frame | 66 | 11 |
puffin | MBCbook | The puffin data set | data.frame | 69 | 6 |
usps358 | MBCbook | The handwritten digits usps358 data set | data.frame | 1756 | 257 |
velib2D | MBCbook | The bivariate Vélib data set | list | | |
velibCount | MBCbook | The discrete version (count data) of the Vélib data set | list | | |
wine27 | MBCbook | The (27-dimensional) Italian Wine data set | data.frame | 178 | 29 |
ACS | ciccr | ACS | data.frame | 17816 | 4 |
ACS_CC | ciccr | ACS_CC | data.frame | 1842 | 4 |
ACS_CP | ciccr | ACS_CP | data.frame | 1842 | 4 |
DZ_CC | ciccr | DZ_CC | data.frame | 689 | 5 |
FG | ciccr | FG | data.frame | 78165 | 5 |
FG_CC | ciccr | FG_CC | data.frame | 4522 | 5 |
FG_CP | ciccr | FG_CP | data.frame | 4522 | 5 |
data | emBayes | simulated gene expression example data | list | | |
Hotel_Long | RMM | Data from a Major Hotel Chain | tbl_df | 8318 | 11 |
Hotel_Wide | RMM | Data from a Major Hotel Chain | tbl_df | 1100 | 22 |
MARMoT_data | MARMoT | Data to showcase MARMoT and ASB functions | data.frame | 8450 | 3 |
deloof_data | MARMoT | Data to showcase deloof and mcdeloof functions | data.frame | 8450 | 2 |
admix | adwave | Simulated Admixed Population Data | list | | |
tideObservation | TideCurves | Sample file of high and low water times and heights | data.frame | 10267 | 3 |
dataset001 | FLR | dataset001 | data.frame | 296 | 25 |
mat | FLR | Graph distance matrix | data.frame | 9 | 9 |
gastro_data | gmmsslm | Gastrointestinal dataset | spec_tbl_df | 76 | 7 |
data_inputparam1 | Ritc | An example set of input parameter data for the function 'fititcdata' | data.frame | 9 | 2 |
data_origin1 | Ritc | An example set of ITC data as exported by Origin7 data sheet | data.frame | 29 | 7 |
dives | diveMove | Sample of TDR data from a fur seal | data.frame | 34199 | 6 |
divesTDR | diveMove | Sample of TDR data from a fur seal | TDRspeed | | |
divesTDRzoc | diveMove | Sample of TDR data from a fur seal | TDRspeed | | |
sealLocs | diveMove | Ringed and Gray Seal ARGOS Satellite Location Data | data.frame | 369 | 5 |
NV_games | datafsm | Empirical prisoner's dilemma games from Nay and Vorobeychik | data.frame | 168386 | 51 |
aggregate_design | idefix | Discrete choice aggregate design. | data.frame | 112 | 9 |
example_design | idefix | Discrete choice design. | matrix | 16 | 6 |
example_design2 | idefix | Discrete choice design. | matrix | 24 | 8 |
nochoice_design | idefix | Discrete choice design with no choice option. | matrix | 24 | 7 |
AFL | season | Australian Football League (AFL) Players' Birthdays for the 2009 Season | list | | |
CVD | season | Cardiovascular Deaths in Los Angeles, 1987-2000 | data.frame | 168 | 8 |
CVDdaily | season | Daily Cardiovascular Deaths in Los Angeles, 1987-2000 | data.frame | 5114 | 17 |
exercise | season | Exercise Data from Queensland, 2005-2007 | data.frame | 1302 | 7 |
indoor | season | Indoor Temperature Data | data.frame | 2021 | 3 |
schz | season | Schizophrenia Births in Australia, 1930-1971 | data.frame | 504 | 6 |
stillbirth | season | Stillbirths in Queensland, 1998-2000 | data.frame | 60110 | 7 |
pisaUSA15 | prcr | student questionnaire data with four variables from the 2015 PISA for students in the United States | tbl_df | 5712 | 4 |
EX | lifelogr | A subset of the data for one user for about one month, from 2017-01-19 to 2017-02-17, containing 'fitbit_daily', 'fitbit_intraday', and 'util' data frames. | Person | | |
GTEx.PrCa.Amatrix | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | matrix | 447 | 167 |
GTEx.PrCa.Geno | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | data.frame | 503 | 447 |
GTEx.PrCa.IVWmarginal.A | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | matrix | 158 | 182 |
GTEx.PrCa.betas.gwas | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | numeric | | |
GTEx.PrCa.betas.se.gwas | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | numeric | | |
GTEx.PrCa.inclusion.indicator | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | matrix | 447 | |
GTEx.PrCa.maf.gwas | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | numeric | | |
GTEx.PrCa.marginal.A | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | matrix | 447 | 167 |
GTEx.PrCa.marginal.A.se | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | matrix | 447 | 183 |
GTEx.PrCa.pvalues.gwas | hJAM | Real data for selecting the genes on chromosome 10 for the risk of prostate cancer | numeric | | |
MI.Amatrix | hJAM | Example data of hJAM | matrix | 210 | 2 |
MI.Geno | hJAM | Example data of hJAM | data.frame | 2467 | 210 |
MI.SNPs_info | hJAM | Example data of hJAM | data.frame | 210 | 5 |
MI.betas.gwas | hJAM | Example data of hJAM | numeric | | |
MI.marginal.Amatrix | hJAM | Example data of hJAM | matrix | 210 | 2 |
PrCa.lipids.Amatrix | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | matrix | 140 | 26 |
PrCa.lipids.Geno | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | data.frame | 503 | 140 |
PrCa.lipids.betas.gwas | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | numeric | | |
PrCa.lipids.betas.se.gwas | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | numeric | | |
PrCa.lipids.maf.gwas | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | numeric | | |
PrCa.lipids.marginal.Amatrix | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | matrix | 140 | 26 |
PrCa.lipids.pvalue.gwas | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | data.frame | 140 | 49 |
PrCa.lipids.rsid | hJAM | Real data for selecting the metabolites for the risk of prostate cancer | character | | |
Simulation.Amatrix | hJAM | Simulation data for EN-hJAM | matrix | 300 | 50 |
Simulation.Geno | hJAM | Simulation data for EN-hJAM | matrix | 500 | |
Simulation.betas.gwas | hJAM | Simulation data for EN-hJAM | numeric | | |
Simulation.betas.se.gwas | hJAM | Simulation data for EN-hJAM | numeric | | |
Simulation.maf.gwas | hJAM | Simulation data for EN-hJAM | numeric | | |
data | BClustLonG | Simulated dataset for testing the algorithm | list | | |
FARS | gamclass | US fatal road accident data for automobiles, 1998 to 2010 | data.frame | 134332 | 16 |
airAccs | gamclass | Aircraft Crash data | data.frame | 5666 | 7 |
bomregions2018 | gamclass | Australian and Related Historical Annual Climate Data, by Region | data.frame | 119 | 35 |
bronchitis | gamclass | Chronic bronchitis in a sample of men in Cardiff | data.frame | 212 | 4 |
coralPval | gamclass | P-values from biological expression array data | matrix | 3072 | 1 |
cvalues | gamclass | Historical speed of light measurements | data.frame | 9 | 3 |
fars2007 | gamclass | US Fatal Road Accident Data, 2007 and 2008 | data.frame | 24179 | 19 |
fars2008 | gamclass | US Fatal Road Accident Data, 2007 and 2008 | data.frame | 22113 | 20 |
frontDeaths | gamclass | Safety Device effectiveness Measures, by Year | list | | |
german | gamclass | German credit scoring data | data.frame | 1000 | 21 |
greatLakesM | gamclass | Monthly Great Lake heights: 1918 - 2019 | data.frame | 1212 | 7 |
loti | gamclass | Global temperature anomalies | data.frame | 140 | 20 |
otherDeaths | gamclass | Safety Device effectiveness Measures, by Year | list | | |
rearDeaths | gamclass | Safety Device effectiveness Measures, by Year | list | | |
relDeaths | gamclass | Yearly Driver deaths, as Fraction of Deaths for All Years | list | | |
sideDeaths | gamclass | Safety Device effectiveness Measures, by Year | list | | |
brauer_2008 | romic | Brauer 2008 | tbl_df | 18000 | 8 |
brauer_2008_tidy | romic | Brauer 2008 | tidy_omic | | |
brauer_2008_triple | romic | Brauer 2008 | triple_omic | | |
cemfgs_rb | diathor | CEMFGS_RB | data.frame | 495 | 2 |
dbc_offline | diathor | DBC (offline) | tbl_df | 8136 | 112 |
des | diathor | DES | data.frame | 528 | 3 |
diat_sampleData | diathor | Sample Data | data.frame | 164 | 109 |
disp | diathor | DISP | data.frame | 141 | 3 |
epid | diathor | EPID | data.frame | 1070 | 3 |
idap | diathor | IDAP | data.frame | 194 | 3 |
idch | diathor | ID-CH | data.frame | 551 | 3 |
idp | diathor | IDP | data.frame | 298 | 2 |
ilm | diathor | ILM | data.frame | 495 | 3 |
ips | diathor | IPS | data.frame | 6914 | 3 |
lobo | diathor | LOBO | data.frame | 297 | 3 |
pbidw | diathor | PBIDW | data.frame | 79 | 2 |
sla | diathor | SLA | data.frame | 976 | 3 |
spear | diathor | SPEAR(h) | data.frame | 300 | 2 |
taxaList | diathor | taxaList | data.frame | 9806 | 1 |
tdi | diathor | TDI | data.frame | 4146 | 3 |
DFR | lazytrade | Table with predicted price change | tbl_df | 35 | 8 |
EURUSDM15X75 | lazytrade | Table with indicator and price change dataset | tbl_df | 184 | 76 |
TradeStatePolicy | lazytrade | Table with Trade States and sample of actual policy for those states | data.frame | 2 | 2 |
data_trades | lazytrade | Table with Trade results samples | tbl_df | 26 | 7 |
indicator_dataset | lazytrade | Table with indicator dataset | tbl_df | 1000 | 29 |
macd_100 | lazytrade | Table with indicator only used to train model, 128 col 1646 rows | tbl_df | 128 | 28 |
macd_ML60M | lazytrade | Table with indicator and market type category used to train model | data.frame | 251 | 65 |
macd_df | lazytrade | Table with one column indicator dataset | tbl_df | 64 | 1 |
policy_tr_systDF | lazytrade | Table with Market Types and sample of actual policy for those states | data.frame | 6 | 2 |
price_dataset | lazytrade | Table with price dataset | tbl_df | 1000 | 29 |
price_dataset_big | lazytrade | Table with price dataset, 30000 rows | spec_tbl_df | 5000 | 29 |
profit_factorDF | lazytrade | Table with Trade results samples | grouped_df | 121 | 7 |
profit_factor_data | lazytrade | Table with Trade results samples | tbl_df | 216 | 7 |
result_R | lazytrade | Table with predicted price change | data.frame | 271 | 1 |
result_R1 | lazytrade | Table with aggregated trade results | data.frame | 600 | 1 |
result_prev | lazytrade | Table with one column as result from the model prediction | data.frame | 1048 | 1 |
test_data_pattern | lazytrade | Table with several columns containing indicator values and Label values | tbl_df | 1048 | 101 |
trading_systemDF | lazytrade | Table with trade data and joined market type info | tbl_df | 16 | 9 |
x_test_model | lazytrade | Table with a dataset to test the Model | tbl_df | 271 | 76 |
y | lazytrade | Table with indicators and price change which is used to train model | tbl_df | 2200 | 20 |
AERparams | EpiReport | Dataset describing the parameters for the epidemiological report production | data.frame | 53 | 24 |
DENGUE2019 | EpiReport | Dataset including Dengue data for 2015-2019 | data.frame | 44332 | 11 |
MSCode | EpiReport | Dataset correspondence table between country names and country code | data.frame | 32 | 4 |
SALM2016 | EpiReport | Dataset including Salmonellosis data for 2012-2016 | data.frame | 60775 | 18 |
aggregation | pald | Aggregation | spec_tbl_df | 788 | 2 |
cognate_dist | pald | Cognate Data Distance Matrix | dist | | |
cultures | pald | Cultures pairwise dissimilarities | matrix | 59 | 59 |
exdata1 | pald | Example Data 1 | tbl_df | 8 | 2 |
exdata2 | pald | Example Data 2 | tbl_df | 16 | 2 |
exdata3 | pald | Example Data 3 | tbl_df | 240 | 2 |
noisy_circles | pald | Noisy circles | spec_tbl_df | 500 | 2 |
noisy_moons | pald | Noisy moons | spec_tbl_df | 500 | 2 |
pald_colors | pald | PaLD Color Palette | character | | |
tissue_dist | pald | Tissue Data Distance Matrix | dist | | |
importexample | qmethod | Import Example | list | | |
lipset | qmethod | _Lipset_ (1963) Q methodology dataset | list | | |
accrualdemo | accrualPlot | Demonstration data set | data.frame | 250 | 2 |
data_binary | clusterSim | Binary data | data.frame | 8 | 10 |
data_interval | clusterSim | Interval data | data.frame | 75 | 5 |
data_mixed | clusterSim | Mixed data | data.frame | 25 | 4 |
data_nominal | clusterSim | Nominal data | data.frame | 26 | 12 |
data_ordinal | clusterSim | Ordinal data | data.frame | 26 | 12 |
data_patternGDM1 | clusterSim | Metric data with 17 objects and 10 variables (8 stimulant variables, 2 destimulant variables) | data.frame | 17 | 10 |
data_patternGDM2 | clusterSim | Ordinal data with 27 objects and 6 variables (3 stimulant variables, 2 destimulant variables and 1 nominant variable) | data.frame | 27 | 6 |
data_ratio | clusterSim | Ratio data | data.frame | 75 | 5 |
data_symbolic | clusterSim | Symbolic interval data | array | | |
data_symbolic_interval_polish_voivodships | clusterSim | The evaluation of Polish voivodships tourism attractiveness level | array | | |
EPL2008_2015 | piratings | English Premier League match outcomes | data.frame | 3040 | 5 |
fatigue | incubate | Small data sets from different publications | numeric | | |
pollution | incubate | Small data sets from different publications | numeric | | |
publication_examples | incubate | Small data sets from different publications | numeric | | |
stankovic | incubate | Survival of mice with glioma under different treatments | tbl_df | 45 | 5 |
susquehanna | incubate | Small data sets from different publications | numeric | | |
greece | FluMoDL | Greece mortality and influenza data | list | | |
pbc | NPCox | Mayo Clinic Primary Biliary Cholangitis Data | data.frame | 418 | 20 |
ecorelevance | mbRes | Biomarker Responses of the Blue Mussels to Organic UV Filters | tbl_df | 30 | 2 |
sokolova2021 | mbRes | Biomarker Responses of the Blue Mussels to Organic UV Filters | tbl_df | 30 | 31 |
OZrain | VLMC | Daily Rainfall in Melbourne, Australia, 1981-1990 | ts | | |
bnrf1EB | VLMC | BNRF1 Gene DNA sequences: Epstein-Barr and Herpes | factor | | |
bnrf1HV | VLMC | BNRF1 Gene DNA sequences: Epstein-Barr and Herpes | factor | | |
assump_data | befproj | assumptions | data.frame | 1111 | 14 |
startpop_data | befproj | Startpopulation | data.frame | 101 | 3 |
kin.data | kin.cohort | sample data for kin-cohort analysis | data.frame | 15820 | 6 |
diabetes | SurvCorr | Diabetes Data | data.frame | 197 | 9 |
kidney | SurvCorr | Kidney Data | data.frame | 38 | 5 |
colon | HTLR | Colon Tissues | list | | |
diabetes392 | HTLR | Pima Indians Diabetes | list | | |
B2721 | qtlpoly | Autotetraploid potato dataset | mappoly.data | | |
hexafake | qtlpoly | Simulated autohexaploid dataset. | mappoly.data | | |
maps4x | qtlpoly | Autotetraploid potato map | list | | |
maps6x | qtlpoly | Simulated autohexaploid map | list | | |
pheno4x | qtlpoly | Autotetraploid potato phenotypes | data.frame | 143 | 3 |
pheno6x | qtlpoly | Simulated phenotypes | data.frame | 300 | 3 |
ChemicalManufacturingProcess | lionfish | Chemical Manufacturing Process Dataset | data.frame | 176 | 58 |
ausActiv | lionfish | Australian Vacation Activities Dataset | matrix | 1003 | 45 |
risk | lionfish | Risk Dataset | data.frame | 563 | 6 |
winterActiv | lionfish | Austrian Vacation Activities Dataset | matrix | 2961 | 27 |
ODMeansSampleData | ODMeans | Origin-Destination points | data.frame | 1700 | 5 |
ODMeansTaxiData | ODMeans | Origin-Destination Taxi data | data.frame | 452166 | 4 |
cla_train | superml | cla_train | data.table | 891 | 12 |
reg_train | superml | reg_train | data.table | 1460 | 81 |
counties | urbnmapr | County shapefile data | tbl_df | 208874 | 12 |
countydata | urbnmapr | County data for mapping | tbl_df | 3142 | 5 |
statedata | urbnmapr | State data for mapping | tbl_df | 51 | 6 |
states | urbnmapr | State shapefile data | tbl_df | 83933 | 9 |
geccoIC2018Test | EventDetectR | geccoIC2018Test | data.frame | 139566 | 11 |
geccoIC2018Train | EventDetectR | geccoIC2018Train | data.frame | 139566 | 11 |
stationBData | EventDetectR | stationBData | data.frame | 7200 | 13 |
rawDrugNamesCoOcEPILONT | epos | List drug terms with their frequency co-occurring with terms from the EPILONT ontology in publications since 2015 from the BioASQ 2020 corpus. | character | | |
rawDrugNamesCoOcEPISEM | epos | List drug terms with their frequency co-occurring with terms from the EPISEM ontology in publications since 2015 from the BioASQ 2020 corpus. | character | | |
rawDrugNamesCoOcESSO | epos | List drug terms with their frequency co-occurring with terms from the ESSO ontology in publications since 2015 from the BioASQ 2020 corpus. | character | | |
rawDrugNamesCoOcEpSO | epos | List drug terms with their frequency co-occurring with terms from the EpSO ontology in publications since 2015 from the BioASQ 2020 corpus. | character | | |
rawDrugNamesCoOcFENICS | epos | List drug terms with their frequency co-occurring with terms from the FENICS ontology in publications from the BioASQ 2020 corpus. | character | | |
anchor_pair_example_count | spatzie | spatzie count correlation data set | interactionData | | |
anchor_pair_example_match | spatzie | spatzie match association data set | interactionData | | |
anchor_pair_example_score | spatzie | spatzie score correlation data set | interactionData | | |
compare_pairs_example | spatzie | compare_motif_pairs example | matrix | 5 | 5 |
filter_pairs_example | spatzie | spatzie score correlation filtered data set | interactionData | | |
int_data_k562 | spatzie | K562 Enhancer - Promoter Interactions Data Set | interactionData | | |
int_data_mslcl | spatzie | MSLCL Enhancer - Promoter Interactions Data Set | interactionData | | |
int_data_yy1 | spatzie | Mouse YY1 Enhancer - Promoter Interactions Data Set | interactionData | | |
interactions_yy1 | spatzie | Mouse YY1 Enhancer - Promoter Interactions Data Set | GenomicInteractions | | |
interactions_yy1_enhancer | spatzie | Mouse YY1 Enhancer - Promoter Interactions Data Set - YY1 enhancers | GenomicInteractions | | |
interactions_yy1_ep | spatzie | Mouse YY1 Enhancer - Promoter Interactions Data Set - YY1 enhancers/promoters | GenomicInteractions | | |
interactions_yy1_promoter | spatzie | Mouse YY1 Enhancer - Promoter Interactions Data Set - YY1 promoters | GenomicInteractions | | |
scan_interactions_example | spatzie | Interactions scanned for motifs - interactionData object | interactionData | | |
scan_interactions_example_filtered | spatzie | Interactions with motifs filtered for significance - interactionData object | interactionData | | |
LDAdata | LDAcoop | LDA (limiting dilution assay) data from a set of cell lines | data.frame | 1677 | 6 |
teachsat | r2mlm | Teacher job satisfaction. | data.frame | 9000 | 8 |
EBMTdata | dscoreMSM | European Bone Marrow Transplantation data obtained from 'mstate' r package | data.frame | 2204 | 13 |
EBMTupdate | dscoreMSM | European Bone Marrow Transplantation data obtained from 'mstate' r package. This is the updated data obtained after applying SPSM. | data.frame | 2204 | 13 |
simulated_data | dscoreMSM | Simulated multistate data | data.frame | 100 | 7 |
sanderlings | moult | Sanderling Moult Data | data.frame | 164 | 2 |
weavers | moult | Weaver Moult Data | data.frame | 5282 | 4 |
ROCR.hiv | ROCR | Data set: Support vector machines and neural networks applied to the prediction of HIV-1 coreceptor usage | list | | |
ROCR.simple | ROCR | Data set: Simple artificial prediction data for use with ROCR | list | | |
ROCR.xval | ROCR | Data set: Artificial cross-validation data for use with ROCR | list | | |
toy | collinear | One response and four predictors with varying levels of multicollinearity | data.frame | 2000 | 5 |
vi | collinear | Example Data With Different Response and Predictor Types | data.frame | 30000 | 68 |
vi_predictors | collinear | All Predictor Names in Example Data Frame vi | character | | |
vi_predictors_categorical | collinear | All Categorical and Factor Predictor Names in Example Data Frame vi | character | | |
vi_predictors_numeric | collinear | All Numeric Predictor Names in Example Data Frame vi | character | | |
HolzingerSwineford | equaltestMI | Holzinger and Swineford (1939) cognitive tests data in 301 children from two schools | data.frame | 301 | 12 |
LeeAlOtaiba | equaltestMI | Lee and Al Otaiba (2015) early literacy skills in four socioeconomic groups | list | | |
events_long | longitudinalcascade | Simulated sample data | data.frame | 60720 | 4 |
Arkansas | SiZer | Time Series of Macroinvertabrates Abundance in the Arkansas River. | data.frame | 90 | 2 |
drS.eg | AssocTests | A toy similarity matrix for dr | matrix | 400 | |
Bizkaia_data | TreeDep | Weather and environmental hourly data in Bizkaia province, Spain | data.frame | 8784 | 16 |
data1X | PDMIF | A synthesized input variable dataset to fit a linear model on a panel dataset. | matrix | 5000 | |
data1Y | PDMIF | A synthesized output variable dataset to fit a linear model on a panel dataset. | matrix | 100 | |
data2X | PDMIF | A synthesized input variable dataset to fit a binomial model on a panel dataset. | matrix | 5000 | |
data2Y | PDMIF | A synthesized output variable dataset to fit a binomial model on a panel dataset. | matrix | 50 | |
data3X | PDMIF | A synthesized input variable dataset to fit a poisson model on a panel dataset. | matrix | 5000 | |
data3Y | PDMIF | A synthesized output variable dataset to fit a poisson model on a panel dataset. | matrix | 50 | |
data4LAB | PDMIF | A synthesized vector of memberships needed to fit a linear model on a panel dataset under known group memberships. | integer | | |
data4X | PDMIF | A synthesized input variable dataset to fit a linear model on a panel dataset under known group memberships. | matrix | 30000 | |
data4Y | PDMIF | A synthesized output variable dataset to fit a linear model on a panel dataset under known group memberships. | matrix | 100 | |
data5X | PDMIF | A synthesized input variable dataset to cluster individuals by heterogeneous panel data models with interactive effects. | matrix | 30000 | |
data5Y | PDMIF | A synthesized output variable dataset to cluster individuals by heterogeneous panel data models with interactive effects. | matrix | 100 | |
data6X | PDMIF | A synthesized input variable dataset to cluster individual units by nonlinear heterogeneous panel data models with interactive effects when the group membership is unknown | matrix | 4500 | |
data6Y | PDMIF | A synthesized output variable dataset to cluster individual units by nonlinear heterogeneous panel data models with interactive effects when the group membership is unknown. | matrix | 50 | |
data7X | PDMIF | A synthesized input variable dataset to fit a quantile panel data model on a panel dataset. | matrix | 20000 | |
data7Y | PDMIF | A synthesized output variable dataset to fit a quantile panel data model on a panel dataset. | matrix | 100 | |
data8Y | PDMIF | A synthesized output variable dataset to fit a quantile VAR model with interactive effects and lag=2. | matrix | 102 | |
nlcvRF_R | nlcv | nlcv results on random data with random forest feature selection | nlcv | | |
nlcvRF_SHS | nlcv | nlcv results on strong hetero signal data with random forest feature selection | nlcv | | |
nlcvRF_SS | nlcv | nlcv results on strong signal data a with random forest feature selection | nlcv | | |
nlcvRF_WHS | nlcv | nlcv results on weak signal data with random forest feature selection | nlcv | | |
nlcvRF_WS | nlcv | nlcv results on weak hetero signal data with random forest feature selection | nlcv | | |
nlcvTT_R | nlcv | nlcv results on random data with t-test feature selection | nlcv | | |
nlcvTT_SHS | nlcv | nlcv results on strong hetero signal data with t-test feature selection | nlcv | | |
nlcvTT_SS | nlcv | nlcv results on strong signal data a with t-test feature selection | nlcv | | |
nlcvTT_WHS | nlcv | nlcv results on weak signal data with t-test feature selection | nlcv | | |
nlcvTT_WS | nlcv | nlcv results on weak hetero signal data with t-test feature selection | nlcv | | |
chest | BayesNetBP | A simulated data from the Chest Clinic example | list | | |
emission | BayesNetBP | A ClusterTree Example of Emission Model | ClusterTree | | |
emission1000 | BayesNetBP | A simulated data from the Emission example | list | | |
liver | BayesNetBP | Mus Musculus HDL QTL data from Leduc et. al. (2012) | list | | |
toytree | BayesNetBP | A ClusterTree Example of Liver Model | ClusterTree | | |
yeast | BayesNetBP | Saccharomyces Cerevisiae eQTL data from Kruglak et. al. (2005) | data.frame | 112 | 50 |
prostate.dat | QHScrnomo | Prostate cancer data set | data.frame | 2000 | 9 |
argo2016 | GpGp | Ocean temperatures from Argo profiling floats | data.frame | 32436 | 6 |
jason3 | GpGp | Windspeed measurements from Jason-3 Satellite | data.frame | 18973 | 4 |
Cell07PNs | nat | Cell07PNs: 40 Sample Projection Neurons from Jefferis, Potter et al 2007 | neuronlist | | |
MBL.surf | nat | Surface object (hxsurf) for the left mushroom body in FCWB template space | hxsurf | | |
dl1neuron | nat | Olfactory Projection Neuron reconstructed from EM data | catmaidneuron | | |
kcs20 | nat | List of 20 Kenyon Cells from Chiang et al 2011 converted to dotprops objects | neuronlist | | |
US.mc.grids | convoSPAT | Mixture component grids for the western United States | list | | |
US.prediction.locs | convoSPAT | Prediction locations for the western United States | matrix | 12884 | |
USprecip97 | convoSPAT | Annual precipitation measurements from the western United States, 1997 | data.frame | 1270 | 4 |
simdata | convoSPAT | Simulated nonstationary dataset | list | | |
BMT | survminer | Bone Marrow Transplant | data.frame | 35 | 3 |
BRCAOV.survInfo | survminer | Breast and Ovarian Cancers Survival Information | data.frame | 1674 | 4 |
myeloma | survminer | Multiple Myeloma Data | data.frame | 256 | 11 |
geccoIC2018Test | EventDetectGUI | geccoIC2018Test | data.frame | 139566 | 11 |
geccoIC2018Train | EventDetectGUI | geccoIC2018Train | data.frame | 139566 | 11 |
stationBData | EventDetectGUI | stationBData | data.frame | 7200 | 13 |
arma_forecast | tstests | Sample ARMA Forecast Data | data.table | 250 | 6 |
garch_forecast | tstests | Sample GARCH Forecast Data | data.table | 250 | 6 |
spy | tstests | SPY ETF Adjusted Close | xts | 7597 | 1 |
auxco | occupationMeasurement | German Auxiliary Classification of Occupations (AuxCO) v1.2.3 | list | | |
isco_08_en | occupationMeasurement | Categories of the The International Standard Classification of Occupations - ISCO-08 | data.table | 619 | 3 |
pretrained_models | occupationMeasurement | Pretrained ML models to be used with the package. | list | | |
Klovan_2D_all_outlier | klovan | Klovan mining dataset | spec_tbl_df | 208 | 13 |
Klovan_Row80 | klovan | Klovan mining dataset | spec_tbl_df | 80 | 13 |
CITEseq_example | CiteFuse | A subset of ECCITE-seq data (control) | list | | |
lr_pair_subset | CiteFuse | A subset of Ligand Receptor Pairs | matrix | 50 | |
sce_control_subset | CiteFuse | A SingleCellExperiment of ECCITE-seq data | SingleCellExperiment | | |
sce_ctcl_subset | CiteFuse | A SingleCellExperiment of ECCITE-seq data | SingleCellExperiment | | |
crs_120E | himach | Asia-centred coordinate reference system | character | | |
crs_Atlantic | himach | Atlantic-centred coordinate reference system | character | | |
crs_N | himach | Arctic-centred coordinate reference system | character | | |
crs_Pacific | himach | Pacific-centred coordinate reference system | character | | |
crs_S | himach | Antarctic-centred coordinate reference system | character | | |
crs_longlat | himach | Lat-long coordinate reference system | character | | |
mach_kph | himach | Speed of sound, for Mach to km conversion | numeric | | |
mcompd | multilevelcoda | Multilevel Compositional Data | data.table | 3540 | 10 |
psub | multilevelcoda | Possible Pairwise Substitutions | data.table | 20 | 5 |
sbp | multilevelcoda | Sequential Binary Partition | matrix | 4 | 5 |
sim | multilevelcoda | multilevelcoda Simulation Study results | list | | |
pupil_data | PupillometryR | Data collected in a pupillometry study by Sylvain Sirois | data.frame | 28800 | 7 |
jagged_lines | smoothr | Jagged lines for smoothing | sf | 9 | 5 |
jagged_lines_3d | smoothr | 3D jagged line with Z-dimension for smoothing | sf | 2 | 4 |
jagged_polygons | smoothr | Jagged polygons for smoothing | sf | 9 | 5 |
jagged_raster | smoothr | Simulated raster for polygonizing and smoothing | PackedSpatRaster | | |
keeley | piecewiseSEM | Data set from Grace & Keeley (2006) | data.frame | 90 | 8 |
meadows | piecewiseSEM | Data set from Grace & Jutila (1999) | data.frame | 354 | 4 |
shipley | piecewiseSEM | Data set from Shipley (2006) | data.frame | 1900 | 9 |
PopHealthData | SangerTools | PopHealthData - Population health data for testing functions | spec_tbl_df | 1000 | 8 |
master_patient_index | SangerTools | Master Patient Index | tbl_df | 10000 | 10 |
uk_pop_standard | SangerTools | Data set of 2018 UK Population | tbl_df | 21 | 2 |
AFDP | MixSemiRob | AFDP data | numeric | | |
NBA | MixSemiRob | NBA data | data.frame | 95 | 4 |
ROE | MixSemiRob | ROE data | numeric | | |
elbow | MixSemiRob | Elbow data | numeric | | |
ethanol | MixSemiRob | Ethanol data | data.frame | 88 | 3 |
tone | MixSemiRob | Tone perception data | data.frame | 150 | 2 |
records | weaana | Demo weather records | WeaAna | | |
data.FAO_country1 | RM.weights | Food insecurity data for a GWP country (Country1). | data.frame | 1000 | 13 |
data.FAO_country2 | RM.weights | Food insecurity data for a GWP pilot country (Country2). | data.frame | 1000 | 13 |
data.FAO_country3 | RM.weights | Food insecurity data for a GWP pilot country (Country3). | data.frame | 1008 | 13 |
data.FAO_country4 | RM.weights | Food insecurity data for a GWP pilot country (Country4). | data.frame | 1000 | 13 |
TR | TreeRingShape | A sample object of class TreeRingShape | classTreeRingShape | | |
TR_ | TreeRingShape | A sample object of class TreeRingShape, shapefile paths and column names only. | classTreeRingShape | | |
fivaqks | MRHawkes | Fiji and Vanuatu Earthquake Data | data.frame | 2509 | 22 |
Coated | MVTests | Coated | data.frame | 15 | 5 |
iris | MVTests | Iris Data | data.frame | 150 | 5 |
mich_lung_xx | sodavis | Gene expression data for Michigan lung cancer study in Beer et al. (2002) | matrix | 86 | 5217 |
mich_lung_yy | sodavis | Gene expression data for Michigan lung cancer study in Beer et al. (2002) | numeric | | |
pumadyn_isample_x | sodavis | Pumadyn dataset | matrix | 4499 | 32 |
pumadyn_isample_y | sodavis | Pumadyn dataset | numeric | | |
pumadyn_osample_x | sodavis | Pumadyn dataset | matrix | 3693 | 32 |
pumadyn_osample_y | sodavis | Pumadyn dataset | numeric | | |
boston_housing | gbts | Boston housing data | list | | |
german_credit | gbts | German credit data | list | | |
chrisEx1 | infoDecompuTE | Randomised Block design consisted of 6 blocks and 3 plots. | data.frame | 18 | 5 |
chrisEx2 | infoDecompuTE | Randomised Block design consisted of 8 blocks and 2 plots. | data.frame | 16 | 5 |
chrisEx3 | infoDecompuTE | Randomised Block design consisted of 4 blocks and 2 plots. | data.frame | 8 | 5 |
bsds | sl3 | Bicycle sharing time series dataset | data.frame | 731 | 16 |
cpp | sl3 | Subset of growth data from the collaborative perinatal project (CPP) | data.frame | 1912 | 33 |
cpp_1yr | sl3 | Subset of growth data from the collaborative perinatal project (CPP) | data.frame | 428 | 33 |
cpp_imputed | sl3 | Subset of growth data from the collaborative perinatal project (CPP) | data.frame | 1441 | 33 |
density_dat | sl3 | Simulated data with continuous exposure | data.frame | 1000 | 7 |
midvd_bt100 | maczic | Example Data for mediate_zi_vcoef, plot_sensitivity and mediate_iv Functions | data.frame | 100 | 9 |
NASAtemp | fdatest | NASA daily temperatures data set | list | | |
response_directions_driving | mapsapi | Sample response from Google Maps Directions API | list | | |
response_directions_transit | mapsapi | Sample response from Google Maps Directions API | list | | |
response_geocode | mapsapi | Sample response from Google Maps Geocode API | list | | |
response_map | mapsapi | Sample response from Maps Static API (as 'stars' raster) | mapsapi_map | | |
response_matrix | mapsapi | Sample response from Google Maps Distance Matrix API | list | | |
sfo_passengers | sfo | SFO Airport Air Traffic Passenger Statistics | data.frame | 50730 | 12 |
sfo_stats | sfo | SFO Airport Air Landings Statistics | data.frame | 57381 | 14 |
Data_Maize | EEMDlstm | Monthly International Maize Price Data | ts | 126 | 1 |
allo | corona | Allometric scaling data. | data.frame | 455 | 7 |
citymap | corona | Citymapper data. | data.frame | 108 | 22 |
cntry | corona | Country data from Our World In Data. | data.frame | 213 | 20 |
djia | corona | Historical Dow Jones Industrial Average prices. | data.frame | 5156 | 5 |
gt | corona | Google trends search for 'coronavirus'. | data.frame | 155 | 3 |
life | corona | The game of life. | data.frame | 213 | 3 |
lock | corona | Approximate dates of full lockdown in various countries. | data.frame | 110 | 3 |
owid | corona | Wide-ranging data from Our World In Data. I only use a tiny part. | data.frame | 27193 | 10 |
stmf | corona | Deaths, by week, for various countries. | data.frame | 22676 | 5 |
vienna | corona | Semmelweis' data on Deaths of parturients in Vienna | data.frame | 98 | 3 |
Anestheticdat | nmaINLA | Data for the Anesthetic example in Greco et al. (2013) | data.frame | 30 | 10 |
Certolizumabdat | nmaINLA | Data for the Certolizumab NMA-network discussed in Dias et al. (2013) | data.frame | 12 | 9 |
CooperStrokedat | nmaINLA | Data for the stroke prevention NMA-network discussed in Cooper et al. (2009) | data.frame | 26 | 18 |
Diabetesdat | nmaINLA | Data for the Diabetes example in Senn et al. (2013) | data.frame | 26 | 11 |
Dietaryfatdat | nmaINLA | Data for the Dietary fat example in Dias et al. (2011) | data.frame | 10 | 10 |
Flourdat | nmaINLA | Data for the Flour NMA example in Dias et al. (2010) | data.frame | 130 | 20 |
IncDiabetesdat | nmaINLA | Data for the Incident Diabetes example in Elliott et al. (2007) | data.frame | 22 | 12 |
KussHeartdat | nmaINLA | Data for the ischemic heart disease sparse pairwise meta-analysis discussed in Kuss (2014) | tbl_df | 60 | 4 |
Parkinsondat | nmaINLA | Data for the Parkinson NMA-network discussed in Dias et al. (2013) | data.frame | 7 | 11 |
Smokdat | nmaINLA | Data for the smoking cessation NMA-network discussed in Dias et al. (2010) | data.frame | 24 | 11 |
Strokedat | nmaINLA | Data for the Stroke NMA regression discussed in Batson et al. (2016) | data.frame | 19 | 18 |
TBdat | nmaINLA | Trials investigating effectiveness of the BCG vaccine against TB | data.frame | 13 | 6 |
Thrombdat | nmaINLA | Data for the thrombolytic NMA-network discussed in Dias et al. (2010) | data.frame | 50 | 10 |
Woodsdat | nmaINLA | Data for the Woods example in Woods et al. (2010) | data.frame | 3 | 14 |
Events | RChronoModel | Events | data.frame | 30000 | 5 |
Phases | RChronoModel | Phases | data.frame | 30000 | 5 |
Phases | RChronoModel | Phases | data.frame | 30000 | 5 |
boolean_input_df | RareComb | Sparse Boolean dataframe with rare variant information and a single outcome variable | data.frame | 5000 | 502 |
boolean_input_mult_df | RareComb | Sparse Boolean dataframe with rare variant information and multiple outcome variables | data.frame | 5000 | 504 |
input_list | RareComb | A list of 50 random input variables | character | | |
aircraft | sm | These data record six characteristics of aircraft designs which appeared during the twentieth century | data.frame | 709 | 8 |
airpc | sm | These data list the first two principal component scores from the aircraft data, which record six characteristics of aircraft designs throughout the twentieth century | data.frame | 709 | 4 |
birth | sm | Low birthweight in babies | data.frame | 189 | 3 |
bissell | sm | Flaws in cloth | data.frame | 32 | 2 |
bonions | sm | Yield-density relationship for Brown Imperial Spanish onions | data.frame | 84 | 3 |
britpts | sm | Coastline of the UK and Ireland | data.frame | 800 | 2 |
citrate | sm | The relationship between plasma citrate and carbohydrate metabolites | data.frame | 10 | 14 |
coalash | sm | Coal ash in mining samples | data.frame | 208 | 3 |
dogs | sm | Coronary sinus potassium in dogs | data.frame | 32 | 8 |
follicle | sm | Ovarian follicle counts | data.frame | 110 | 3 |
geys3d | sm | Duration and the time between eruptions for the Old Faithful Geyser | data.frame | 298 | 3 |
geyser | sm | Old Faithful Geyser Data | data.frame | 299 | 2 |
lcancer | sm | Spatial positions of cases of laryngeal cancer | data.frame | 974 | 3 |
mackerel | sm | The abundance of mackerel eggs | data.frame | 279 | 6 |
magrem | sm | Magnetic remanence | data.frame | 107 | 2 |
mildew | sm | Mildew control | data.frame | 36 | 12 |
mosses | sm | Heavy metals in mosses in Galicia. | list | | |
muscle | sm | Rat skeletal muscles | data.frame | 25 | 4 |
nile | sm | Water level of the River Nile | data.frame | 100 | 2 |
poles | sm | Positions of the south pole | data.frame | 50 | 2 |
radioc | sm | Radiocarbon in Irish oak | data.frame | 343 | 3 |
smacker | sm | Mackerel data from a Spanish survey | data.frame | 417 | 6 |
stanford | sm | Survival times from the Stanford Heart Transplant Study | data.frame | 184 | 3 |
tephra | sm | Tephra layer | data.frame | 59 | 1 |
trawl | sm | Trawl data from the Great Barrier Reef | data.frame | 155 | 7 |
trout | sm | Potassium cyanate and trout eggs | data.frame | 48 | 4 |
wonions | sm | Yield-density relationship for White Imperial Spanish onion plants | data.frame | 84 | 3 |
worm | sm | Human parasitic worm infections | data.frame | 304 | 3 |
droso | EncDNA | An example dataset consisting of true and false donor splice sites of Drosophila melanogaster. | list | | |
brightstar | logcondens | Bright star dataset used to illustrate log-concave density estimation | data.frame | 9110 | 3 |
pancreas | logcondens | Data from pancreatic cancer serum biomarker study | data.frame | 141 | 3 |
reliability | logcondens | Reliability dataset used to illustrate log-concave density estimation | numeric | | |
X | SparseM | A Design Matrix for a Triogram Problem | matrix.csr | | |
lsq | SparseM | Least Squares Problems in Surveying | matrix.csc.hb | | |
kyoto_districts | kyotocities | Kyoto prefecture administrative district data | sf | 36 | 5 |
kyoto_fire_stations | kyotocities | Kyoto Fire Station Data | sf | 122 | 2 |
kidney.donors | transplantr | Simulated dataset of donors to illustrate KDRI vignette. | spec_tbl_df | 4 | 7 |
liver.pts | transplantr | Simulated dataset to illustrate MELD calculator vignette. | data.frame | 4 | 6 |
mismatches | transplantr | Simulated dataset to illustrate mismatches for HLA vignette. | data.frame | 4 | 5 |
results | transplantr | Simulated dataset to illustrate eGFR calculator vignette. | spec_tbl_df | 4 | 6 |
results_US | transplantr | Simulated dataset to illustrate eGFR calculator vignette. | spec_tbl_df | 4 | 6 |
serial.results | transplantr | Simulated dataset to illustrate serial results eGFR calculator vignette. | spec_tbl_df | 4 | 5 |
md_exp | MinimumDistance | An example 'MinDistExperiment' | MinDistExperiment | | |
md_gr | MinimumDistance | An example 'MinDistGRanges' object | MinDistGRanges | | |
trioSetList | MinimumDistance | An example 'TrioSetList' object | TrioSetList | | |
shpdata1 | spsh | Measured soil hydraulic property data | list | | |
Sigma | GLMMselect | A list of covariance matrices | list | | |
X | GLMMselect | The matrix of candidate covariates | matrix | 100 | |
Y | GLMMselect | The vector of observations | integer | | |
Z | GLMMselect | A list of design matrices | list | | |
lipcancer_Sigma | GLMMselect | A list of covariance matrices | list | | |
lipcancer_X | GLMMselect | The matrix of candidate covariates | matrix | 56 | |
lipcancer_Y | GLMMselect | The vector of observations for lip cancer case study | numeric | | |
lipcancer_Z | GLMMselect | A list of design matrices | list | | |
lipcancer_offset | GLMMselect | The vector of priori information for lip cancer case study | matrix | 56 | |
promoterAnnotation.gencode.v34.subset | proActiv | Promoter annotation for Gencode.v34 (subset) | PromoterAnnotation | | |
ratMix3 | debCAM | Gene expression data downsampled from GSE19380 | list | | |
gtex_expr | GWENA | Transcriptomic muscle data from GTEx consorsium RNA-seq data | data.frame | 50 | 15000 |
gtex_traits | GWENA | Traits data linked to samples in transcriptomic data from GTEx | data.frame | 50 | 4 |
kuehne_expr | GWENA | Transcriptomic data from the Kuehne et al. publication | data.frame | 48 | 15801 |
kuehne_traits | GWENA | Traits data linked to samples in transcriptomic data from the Kuehne et al. publication | data.frame | 48 | 5 |
referenceReadCounts | CNVPanelizer | Reference sample data | matrix | 110 | 100 |
sampleReadCounts | CNVPanelizer | Test sample data | matrix | 110 | 4 |
real_peaks | target | AR peaks in LNCaP cell line | GRanges | | |
real_transcripts | target | Differential expression of DHT treated LNCaP cell line | GRanges | | |
sim_peaks | target | Simulated peaks | GRanges | | |
sim_transcripts | target | Simulated transcripts The transcripts chromosome 1 of the mm10 mouse genome with randomly singed statistics assigned to each. | GRanges | | |
recount_abstract | recount | Summary information at the project level for the recount project | data.frame | 2041 | 4 |
recount_exons | recount | Exon annotation used in recount | CompressedGRangesList | | |
recount_genes | recount | Gene annotation used in recount | GRanges | | |
recount_url | recount | Files and URLs hosted by the recount project | data.frame | 97229 | 6 |
rse_gene_SRP009615 | recount | RangedSummarizedExperiment at the gene level for study SRP009615 | RangedSummarizedExperiment | | |
maskCNV_BRCA | CINmetrics | Breast Cancer Data from TCGA Data Release 25.0 GDC Product: Data Release Date: July 22, 2020 Masked Copy Number variation data for Breast Cancer for 10 unique samples selected randomly from TCGA | data.frame | 1650 | 7 |
achv_color_map | gophr | OHA Achievement Colors | tbl_df | 4 | 3 |
cascade_ind | gophr | MER Clinical Cascade indicators | character | | |
snapshot_ind | gophr | MER Snapshot indicators | character | | |
china | mapchina | China administraive division shapefile data | sf | 2901 | 14 |
EuroUnemployment | mclust | Unemployment data for European countries in 2014 | data.frame | 31 | 3 |
GvHD.control | mclust | GvHD Dataset | data.frame | 6809 | 4 |
GvHD.pos | mclust | GvHD Dataset | data.frame | 9083 | 4 |
Test1D | mclust | Simulated Example Datasets From Baudry et al. (2010) | data.frame | 200 | 1 |
acidity | mclust | Acidity data | numeric | | |
banknote | mclust | Swiss banknotes data | data.frame | 200 | 7 |
chevron | mclust | Simulated minefield data | data.frame | 1104 | 3 |
cross | mclust | Simulated Cross Data | data.frame | 500 | 3 |
diabetes | mclust | Diabetes Data (flawed) | data.frame | 145 | 4 |
ex4.1 | mclust | Simulated Example Datasets From Baudry et al. (2010) | data.frame | 600 | 2 |
ex4.2 | mclust | Simulated Example Datasets From Baudry et al. (2010) | data.frame | 600 | 2 |
ex4.3 | mclust | Simulated Example Datasets From Baudry et al. (2010) | data.frame | 200 | 2 |
ex4.4.1 | mclust | Simulated Example Datasets From Baudry et al. (2010) | data.frame | 800 | 2 |
ex4.4.2 | mclust | Simulated Example Datasets From Baudry et al. (2010) | data.frame | 300 | 3 |
thyroid | mclust | UCI Thyroid Gland Data | data.frame | 215 | 6 |
wdbc | mclust | UCI Wisconsin Diagnostic Breast Cancer Data | data.frame | 569 | 32 |
wreath | mclust | Data Simulated from a 14-Component Mixture | matrix | 1000 | 2 |
icons | scifigure | scifigure icons | list | | |
icons_diff | scifigure | repfigure icons_diff | list | | |
lackinfo | IntervalQuestionStat | Lack of information in expository face-to-face lessons data set | data.frame | 50 | 12 |
Math | bmemLavaan | Parents' education levels and adolescent mathematics achievement of 76 families in 1986 | data.frame | 76 | 4 |
birds | mldr.datasets | Dataset with sounds produced by birds and the species they belong to | mldr | | |
cal500 | mldr.datasets | Dataset with music data along with labels for emotions, instruments, genres, etc. | mldr | | |
emotions | mldr.datasets | Dataset with features extracted from music tracks and the emotions they produce | mldr | | |
flags | mldr.datasets | Dataset with features correspoinding to world flags | mldr | | |
genbase | mldr.datasets | Dataset with genes data and their functional expression | mldr | | |
langlog | mldr.datasets | Dataset with data from the Language forum discussion | mldr | | |
medical | mldr.datasets | Dataset generated from medical reports | mldr | | |
ng20 | mldr.datasets | Dataset with news messages and the news groups they belong to | mldr | | |
slashdot | mldr.datasets | Dataset generated from slashdot.org site entries | mldr | | |
stackex_chess | mldr.datasets | Dataset from the Stack Exchange's chess forum | mldr | | |
flying | dropout | Flying Etiquette Survey Data | tbl_df | 1040 | 27 |
herring | FinePop | An example dataset of Atlantic herring. | list | | |
jsmackerel | FinePop | An example dataset of Japanese Spanich mackerel in GENEPOP and frequency format. | list | | |
mayo | survivalROC | Mayo Marker data | data.frame | 312 | 4 |
galleria | fluoSurv | Fluorescence data measured on _Galleria mellonella_ | data.frame | 382944 | 8 |
setup | fluoSurv | An injection experimental setup | data.frame | 96 | 4 |
salmon | idr | Salmon data | data.frame | 28 | 3 |
simu.idr | idr | Simulated data | data.frame | 1000 | 3 |
Body | Brq | Exploring Relationships in Body Dimensions | data.frame | 507 | 25 |
ImmunogG | Brq | Immunoglobulin G Data | data.frame | 298 | 2 |
Prostate | Brq | Prostate Cancer Data | data.frame | 97 | 9 |
USgirl | Brq | Weight and age for a sample of 4011 US girl data | data.frame | 4011 | 2 |
salary | Brq | Salaries of 459 US statistics professor data | data.frame | 459 | 2 |
wheat | Brq | Wheat Data | data.frame | 113 | 6 |
fly | popgenr | Drosphila melanogaster bw75 data | matrix | 20 | 33 |
genotypes | popgenr | Genotype data from Aleppo Pines | data.frame | 181 | 20 |
moth | popgenr | Temporal allele frequency shifts | data.frame | 8 | 2 |
snp | popgenr | SNP information for 25 loci | data.frame | 25 | 6 |
thirteen | popgenr | Genotypes across 13 CODIS loci | data.frame | 1036 | 27 |
whale | popgenr | Genotypes of 246 South Pacific Blue Whales | data.frame | 264 | 15 |
Example | FESta | Time series of commercial fish landings and fishing effort along Andhra Pradesh coastline (1997-2018) | list | | |
artms_config | artMS | artMS configuration template | list | | |
artms_data_corum_mito_database | artMS | CORUM Protein Complexes database use for complex enrichment analysis | data.frame | 3616 | 20 |
artms_data_pathogen_LPN | artMS | LPN PATHOGEN: Legionella pneumophila subsp. pneumophila (strain Philadelphia 1 / ATCC 33152 / DSM 7513) UNIPROT IDS | data.frame | 2930 | 1 |
artms_data_pathogen_TB | artMS | TB PATHOGEN: Mycobacterium tuberculosis (strain ATCC 35801 / TMC 107 / Erdman) UNIPROTS IDS | data.frame | 4222 | 1 |
artms_data_ph_config | artMS | artMS configuration for the available PH dataset | list | | |
artms_data_ph_contrast | artMS | Contrast example for the PH dataset | character | | |
artms_data_ph_evidence | artMS | Evidence file example | data.frame | 5004 | 36 |
artms_data_ph_keys | artMS | Keys File Example | data.frame | 4 | 5 |
artms_data_ph_msstats_modelqc | artMS | MSstats modelQC example | data.frame | 24162 | 13 |
artms_data_ph_msstats_results | artMS | MSstats results example | data.frame | 3413 | 11 |
artms_data_randomDF | artMS | Random data set | data.frame | 100 | 10 |
CO2data | mixtools | GNP and CO2 Data Set | data.frame | 28 | 3 |
Habituationdata | mixtools | Infant habituation data | data.frame | 51 | 2 |
NOdata | mixtools | Ethanol Fuel Data Set | data.frame | 88 | 2 |
RTdata | mixtools | Reaction Time (RT) Data Set | data.frame | 197 | 6 |
RTdata2 | mixtools | Reaction Time (RT) Data Set (No. 2) | data.frame | 82 | 24 |
RanEffdata | mixtools | Simulated Data from 2-Component Mixture of Regressions with Random Effects | list | | |
RodFramedata | mixtools | Rod and Frame Task Data Set | data.frame | 140 | 56 |
Waterdata | mixtools | Water-Level Task Data Set | data.frame | 405 | 10 |
WaterdataFull | mixtools | Water-Level Task Data Set | data.frame | 579 | 10 |
tonedata | mixtools | Tone perception data | data.frame | 150 | 2 |
gcTheoretical | ngsReports | Theoretical GC content | TheoreticalGC | | |
mtcars2 | qwraps2 | mtcars2 | data.frame | 32 | 19 |
pefr | qwraps2 | pefr | data.frame | 68 | 4 |
spambase | qwraps2 | Spambase | data.frame | 4601 | 58 |
artificial_networks | snowboot | 10 Simulated Networks of Order 2000 with Polylogarithmic (0.1, 2) Degree Distributions | list | | |
rice_salt | BootMRMR | A gene expression dataset of rice under salinity stress | data.frame | 201 | 40 |
directAntivirals | BIGL | Partial data with combination experiments of direct-acting antivirals | data.frame | 3520 | 6 |
directAntivirals_ALL | BIGL | Full data with combination experiments of direct-acting antivirals | data.frame | 4224 | 6 |
dummydata | ezec | dummydata | data.frame | 96 | 6 |
hla.freqs | asymLD | Pairwise haplotype frequencies for 7 HLA loci | data.frame | 3063 | 5 |
snp.freqs | asymLD | Pairwise haplotype frequencies for 4 SNP loci | data.frame | 20 | 5 |
CETdaily | multitaper | Central England Temperature daily time series | data.frame | 87566 | 4 |
CETmonthly | multitaper | Central England Temperature monthly time series | data.frame | 4236 | 3 |
HadCRUTnh | multitaper | HadCRUT Land Temperature Anomaly (Northern Hemisphere) Series | data.frame | 619 | 3 |
mlco2 | multitaper | Mauna Loa Observatory CO2 Monthly Averages | data.frame | 619 | 3 |
percivalAR4 | multitaper | Auto Regressive Series generated by Don Percival at Applied Physics Laboratory | ts | | |
willamette | multitaper | Willamette River time series | numeric | | |
AgPalik | dielectric | Silver in the visible, from Palik | dielectric | | |
AlRakic | dielectric | Aluminium in the visible | dielectric | | |
AuJC | dielectric | Gold in the visible | dielectric | | |
Chromium | dielectric | Chromium in the visible | dielectric | | |
Ti | dielectric | Amorphous silicon in the visible | dielectric | | |
aSi | dielectric | Chromium in the visible | dielectric | | |
constants | dielectric | Various physical constants | list | | |
sapphire | dielectric | Sapphire in the visible | dielectric | | |
aarp | blm | Nested case-control data set of bladder cancer in the NIH-AARP Diet and Health Study | data.frame | 584 | 7 |
ccdata | blm | Simulated case-control dataset | data.frame | 756 | 5 |
LOD | EPT | Length of Day Data | list | | |
SolarRadiation | EPT | Solar Radiation | data.frame | 696 | 4 |
genomeData | derfinder | Genome samples processed data | list | | |
genomeDataRaw | derfinder | Genome samples processed data | list | | |
genomeFstats | derfinder | F-statistics for the example data | Rle | | |
genomeInfo | derfinder | Genome samples information | data.frame | 31 | 5 |
genomeRegions | derfinder | Candidate DERs for example data | list | | |
genomicState | derfinder | Genomic State for Hsapiens.UCSC.hg19.knownGene | CompressedGRangesList | | |
memberstates | memberstates | Member states of various international organizations. | list | | |
binaryalphadigits | RnavGraphImageData | Binary Alphadigits | data.frame | 1404 | 320 |
digits | RnavGraphImageData | USPS Handwritten Digits | data.frame | 256 | 11000 |
faces | RnavGraphImageData | Olivetti Faces | data.frame | 4096 | 400 |
frey | RnavGraphImageData | Frey Face | data.frame | 560 | 1965 |
ordalphadigits | RnavGraphImageData | Dissimilarity object of class 'isomap' for Binary Alphadigits data | isomap | | |
ordfrey | RnavGraphImageData | Dissimilarity object of class 'isomap' for Frey Faces data | isomap | | |
La04_ecc_6_8 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 2001 | 2 |
La04_obl_6_8 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 2001 | 2 |
La04_pre_0_20 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 20001 | 2 |
La04_pre_obl_5_9 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 4001 | 3 |
ace | DecomposeR | Datasets for Testing DecomposeR | data.frame | 6000 | 2 |
cip1 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 1146 | 2 |
cip1_input | DecomposeR | Datasets for Testing DecomposeR | data.frame | 2001 | 2 |
cip1_raw | DecomposeR | Datasets for Testing DecomposeR | data.frame | 1146 | |
cip2 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 1002 | 2 |
cip3 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 2631 | 2 |
sc97amp | DecomposeR | Datasets for Testing DecomposeR | data.frame | 401 | 2 |
w17 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 2070 | 3 |
z13 | DecomposeR | Datasets for Testing DecomposeR | data.frame | 7191 | 3 |
z13amp | DecomposeR | Datasets for Testing DecomposeR | data.frame | 9952 | 2 |
Lizard_Size_Data | hmde | Skink size data - Lampropholis delicata | tbl_df | 328 | 4 |
Tree_Size_Data | hmde | Garcinia recondita - Barro Colorado Island data | tbl_df | 300 | 4 |
Tree_Size_Ests | hmde | Garcinia recondita model estimates - Barro Colorado Island data | list | | |
Trout_Size_Data | hmde | SUSTAIN Salmo trutta data | tbl_df | 135 | 4 |
slide_dim | tracetheme | Epiverse-TRACE default plotting dimensions for slides | list | | |
vignette_dim | tracetheme | Epiverse-TRACE default plotting dimensions for vignettes | list | | |
ex_linquad | bootImpute | Simulated example data with continuous outcome and quadratic covariate effects | data.frame | 1000 | 5 |
movie_review | text2vec | IMDB movie reviews | data.frame | 5000 | 3 |
BinomialExample | glmnet | Synthetic dataset with binary response | list | | |
CoxExample | glmnet | Synthetic dataset with right-censored survival response | list | | |
MultiGaussianExample | glmnet | Synthetic dataset with multiple Gaussian responses | list | | |
MultinomialExample | glmnet | Synthetic dataset with multinomial response | list | | |
PoissonExample | glmnet | Synthetic dataset with count response | list | | |
QuickStartExample | glmnet | Synthetic dataset with Gaussian response | list | | |
SparseExample | glmnet | Synthetic dataset with sparse design matrix | list | | |
beta_CVX | glmnet | Simulated data for the glmnet vignette | numeric | | |
lineup2ex | lineup2 | Example dataset for lineup2 package | list | | |
price6 | gpindex | Sample price/quantity data | data.frame | 6 | 5 |
quantity6 | gpindex | Sample price/quantity data | data.frame | 6 | 5 |
ABHt32 | psychometric | Table 3.2 from Arthur et al | data.frame | 10 | 7 |
HSJt35 | psychometric | Table 3.5 Hunter et al. | data.frame | 8 | 7 |
TestScores | psychometric | Fictitious Test Scores for Illustrative Purposes | matrix | 30 | 11 |
ants | timeordered | Ant interaction data | data.frame | 1911 | 4 |
s.oryzae | CINID | Data on Sitophilus orizae | list | | |
homecare | dunn.test | Occupation and Home Care Eligibility | data.frame | 383 | 2 |
NCA_values | Planesmuestra | Data: AQL levels for MIL STD 105E acceptance sampling plans. | matrix | 26 | 1 |
ap_DR | Planesmuestra | Data: Dodge Romig table of Nonconforming fraction levels for AOQL and LPTD plans | data.frame | 6 | 2 |
code_letter | Planesmuestra | Data: Inspection level and the code letter for a MIL STD 105E acceptance sampling plan. | data.frame | 15 | 7 |
code_letter.milstd414 | Planesmuestra | Data: Inspection level and the code letter for a MIL STD 414 acceptance sampling plan and normal inspection. | data.frame | 17 | 5 |
k_plans.milstd414 | Planesmuestra | Data: Extract the sample size and k value for MIL STD 414 variable acceptance sampling plans and normal type. | data.frame | 432 | 5 |
lot_size | Planesmuestra | Data: Lot size levels for MIL STD 105 E acceptance sampling plans | data.frame | 15 | 1 |
lot_size.milstd414 | Planesmuestra | Data: Lot size levels for MIL STD 414 variable acceptance sampling plans | data.frame | 17 | 1 |
lot_size_DR | Planesmuestra | Data: Lot size for Dodge Romig acceptance sampling plan | data.frame | 222 | 6 |
milstd105eplans | Planesmuestra | Data: Extract the sample size and the acceptance number for MIL STD 105E acceptance sampling plans. | data.frame | 1274 | 5 |
snp_gr | G4SNVHunter | Single Nucleotide Polymorphisms GRanges Object | GRanges | | |
snv_gr | G4SNVHunter | Single Nucleotide Variant GRanges Object | GRanges | | |
scenario_results_beijing | urbanAnnualRunoff | Results of ABIMO Scenario Analysis For Beijing | tbl_df | 7 | 20 |
scenario_results_jinxi | urbanAnnualRunoff | Results of ABIMO Scenario Analysis For Jinxi | tbl_df | 10 | 20 |
df_bezier_skeleton | ggbenjamini | Example bezier dataframe to grow leaves on | tbl_df | 40 | 4 |
metrics | tidycenso | Available units of measurement | tbl_df | 51 | 4 |
tables | tidycenso | Available tables | tbl_df | 7 | 3 |
variables | tidycenso | Available variables | tbl_df | 395 | 4 |
nwt_morts | cyclomort | Mortality data for Northwest territory boreal woodland caribou. | data.frame | 370 | 5 |
seasonalsex | cyclomort | Simulated data of seasonal mortality data for two sex groups | data.frame | 200 | 2 |
timetoeventprediction | cyclomort | Example fitted time to event prediction | list | | |
wah_morts | cyclomort | Mortality data for Western Arctic Herd Caribou | data.frame | 90 | 4 |
Numbers | broman | Numbers spelled out in English | character | | |
numbers | broman | Numbers spelled out in English | character | | |
data.starts01a | STARTS | Datasets in the 'STARTS' Package | data.frame | 890 | 16 |
data.starts01b | STARTS | Datasets in the 'STARTS' Package | data.frame | 3215 | 17 |
data.starts02 | STARTS | Datasets in the 'STARTS' Package | list | | |
data.starts03a | STARTS | Datasets in the 'STARTS' Package | list | | |
data.starts03b | STARTS | Datasets in the 'STARTS' Package | list | | |
data.starts03c | STARTS | Datasets in the 'STARTS' Package | list | | |
country_code_data | rfisheries | country_code_data | data.frame | 239 | 2 |
species_code_data | rfisheries | species_code_data | data.frame | 11562 | 5 |
artData | dataReporter | Semi-artificial data about masterpieces of art | data.frame | 200 | 11 |
bigPresidentData | dataReporter | Semi-artificial data about the US presidents (extended version) | data.frame | 47 | 15 |
exampleData | dataReporter | Example data with zero-inflated variables | data.frame | 300 | 6 |
presidentData | dataReporter | Semi-artificial data about the US presidents | data.frame | 46 | 11 |
testData | dataReporter | Extended example data to test the features of dataReporter | data.frame | 15 | 15 |
toyData | dataReporter | Small example data to show the features of dataReporter | tbl_df | 15 | 6 |
useR_2008_abstracts | movMF | useR! 2008 Abstracts | data.frame | 177 | 5 |
my_snapchatads_data | snapchatadsR | Sample of digital marketing data from Snapchat Ads downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
auto | corrgram | Statistics of 1979 automobile models | data.frame | 74 | 14 |
baseball | corrgram | Baseball Hitter's Data | data.frame | 322 | 22 |
vote | corrgram | Voting correlations | matrix | 12 | 12 |
github_data | codesamples | Github R Snippets | data.frame | 9738 | 4 |
package_examples | codesamples | Package examples | tbl_df | 6323 | 3 |
so_questions | codesamples | Stack Overflow R Snippets | tbl_df | 11013 | 3 |
express | lcra | Small simulated data set | data.frame | 150 | 8 |
latent3 | lcra | Simulated data set number 2 (continuous regression outcome) | data.frame | 350 | 17 |
latent3_binary | lcra | Simulated data set number 2 (discrete regression outcome) | data.frame | 350 | 17 |
paper_sim | lcra | Simulated data set (continuous regression outcome) | data.frame | 100 | 13 |
paper_sim_binary | lcra | Simulated data set (discrete regression outcome) | data.frame | 100 | 13 |
UNlocations | wpp2012 | United Nations Table of Locations | data.frame | 269 | 7 |
e0F | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 236 | 16 |
e0F_supplemental | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Fproj | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 236 | 20 |
e0Fproj80l | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0Fproj80u | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0Fproj95l | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0Fproj95u | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0M | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 236 | 16 |
e0M_supplemental | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Mproj | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 236 | 20 |
e0Mproj80l | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0Mproj80u | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0Mproj95l | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
e0Mproj95u | wpp2012 | United Nations Time Series of Life Expectancy | data.frame | 162 | 20 |
migrationF | wpp2012 | Datasets on Migration | data.frame | 4956 | 33 |
migrationM | wpp2012 | Datasets on Migration | data.frame | 4956 | 33 |
mxF | wpp2012 | Age-specific Mortality Data | data.frame | 5324 | 33 |
mxM | wpp2012 | Age-specific Mortality Data | data.frame | 5324 | 33 |
percentASFR | wpp2012 | Datasets on Age-specific Distribution of Fertility Rates | data.frame | 1652 | 33 |
popF | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 16 |
popFprojHigh | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 21 |
popFprojLow | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 21 |
popFprojMed | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 21 |
popM | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 16 |
popMprojHigh | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 21 |
popMprojLow | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 21 |
popMprojMed | wpp2012 | Estimates and Projections of Population Counts | data.frame | 4956 | 21 |
popproj80l | wpp2012 | Estimates and Projections of Population Counts | data.frame | 268 | 20 |
popproj80u | wpp2012 | Estimates and Projections of Population Counts | data.frame | 268 | 20 |
popproj95l | wpp2012 | Estimates and Projections of Population Counts | data.frame | 268 | 21 |
popproj95u | wpp2012 | Estimates and Projections of Population Counts | data.frame | 268 | 21 |
popprojHigh | wpp2012 | Estimates and Projections of Population Counts | data.frame | 268 | 20 |
popprojLow | wpp2012 | Estimates and Projections of Population Counts | data.frame | 268 | 20 |
sexRatio | wpp2012 | Sex Ratio at Birth | data.frame | 236 | 32 |
tfr | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 236 | 16 |
tfr_supplemental | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 103 | 45 |
tfrproj80l | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 20 |
tfrproj80u | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 20 |
tfrproj95l | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 20 |
tfrproj95u | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 201 | 20 |
tfrprojHigh | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 236 | 20 |
tfrprojLow | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 236 | 20 |
tfrprojMed | wpp2012 | United Nations Time Series of Total Fertility Rate | data.frame | 236 | 20 |
SquaredSquare | ResistorArray | A Squared square | matrix | 13 | |
brodmann | ggsegBrodmann | Brodmann atlas | brain_atlas | | |
brodmann_3d | ggsegBrodmann | Brodmann atlas | ggseg3d_atlas | 4 | 4 |
SP500_symbols | portfolioBacktest | Stock symbols of the S&P 500 constituents | character | | |
dataset10 | portfolioBacktest | Ten datasets obtained by resampling the S&P 500 | list | | |
flint | quantileCI | Water Monitoring Sample from Flint, Michigan, 2015 | data.frame | 71 | 2 |
metadata | POSTm | Example Dataset For Illustration | data.frame | 39 | 3 |
otu | POSTm | Example Dataset For Illustration | matrix | 39 | 189 |
otuseq | POSTm | Example Dataset For Illustration | character | | |
otutree | POSTm | Example Dataset For Illustration | phylo | | |
nhanes_example | cforward | Example Data from National Health and Nutrition Examination Survey ('NHANES') | tbl_df | 3653 | 9 |
Data | yuima | Five minutes Log SPX prices | list | | |
MWK151 | yuima | Graybill - Methuselah Walk - PILO - ITRDB CA535 | zoo | | |
FF25 | REN | FF25 Dataset | data.frame | 25670 | 27 |
antidiabetic | NMA | Phung et al. (2010)'s network meta-analysis data | data.frame | 42 | 5 |
diabetes | NMA | Elliott and Mayer (2007)'s network meta-analysis data | data.frame | 48 | 4 |
heartfailure | NMA | Sciarretta et al. (2011)'s network meta-analysis data | data.frame | 54 | 8 |
smoking | NMA | Smoking cessation data | data.frame | 50 | 4 |
my_appleads_data | appleadsR | Sample of digital marketing data from Apple Search Ads downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
amplifDat | heatmap4 | Gene amplification status | matrix | 3469 | |
anno | heatmap4 | Gene information | data.frame | 3469 | 2 |
chrInfo | heatmap4 | Chromosomal information | data.frame | 24 | 3 |
genomDat | heatmap4 | Gene expression data | matrix | 3469 | 39 |
phen | heatmap4 | Phenotypic (patient information) data | data.frame | 39 | 7 |
REMIND_FinalEnergy | rmndt | A random REMIND FE trajectory. | data.table | 2011 | 6 |
REMIND_GDP | rmndt | REMIND GDP Trajectories for ISO countries | data.table | 9462 | 4 |
REMIND_RegionMap | rmndt | The mapping between REMIND-12 regions and ISO countries | data.table | 249 | 3 |
mm | area | Planar straight line graphs and triangulations | pslg | | |
mm_tri | area | Planar straight line graphs and triangulations | triangulation | | |
tas | area | Planar straight line graphs and triangulations | pslg | | |
dataset_size | onsr | The Dataset with a size column | data.frame | 41 | 3 |
ECIMCI_profiles | textcat | ECI/MCI N-Gram Profiles | textcat_profile_db | | |
TC_byte_profiles | textcat | TextCat N-Gram Profiles | textcat_profile_db | | |
TC_char_profiles | textcat | TextCat N-Gram Profiles | textcat_profile_db | | |
simple | timedelay | Simulated simple data of a doubly-lensed quasar | matrix | 78 | 5 |
simple.band1 | timedelay | Simulated simple data of a doubly-lensed quasar observed in band 1 | data.frame | 50 | 5 |
simple.band2 | timedelay | Simulated simple data of a doubly-lensed quasar observed in band 2 | data.frame | 30 | 5 |
RRprofile | ocedata | Seawater profile used by Reiniger and Ross (1968) | data.frame | 19 | 3 |
beaufort | ocedata | Beaufort Scale relationship wave height to wind speed | data.frame | 11 | 2 |
buoy | ocedata | Measurements made at a buoy off Halifax | data.frame | 1078 | 9 |
coastlineWorldFine | ocedata | World coastline at fine-scale (1:10M) resolution | coastline | | |
coastlineWorldMedium | ocedata | World coastline at medium-scale (1:50M) resolution | coastline | | |
conveyor | ocedata | Conveyor-belt path | data.frame | 40 | 3 |
drag | ocedata | Wind drag coefficient | data.frame | 31 | 4 |
endeavour | ocedata | Cook's Endeavour cruise track | data.frame | 376 | 3 |
geosecs235 | ocedata | GEOSECS station 235 data | ctd | | |
giss | ocedata | Time series of NASA/GISS land-ocean temperature index | data.frame | 1656 | 2 |
gs | ocedata | Gulf Stream position | list | | |
levitus | ocedata | Annually-averaged sea-surface temperature and salinity | list | | |
munk | ocedata | Munk's (1966) temperature profile | data.frame | 24 | 2 |
nao | ocedata | North Atlantic Oscillation Index | data.frame | 792 | 2 |
oceans | ocedata | Geometries of the five world oceans | data.frame | 5 | 3 |
papa | ocedata | OWS Papa hydrographic record during 2010 | list | | |
redfieldNC | ocedata | Redfield's (1934) NO3 and total CO2 data | data.frame | 141 | 2 |
redfieldNP | ocedata | Redfield's (1934) NO3 and PO4 data | data.frame | 119 | 2 |
redfieldPlankton | ocedata | Redfield's (1934) Table II | data.frame | 11 | 4 |
riley | ocedata | Riley's (1946) observation/theory of phytoplankton concentration | list | | |
schmitt | ocedata | Schmitt's (1981) NACW temperature-salinity data | data.frame | 14 | 2 |
secchi | ocedata | Secchi depth dataset | data.frame | 40829 | 4 |
soi | ocedata | Southern Oscillation Index | data.frame | 1824 | 2 |
topo2 | ocedata | World topography on a 2-degree grid | matrix | 180 | |
turbulence | ocedata | Grant et al. (1962) turbulence data | data.frame | 15 | 2 |
wilson | ocedata | Wilson's (1963) table of seafloor spreading | data.frame | 19 | 4 |
nlsy | logistic4p | An example data set | data.frame | 5399 | 5 |
sparrowDetectionData | Rdistance | Brewer's Sparrow detection data | data.frame | 356 | 5 |
sparrowSiteData | Rdistance | Brewer's Sparrow site data | data.frame | 72 | 8 |
thrasherDetectionData | Rdistance | Sage Thrasher detection data | data.frame | 193 | 3 |
thrasherSiteData | Rdistance | thrasherSiteData - Sage Thrasher site data. | data.frame | 120 | 6 |
comet_tags | WaCSE | comet_wa tags | data.frame | 122 | 7 |
comet_wa | WaCSE | Greenhouse gas emission reduction coefficients | tbl_df | 17584 | 14 |
cell_line_fsa_list | trace | A list of fsa files | list | | |
example_data | trace | example_data | data.frame | 1719 | 9 |
example_data_repeat_table | trace | example_data_repeat_table | data.frame | 976 | 3 |
metadata | trace | metadata | data.frame | 19 | 9 |
Arabidopsis | lme4 | Arabidopsis clipping/fertilization data | data.frame | 625 | 8 |
Dyestuff | lme4 | Yield of dyestuff by batch | data.frame | 30 | 2 |
Dyestuff2 | lme4 | Yield of dyestuff by batch | data.frame | 30 | 2 |
InstEval | lme4 | University Lecture/Instructor Evaluations by Students at ETH | data.frame | 73421 | 7 |
Pastes | lme4 | Paste strength by batch and cask | data.frame | 60 | 4 |
Penicillin | lme4 | Variation in penicillin testing | data.frame | 144 | 3 |
VerbAgg | lme4 | Verbal Aggression item responses | data.frame | 7584 | 9 |
cake | lme4 | Breakage Angle of Chocolate Cakes | data.frame | 270 | 5 |
cbpp | lme4 | Contagious bovine pleuropneumonia | data.frame | 56 | 4 |
grouseticks | lme4 | Data on red grouse ticks from Elston et al. 2001 | data.frame | 403 | 7 |
grouseticks_agg | lme4 | Data on red grouse ticks from Elston et al. 2001 | data.frame | 118 | 7 |
sleepstudy | lme4 | Reaction times in a sleep deprivation study | data.frame | 180 | 3 |
power_studies_results | Rgof | power_studies_results | list | | |
pvaluecdf | Rgof | pvaluecdf | matrix | 250 | 3 |
A.illuminant.spct | photobiology | CIE A illuminant data | source_spct | 531 | 2 |
D2.UV586 | photobiology | Data for typical calibration lamps | polynomial | | |
D2.UV653 | photobiology | Data for typical calibration lamps | polynomial | | |
D2.UV654 | photobiology | Data for typical calibration lamps | polynomial | | |
D50.illuminant.spct | photobiology | CIE D50 illuminant data | source_spct | 531 | 2 |
D65.illuminant.spct | photobiology | CIE D65 illuminant data | source_spct | 531 | 2 |
FEL.BN.9101.165 | photobiology | Data for typical calibration lamps | list | | |
Ler_leaf.spct | photobiology | Green Arabidopsis leaf reflectance and transmittance. | object_spct | 2401 | 3 |
Ler_leaf_rflt.spct | photobiology | Green Arabidopsis leaf reflectance and transmittance. | reflector_spct | 1750 | 2 |
Ler_leaf_trns.spct | photobiology | Green Arabidopsis leaf reflectance and transmittance. | filter_spct | 1753 | 2 |
Ler_leaf_trns_i.spct | photobiology | Green Arabidopsis leaf reflectance and transmittance. | filter_spct | 2401 | 3 |
beesxyzCMF.spct | photobiology | Honeybee xyz chromaticity colour matching function data | chroma_spct | 401 | 4 |
black_body.spct | photobiology | Theoretical optical bodies | object_spct | 4 | 3 |
ccd.spct | photobiology | Spectral response of a back-thinned CCD image sensor. | response_spct | 186 | 2 |
ciev10.spct | photobiology | Linear energy CIE 2008 luminous efficiency function 10 deg data | response_spct | 441 | 2 |
ciev2.spct | photobiology | Linear energy CIE 2008 luminous efficiency function 2 deg data | response_spct | 441 | 2 |
ciexyzCC10.spct | photobiology | CIE xyz chromaticity coordinates (CC) 10 deg data | chroma_spct | 441 | 4 |
ciexyzCC2.spct | photobiology | CIE xyz chromaticity coordinates 2 deg data | chroma_spct | 441 | 4 |
ciexyzCMF10.spct | photobiology | Linear energy CIE xyz colour matching function (CMF) 10 deg data | chroma_spct | 441 | 4 |
ciexyzCMF2.spct | photobiology | Linear energy CIE xyz colour matching function (CMF) 2 deg data | chroma_spct | 441 | 4 |
clear.spct | photobiology | Theoretical spectrum of clear and apaque materials | filter_spct | 4 | 2 |
clear_body.spct | photobiology | Theoretical optical bodies | object_spct | 4 | 3 |
cone_fundamentals10.mspct | photobiology | Ten-degree cone fundamentals | response_mspct | | |
cone_fundamentals10.spct | photobiology | Ten-degree cone fundamentals | chroma_spct | 4 | 4 |
filter_cps.mspct | photobiology | Counts per second from a measurement of a plastic film | cps_mspct | | |
green_leaf.spct | photobiology | Green birch leaf reflectance. | reflector_spct | 226 | 2 |
opaque.spct | photobiology | Theoretical spectrum of clear and apaque materials | filter_spct | 4 | 2 |
phenylalanine.spct | photobiology | Molar spectral attenuation coefficient of phenylalanine | solute_spct | 1993 | 2 |
photodiode.spct | photobiology | Spectral response of a GaAsP photodiode | response_spct | 94 | 2 |
polyester.spct | photobiology | Transmittance spectrum of plastic films | filter_spct | 561 | 2 |
r4p_pkgs | photobiology | Packages in R for Photobiology suite | character | | |
sun.daily.data | photobiology | Daily solar spectral irradiance (simulated) | tbl_df | 511 | 3 |
sun.daily.spct | photobiology | Daily solar spectral irradiance (simulated) | source_spct | 522 | 3 |
sun.data | photobiology | Solar spectral irradiance (simulated) | data.frame | 508 | 3 |
sun.spct | photobiology | Solar spectral irradiance (simulated) | source_spct | 522 | 3 |
sun_daily.data | photobiology | Daily solar spectral irradiance (simulated) | tbl_df | 511 | 3 |
sun_daily.spct | photobiology | Daily solar spectral irradiance (simulated) | source_spct | 522 | 3 |
sun_evening.mspct | photobiology | Time series of solar spectral irradiance (measured) | source_mspct | | |
sun_evening.spct | photobiology | Time series of solar spectral irradiance (measured) | source_spct | 7965 | 3 |
two_filters.mspct | photobiology | Transmittance spectrum of plastic films | filter_mspct | | |
two_filters.spct | photobiology | Transmittance spectrum of plastic films | filter_spct | 1172 | 3 |
two_sensors.mspct | photobiology | Spectral response of two light sensors. | response_mspct | | |
two_sensors.spct | photobiology | Spectral response of two light sensors. | response_spct | 280 | 4 |
water.spct | photobiology | Molar spectral attenuation coefficient of water | solute_spct | 251 | 2 |
white_body.spct | photobiology | Theoretical optical bodies | object_spct | 4 | 3 |
white_led.cps_spct | photobiology | White led bulb spectrum | cps_spct | 2068 | 2 |
white_led.raw_spct | photobiology | White led bulb spectrum | raw_spct | 2068 | 4 |
white_led.source_spct | photobiology | White led bulb spectrum | source_spct | 1421 | 2 |
yellow_gel.spct | photobiology | Transmittance spectrum of plastic films | filter_spct | 611 | 2 |
Kline | loo | Datasets for loo examples and vignettes | data.frame | 10 | 5 |
milk | loo | Datasets for loo examples and vignettes | data.frame | 29 | 8 |
voice | loo | Datasets for loo examples and vignettes | data.frame | 126 | 313 |
voice_loo | loo | Datasets for loo examples and vignettes | data.frame | 126 | 3 |
nemsqar_airway_table | nemsqar | Synthetic Test Data for eAirway Fields in National EMS Information System | tbl_df | 10000 | 8 |
nemsqar_arrest_table | nemsqar | Synthetic Test Data for eArrest Fields in National EMS Information System | tbl_df | 10000 | 28 |
nemsqar_disposition_table | nemsqar | Synthetic Test Data for eDisposition Fields in National EMS Information System | tbl_df | 10000 | 13 |
nemsqar_exam_table | nemsqar | Synthetic Test Data for eExam Fields in National EMS Information System | tbl_df | 10000 | 11 |
nemsqar_injury_table | nemsqar | Synthetic Test Data for eInjury Fields in National EMS Information System | tbl_df | 10000 | 8 |
nemsqar_medications_table | nemsqar | Synthetic Test Data for eMedications Fields in National EMS Information System | tbl_df | 10000 | 8 |
nemsqar_patient_scene_table | nemsqar | Synthetic ePatient Data from the National Emergency Medical Services Information System (NEMSIS) | tbl_df | 10000 | 6 |
nemsqar_procedures_table | nemsqar | Synthetic eProcedures Data from the National Emergency Medical Services Information System (NEMSIS) | tbl_df | 10000 | 8 |
nemsqar_response_table | nemsqar | Synthetic eResponse Data from the National Emergency Medical Services Information System (NEMSIS) | tbl_df | 10000 | 5 |
nemsqar_situation_table | nemsqar | Synthetic Test Data for eSituation Fields in National EMS Information System | tbl_df | 10000 | 18 |
nemsqar_vitals_table | nemsqar | Synthetic eVitals Data for NEMSIS from the National Emergency Medical Services Information System (NEMSIS) | tbl_df | 10000 | 19 |
fauxmesa_edges | ideanet | Goodreau's Faux Mesa High School (Edgelist) | data.frame | 203 | 2 |
fauxmesa_nodes | ideanet | Goodreau's Faux Mesa High School (Nodelist) | data.frame | 205 | 4 |
florentine_edges | ideanet | Edgelist of marriage alliances and business relationships between Florentine families during the Italian Renaissance | data.frame | 35 | 4 |
florentine_nodes | ideanet | Nodelist of marriage alliances and business relationships between Florentine families during the Italian Renaissance | data.frame | 16 | 2 |
hightech | ideanet | Multiplex Network of Relationships Between Managers of a High-Tech Company | data.frame | 312 | 4 |
marvel | ideanet | Character Relations in Marvel Comics | spec_tbl_df | 9891 | 3 |
ngq_aa | ideanet | Ego Networks Elicited from the "Important Matters" Name Generator Question (Alter-Alter Edgelist) | tbl_df | 123 | 5 |
ngq_alters | ideanet | Ego Networks Elicited from the "Important Matters" Name Generator Question (Alter List) | data.frame | 67 | 14 |
ngq_egos | ideanet | Ego Networks Elicited from the "Important Matters" Name Generator Question (Nodelist) | tbl_df | 20 | 9 |
triad | ideanet | A Small Network Containing all Triads and Motifs | matrix | 9 | 9 |
chatgpt | volker | ChatGPT Adoption Dataset CG-GE-APR23 | tbl_df | 101 | 19 |
cultures_data | mpactr | LC-MS/MS sample data | filter_pactr | | |
alcohol | RobStatTM | Alcohol data | data.frame | 44 | 7 |
algae | RobStatTM | Algae data | data.frame | 90 | 12 |
biochem | RobStatTM | Biochem data | data.frame | 12 | 2 |
breslow.dat | RobStatTM | Breslow Data | data.frame | 59 | 12 |
bus | RobStatTM | Bus data | data.frame | 218 | 18 |
flour | RobStatTM | Flour data | data.frame | 24 | 1 |
glass | RobStatTM | Glass data | data.frame | 76 | 7 |
hearing | RobStatTM | Hearing data | data.frame | 7 | 7 |
image | RobStatTM | Image data | data.frame | 1573 | 6 |
leuk.dat | RobStatTM | Leukemia Data | data.frame | 33 | 3 |
mineral | RobStatTM | Mineral data | data.frame | 53 | 2 |
neuralgia | RobStatTM | Neuralgia data | data.frame | 18 | 5 |
oats | RobStatTM | Oats data | data.frame | 40 | 4 |
resex | RobStatTM | Resex data | numeric | | |
shock | RobStatTM | Shock data | data.frame | 16 | 2 |
skin | RobStatTM | Skin data | data.frame | 39 | 3 |
stackloss | RobStatTM | Stackloss data | data.frame | 21 | 5 |
vehicle | RobStatTM | Vehicle data | data.frame | 217 | 18 |
waste | RobStatTM | Waste data | data.frame | 40 | 6 |
wine | RobStatTM | Wine data | data.frame | 59 | 13 |
Cliff_walking | markovDP | Cliff Walking Gridworld MDP | MDP | | |
DynaMaze | markovDP | The Dyna Maze | MDP | | |
Maze | markovDP | Steward Russell's 4x3 Maze Gridworld MDP | MDP | | |
Windy_gridworld | markovDP | Windy Gridworld MDP Windy Gridworld MDP | MDP | | |
slim_classes | slimr | SLiM Classes | tbl_df | 20 | 3 |
slim_recipes | slimr | Dataset of all recipes available in SLiM | list | | |
dube | DiSCos | Data from (Dube 2019) | data.table | 652870 | 3 |
biome4_classes | pastclim | BIOME4 classes. | data.frame | 29 | 3 |
koeppen_classes | pastclim | Koeppen-Geiger classes. | data.frame | 30 | 5 |
mis_boundaries | pastclim | Time boundaries of marine isotope stages (MIS). | data.frame | 24 | 3 |
region_extent | pastclim | Region extents. | list | | |
region_outline | pastclim | Region outlines. | sfc_GEOMETRY | | |
region_outline_union | pastclim | Region outlines unioned. | sfc_POLYGON | | |
dat2way | semTools | Simulated Dataset to Demonstrate Two-way Latent Interaction | matrix | 10000 | 9 |
dat3way | semTools | Simulated Dataset to Demonstrate Three-way Latent Interaction | matrix | 1000 | 12 |
datCat | semTools | Simulated Data set to Demonstrate Categorical Measurement Invariance | data.frame | 200 | 9 |
exLong | semTools | Simulated Data set to Demonstrate Longitudinal Measurement Invariance | data.frame | 200 | 10 |
simParcel | semTools | Simulated Data set to Demonstrate Random Allocations of Parcels | data.frame | 100 | 18 |
Mm.c2 | OmicNavigator | Mm.c2 from Bioconductor workflow RNAseq123 | list | | |
basal.vs.lp | OmicNavigator | basal.vs.lp from Bioconductor workflow RNAseq123 | data.frame | 24 | 8 |
basal.vs.ml | OmicNavigator | basal.vs.ml from Bioconductor workflow RNAseq123 | data.frame | 24 | 8 |
cam.BasalvsLP | OmicNavigator | cam.BasalvsLP from Bioconductor workflow RNAseq123 | data.frame | 4 | 4 |
cam.BasalvsML | OmicNavigator | cam.BasalvsML from Bioconductor workflow RNAseq123 | data.frame | 4 | 4 |
group | OmicNavigator | group from Bioconductor workflow RNAseq123 | factor | | |
lane | OmicNavigator | lane from Bioconductor workflow RNAseq123 | factor | | |
lcpm | OmicNavigator | lcpm from Bioconductor workflow RNAseq123 | matrix | 24 | 9 |
samplenames | OmicNavigator | samplenames from Bioconductor workflow RNAseq123 | character | | |
congreveLamsdellMatrices | TreeSearch | 100 simulated data matrices | list | | |
inapplicable.citations | TreeSearch | Thirty datasets with inapplicable data | character | | |
inapplicable.datasets | TreeSearch | Thirty datasets with inapplicable data | list | | |
inapplicable.phyData | TreeSearch | Thirty datasets with inapplicable data | list | | |
inapplicable.trees | TreeSearch | Thirty datasets with inapplicable data | list | | |
profiles | TreeSearch | Empirically counted profiles for small trees | list | | |
referenceTree | TreeSearch | Tree topology for matrix simulation | phylo | | |
psymas | siren | psymas database | data.frame | 1309 | 10 |
crooked_river | hatchR | Example dataset: Crooked River, Idaho | spec_tbl_df | 1826 | 2 |
idaho | hatchR | Central Idaho Water Temperature Data | tbl_df | 773681 | 3 |
model_table | hatchR | Table of phenology models | spec_tbl_df | 51 | 5 |
woody_island | hatchR | Example dataset: Woody Island, Lake Iliamna, Alaska | spec_tbl_df | 735 | 2 |
adr_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 2133 | 4 |
demo_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 750 | 7 |
drug_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 3514 | 12 |
ex_ | vigicaen | Data for the immune checkpoint inhibitors example | list | | |
followup_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 902 | 2 |
ind_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 2426 | 2 |
link_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 5136 | 11 |
meddra_ | vigicaen | Sample of Meddra. | data.table | 677 | 15 |
mp_ | vigicaen | Sample of WHODrug | data.table | 14147 | 8 |
out_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 747 | 3 |
smq_content_ | vigicaen | Sample of Meddra. | data.table | 3386 | 9 |
smq_list_ | vigicaen | Sample of Meddra. | data.table | 11 | 9 |
smq_list_content_ | vigicaen | Sample of Meddra. | data.table | 3386 | 19 |
srce_ | vigicaen | Data of immune checkpoint inhibitors. | data.table | 729 | 2 |
thg_ | vigicaen | Sample of WHODrug | data.table | 4079 | 5 |
ens | abms | Chilean National Health Survey (2016-2017) | data.frame | 3211 | 12 |
LEAD | peacesciencer | (An Abbreviation of) The LEAD Data Set | tbl_df | 3409 | 12 |
archigos | peacesciencer | Archigos: A (Subset of a) Dataset on Political Leaders | tbl_df | 3409 | 11 |
atop_alliance | peacesciencer | Alliance Treaty Obligations and Provisions (ATOP) Project Data (v. 5.0) | tbl_df | 272046 | 8 |
ccode_democracy | peacesciencer | Democracy data for all Correlates of War states | tbl_df | 16731 | 5 |
cow_alliance | peacesciencer | Data sets that have been deprecated | tbl_df | 120784 | 7 |
cow_capitals | peacesciencer | A complete list of capitals and capital transitions for Correlates of War state system members | tbl_df | 252 | 7 |
cow_contdir | peacesciencer | Correlates of War Direct Contiguity Data (v. 3.2) | tbl_df | 1694 | 5 |
cow_ddy | peacesciencer | A directed dyad-year data frame of Correlates of War state system members | tbl_df | 2139270 | 3 |
cow_gw_years | peacesciencer | Correlates of War and Gleditsch-Ward states, by year | tbl_df | 16936 | 6 |
cow_igo_ndy | peacesciencer | Correlates of War Non-Directed Dyad-Year International Governmental Organizations (IGOs) Data | tbl_df | 917695 | 4 |
cow_igo_sy | peacesciencer | Correlates of War State-Year International Governmental Organizations (IGOs) Data | tbl_df | 15557 | 6 |
cow_majors | peacesciencer | Correlates of War Major Powers Data (1816-2016) | tbl_df | 14 | 8 |
cow_mid_ddydisps | peacesciencer | Directed Dyadic Dispute-Year Data with No Duplicate Dyad-Years (CoW-MID, v. 5.0) | tbl_df | 10234 | 24 |
cow_mid_dirdisps | peacesciencer | Directed Dyadic Dispute-Year Data (CoW-MID, v. 5.0) | tbl_df | 11390 | 18 |
cow_mid_disps | peacesciencer | Abbreviated CoW-MID Dispute-level Data (v. 5.0) | tbl_df | 2436 | 11 |
cow_mindist | peacesciencer | The Minimum Distance Between States in the Correlates of War System, 1886-2019 | tbl_df | 952244 | 4 |
cow_nmc | peacesciencer | Correlates of War National Military Capabilities Data | tbl_df | 15951 | 9 |
cow_sdp_gdp | peacesciencer | (Surplus and Gross) Domestic Product for Correlates of War States | tbl_df | 27753 | 6 |
cow_states | peacesciencer | Correlates of War State System Membership Data (1816-2016) | spec_tbl_df | 243 | 10 |
cow_trade_sy | peacesciencer | Correlates of War National Trade Data Set (v. 4.0) | tbl_df | 14410 | 4 |
cow_war_inter | peacesciencer | Correlates of War Inter-State War Data (v. 4.0) | tbl_df | 1932 | 15 |
cow_war_intra | peacesciencer | Correlates of War Intra-State War Data (v. 4.1) | tbl_df | 1361 | 17 |
creg | peacesciencer | Composition of Religious and Ethnic Groups (CREG) Fractionalization/Polarization Estimates | tbl_df | 11523 | 8 |
false_cow_dyads | peacesciencer | False Correlates of War Directed Dyad-Years | tbl_df | 60 | 4 |
false_gw_dyads | peacesciencer | False Gleditsch-Ward Directed Dyad-Years | tbl_df | 38 | 4 |
gml_dirdisp | peacesciencer | Data sets that have been deprecated | spec_tbl_df | 10276 | 39 |
gml_mid_ddlydisps | peacesciencer | Data sets that have been deprecated | tbl_df | 10708 | 16 |
gml_mid_ddydisps | peacesciencer | Data sets that have been deprecated | tbl_df | 9284 | 24 |
gml_mid_dirleaderdisps | peacesciencer | Data sets that have been deprecated | tbl_df | 11686 | 16 |
gml_mid_disps | peacesciencer | Data sets that have been deprecated | tbl_df | 2174 | 11 |
gml_part | peacesciencer | Data sets that have been deprecated | tbl_df | 5217 | 19 |
grh_arms_races | peacesciencer | Conventional Arms Races During Periods of Rivalry | tbl_df | 71 | 5 |
gw_capitals | peacesciencer | A complete list of capitals and capital transitions for Gleditsch-Ward state system members | tbl_df | 252 | 7 |
gw_cow_years | peacesciencer | Gleditsch-Ward states and Correlates of War, by year | tbl_df | 18425 | 6 |
gw_ddy | peacesciencer | A directed dyad-year data frame of Gleditsch-Ward state system members | tbl_df | 2089826 | 3 |
gw_mindist | peacesciencer | The Minimum Distance Between States in the Gleditsch-Ward System, 1886-2019 | tbl_df | 871034 | 4 |
gw_sdp_gdp | peacesciencer | (Surplus and Gross) Domestic Product for Gleditsch-Ward States | tbl_df | 27387 | 6 |
gw_states | peacesciencer | Gleditsch-Ward (Independent States) System Membership Data (1816-2017) | spec_tbl_df | 216 | 5 |
gwcode_democracy | peacesciencer | Democracy data for all Gleditsch-Ward states | tbl_df | 18289 | 5 |
hief | peacesciencer | Historical Index of Ethnic Fractionalization data | tbl_df | 8808 | 4 |
leader_codes | peacesciencer | A Data Set of Leader Codes Across Archigos 4.1, Archigos 2.9, and the LEAD Data | tbl_df | 3409 | 4 |
lwuf | peacesciencer | Leader Willingness to Use Force | tbl_df | 3409 | 9 |
maoz_powers | peacesciencer | Zeev Maoz' Regional/Global Power Data | tbl_df | 20 | 5 |
ps_bib | peacesciencer | A 'BibTeX' Data Frame of Citations | tbl_df | 47 | 37 |
ps_data_version | peacesciencer | The Version Numbers for Data Included in 'peacesciencer' | tbl_df | 32 | 4 |
rugged | peacesciencer | Rugged/Mountainous Terrain Data | tbl_df | 192 | 4 |
td_rivalries | peacesciencer | Thompson and Dreyer's (2012) Strategic Rivalries, 1494-2010 | tbl_df | 197 | 10 |
tss_rivalries | peacesciencer | Thompson et al. (2021) Strategic Rivalries, 1494-2020 | tbl_df | 264 | 12 |
ucdp_acd | peacesciencer | UCDP Armed Conflict Data (ACD) (v. 20.1) | tbl_df | 4164 | 15 |
ucdp_onsets | peacesciencer | UCDP Onset Data (v. 19.1) | tbl_df | 10142 | 8 |
bike | glex | Bikesharing data | data.table | 8645 | 11 |
COelev | evgam | Colorado daily precipitation accumulations | list | | |
COprcp | evgam | Colorado daily precipitation accumulations | data.frame | 404326 | 3 |
COprcp_meta | evgam | Colorado daily precipitation accumulations | data.frame | 64 | 5 |
FCtmax | evgam | Fort Collins, Colorado, US daily max. temperatures | data.frame | 18156 | 2 |
fremantle | evgam | Annual Maximum Sea Levels at Fremantle, Western Australia | data.frame | 86 | 3 |
a | mvp | Single-letter symbols | mvp | | |
b | mvp | Single-letter symbols | mvp | | |
c | mvp | Single-letter symbols | mvp | | |
d | mvp | Single-letter symbols | mvp | | |
e | mvp | Single-letter symbols | mvp | | |
f | mvp | Single-letter symbols | mvp | | |
g | mvp | Single-letter symbols | mvp | | |
h | mvp | Single-letter symbols | mvp | | |
i | mvp | Single-letter symbols | mvp | | |
j | mvp | Single-letter symbols | mvp | | |
k | mvp | Single-letter symbols | mvp | | |
l | mvp | Single-letter symbols | mvp | | |
m | mvp | Single-letter symbols | mvp | | |
n | mvp | Single-letter symbols | mvp | | |
o | mvp | Single-letter symbols | mvp | | |
p | mvp | Single-letter symbols | mvp | | |
q | mvp | Single-letter symbols | mvp | | |
r | mvp | Single-letter symbols | mvp | | |
s | mvp | Single-letter symbols | mvp | | |
t | mvp | Single-letter symbols | mvp | | |
u | mvp | Single-letter symbols | mvp | | |
v | mvp | Single-letter symbols | mvp | | |
w | mvp | Single-letter symbols | mvp | | |
x | mvp | Single-letter symbols | mvp | | |
y | mvp | Single-letter symbols | mvp | | |
z | mvp | Single-letter symbols | mvp | | |
california_ev_model | evsim | EV model example | evmodel | | |
california_ev_sessions | evsim | EV charging sessions example | tbl_df | 30114 | 12 |
california_ev_sessions_profiles | evsim | Clustered EV charging sessions example | tbl_df | 28447 | 15 |
sessions_feature_names | evsim | Names of standard features of a sessions dataset | character | | |
ppiAFG2012 | ppitables | Poverty Probability Index (PPI) lookup table for Afghanistan | data.frame | 101 | 7 |
ppiAGO2015 | ppitables | Poverty Probability Index (PPI) lookup table for Angola | data.frame | 101 | 9 |
ppiBEN2012 | ppitables | Poverty Probability Index (PPI) lookup table for Benin | data.frame | 101 | 7 |
ppiBEN2022_11q | ppitables | Poverty Probability Index (PPI) lookup table for Benin for 2022 for 11 questions score card | tbl_df | 101 | 14 |
ppiBEN2022_6q | ppitables | Poverty Probability Index (PPI) lookup table for Benin for 2022 for 6 questions score card | tbl_df | 101 | 14 |
ppiBFA2011 | ppitables | Poverty Probability Index (PPI) lookup table for Burkina Faso | data.frame | 101 | 8 |
ppiBFA2014 | ppitables | Poverty Probability Index (PPI) lookup table for Burkina Faso | data.frame | 101 | 18 |
ppiBFA2017 | ppitables | Poverty Probability Index (PPI) lookup table for Burkina Faso | data.frame | 101 | 15 |
ppiBFA2023 | ppitables | Poverty Probability Index (PPI) lookup table for Burkina Faso for 2023 | tbl_df | 101 | 14 |
ppiBGD2013 | ppitables | Poverty Probability Index (PPI) lookup table for Bangladesh | data.frame | 101 | 10 |
ppiBOL2015 | ppitables | Poverty Probability Index (PPI) lookup table for Bolivia | data.frame | 101 | 10 |
ppiBOL2023 | ppitables | Poverty Probability Index (PPI) lookup table for Bolivia for 2023 | tbl_df | 101 | 15 |
ppiBRA2010 | ppitables | Poverty Probability Index (PPI) lookup table for Brazil | data.frame | 101 | 10 |
ppiCIV2013 | ppitables | Poverty Probability Index (PPI) lookup table for Ivory Coast | data.frame | 101 | 9 |
ppiCIV2018 | ppitables | Poverty Probability Index (PPI) lookup table for Ivory Coast | data.frame | 101 | 15 |
ppiCMR2013 | ppitables | Poverty Probability Index (PPI) lookup table for Cameroon | data.frame | 101 | 8 |
ppiCOL2012 | ppitables | Poverty Probability Index (PPI) lookup table for Colombia | data.frame | 101 | 10 |
ppiCOL2012_a | ppitables | Poverty Probability Index (PPI) lookup table for Colombia | data.frame | 101 | 12 |
ppiCOL2018 | ppitables | Poverty Probability Index (PPI) lookup table for Colombia | data.frame | 101 | 19 |
ppiCOL2024 | ppitables | Poverty Probability Index (PPI) lookup table for Colombia based on data from the 2022 Gran Encuesta Integrada de Hogares (GEIH). | tbl_df | 101 | 15 |
ppiDOM2010 | ppitables | Poverty Probability Index (PPI) lookup table for Dominican Republic | data.frame | 101 | 11 |
ppiDOM2018 | ppitables | Poverty Probability Index (PPI) lookup table for Dominican Republic | data.frame | 101 | 16 |
ppiDOM2024 | ppitables | Poverty Probability Index (PPI) lookup table for Dominican Republic based on data from the 2022 Encuesta Continua de Fuerza de Trabajo - ENCFT conducted by the National Statistics Office (ONE) | tbl_df | 101 | 9 |
ppiECU2015 | ppitables | Poverty Probability Index (PPI) lookup table for Ecuador | data.frame | 101 | 11 |
ppiECU2022 | ppitables | Poverty Probability Index (PPI) lookup table for Ecuador for 2022 | tbl_df | 101 | 20 |
ppiEGY2010 | ppitables | Poverty Probability Index (PPI) lookup table for Egypt | data.frame | 101 | 8 |
ppiETH2016 | ppitables | Poverty Probability Index (PPI) lookup table for Ethiopia | data.frame | 101 | 21 |
ppiETH2023 | ppitables | Poverty Probability Index (PPI) lookup table for Ethiopia for 2023 | tbl_df | 101 | 20 |
ppiFJI2014 | ppitables | Poverty Probability Index (PPI) lookup table for Fiji | data.frame | 101 | 8 |
ppiGHA2015 | ppitables | Poverty Probability Index (PPI) lookup table for Ghana based on legacy definitions | data.frame | 101 | 8 |
ppiGHA2015_a | ppitables | Poverty Probability Index (PPI) lookup table for Ghana using poverty definitions deflated with Ghana's CPI | data.frame | 101 | 13 |
ppiGHA2015_b | ppitables | Poverty Probability Index (PPI) lookup table for Ghana using poverty definitions deflated with the change in 100% of national poverty line | data.frame | 101 | 8 |
ppiGHA2019 | ppitables | Poverty Probability Index (PPI) lookup table for Ghana | tbl_df | 101 | 20 |
ppiGTM2016 | ppitables | Poverty Probability Index (PPI) lookup table for Guatemala | data.frame | 101 | 17 |
ppiGTM2023 | ppitables | Poverty Probability Index (PPI) lookup table for Guatemala for 2023 | tbl_df | 101 | 11 |
ppiHND2010 | ppitables | Poverty Probability Index (PPI) lookup table for Honduras | data.frame | 101 | 7 |
ppiHND2023 | ppitables | Poverty Probability Index (PPI) lookup table for Honduras for 2023 | tbl_df | 101 | 18 |
ppiHTI2016 | ppitables | Poverty Probability Index (PPI) lookup table for Haiti | data.frame | 101 | 10 |
ppiIDN2012 | ppitables | Poverty Probability Index (PPI) lookup table for Indonesia using legacy poverty definitions | data.frame | 101 | 4 |
ppiIDN2012_a | ppitables | Poverty Probability Index (PPI) lookup table for Indonesia using new poverty definitions | data.frame | 101 | 9 |
ppiIDN2020 | ppitables | Poverty Probability Index (PPI) lookup table for Indonesia | tbl_df | 100 | 20 |
ppiIDN2023 | ppitables | Poverty Probability Index (PPI) lookup table for Indonesia for 2023 | tbl_df | 101 | 10 |
ppiIND2016_r59 | ppitables | Poverty Probability Index (PPI) lookup table for India using r59 poverty definitions | data.frame | 101 | 4 |
ppiIND2016_r62 | ppitables | Poverty Probability Index (PPI) lookup table for India using r62 poverty definitions | data.frame | 101 | 7 |
ppiIND2016_r66 | ppitables | Poverty Probability Index (PPI) lookup table for India using r66 poverty definitions | data.frame | 101 | 8 |
ppiIND2016_r68 | ppitables | Poverty Probability Index (PPI) lookup table for India using r68 poverty definitions | data.frame | 101 | 16 |
ppiJOR2010 | ppitables | Poverty Probability Index (PPI) lookup table for Jordan | data.frame | 101 | 10 |
ppiKEN2011 | ppitables | Poverty Probability Index (PPI) lookup table for Kenya | data.frame | 101 | 11 |
ppiKEN2018 | ppitables | Poverty Probability Index (PPI) lookup table for Kenya | data.frame | 101 | 17 |
ppiKEN2024 | ppitables | Poverty Probability Index (PPI) lookup table for Kenya based on data from the 2021 Kenya Integrated Household Budget Survey (KIHBS) | tbl_df | 101 | 13 |
ppiKGZ2015 | ppitables | Poverty Probability Index (PPI) lookup table for Kyrgyzstan | data.frame | 101 | 9 |
ppiKHM2015 | ppitables | Poverty Probability Index (PPI) lookup table for Cambodia | data.frame | 101 | 6 |
ppiKHM2015_gov | ppitables | Poverty Probability Index (PPI) lookup table for Cambodia | data.frame | 101 | 9 |
ppiKHM2015_wb | ppitables | Poverty Probability Index (PPI) lookup table for Cambodia | data.frame | 101 | 9 |
ppiKHM2023 | ppitables | Poverty Probability Index (PPI) lookup table for Cambodia for 2023 | tbl_df | 101 | 14 |
ppiLKA2016 | ppitables | Poverty Probability Index (PPI) lookup table for Sri Lanka | data.frame | 101 | 16 |
ppiMAR2013 | ppitables | Poverty Probability Index (PPI) lookup table for Morocco | data.frame | 101 | 9 |
ppiMDG2015 | ppitables | Poverty Probability Index (PPI) lookup table for Madagascar | data.frame | 101 | 9 |
ppiMEX2017 | ppitables | Poverty Probability Index (PPI) lookup table for Mexico using legacy definitions | data.frame | 101 | 8 |
ppiMEX2017_a | ppitables | Poverty Probability Index (PPI) lookup table for Mexico using new poverty definitions | data.frame | 101 | 17 |
ppiMEX2024 | ppitables | Poverty Probability Index (PPI) lookup table for Mexico based on data from Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) from 2022 produced by the Instituto Nacional de Estadística y Geografía (INEGI) | tbl_df | 101 | 13 |
ppiMLI2010 | ppitables | Poverty Probability Index (PPI) lookup table for Mali | data.frame | 101 | 6 |
ppiMMR2012 | ppitables | Poverty Probability Index (PPI) lookup table for Myanmar | data.frame | 101 | 8 |
ppiMMR2019 | ppitables | Poverty Probability Index (PPI) lookup table for Myanmar | tbl_df | 101 | 20 |
ppiMNG2016 | ppitables | Poverty Probability Index (PPI) lookup table for Mongolia | data.frame | 101 | 18 |
ppiMOZ2013 | ppitables | Poverty Probability Index (PPI) lookup table for Mozambique | data.frame | 101 | 7 |
ppiMOZ2019 | ppitables | Poverty Probability Index (PPI) lookup table for Mozambique | tbl_df | 101 | 15 |
ppiMOZ2024 | ppitables | Poverty Probability Index (PPI) lookup table for Mozambique | tbl_df | 101 | 6 |
ppiMWI2015 | ppitables | Poverty Probability Index (PPI) lookup table for Malawi using legacy poverty definitions | data.frame | 101 | 3 |
ppiMWI2015_gov | ppitables | Poverty Probability Index (PPI) lookup table for Malawi using government poverty definitions | data.frame | 101 | 14 |
ppiMWI2015_pbm | ppitables | Poverty Probability Index (PPI) lookup table for Malawi using PBM poverty definitions | data.frame | 101 | 13 |
ppiMWI2020 | ppitables | Poverty Probability Index (PPI) lookup table for Malawi | tbl_df | 100 | 16 |
ppiMWI2023 | ppitables | Poverty Probability Index (PPI) lookup table for Malawi for 2023 | tbl_df | 101 | 13 |
ppiNAM2013 | ppitables | Poverty Probability Index (PPI) lookup table for Namibia | data.frame | 101 | 9 |
ppiNER2013 | ppitables | Poverty Probability Index (PPI) lookup table for Niger | data.frame | 101 | 9 |
ppiNGA2015 | ppitables | Poverty Probability Index (PPI) lookup table for Nigeria | data.frame | 101 | 13 |
ppiNIC2013 | ppitables | Poverty Probability Index (PPI) lookup table for Nicaragua | data.frame | 101 | 10 |
ppiNPL2013 | ppitables | Poverty Probability Index (PPI) lookup table for Nepal using legacy poverty definitions | data.frame | 101 | 4 |
ppiNPL2013_a | ppitables | Poverty Probability Index (PPI) lookup table for Nepal using new poverty definitions | data.frame | 101 | 9 |
ppiPAK2009 | ppitables | Poverty Probability Index (PPI) lookup table for Pakistan | data.frame | 101 | 10 |
ppiPER2012 | ppitables | Poverty Probability Index (PPI) lookup table for Peru | data.frame | 101 | 9 |
ppiPER2018 | ppitables | Poverty Probability Index (PPI) lookup table for Peru | data.frame | 101 | 19 |
ppiPER2024 | ppitables | Poverty Probability Index (PPI) lookup table for Peru based on data from the 2022 Encuesta Nacional de Hogares (ENAHO) | tbl_df | 101 | 15 |
ppiPHL2014 | ppitables | Poverty Probability Index (PPI) lookup table for Philippines using legacy poverty definitions | data.frame | 101 | 6 |
ppiPHL2014_a | ppitables | Poverty Probability Index (PPI) lookup table for Philippines using new poverty definitions | data.frame | 101 | 11 |
ppiPHL2018 | ppitables | Poverty Probability Index (PPI) lookup table for Philippines | data.frame | 101 | 18 |
ppiPHL2023 | ppitables | Poverty Probability Index (PPI) lookup table for Philippines for 2023 | tbl_df | 101 | 13 |
ppiPNG2023 | ppitables | Poverty Probability Index (PPI) lookup table for Papua New Guinea 2023 | tbl_df | 101 | 9 |
ppiPRY2012 | ppitables | Poverty Probability Index (PPI) lookup table for Paraguay | data.frame | 101 | 8 |
ppiPSE2014 | ppitables | Poverty Probability Index (PPI) lookup table for Palestine | data.frame | 101 | 11 |
ppiROU2009 | ppitables | Poverty Probability Index (PPI) lookup table for Romania | data.frame | 101 | 9 |
ppiRUS2010 | ppitables | Poverty Probability Index (PPI) lookup table for Russia | data.frame | 101 | 4 |
ppiRWA2016 | ppitables | Poverty Probability Index (PPI) lookup table for Rwanda | data.frame | 101 | 11 |
ppiRWA2019 | ppitables | Poverty Probability Index (PPI) lookup table for Rwanda | tbl_df | 101 | 20 |
ppiSEN2009 | ppitables | Poverty Probability Index (PPI) lookup table for Senegal | data.frame | 101 | 11 |
ppiSEN2018 | ppitables | Poverty Probability Index (PPI) lookup table for Senegal | data.frame | 101 | 16 |
ppiSLE2011 | ppitables | Poverty Probability Index (PPI) lookup table for Sierra Leone | data.frame | 101 | 8 |
ppiSLV2010 | ppitables | Poverty Probability Index (PPI) lookup table for El Salvador | data.frame | 101 | 9 |
ppiSLV2021 | ppitables | Poverty Probability Index (PPI) lookup table for El Salvador for 2021 | tbl_df | 101 | 21 |
ppiSYR2010 | ppitables | Poverty Probability Index (PPI) lookup table for Syria | data.frame | 101 | 8 |
ppiTGO2018 | ppitables | Poverty Probability Index (PPI) lookup table for Togo | data.frame | 101 | 15 |
ppiTGO2023 | ppitables | Poverty Probability Index (PPI) lookup table for Togo for 2023 | tbl_df | 101 | 14 |
ppiTJK2015 | ppitables | Poverty Probability Index (PPI) lookup table for Tajikistan | data.frame | 101 | 9 |
ppiTLS2013 | ppitables | Poverty Probability Index (PPI) lookup table for Timor Leste | data.frame | 101 | 8 |
ppiTZA2016 | ppitables | Poverty Probability Index (PPI) lookup table for Tanzania | data.frame | 101 | 19 |
ppiTZA2022 | ppitables | Poverty Probability Index (PPI) lookup table for Tanzania 2022 | tbl_df | 100 | 21 |
ppiUGA2015 | ppitables | Poverty Probability Index (PPI) lookup table for Uganda | data.frame | 101 | 13 |
ppiUGA2022 | ppitables | Poverty Probability Index (PPI) lookup table for Uganda 2022 | tbl_df | 100 | 13 |
ppiVNM2009 | ppitables | Poverty Probability Index (PPI) lookup table for Vietnam | data.frame | 101 | 8 |
ppiVNM2023 | ppitables | Poverty Probability Index (PPI) lookup table for Vietnam for 2023 | tbl_df | 101 | 5 |
ppiYEM2009 | ppitables | Poverty Probability Index (PPI) lookup table for Yemen | data.frame | 101 | 8 |
ppiZAF2009 | ppitables | Poverty Probability Index (PPI) lookup table for South Africa | data.frame | 101 | 8 |
ppiZAF2023 | ppitables | Poverty Probability Index (PPI) lookup table for South Africa for 2023 | tbl_df | 101 | 6 |
ppiZMB2013_cso | ppitables | Poverty Probability Index (PPI) lookup table for Zambia | data.frame | 101 | 9 |
ppiZMB2013_got | ppitables | Poverty Probability Index (PPI) lookup table for Zambia | data.frame | 101 | 9 |
ppiZMB2017 | ppitables | Poverty Probability Index (PPI) lookup table for Zambia | data.frame | 101 | 17 |
ppiZMB2017_a | ppitables | Poverty Probability Index (PPI) lookup table for Zambia | data.frame | 101 | 16 |
card | sampcompR | card | data.frame | 3010 | 34 |
namcs2019sv | surveytable | Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) | survey.design2 | | |
namcs2019sv_df | surveytable | Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) | data.frame | 8250 | 33 |
rccsu2018 | surveytable | National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) | survey.design2 | | |
uspop2019 | surveytable | US Population in 2019 | list | | |
arnold2013 | TreeBUGS | Data of a Source-Monitoring Experiment | data.frame | 48 | 13 |
california_GMM | evprof | Gaussian Mixture Models examples | list | | |
california_ev_model | evprof | EV model example | evmodel | | |
california_ev_sessions | evprof | EV charging sessions example | tbl_df | 30114 | 12 |
california_ev_sessions_profiles | evprof | Clustered EV charging sessions example | tbl_df | 28447 | 15 |
sessions_feature_names | evprof | Names of standard features of a sessions dataset | character | | |
sessions_summary_feature_names | evprof | Names of features to summarise in evprof functions | character | | |
pop_dat | ageutils | Aggregated population data | data.frame | 19 | 4 |
o_assen | movepub | Sample Movebank dataset with GPS tracking data | datapackage | | |
anthyllis | lefko3 | Matrix Set of _Anthyllis vulneraria_ Populations in Belgium | lefkoMat | | |
cypdata | lefko3 | Demographic Dataset of _Cypripedium candidum_ Population, in Horizontal Format | data.frame | 77 | 29 |
cypvert | lefko3 | Demographic Dataset of _Cypripedium candidum_ Population, in Vertical Format | data.frame | 322 | 14 |
lathyrus | lefko3 | Demographic Dataset of _Lathyrus vernus_ Population | data.frame | 1119 | 38 |
pyrola | lefko3 | Demographic Dataset of _Pyrola japonica_ and _Pyrola subaphylla_ Populations, in Horizontal Format | data.frame | 454 | 51 |
er_network | missSBM | ER ego centered network | dgCMatrix | | |
frenchblog2007 | missSBM | Political Blogosphere network prior to 2007 French presidential election | igraph | | |
war | missSBM | War data set | list | | |
Arca | stevedata | NYSE Arca Steel Index data, 2017–present | tbl_df | 966 | 5 |
CFT15 | stevedata | Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate | tbl_df | 1390 | 9 |
CP77 | stevedata | Education Expenditure Data (Chatterjee and Price, 1977) | tbl_df | 50 | 6 |
DAPO | stevedata | Determinants of Arab Public Opinion | tbl_df | 91 | 11 |
DCE12 | stevedata | Domestic Conflict Events, 2012 | tbl_df | 198 | 19 |
DJIA | stevedata | Dow Jones Industrial Average, 1885-Present | tbl_df | 37931 | 2 |
DST | stevedata | Casualties/Fatalities in the U.S. for Drunk-Driving, Suicide, and Terrorism | tbl_df | 49 | 5 |
Datasaurus | stevedata | The Datasaurus Dozen | spec_tbl_df | 1846 | 3 |
Dee04 | stevedata | Are There Civics Returns to Education? | tbl_df | 9227 | 8 |
EBJ | stevedata | The Economic Benefits of Justice | tbl_df | 95 | 12 |
ESS10NO | stevedata | Norwegian Attitudes toward European Integration (2021-2022) | tbl_df | 1411 | 24 |
ESS9GB | stevedata | British Attitudes Toward Immigration (2018-19) | tbl_df | 1905 | 19 |
ESSBE5 | stevedata | Trust in the Police in Belgium (European Social Survey, Round 5) | tbl_df | 1704 | 10 |
GHR04 | stevedata | Comparative Public Health: The Political Economy of Human Misery and Well-Being | tbl_df | 182 | 15 |
Guber99 | stevedata | School Expenditures and Test Scores for 50 States, 1994-95 | tbl_df | 50 | 8 |
LOTI | stevedata | Land-Ocean Temperature Index, 1880-2022 | tbl_df | 1716 | 2 |
LTPT | stevedata | Long-Term Price Trends for Computers, TVs, and Related Items | tbl_df | 1704 | 3 |
LTWT | stevedata | "Let Them Watch TV" | tbl_df | 2377 | 3 |
Lipset59 | stevedata | Democracy and Economic Development (Around) 1949-50 | tbl_df | 48 | 11 |
Mitchell68 | stevedata | Inequality and Insurgency: A Statistical Study of South Vietnam (Mitchell, 1968) | tbl_df | 26 | 9 |
Newhouse77 | stevedata | Medical-Care Expenditure: A Cross-National Survey (Newhouse, 1977) | tbl_df | 13 | 5 |
ODGI | stevedata | Ozone Depleting Gas Index Data, 1992-2022 | tbl_df | 62 | 16 |
OODTPT | stevedata | Data for "Optimal Obfuscation: Democracy and Trade Policy Transparency" | tbl_df | 75 | 16 |
PPGE | stevedata | Partisan Politics in the Global Economy | tbl_df | 1020 | 14 |
PRDEG | stevedata | Property Rights, Democracy, and Economic Growth | tbl_df | 147 | 10 |
Parvin73 | stevedata | Economic Determinants of Political Unrest (Parvin, 1973) | tbl_df | 26 | 9 |
Presidents | stevedata | U.S. Presidents and Their Terms in Office | tbl_df | 45 | 3 |
Russett64 | stevedata | Inequality and Instability: The Relation of Land Tenure to Politics (Russett, 1964) | tbl_df | 47 | 10 |
SBCD | stevedata | Systemic Banking Crises Database II | tbl_df | 547 | 4 |
SCP16 | stevedata | South Carolina County GOP/Democratic Primary Data, 2016 | spec_tbl_df | 46 | 15 |
TV16 | stevedata | The Individual Correlates of the Trump Vote in 2016 | tbl_df | 64600 | 21 |
USFAHR | stevedata | U.S. Foreign Aid and Human Rights in Assorted Years | tbl_df | 1654 | 18 |
af_crime93 | stevedata | Statewide Crime Data (1993) | tbl_df | 51 | 8 |
african_coups | stevedata | Modeling Coups in Africa, 1960 to 1975 (1982) | tbl_df | 45 | 11 |
aluminum_premiums | stevedata | LME Aluminum Premiums Data | tbl_df | 3664 | 3 |
anes_partytherms | stevedata | Major Party (Democrat, Republican) Thermometer Index Data (1978-2012) | tbl_df | 33830 | 19 |
anes_prochoice | stevedata | Abortion Attitudes (ANES, 2012) | tbl_df | 5914 | 14 |
anes_vote84 | stevedata | Simple Data for a Simple Model of Individual Voter Turnout (ANES, 1984) | tbl_df | 2257 | 9 |
arcticseaice | stevedata | Arctic Sea Ice Extent Data, 1901-2015 | tbl_df | 115 | 4 |
arg_tariff | stevedata | Simple Mean Tariff Rate for Argentina | tbl_df | 39 | 3 |
asn_stats | stevedata | Aviation Safety Network Statistics, 1942-2019 | tbl_df | 78 | 7 |
chile88 | stevedata | Voting Intentions in the 1988 Chilean Plebiscite | tbl_df | 2700 | 8 |
china_peace | stevedata | Drivers of China's Peace Engagement in Conflict-affected Countries | tbl_df | 884 | 13 |
clemson_temps | stevedata | Daily Clemson Temperature Data | tbl_df | 33148 | 3 |
co2emissions | stevedata | Carbon Dioxide Emissions Data | tbl_df | 3099 | 2 |
coffee_imports | stevedata | Coffee Imports for Select Importing Countries | tbl_df | 4530 | 4 |
coffee_price | stevedata | The Primary Commodity Price for Coffee (Arabica, Robustas) | tbl_df | 499 | 3 |
commodity_prices | stevedata | Select World Bank Commodity Price Data (Monthly) | tbl_df | 756 | 11 |
country_isocodes | stevedata | ISO 3166 Country Codes (Two-Character, Three-Character, Numeric) | tbl_df | 249 | 4 |
eight_schools | stevedata | The Effect of Special Preparation on SAT-V Scores in Eight Randomized Experiments | tbl_df | 8 | 6 |
election_turnout | stevedata | State-Level Education and Voter Turnout in 2016 | tbl_df | 51 | 14 |
epl_odds | stevedata | Odds for 2024-25 English Premier League Clubs | tbl_df | 20 | 7 |
eq_passengercars | stevedata | Export Quality Data for Passenger Cars, 1963-2014 | tbl_df | 60424 | 6 |
eu_ua_fta24 | stevedata | A Roll Call Vote on Extending Temporary Trade Liberalization Measures Applicable to Ukrainian products under the EU/Euratom/Ukraine Association Agreement | tbl_df | 705 | 9 |
eurostat_codes | stevedata | Eurostat Country Codes | tbl_df | 56 | 3 |
eustates | stevedata | EU Member States (Current as of 2019) | tbl_df | 28 | 3 |
fakeAPI | stevedata | Hypothetical (Fake) Data on Academic Performance | tbl_df | 10000 | 11 |
fakeHappiness | stevedata | Fake Data on Happiness | tbl_df | 1000 | 8 |
fakeLogit | stevedata | Fake Data for a Logistic Regression | tbl_df | 10000 | 2 |
fakeTSCS | stevedata | Fake Data for a Time-Series Cross-Section | tbl_df | 2500 | 8 |
fakeTSD | stevedata | Fake Data for a Time-Series | tbl_df | 100 | 5 |
gas_demand | stevedata | Gasoline Demand in the OECD, 1960-1978 | tbl_df | 342 | 6 |
gatt_members | stevedata | The 128 Countries That Had Signed GATT by 1994 | tbl_df | 128 | 3 |
ghp100k | stevedata | Gun Homicide Rate per 100,000 People, by Country | tbl_df | 561 | 3 |
gss_abortion | stevedata | Abortion Opinions in the General Social Survey | tbl_df | 64814 | 18 |
gss_spending | stevedata | Attitudes Toward National Spending in the General Social Survey (2018) | tbl_df | 2348 | 33 |
gss_wages | stevedata | The Gender Pay Gap in the General Social Survey | tbl_df | 61697 | 11 |
illiteracy30 | stevedata | Illiteracy in the Population 10 Years Old and Over, 1930 | tbl_df | 49 | 11 |
inglehart03 | stevedata | "How Solid is Mass Support for Democracy-And How Can We Measure It?" | spec_tbl_df | 77 | 4 |
min_wage | stevedata | History of Federal Minimum Wage Rates Under the Fair Labor Standards Act, 1938-2009 | tbl_df | 23 | 2 |
mm_mlda | stevedata | Minimum Legal Drinking Age Fatalities Data | tbl_df | 50 | 19 |
mm_nhis | stevedata | Data from the 2009 National Health Interview Survey (NHIS) | tbl_df | 18790 | 10 |
mm_randhie | stevedata | Data from the RAND Health Insurance Experiment (HIE) | list | | |
mvprod | stevedata | Motor Vehicle Production by Country, 1950-2019 | tbl_df | 1206 | 3 |
nesarc_drinkspd | stevedata | The Usual Daily Drinking Habits of Americans (NESARC, 2001-2) | tbl_df | 43093 | 8 |
pwt_sample | stevedata | Penn World Table (10.0) Macroeconomic Data for Select Countries, 1950-2019 | tbl_df | 1540 | 12 |
quartets | stevedata | Anscombe's (1973) Quartets | tbl_df | 44 | 3 |
recessions | stevedata | United States Recessions, 1855-present | tbl_df | 35 | 8 |
rok_unga | stevedata | The Correlates of Dyadic Voting Similiarities in the UN General Assembly for South Korea | tbl_df | 6095 | 17 |
scb_regions | stevedata | Region Codes in the Central Bureau of Statistics ("Statistiska centralbyrån") in Sweden | tbl_df | 312 | 2 |
sealevels | stevedata | Global Average Absolute Sea Level Change, 1880–2015 | tbl_df | 136 | 5 |
so2concentrations | stevedata | Sulfur Dioxide Emissions, 1980-2020 | tbl_df | 41 | 4 |
states_war | stevedata | State Performance in Inter-State Wars | tbl_df | 284 | 23 |
steves_clothes | stevedata | Steve's (Professional) Clothes, as of March 20, 2022 | tbl_df | 86 | 4 |
sugar_price | stevedata | IMF Primary Commodity Price Data for Sugar | tbl_df | 1316 | 3 |
sweden_counties | stevedata | The Counties of Sweden | tbl_df | 21 | 6 |
thatcher_approval | stevedata | Margaret Thatcher Satisfaction Ratings, 1980-1990 | tbl_df | 125 | 8 |
therms | stevedata | Thermometer Ratings for Donald Trump and Barack Obama | tbl_df | 3080 | 2 |
turnips | stevedata | Turnip prices in Animal Crossing (New Horizons) | tbl_df | 1429 | 3 |
ukg_eeri | stevedata | United Kingdom Effective Exchange Rate Index Data, 1990-2022 | tbl_df | 8340 | 2 |
uniondensity | stevedata | Cross-National Rates of Trade Union Density | tbl_df | 20 | 5 |
usa_chn_gdp_forecasts | stevedata | United States-China GDP and GDP Forecasts, 1960-2050 | tbl_df | 182 | 12 |
usa_computers | stevedata | Percentage of U.S. Households with Computer Access, by Year | tbl_df | 19 | 2 |
usa_migration | stevedata | U.S. Inbound/Outbound Migration Data, 1990-2017 | tbl_df | 3535 | 5 |
usa_states | stevedata | State Abbreviations, Names, and Regions/Divisions | tbl_df | 51 | 4 |
usa_tradegdp | stevedata | U.S. Trade and GDP, 1790-2018 | tbl_df | 229 | 5 |
voteincome | stevedata | Sample Turnout and Demographic Data from the 2000 Current Population Survey | spec_tbl_df | 1500 | 7 |
wb_groups | stevedata | World Bank Country Groups | tbl_df | 2085 | 4 |
wbd_example | stevedata | A Simple Panel drawn from World Bank Open Data | tbl_df | 4537 | 7 |
wvs_ccodes | stevedata | Syncing Word Values Survey Country Codes with CoW Codes | spec_tbl_df | 112 | 3 |
wvs_immig | stevedata | Attitudes about Immigration in the World Values Survey | tbl_df | 310388 | 6 |
wvs_justifbribe | stevedata | Attitudes about the Justifiability of Bribe-Taking in the World Values Survey | tbl_df | 348532 | 6 |
wvs_usa_abortion | stevedata | Attitudes on the Justifiability of Abortion in the United States (World Values Survey, 1982-2011) | tbl_df | 10387 | 16 |
wvs_usa_educat | stevedata | Education Categories for the United States in the World Values Survey | spec_tbl_df | 42 | 6 |
wvs_usa_regions | stevedata | Region Categories for the United States in the World Values Survey | tbl_df | 63 | 6 |
yugo_sales | stevedata | Yugo Sales in the United States, 1985-1992 | tbl_df | 24 | 3 |
animals | tidyplots | Animals data | tbl_df | 60 | 14 |
climate | tidyplots | Climate data | tbl_df | 1596 | 5 |
dinosaurs | tidyplots | Dinosaurs data | tbl_df | 31 | 14 |
distributions | tidyplots | Distributions data | tbl_df | 24 | 3 |
energy | tidyplots | Energy data | tbl_df | 344 | 5 |
energy_week | tidyplots | Energy week data | tbl_df | 10080 | 5 |
eu_countries | tidyplots | EU countries data | tbl_df | 27 | 10 |
gene_expression | tidyplots | RNA-Seq expression data | spec_tbl_df | 800 | 11 |
spendings | tidyplots | Spending data | spec_tbl_df | 19 | 4 |
study | tidyplots | Study data | tbl_df | 20 | 7 |
time_course | tidyplots | Time course data | spec_tbl_df | 1710 | 4 |
deciduous_polygon | sephora | 128 NDVI pixels from a MOD13Q1 time series | matrix | 128 | 506 |
PEN_death | BayesGP | The monthly all-cause mortality for male with age less than 40 in Pennsylvania. | data.frame | 298 | 8 |
ccData | BayesGP | A simulated dataset from the case-crossover model. | data.frame | 3596 | 6 |
covid_canada | BayesGP | The COVID-19 daily death data in Canada. | data.frame | 787 | 10 |
NDVI | GPoM | A time series of vegetation index measured from satellite | data.frame | 1000 | 4 |
P1FxCh | GPoM | A data set for testing periodicity | matrix | 1000 | 6 |
P1FxChP2 | GPoM | A data set for testing periodicity | matrix | 1000 | 8 |
RosYco | GPoM | Twelve Rossler-1976 time series (exclusively variable y) | matrix | 3000 | 12 |
Ross76 | GPoM | Time series of the Rossler-1976 system | deSolve | 4000 | 4 |
TS | GPoM | Time series resulting from the integration of a non stationary system | matrix | 6001 | |
TSallMod_nVar3_dMax2 | GPoM | Time series of three-dimensional chaotic sytems (for vignette 'VII_Retro-Modelling') | list | | |
allMod_nVar3_dMax2 | GPoM | Numerical description of a list of eighteen three-dimensional chaotic sytems (see vignette '7_Retro-Modelling') | list | | |
allToTest | GPoM | A list providing the description of six models tested by the function 'autoGPoMoTest'. | list | | |
data_vignetteIII | GPoM | Output of the vignette 'III_Modelling' | list | | |
data_vignetteVI | GPoM | Output of the vignette 'VI_Sensitivity' | list | | |
data_vignetteVII | GPoM | Output of the vignette 'VII_Retro-Modelling' | list | | |
svrlTS | GPoM | A data set for the global modeling of time series in association | list | | |
Aar | compositions | Composition of glaciar sediments from the Aar massif (Switzerland) | tbl_df | 87 | 31 |
Activity10 | compositions | Activity patterns of a statistician for 20 days | matrix | 20 | 6 |
Activity31 | compositions | Activity patterns of a statistician for 20 days | matrix | 20 | 6 |
AnimalVegetation | compositions | Animal and vegetation measurement | matrix | 100 | 6 |
ArcticLake | compositions | Artic lake sediment samples of different water depth | matrix | 39 | 4 |
Bayesite | compositions | Permeabilities of bayesite | matrix | 21 | 6 |
Blood23 | compositions | Blood samples | matrix | 40 | 3 |
Boxite | compositions | Compositions and depth of 25 specimens of boxite | matrix | 25 | 6 |
ClamEast | compositions | Color-size compositions of 20 clam colonies from East Bay | matrix | 20 | 6 |
ClamWest | compositions | Color-size compositions of 20 clam colonies from West Bay | matrix | 20 | 6 |
Coxite | compositions | Compositions, depths and porosities of 25 specimens of coxite | matrix | 25 | 7 |
DiagnosticProb | compositions | Diagnostic probabilities | matrix | 30 | 4 |
Firework | compositions | Firework mixtures | matrix | 81 | 7 |
Glacial | compositions | Compositions and total pebble counts of 92 glacial tills | matrix | 92 | 5 |
Hongite | compositions | Compositions of 25 specimens of hongite | matrix | 25 | 5 |
HouseholdExp | compositions | Household Expenditures | matrix | 40 | 5 |
Hydrochem | compositions | Hydrochemical composition data set of Llobregat river basin water (NE Spain) | data.frame | 485 | 19 |
Kongite | compositions | Compositions of 25 specimens of kongite | matrix | 25 | 5 |
Metabolites | compositions | Steroid metabolite patterns in adults and children | matrix | 67 | 4 |
PogoJump | compositions | Honk Kong Pogo-Jumps Championship | matrix | 28 | 4 |
Sediments | compositions | Proportions of sand, silt and clay in sediments specimens | matrix | 21 | 4 |
SerumProtein | compositions | Serum Protein compositions of blood samples | matrix | 36 | 5 |
ShiftOperators | compositions | Shifts of machine operators | matrix | 27 | 4 |
Skulls | compositions | Measurement of skulls | matrix | 102 | 7 |
SkyeAFM | compositions | AFM compositions of 23 aphyric Skye lavas | matrix | 23 | 3 |
Supervisor | compositions | Proportions of supervisor's statements assigned to different categories | matrix | 18 | 13 |
WhiteCells | compositions | White-cell composition of 30 blood samples by two different methods | matrix | 30 | 6 |
Yatquat | compositions | Yatquat fruit evaluation | matrix | 40 | 7 |
jura259 | compositions | The jura dataset | data.frame | 359 | 11 |
juraset | compositions | The jura dataset | data.frame | 359 | 11 |
sa.dirichlet | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.dirichlet.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.dirichlet.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.dirichlet5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.dirichlet5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.dirichlet5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.groups | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.groups.area | compositions | Simulated amount datasets | factor | | |
sa.groups.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.groups.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.groups5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.groups5.area | compositions | Simulated amount datasets | factor | | |
sa.groups5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.groups5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.lognormals | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.lognormals.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.lognormals.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.lognormals5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.lognormals5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.lognormals5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.missings | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.missings5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.outliers1 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers2 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers3 | compositions | Simulated amount datasets | acomp | 101 | 3 |
sa.outliers4 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers5 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers6 | compositions | Simulated amount datasets | acomp | 120 | 3 |
sa.tnormals | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.tnormals.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.tnormals.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.tnormals5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.tnormals5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.tnormals5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.uniform | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.uniform.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.uniform.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.uniform5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.uniform5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.uniform5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sampleDepthData | s2dv | Sample of Experimental Data for Forecast Verification In Function Of Latitudes And Depths | list | | |
sampleMap | s2dv | Sample Of Observational And Experimental Data For Forecast Verification In Function Of Longitudes And Latitudes | list | | |
sampleTimeSeries | s2dv | Sample Of Observational And Experimental Data For Forecast Verification As Area Averages | list | | |
ann.data | disprose | Chlamydia pneumoniae genome annotation. | spec_tbl_df | 2218 | 9 |
blast.fill | disprose | Local BLAST results with added content. | data.frame | 72 | 19 |
blast.raw | disprose | Local BLAST results. | data.frame | 72 | 19 |
meta.all | disprose | Metadata of all available Chlamydia pneumoniae's sequences. | data.frame | 9062 | 21 |
meta.target | disprose | Metadata of target Chlamydia pneumoniae's sequences. | data.frame | 183 | 21 |
long_data | CIMPLE | long_data | data.table | 7214 | 6 |
surv_data | CIMPLE | long_data | data.table | 1828 | 6 |
train_data | CIMPLE | long_data | data.frame | 6727 | 5 |
invData | qape | Population data - investments in Poland at NUTS 4 level | data.frame | 2280 | 6 |
realestData | qape | Population data - real estate in Poland at NUTS 4 level | data.frame | 1504 | 7 |
maize | heteromixgm | Maize data | list | | |
AMR | FishResp | Active Metabolic Rate: Final Data | data.frame | 12 | 16 |
AMR.clean | FishResp | Active Metabolic Rate: Corrected Raw Data | data.frame | 7200 | 17 |
AMR.raw | FishResp | Active Metabolic Rate: Raw Data | data.frame | 1800 | 16 |
AMR.slope | FishResp | Active Metabolic Rate: Extracted Slope(s) | data.frame | 12 | 12 |
SMR | FishResp | Standard Metabolic Rate: Final Data | data.frame | 12 | 16 |
SMR.clean | FishResp | Standard Metabolic Rate: Corrected Raw Data | data.frame | 76800 | 17 |
SMR.raw | FishResp | Standard Metabolic Rate: Raw Data | data.frame | 19200 | 16 |
SMR.slope | FishResp | Standard Metabolic Rate: Extracted Slope(s) | data.frame | 12 | 12 |
info | FishResp | Info about Individuals and Chambers | data.frame | 4 | 4 |
post | FishResp | Post Raw Data | data.frame | 2400 | 7 |
pre | FishResp | Pre Raw Data | data.frame | 4800 | 7 |
results | FishResp | Results of Analysis: SMR, AMR and MS | data.frame | 36 | 18 |
x_obs | absorber | Observation matrix x of five variables | matrix | 700 | |
y_obs | absorber | Values of the response variable of the noisy observation set of five input variables | numeric | | |
brca | iDINGO | Modified TCGA Breast Cancer data | list | | |
gbm | iDINGO | Modified TCGA Glioblastoma data | data.frame | 156 | 19 |
maas | sgeostat | maas- zinc measurements | data.frame | 155 | 3 |
maas.bank | sgeostat | maas.bank - coordinates | data.frame | 37 | 2 |
Balt | TrendSLR | Ocean water level data for Baltimore, USA | data.frame | 115 | 2 |
s | TrendSLR | sample 'msl.trend' object | msl.trend | | |
t | TrendSLR | sample 'custom.trend' object | custom.trend | | |
ch1.GM03563 | breakpoint | Fibroblast cell line (GM03563) data | data.frame | 135 | 1 |
X | adapt4pv | Simulated data for the adapt4pv package | dgCMatrix | | |
Y | adapt4pv | Simulated data for the adapt4pv package | numeric | | |
counties | vipor | Census ata on US counties | data.frame | 3143 | 8 |
integrations | vipor | Data on HIV integration sites from several studies | data.frame | 12436 | 4 |
data_n | sdpdth | A simulated data set for testing | list | | |
data_nw | sdpdth | A simulated data set for testing | matrix | 12 | |
data_th | sdpdth | A simulated data set for testing | list | | |
data_w | sdpdth | A simulated data set for testing | matrix | 16 | |
dental | dstat | Dental Problems Caused by Smoking | data.frame | 441 | 5 |
lalive | dstat | Unemployment Duration Following an Increase in Unemployment Benefits | data.frame | 2782 | 17 |
yeast | som | yeast cell cycle | data.frame | 6601 | 18 |
plasma | rlmDataDriven | plasma | data.frame | 273 | 3 |
WGScan.example | WGScan | Data example for WGScan (A Genome-Wide Scan Statistic Framework For Whole-Genome Sequence Data Analysis) | list | | |
WGScan.info | WGScan | hg19 chromosome sizes | list | | |
ptg_stud_data | LogRegEquiv | Student Performance Data Set | data.frame | 649 | 31 |
ptg_stud_f_test | LogRegEquiv | Student Performance Data Set - female testing data | data.frame | 77 | 30 |
ptg_stud_f_train | LogRegEquiv | Student Performance Data Set - female training data | data.frame | 306 | 30 |
ptg_stud_m_test | LogRegEquiv | Student Performance Data Set - male testing data | data.frame | 53 | 30 |
ptg_stud_m_train | LogRegEquiv | Student Performance Data Set - male training data | data.frame | 213 | 30 |
D243 | RobustAFT | Sample of 100 hospital stays for medical back problems | data.frame | 100 | 11 |
MCI | RobustAFT | Sample of 75 Hospital Stays | data.frame | 75 | 6 |
Z243 | RobustAFT | Sample of 100 hospital stays for medical back problems | data.frame | 100 | 14 |
data | lddmm | Example dataset | tbl_df | 24254 | 6 |
choe2.L | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | numeric | | |
choe2.degenes | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | logical | | |
choe2.mapping | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | numeric | | |
choe2.mat | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | matrix | 6 | 11475 |
choe2.probe.name | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | character | | |
choe2.symbol.name | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | character | | |
pmf_list | binomialRF | A prebuilt distribution for correlated binary data | list | | |
agpop | SDAResources | agpop data | tbl_df | 3078 | 15 |
agpps | SDAResources | agpps data | tbl_df | 15 | 34 |
agsrs | SDAResources | agsrs data | tbl_df | 300 | 15 |
agstrat | SDAResources | agstrat data | tbl_df | 300 | 17 |
algebra | SDAResources | algebra data | tbl_df | 299 | 3 |
anthrop | SDAResources | anthrop data | tbl_df | 3000 | 2 |
anthsrs | SDAResources | anthsrs data | tbl_df | 200 | 3 |
anthuneq | SDAResources | anthuneq data | tbl_df | 200 | 3 |
artifratio | SDAResources | artifratio data | tbl_df | 70 | 10 |
asafellow | SDAResources | asafellow data | tbl_df | 106 | 7 |
auditresult | SDAResources | auditresult data | tbl_df | 25 | 4 |
auditselect | SDAResources | auditselect data | tbl_df | 44 | 6 |
azcounties | SDAResources | azcounties data | tbl_df | 13 | 5 |
baseball | SDAResources | baseball data | tbl_df | 797 | 30 |
books | SDAResources | books data | tbl_df | 60 | 5 |
census1920 | SDAResources | census1920 data | tbl_df | 48 | 2 |
census2010 | SDAResources | census2010 data | tbl_df | 50 | 2 |
cherry | SDAResources | cherry data | tbl_df | 31 | 3 |
classes | SDAResources | classes data | tbl_df | 15 | 2 |
classpps | SDAResources | classpps data | tbl_df | 20 | 4 |
classppsjp | SDAResources | classppsjp data | tbl_df | 5 | 9 |
college | SDAResources | college data | tbl_df | 1372 | 29 |
collegerg | SDAResources | collegerg data | tbl_df | 50 | 32 |
collshr | SDAResources | collshr data | tbl_df | 10 | 34 |
coots | SDAResources | coots data | tbl_df | 368 | 6 |
counties | SDAResources | counties data | tbl_df | 100 | 18 |
crimes | SDAResources | crimes data | tbl_df | 5000 | 7 |
deadtrees | SDAResources | deadtrees data | tbl_df | 25 | 2 |
divorce | SDAResources | divorce data | tbl_df | 32 | 20 |
gini | SDAResources | gini data | tbl_df | 213 | 14 |
golfsrs | SDAResources | golfsrs data | tbl_df | 120 | 16 |
gpa | SDAResources | gpa data | tbl_df | 20 | 3 |
healthjournals | SDAResources | healthjournals data | tbl_df | 198 | 7 |
htcdf | SDAResources | htcdf data | tbl_df | 65 | 4 |
htpop | SDAResources | htpop data | tbl_df | 2000 | 2 |
htsrs | SDAResources | htsrs data | tbl_df | 200 | 3 |
htstrat | SDAResources | htstrat data | tbl_df | 200 | 3 |
hunting | SDAResources | hunting data | tbl_df | 36 | 5 |
impute | SDAResources | impute data | tbl_df | 20 | 6 |
integerwt | SDAResources | integerwt data | tbl_df | 2000 | 2 |
intellonline | SDAResources | intellonline data | tbl_df | 983 | 8 |
intelltel | SDAResources | intelltel data | tbl_df | 1838 | 8 |
intellwts | SDAResources | intellwts data | tbl_df | 8 | 7 |
ipums | SDAResources | ipums data | tbl_df | 53461 | 15 |
journal | SDAResources | journal data | tbl_df | 26 | 3 |
measles | SDAResources | measles data | tbl_df | 307 | 13 |
mysteries | SDAResources | mysteries data | tbl_df | 60 | 17 |
nhanes | SDAResources | nhanes data | tbl_df | 9971 | 25 |
nybight | SDAResources | nybight data | tbl_df | 107 | 7 |
otters | SDAResources | otters data | tbl_df | 82 | 3 |
ozone | SDAResources | ozone data | tbl_df | 730 | 27 |
pitcount | SDAResources | pitcount data | tbl_df | 100 | 7 |
profresp | SDAResources | profresp data | tbl_df | 2404 | 15 |
profrespacs | SDAResources | profrespacs data | tbl_df | 18 | 4 |
radon | SDAResources | radon data | tbl_df | 1003 | 5 |
rectlength | SDAResources | rectlength data | tbl_df | 100 | 2 |
rnt | SDAResources | rnt data | data.frame | 50 | 6 |
sample70 | SDAResources | sample70 data | tbl_df | 70 | 10 |
santacruz | SDAResources | santacruz data | tbl_df | 10 | 3 |
schools | SDAResources | schools data | tbl_df | 200 | 8 |
seals | SDAResources | seals data | tbl_df | 40 | 2 |
shapespop | SDAResources | shapespop data | tbl_df | 20000 | 5 |
shorebirds | SDAResources | shorebirds data | tbl_df | 201 | 3 |
sp500 | SDAResources | sp500 data | tbl_df | 500 | 6 |
spanish | SDAResources | spanish data | tbl_df | 196 | 3 |
srs30 | SDAResources | srs30 data | tbl_df | 30 | 1 |
ssc | SDAResources | ssc data | tbl_df | 150 | 3 |
statepop | SDAResources | statepop data | tbl_df | 100 | 12 |
statepps | SDAResources | statepps data | tbl_df | 51 | 5 |
swedishlcs | SDAResources | swedishlcs data | tbl_df | 13 | 6 |
syc | SDAResources | syc data | tbl_df | 2621 | 28 |
teachers | SDAResources | teachers data | tbl_df | 310 | 6 |
teachmi | SDAResources | teachmi data | tbl_df | 31 | 4 |
teachnr | SDAResources | teachnr data | data.frame | 26 | 4 |
uneqvar | SDAResources | uneqvar data | tbl_df | 100 | 2 |
vietnam | SDAResources | vietnam data | tbl_df | 2064 | 8 |
vius | SDAResources | vius data | tbl_df | 98682 | 26 |
viusca | SDAResources | viusca data | tbl_df | 2192 | 26 |
winter | SDAResources | winter data | tbl_df | 985 | 18 |
wtshare | SDAResources | wtshare data | tbl_df | 114 | 4 |
AST | CoDiNA | AST | data.frame | 3384 | 3 |
CTR | CoDiNA | CTR | data.frame | 17471 | 3 |
GLI | CoDiNA | GLI | data.frame | 172245 | 3 |
OLI | CoDiNA | OLI | data.frame | 64791 | 3 |
motorcycledata | adlift | Motorcycle data. | data.frame | 133 | 2 |
yeastG1 | PGEE | Yeast cell-cycle gene expression data | data.frame | 1132 | 99 |
Cardiological.CR | iRegression | Cardiological Interval Data Set (Centre and Range) | data.frame | 11 | 6 |
Cardiological.MinMax | iRegression | Cardiological Interval Data Set | data.frame | 11 | 6 |
soccer.bivar | iRegression | Soccer Interval Data Set | data.frame | 20 | 6 |
primlist | schoolmath | A vector containing primes from 0 to 9999999 | numeric | | |
CarMileageData | gvlma | Car Mileage Data Recorded at Each Gasoline Fill-Up | data.frame | 205 | 7 |
microscopy | fiberLD | Data of uncut fiber lengths in the increment core | numeric | | |
ofa | fiberLD | Example of increment core data | numeric | | |
ei_NZ_2002 | ei.Datasets | Ecological inference data sets of the 2002 New Zealand General Election. | tbl_df | 69 | 6 |
ei_NZ_2005 | ei.Datasets | Ecological inference data sets of the 2005 New Zealand General Election. | tbl_df | 69 | 6 |
ei_NZ_2008 | ei.Datasets | Ecological inference data sets of the 2008 New Zealand General Election. | tbl_df | 70 | 6 |
ei_NZ_2011 | ei.Datasets | Ecological inference data sets of the 2011 New Zealand General Election. | tbl_df | 70 | 6 |
ei_NZ_2014 | ei.Datasets | Ecological inference data sets of the 2014 New Zealand General Election. | tbl_df | 71 | 6 |
ei_NZ_2017 | ei.Datasets | Ecological inference data sets of the 2017 New Zealand General Election. | tbl_df | 71 | 6 |
ei_NZ_2020 | ei.Datasets | Ecological inference data sets of the 2020 New Zealand General Election. | tbl_df | 72 | 6 |
ei_SCO_2007 | ei.Datasets | Ecological inference data sets of the 2007 Scottish National Assembly. | tbl_df | 73 | 6 |
SDcorn | saws | Mammary tumors in Sprague-Dawley rats fed Corn Oil | data.frame | 104 | 10 |
dietfat | saws | Mammary Tumors and Different Types of Dietary Fat in Rodents | data.frame | 442 | 9 |
micefat | saws | Dietary fat and Mammary tumors in Mice | data.frame | 57 | 5 |
taxus_bin | diffval | _Taxus baccata_ forests | matrix | 209 | 33 |
dataME | saeME | dataME | data.frame | 100 | 5 |
datamix | saeME | datamix | data.frame | 100 | 9 |
X1 | vottrans | Example data X1 | matrix | 119 | 7 |
Y1 | vottrans | Example Data Y1 | matrix | 119 | 7 |
diabetes | elasticnet | Blood and other measurements in diabetics | data.frame | 442 | 3 |
pitprops | elasticnet | Pitprops correlation data | matrix | 13 | 13 |
HDL | informedSen | Light Daily Alcohol and HDL Cholesterol Levels | data.frame | 800 | 9 |
ECBYieldCurve | YieldCurve | Yield curve data spot rate, AAA-rated bonds, maturities from 3 months to 30 years | xts | 655 | 32 |
FedYieldCurve | YieldCurve | Federal Reserve interest rates | xts | 372 | 8 |
ascarr | cgwtools | Banner Versions of Characters | array | | |
elec92 | optiscale | Public Opinion During the 1992 U.S. Presidential Election | data.frame | 1653 | 7 |
conflictData | clarkeTest | Conflict Data | data.frame | 4381 | 9 |
elas | OptimalCutpoints | Leukocyte Elastase Data | data.frame | 141 | 3 |
esetProstate | stageR | Transcript-level abundance estimates in 14 Chinese prostate cancer patients | ExpressionSet | | |
hammer.eset | stageR | Hammer dataset | ExpressionSet | | |
BITR | refsplitr | Data from the journal BioTropica (pulled from Web of Knowledge) | data.frame | 10 | 32 |
BITR_geocode | refsplitr | Georeferenced data from the journal BioTropica (pulled from Web of Science) | data.frame | 41 | 15 |
countries | refsplitr | Names of all the countries in the world | character | | |
ex1 | SplitSplitPlot | Dados de exemplo de um experimento em DQL. | data.frame | 288 | 5 |
sequences | bold | List of 3 nucleotide sequences to use in examples for the 'bold_identify' function | list | | |
arxiv_cats | aRxiv | arXiv subject classifications | data.frame | 155 | 5 |
query_terms | aRxiv | arXiv query field terms | data.frame | 10 | 2 |
Fcor | BFpack | Student t approximations of Fisher transformed correlations | data.frame | 39 | 3 |
actors | BFpack | Actors from a small hypothetical network | data.frame | 25 | 3 |
attention | BFpack | Multiple Sources of Attentional Dysfunction in Adults With Tourette's Syndrome | data.frame | 51 | 2 |
fmri | BFpack | fMRI data | data.frame | 13 | 5 |
memory | BFpack | Memory data on health and schizophrenic patients | data.frame | 40 | 7 |
relevents | BFpack | A sequence of innovation-related e-mail messages | data.frame | 227 | 3 |
same_culture | BFpack | Same culture event statistic | data.frame | 25 | 25 |
same_location | BFpack | Same location event statistic | data.frame | 25 | 25 |
sivan | BFpack | Wason task performance and morality | data.frame | 887 | 12 |
therapeutic | BFpack | Data come from an experimental study (Rosa, Rosa, Sarner, and Barrett, 1998) that were also used in Howell (2012, p.196). An experiment was conducted to investigate if Therapeutic Touch practitioners who were blindfolded can effectively identify which of their hands is below the experimenter¡¯s. Twenty-eight practitioners were involved and tested 10 times in the experiment. Researchers expected an average of 5 correct answers from each practitioner as it is the number by chance if they do not outperform others. | data.frame | 28 | 1 |
timssICC | BFpack | Trends in International Mathematics and Science Study (TIMSS) 2011-2015 | data.frame | 16770 | 15 |
tvprices | BFpack | Precision of the Anchor Influences the Amount of Adjustment | data.frame | 59 | 3 |
wilson | BFpack | Facial trustworthiness and criminal sentencing | data.frame | 742 | 13 |
augusta_nlcd | landscapemetrics | Augusta NLCD 2011 | PackedSpatRaster | | |
landscape | landscapemetrics | Example map (random cluster neutral landscape model). | PackedSpatRaster | | |
lsm_abbreviations_names | landscapemetrics | Tibble of abbreviations coming from FRAGSTATS | tbl_df | 133 | 5 |
podlasie_ccilc | landscapemetrics | Podlasie ESA CCI LC | PackedSpatRaster | | |
data_hlme | lcmm | Simulated dataset for hlme function | data.frame | 326 | 6 |
data_lcmm | lcmm | Simulated dataset for lcmm and Jointlcmm functions | data.frame | 1678 | 12 |
paquid | lcmm | Longitudinal data on cognitive and physical aging in the elderly | data.frame | 2250 | 12 |
simdataHADS | lcmm | Simulated dataset simdataHADS | data.frame | 1140 | 13 |
territories | arealDB | Example 'gazetteer' | onto | | |
ecoffs | MIC | ECOFF data | tbl_df | 85 | 25 |
example_mics | MIC | Example MIC data | data.frame | 300 | 4 |
grwth_data | SynergyLMM | Example Tumor Growth Data | data.frame | 352 | 4 |
anchoring | sdamr | Anchoring | data.frame | 4632 | 10 |
cheerleader | sdamr | Data from Experiment 1 of Carragher, D.J., Thomas, N.A., Gwinn, O.S. et al. (2019) Limited evidence of hierarchical encoding in the cheerleader effect. Scientific Reports, 9, 9329. https://doi.org/10.1038/s41598-019-45789-6 | data.frame | 192 | 9 |
expBelief | sdamr | Data from Experiment 5 of Gilder, T. S. E., & Heerey, E. A. (2018). The Role of Experimenter Belief in Social Priming. Psychological Science, 29(3), 403–417. | data.frame | 400 | 16 |
fifa2010 | sdamr | Predictions by Paul the Octopus in the 2010 FIFA World Cup. | data.frame | 8 | 4 |
fifa2010teams | sdamr | FIFA 2010 team statistics | data.frame | 32 | 11 |
gestures | sdamr | Data from Winter, B., & Burkner, P. (2021) Poisson regression for linguists: A tutorial introduction to modelling count data with brms. Language and Linguistics Compass, 15, e12439 doi:10.1111/lnc3.12439 <https://doi.org/10.1111/lnc3.12439> | data.frame | 54 | 6 |
legacy2015 | sdamr | Legacy motives and pro-environmental behaviour | data.frame | 237 | 9 |
metacognition | sdamr | Data from Rausch, M. & Zehetleitner, M. (2016) Visibility is not equivalent to confidence in a low contrast orientation discrimination task. Frontiers in Psychology, 7, p. 591 doi:10.3389/fpsyg.2016.00591 <https://doi.org/10.3389/fpsyg.2016.00591> | data.frame | 7560 | 10 |
papervotes | sdamr | Data based on a post-election survey by YouGov (see <https://yougov.co.uk/topics/politics/articles-reports/2017/06/13/how-britain-voted-2017-general-election>). Note that the data was recreated by combining frequency and percentage results reported in <https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/smo1w49ph1/InternalResults_170613_2017Election_Demographics_W.pdf>. Due to rounding and other potential inconsistencies, this data set will likely differ from the actual results. | data.frame | 90 | 3 |
redist2015 | sdamr | Redistribution of wealth | data.frame | 305 | 12 |
rps | sdamr | Data from Experiment 1 in Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342 doi:10.1007/s42113-022-00133-6 <https://doi.org/10.1007/s42113-022-00133-6>. Participants (n=52) each play 50 rounds of Rock-Paper-Scissors against an AI player who either adopts a "level-1" or "level-2" strategy. A level-1 strategy assumes the opponent will repeat their last action, and chooses the action that beats this. A level-2 strategy assumes the opponent adopts a level-1 strategy, and chooses the action that beats this. On 10% of rounds, the AI players pick a random action. On the remainder, they act according to their strategy. | data.frame | 2600 | 6 |
speeddate | sdamr | Speed dating | data.frame | 1562 | 38 |
tetris2015 | sdamr | Tetris and intrusive memories | data.frame | 72 | 28 |
trump2016 | sdamr | Trump votes in 2016 for 50 US states and the District of Columbia | data.frame | 51 | 7 |
uefa2008 | sdamr | Predictions by Paul the Octopus in the 2008 UEFA Cup. | data.frame | 6 | 4 |
data.Ct | RQdeltaCT | Gene expression dataset for RQdeltaCT package. | data.frame | 1288 | 5 |
data.Ct.10vs10 | RQdeltaCT | Gene expression dataset for RQdeltaCT package - a reduced version. | data.frame | 410 | 5 |
data.Ct.3groups | RQdeltaCT | Gene expression dataset for RQdeltaCT package - variant with three groups. | data.frame | 2048 | 5 |
data.Ct.pairwise | RQdeltaCT | Gene expression dataset for RQdeltaCT package. Siutable for pairwise analysis. | tbl_df | 756 | 4 |
catalysis | iBART | Single-Atom Catalysis Data | list | | |
iBART_real_data | iBART | iBART Real Data Result | list | | |
iBART_sim | iBART | iBART Simulation Result | list | | |
grid_ahvenanmaa | geofi | custom geofacet grid for Ahvenanmaa region | data.frame | 16 | 4 |
grid_etela_karjala | geofi | custom geofacet grid for Etelä-Karjala region as in 2020 | data.frame | 9 | 4 |
grid_etela_pohjanmaa | geofi | custom geofacet grid for Etelä-Pohjanmaa | data.frame | 18 | 4 |
grid_etela_savo | geofi | custom geofacet grid for Etelä-Savo | data.frame | 11 | 4 |
grid_hyvinvointialue | geofi | custom geofacet grid for Wellbeing services counties | data.frame | 23 | 4 |
grid_kainuu | geofi | custom geofacet grid for Kainuu region | data.frame | 8 | 4 |
grid_kanta_hame | geofi | custom geofacet grid for Kanta-Häme region | data.frame | 11 | 4 |
grid_keski_pohjanmaa | geofi | custom geofacet grid for Keski-Pohjanmaa region | data.frame | 8 | 4 |
grid_keski_suomi | geofi | custom geofacet grid for Keski-Suomi region as in 2020 | data.frame | 22 | 4 |
grid_kymenlaakso | geofi | custom geofacet grid for Kymenlaakso region | data.frame | 6 | 4 |
grid_lappi | geofi | custom geofacet grid for Lappi region as in 2020 | data.frame | 21 | 4 |
grid_maakunta | geofi | custom geofacet grid for regions | data.frame | 19 | 4 |
grid_paijat_hame | geofi | custom geofacet grid for Päijät-Häme region | data.frame | 10 | 4 |
grid_pirkanmaa | geofi | custom geofacet grid for Pirkanmaa region | data.frame | 23 | 4 |
grid_pohjanmaa | geofi | custom geofacet grid for Pohjanmaa region | data.frame | 14 | 4 |
grid_pohjois_karjala | geofi | custom geofacet grid for Pohjois-Karjala region | data.frame | 13 | 4 |
grid_pohjois_pohjanmaa | geofi | custom geofacet grid for Pohjois-Pohjanmaa region | data.frame | 30 | 4 |
grid_pohjois_savo | geofi | custom geofacet grid for Pohjois-Savo region | data.frame | 19 | 4 |
grid_sairaanhoitop | geofi | custom geofacet grid for health care districts | data.frame | 21 | 4 |
grid_satakunta | geofi | custom geofacet grid for Satakunta region | data.frame | 16 | 4 |
grid_uusimaa | geofi | custom geofacet grid for Uusimaa region | data.frame | 26 | 4 |
grid_varsinais_suomi | geofi | custom geofacet grid for Varsinais-Suomi region | data.frame | 27 | 4 |
municipality_central_localities | geofi | A simple feature point data containing locations of municipalities central localities | sf | 311 | 22 |
municipality_key | geofi | Aggregated municipality key table for years 2013-2025 | tbl_df | 4057 | 81 |
municipality_key_2013 | geofi | municipality_key_2013 | tbl_df | 320 | 35 |
municipality_key_2014 | geofi | municipality_key_2014 | tbl_df | 320 | 39 |
municipality_key_2015 | geofi | municipality_key_2015 | tbl_df | 317 | 39 |
municipality_key_2016 | geofi | municipality_key_2016 | tbl_df | 313 | 39 |
municipality_key_2017 | geofi | municipality_key_2017 | tbl_df | 311 | 55 |
municipality_key_2018 | geofi | Municipality key table for 2018 | tbl_df | 311 | 59 |
municipality_key_2019 | geofi | Municipality key table for 2019 | tbl_df | 311 | 63 |
municipality_key_2020 | geofi | Municipality key table for 2020 | tbl_df | 310 | 63 |
municipality_key_2021 | geofi | Municipality key table for 2021 | tbl_df | 309 | 63 |
municipality_key_2022 | geofi | Municipality key table for 2022 | tbl_df | 309 | 65 |
municipality_key_2023 | geofi | Municipality key table for 2023 | tbl_df | 309 | 65 |
municipality_key_2024 | geofi | Municipality key table for 2024 | tbl_df | 309 | 61 |
municipality_key_2025 | geofi | Municipality key table for 2025 | tbl_df | 308 | 69 |
sotkadata_population | geofi | Municipality level population data from Sotkanet | data.frame | 7107 | 3 |
sotkadata_swedish_speaking_pop | geofi | Municipality level Swedish speaking population numbers from Sotkanet | data.frame | 5761 | 3 |
statfi_zipcode_population | geofi | Zipcode level population data from Statistics Finland | data.frame | 3027 | 2 |
example_data | eventstudyr | Sample dataset obtained from the replication archive for Freyaldenhoven et al. (2021) | tbl_df | 2000 | 12 |
ben_dhs06 | mpitbR | Preprocessed Benin DHS 06 dataset | data.frame | 89282 | 18 |
ben_dhs17_18 | mpitbR | Preprocessed Benin DHS 17-18 dataset | data.frame | 73728 | 18 |
syn_cdta | mpitbR | Synthetic dataset with a typical household survey design | data.frame | 15000 | 17 |
CVF | QCA | Ethnic protest in Europe | data.frame | 29 | 6 |
CVR | QCA | Ethnic protest in Europe | data.frame | 29 | 6 |
Emme | QCA | Internal Functions | data.frame | 19 | 7 |
HC | QCA | Time-Difference | data.frame | 15 | 5 |
HarKem | QCA | Internal Functions | data.frame | 48 | 7 |
Krook | QCA | Internal Functions | data.frame | 22 | 6 |
LC | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
LF | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
LM | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
LR | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
NF | QCA | Class voting in post-World War era | data.frame | 12 | 5 |
Quine | QCA | Internal Functions | data.frame | 6 | 4 |
RS | QCA | University recognition of a graduate student union | data.frame | 17 | 6 |
RagStr | QCA | Internal Functions | data.frame | 17 | 6 |
Rokkan | QCA | Internal Functions | data.frame | 16 | 5 |
d.AS | QCA | Internal Functions | data.frame | 14 | 8 |
d.BWB | QCA | Internal Functions | data.frame | 27 | 18 |
d.Bas | QCA | Internal Functions | data.frame | 30 | 9 |
d.CS | QCA | Internal Functions | data.frame | 15 | 10 |
d.CZH | QCA | Internal Functions | data.frame | 17 | 5 |
d.Emm | QCA | Internal Functions | data.frame | 19 | 7 |
d.HK | QCA | Internal Functions | data.frame | 48 | 7 |
d.HMN | QCA | Internal Functions | data.frame | 31 | 6 |
d.Kil | QCA | Internal Functions | data.frame | 14 | 6 |
d.Kro | QCA | Internal Functions | data.frame | 22 | 6 |
d.RS | QCA | Internal Functions | data.frame | 17 | 6 |
d.SA | QCA | Internal Functions | data.frame | 21 | 10 |
d.SS | QCA | Internal Functions | data.frame | 14 | 7 |
d.autonomy | QCA | Internal Functions | data.frame | 30 | 9 |
d.biodiversity | QCA | Internal Functions | data.frame | 30 | 9 |
d.educate | QCA | Internal Functions | data.frame | 8 | 5 |
d.education | QCA | Internal Functions | data.frame | 14 | 7 |
d.graduate | QCA | Internal Functions | data.frame | 17 | 6 |
d.health | QCA | Internal Functions | data.frame | 27 | 18 |
d.homeless | QCA | Internal Functions | data.frame | 15 | 10 |
d.jobsecurity | QCA | Internal Functions | data.frame | 19 | 7 |
d.napoleon | QCA | Internal Functions | data.frame | 14 | 8 |
d.partybans | QCA | Internal Functions | data.frame | 48 | 7 |
d.pban | QCA | Internal Functions | data.frame | 48 | 5 |
d.represent | QCA | Internal Functions | data.frame | 22 | 6 |
d.socialsecurity | QCA | Internal Functions | data.frame | 31 | 6 |
d.stakeholder | QCA | Internal Functions | data.frame | 17 | 5 |
d.transport | QCA | Internal Functions | data.frame | 21 | 10 |
d.urban | QCA | Internal Functions | data.frame | 14 | 6 |
d.women | QCA | Internal Functions | data.frame | 22 | 6 |
. | permutations | Group-theoretic commutator: the dot object | dot | | |
DB | permutations | megaminx | permutation | | |
DG | permutations | megaminx | permutation | | |
DY | permutations | megaminx | permutation | | |
Gy | permutations | megaminx | permutation | | |
LB | permutations | megaminx | permutation | | |
LG | permutations | megaminx | permutation | | |
LY | permutations | megaminx | permutation | | |
O | permutations | megaminx | permutation | | |
Pi | permutations | megaminx | permutation | | |
Pu | permutations | megaminx | permutation | | |
R | permutations | megaminx | permutation | | |
W | permutations | megaminx | permutation | | |
megaminx | permutations | megaminx | permutation | | |
megaminx_colours | permutations | megaminx | noquote | | |
superflip | permutations | megaminx | permutation | 1 | |
growth.curves | enveomics.R | Bacterial growth curves for three Escherichia coli mutants | data.frame | 16 | 10 |
phyla.counts | enveomics.R | Counts of microbial phyla in four sites | data.frame | 9 | 4 |
ups | baldur | Spiked-in data set of peptides | spec_tbl_df | 10599 | 13 |
yeast | baldur | Spiked-in data set of reversibly oxidized cysteines | spec_tbl_df | 2235 | 7 |
df_complex_conditions | ggupset | A fictional biological dataset with a complex experimental design | tbl_df | 360 | 4 |
gene_pathway_membership | ggupset | A fictional dataset describing which genes belong to certain pathways | matrix | 6 | 37 |
tidy_movies | ggupset | Tidy version of the movies dataset from the ggplot2 package | tbl_df | 50000 | 10 |
rnaedit_df | rnaEditr | Example breast cancer RNA editing dataset. | data.frame | 272 | 221 |
t_rnaedit_df | rnaEditr | Transposed breast cancer example dataset. | data.frame | 50 | 20 |
BeechBnd | R2BayesX | Beech Location Map | bnd | | |
BeechGra | R2BayesX | Beech Neighborhood Information | gra | 83 | 83 |
FantasyBnd | R2BayesX | Fantasy Map | list | | |
ForestHealth | R2BayesX | Forest Health Data | data.frame | 1793 | 16 |
GAMart | R2BayesX | GAM Artificial Data Set | data.frame | 500 | 12 |
GermanyBnd | R2BayesX | Germany Map | bnd | | |
MunichBnd | R2BayesX | Munich Map | bnd | | |
ZambiaBnd | R2BayesX | Zambia Map | bnd | | |
ZambiaNutrition | R2BayesX | Determinants of Childhood Malnutrition in Zambia | data.frame | 4847 | 8 |
brfarmers | netdiffuseR | Brazilian Farmers | data.frame | 692 | 148 |
brfarmersDiffNet | netdiffuseR | 'diffnet' version of the Brazilian Farmers data | diffnet | | |
fakeDynEdgelist | netdiffuseR | Fake dynamic edgelist | data.frame | 22 | 4 |
fakeEdgelist | netdiffuseR | Fake static edgelist | data.frame | 11 | 3 |
fakesurvey | netdiffuseR | Fake survey data | data.frame | 9 | 9 |
fakesurveyDyn | netdiffuseR | Fake longitudinal survey data | data.frame | 18 | 10 |
kfamily | netdiffuseR | Korean Family Planning | data.frame | 1047 | 432 |
kfamilyDiffNet | netdiffuseR | 'diffnet' version of the Korean Family Planning data | diffnet | | |
medInnovations | netdiffuseR | Medical Innovation | data.frame | 125 | 60 |
medInnovationsDiffNet | netdiffuseR | 'diffnet' version of the Medical Innovation data | diffnet | | |
x | flowMeans | xSample | data.frame | 17640 | 6 |
aquifer | npsp | Wolfcamp aquifer data | data.frame | 85 | 3 |
earthquakes | npsp | Earthquake data | data.frame | 1859 | 6 |
precipitation | npsp | Precipitation data | SpatialPointsDataFrame | | |
c57_nos2KO_mouse_countDF | omu | c57b6J nos2KO metabolomics count matrix | data.frame | 668 | 31 |
c57_nos2KO_mouse_metadata | omu | c57b6J nos2KO meta data | data.frame | 29 | 4 |
besag | catsim | A hand-constructed image from Besag (1986) | matrix | 100 | |
hoffmanphantom | catsim | An example fMRI phantom | array | | |
funk_table | ProTrackR | ProTracker Funk Table | numeric | | |
mod.intro | ProTrackR | Example of a PTModule object | PTModule | | |
paula_clock | ProTrackR | Paula clock table | data.frame | 2 | 2 |
period_table | ProTrackR | ProTracker Period Table | data.frame | 48 | 14 |
aiterhofen_oedmuehlen | mortAAR | aiterhofen_oedmuehlen: Life table example | data.frame | 13 | 11 |
gallery_graves | mortAAR | gallery_graves: Life table example | data.frame | 129 | 4 |
magdalenenberg | mortAAR | magdalenenberg: Life table example | data.frame | 13 | 2 |
muensingen | mortAAR | muensingen: Life table example | data.frame | 71 | 4 |
nitra | mortAAR | nitra: Life table example | data.frame | 75 | 5 |
odagsen_cm | mortAAR | odagsen_corpus.mandibulae: Life table example | data.frame | 10 | 9 |
odagsen_mo | mortAAR | odagsen_margo.orbitalis: Life table example | data.frame | 10 | 9 |
schleswig_ma | mortAAR | schleswig_ma: Life table example | data.frame | 11 | 9 |
CGC_20211118 | PathwaySpace | COSMIC-CGC genes mapped to PathwaySpace images. | data.frame | 723 | 3 |
PCv12_pruned_igraph | PathwaySpace | A pruned and laid out igraph object from Pathway Commons V12. | igraph | | |
gdist.toy | PathwaySpace | A pruned and laid out igraph object from Pathway Commons V12. | matrix | 500 | 500 |
gimage | PathwaySpace | An image matrix. | matrix | 500 | |
gtoy1 | PathwaySpace | Toy 'igraph' objects. | igraph | | |
gtoy2 | PathwaySpace | Toy 'igraph' objects. | igraph | | |
hallmarks | PathwaySpace | A list with Hallmark gene sets (v2023.1). | list | | |
welding | statmod | Data: Tensile Strength of Welds | data.frame | 16 | 10 |
GBSG | mfp | German Breast Cancer Study Group | data.frame | 686 | 11 |
bodyfat | mfp | percentage of body fat determined by underwater weighing | data.frame | 252 | 17 |
Bact1 | DivE | Count of Medically Important Bacteria Species in a Sample | data.frame | 144 | 2 |
ModelSet | DivE | List of 58 candidate models to fit to data | list | | |
ParamRanges | DivE | List of 58 sets of upper and lower bounds for models evaluated by DivE | list | | |
ParamSeeds | DivE | List of 58 matrices of model seeding parameters. | list | | |
intcal | rintcal | IntCal20 json file | list | | |
testdata | RPscoring | Test Dataset | matrix | 8 | |
train_choice | RprobitB | Stated Preferences for Train Traveling | data.frame | 2929 | 11 |
ihdp | bartcs | Infant Health and Development Program Data | data.frame | 747 | 30 |
amt_fisher | amt | GPS tracks from four fishers | track_xyt | 14230 | 6 |
amt_fisher_covar | amt | Environmental data for fishers | list | | |
deer | amt | Relocations of 1 red deer | track_xyt | 826 | 4 |
sh | amt | Relocations of one Red Deer | data.frame | 1500 | 4 |
sh_forest | amt | Forest cover | PackedSpatRaster | | |
uhc_hab | amt | Simulated habitat rasters for demonstrating UHC plots | data.frame | 6400 | 9 |
uhc_hsf_locs | amt | Simulated HSF location data for demonstrating UHC plots | data.frame | 1894 | 2 |
uhc_issf_locs | amt | Simulated iSSF location data for demonstrating UHC plots | data.frame | 2135 | 3 |
chr | bWGR | Tetra-seed Pods | integer | | |
fam | bWGR | Tetra-seed Pods | numeric | | |
gen | bWGR | Tetra-seed Pods | matrix | 196 | 376 |
y | bWGR | Tetra-seed Pods | numeric | | |
med | regmed | Simulated dataset for regmed package | matrix | 100 | 200 |
x | regmed | Simulated dataset for regmed package | matrix | 100 | 10 |
y | regmed | Simulated dataset for regmed package | matrix | 100 | 2 |
simdata | fect | Simulated data | data.frame | 7000 | 18 |
simdata1 | fect | Simulated data | data.frame | 7000 | 17 |
turnout | fect | EDR and voter turnout in the US | data.frame | 1128 | 6 |
usmacro_growth | bayesianVARs | Data from the US-economy | matrix | 247 | 21 |
intergrated | GseaVis | This is a test data for this package test data describtion | list | | |
psid | bife | Female labor force participation | data.table | 13149 | 8 |
mpdta | did | County Teen Employment Dataset | data.frame | 2500 | 6 |
cfa_example | semptools | Sample dataset pa_example | data.frame | 200 | 14 |
pa_example | semptools | Sample dataset pa_example | data.frame | 100 | 4 |
pa_example_3covs | semptools | Sample dataset pa_example_3covs | data.frame | 100 | 7 |
sem_2nd_order_example | semptools | Sample dataset sem_2nd_order_example | data.frame | 500 | 21 |
sem_example | semptools | Sample dataset sem_example | data.frame | 200 | 14 |
TDHeat05 | statgenSTA | Field data for a wheat experiment in Mexico | TD | | |
TDMaize | statgenSTA | Field data for a maize experiment in Tlaltizapan, Mexico | TD | | |
dropsRaw | statgenSTA | DROPS data set | data.frame | 6486 | 26 |
Lai2005fig4 | anomalous | Normalized glioblastoma profile for an excerpt of chromosome 7, the EGFR locus. | data.frame | 193 | 5 |
machinetemp | anomalous | Machine temperature data. | data.frame | 22695 | 2 |
sim.data | anomalous | Simulated data. | matrix | 500 | |
wind | anomalous | Ireland wind data, 1961-1978 | data.frame | 6574 | 15 |
wind.loc | anomalous | Ireland wind data, 1961-1978 | data.frame | 12 | 5 |
DSR_data | PHEindicatormethods | SII test datasets - DSR | data.frame | 30 | 6 |
LE_data | PHEindicatormethods | SII test datasets - Life Expectancy | data.frame | 7222 | 7 |
esp2013 | PHEindicatormethods | European Standard Population 2013 | numeric | | |
prevalence_data | PHEindicatormethods | SII test datasets - Prevalence | data.frame | 240 | 10 |
keys | selenider | Special keys | list | | |
Betula_ermanii.mspct | photobiologyPlants | Spectral data for 'Betula ermanii' leaves | object_mspct | | |
CRYs.mspct | photobiologyPlants | CRY1, CRY2 and CRY3 absorbance spectra. | filter_mspct | | |
McCree_photosynthesis.mspct | photobiologyPlants | McCree's action spectra for whole-leaf photosynthesis. | response_mspct | | |
PHOTs.mspct | photobiologyPlants | PHOT1 and PHOT2 absorbance spectra. | filter_mspct | | |
PHYs.mspct | photobiologyPlants | Tabulated data for Phytochrome Sigma | generic_mspct | | |
Solidago_altissima.mspct | photobiologyPlants | Spectral optical data for 'Solidago altissima' leaves | object_mspct | | |
UVR8s.mspct | photobiologyPlants | UVR8 absorbance spectrum | filter_mspct | | |
ZTLs.mspct | photobiologyPlants | ZTL absorbance spectra. | filter_mspct | | |
carotenoids.mspct | photobiologyPlants | Absorbance spectra for carotenoids. | filter_mspct | | |
chlorophylls.mspct | photobiologyPlants | Absorbance spectra for chlorophylls. | filter_mspct | | |
chlorophylls_fluorescence.mspct | photobiologyPlants | Fluorescence emission spectra for chlorophylls. | source_mspct | | |
leaf_fluorescence.mspct | photobiologyPlants | Fluorescence emission spectra of leaves. | source_mspct | | |
phytochrome.spct | photobiologyPlants | Tabulated data for Phytochrome Sigma | generic_spct | 49 | 3 |
fl | multe | ECLS data from Fryer and Levitt (2013) | data.frame | 8806 | 21 |
example_data | mixedbiastest | Example Dataset for the mixedbiastest Package | data.frame | 97 | 3 |
exdata01 | rqlm | A simulated example dataset | data.frame | 40 | 5 |
exdata02 | rqlm | A simulated example dataset | data.frame | 1200 | 5 |
exdata03 | rqlm | A simulated example dataset with missing covariates | data.frame | 1200 | 5 |
mch | rqlm | A cluster-randomised trial dataset for the maternal and child health handbook | data.frame | 500 | 13 |
madelon | MDFS | An artificial dataset called MADELON | list | | |
compendiums | sketchy | List with compendium skeletons | list | | |
NHANES | hexbin | NHANES Data : National Health and Nutrition Examination Survey | data.frame | 9575 | 15 |
rasch1000 | ConjointChecks | 1000 sampled 3-matrices from simulated Rasch data. | checks | | |
control.points | vec2dtransf | Sample control points data.frame | data.frame | 16 | 5 |
test_container | scITD | Data container for testing tensor formation steps | environment | | |
SP500_idioerr | bspcov | SP500 dataset | list | | |
colon | bspcov | colon dataset | data.frame | 2000 | 62 |
tissues | bspcov | tissues dataset | numeric | | |
between.1 | bruceR | Demo data. | data.frame | 32 | 2 |
between.2 | bruceR | Demo data. | data.frame | 24 | 3 |
between.3 | bruceR | Demo data. | data.frame | 32 | 4 |
mixed.2_1b1w | bruceR | Demo data. | data.frame | 8 | 4 |
mixed.3_1b2w | bruceR | Demo data. | data.frame | 8 | 5 |
mixed.3_2b1w | bruceR | Demo data. | data.frame | 16 | 4 |
within.1 | bruceR | Demo data. | data.frame | 8 | 5 |
within.2 | bruceR | Demo data. | data.frame | 4 | 7 |
within.3 | bruceR | Demo data. | data.frame | 4 | 9 |
coloc_test_data | coloc | Simulated data to use in testing and vignettes in the coloc package | list | | |
RP | FastRet | Retention Times (RT) Measured on a Reverse Phase (RP) Column | data.frame | 442 | 3 |
alpinaCompData | chemodiv | Arabis alpina floral scent compounds | data.frame | 15 | 3 |
alpinaCompDis | chemodiv | Arabis alpina floral scent compound dissimilarity matrix | matrix | 15 | 15 |
alpinaMolNet | chemodiv | Arabis alpina floral scent molecular network | tbl_graph | | |
alpinaNPCTable | chemodiv | Arabis alpina floral scent NPClassifier table | data.frame | 15 | 6 |
alpinaPopData | chemodiv | Arabis alpina populations | data.frame | 87 | 1 |
alpinaSampData | chemodiv | Arabis alpina floral scent data | data.frame | 87 | 15 |
alpinaSampDis | chemodiv | Arabis alpina floral scent sample dissimilarity matrix | matrix | 87 | 87 |
minimalCompData | chemodiv | Minimal compound dataset | data.frame | 3 | 3 |
minimalCompDis | chemodiv | Minimal compound dissimilarity matrix | matrix | 3 | 3 |
minimalMolNet | chemodiv | Minimal molecular network | tbl_graph | | |
minimalNPCTable | chemodiv | Minimal NPClassifier table | data.frame | 3 | 7 |
minimalSampData | chemodiv | Minimal sample dataset | data.frame | 4 | 3 |
minimalSampDis | chemodiv | Minimal sample dissimilarity matrix | matrix | 4 | 4 |
config | scROSHI | Test config file | data.frame | 7 | 2 |
marker_list | scROSHI | Marker gene list for the test SCE data Set | list | | |
test_sce_data | scROSHI | Test SCE Data Set | SingleCellExperiment | | |
coral6 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a branching coral, identified hereon as “coral6”. | data.frame | 300 | 5 |
foram166shot7 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a planktonic foraminifera | data.frame | 144 | 8 |
foram174shot4 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a planktonic foraminifera | data.frame | 144 | 8 |
foram72shot3 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a planktonic foraminifera | data.frame | 144 | 8 |
ICU_data | stratamatch | Demographics and comorbidities of 10,157 ICU patients | tbl_df | 10157 | 13 |
case_study | countfitteR | Short version of the 'case_study_FITC' | data.frame | 117 | 16 |
case_study_APC | countfitteR | Case study for APC dye | data.frame | 117 | 120 |
case_study_FITC | countfitteR | Case study for FITC dye | data.frame | 117 | 120 |
case_study_all | countfitteR | Case study with two fluorescent dyes | data.frame | 117 | 240 |
sim_dat | countfitteR | Data created from simulation of NB Poiss | tbl_df | 24000 | 5 |
seniors | poissonreg | Alcohol, Cigarette, and Marijuana Use for High School Seniors | tbl_df | 8 | 4 |
census.at.school.5000 | iNZightMR | Census at School 5000 | data.frame | 5000 | 72 |
gallupGPS6 | GeomArchetypal | Gallup Global Preferences Study processed data set of six variables | data.frame | 76132 | 6 |
GerberGreenImai | Matching | Gerber and Green Dataset used by Imai | data.frame | 10829 | 26 |
lalonde | Matching | Lalonde Dataset | data.frame | 445 | 12 |
cranJuly2014 | miniCRAN | Stored version of available.packages() | matrix | 5588 | 17 |
hfdata | PeerPerformance | Hedge fund data | matrix | 60 | 100 |
europeananews | nametagger | Tagged news paper articles from Europeana | data.frame | 533893 | 4 |
Conf_griffin | sequoia | Example output from estimating confidence probabilities: griffins | list | | |
FieldMums_griffin | sequoia | Example field-observed mothers: griffins | data.frame | 143 | 2 |
Geno_HSg5 | sequoia | Example genotype file: 'HSg5' | matrix | 920 | |
Geno_griffin | sequoia | Example genotype file: Griffins | matrix | 142 | |
Inherit_patterns | sequoia | Inheritance patterns | array | | |
LH_HSg5 | sequoia | Example life history file: 'HSg5' | data.frame | 1000 | 3 |
LH_griffin | sequoia | Example life history data: griffins | data.frame | 200 | 3 |
MaybeRel_griffin | sequoia | Example output from check for relatives: griffins | list | | |
Ped_HSg5 | sequoia | Example pedigree: 'HSg5' | data.frame | 1000 | 3 |
Ped_griffin | sequoia | Example pedigree: griffins | data.frame | 200 | 4 |
SeqOUT_HSg5 | sequoia | Example output from pedigree inference: 'HSg5' | list | | |
SeqOUT_griffin | sequoia | Example output from pedigree inference: griffins | list | | |
SimGeno_example | sequoia | Example genotype file: 'HSg5' | matrix | 214 | 200 |
pie | lulcc | Land use change dataset for Plum Island Ecosystem | list | | |
sibuyan | lulcc | Land use change dataset for Sibuyan Island | list | | |
bivariate_missingness | dosearch | Systematic Analysis of Bivariate Missing Data Problems | data.frame | 6144 | 8 |
ga_topo | textures | Topographic image | list | | |
GermanIndustry | micEconCES | Aggregated Time Series Data for the West German Industry | data.frame | 34 | 33 |
MishraCES | micEconCES | Mishra's (2006) CES data | data.frame | 50 | 6 |
cytokine | varbvs | Cytokine signaling genes SNP annotation. | numeric | | |
leukemia | varbvs | Expression levels recorded in leukemia patients. | list | | |
reference | SeqNet | RNA-seq reference dataset | list | | |
scale_tabs | goeveg | Conversion tables for cover-abundance scales | list | | |
schedenenv | goeveg | Header data for Vegetation releves from Scheden | data.frame | 28 | 11 |
schedenveg | goeveg | Vegetation releves from Scheden | data.frame | 28 | 155 |
common_na_numbers | naniar | Common number values for NA | numeric | | |
common_na_strings | naniar | Common string values for NA | character | | |
oceanbuoys | naniar | West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997. | tbl_df | 736 | 8 |
pedestrian | naniar | Pedestrian count information around Melbourne for 2016 | tbl_df | 37700 | 9 |
riskfactors | naniar | The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009. | tbl_df | 245 | 34 |
ibd | ZIBR | Longitudinal human microbiome data | data.frame | 236 | 5 |
umaru | interactionRCS | UMARU IMPACT Study data | data.frame | 575 | 15 |
students | Kernelheaping | Student0405 | data.frame | 690 | 7 |
income | kernlab | Income Data | data.frame | 8993 | 14 |
musk | kernlab | Musk data set | data.frame | 476 | 167 |
promotergene | kernlab | E. coli promoter gene sequences (DNA) | data.frame | 106 | 58 |
reuters | kernlab | Reuters Text Data | list | | |
rlabels | kernlab | Reuters Text Data | factor | | |
spam | kernlab | Spam E-mail Database | data.frame | 4601 | 58 |
spirals | kernlab | Spirals Dataset | matrix | 300 | |
ticdata | kernlab | The Insurance Company Data | data.frame | 9822 | 86 |
bechdel | epoxy | Top 10 Highest-Rated, Bechdel-Passing Movies | tbl_df | 10 | 18 |
lazega | Bergm | Lazega lawyers network data | network | | |
R_example | unusualprofile | An example correlation matrix | matrix | 8 | 8 |
d_example | unusualprofile | An example data.frame | tbl_df | 1 | 8 |
Users | Authenticate | Users Dataset | data.frame | 3 | 4 |
seldata | selection.index | Selection Index DataSet | data.frame | 75 | 9 |
weight | selection.index | Weight dataset | data.frame | 7 | 3 |
C10E14 | AMCP | The data used in Chapter 10, Exercise 14 | data.frame | 63 | 4 |
C10E7 | AMCP | The data used in Chapter 10, Exercise 7 | data.frame | 45 | 3 |
C10E9 | AMCP | The data used in Chapter 10, Exercise 9 | data.frame | 72 | 4 |
C10T5 | AMCP | The data used in Chapter 10, Table 5 | data.frame | 40 | 3 |
C10T9 | AMCP | The data used in Chapter 10, Table 9 | data.frame | 24 | 3 |
C11E17 | AMCP | The data used in Chapter 11, Exercise 17 | data.frame | 14 | 4 |
C11E18 | AMCP | The data used in Chapter 11, Exercise 18 | data.frame | 12 | 3 |
C11E19 | AMCP | The data used in Chapter 11, Exercise 19 | data.frame | 14 | 4 |
C11E21 | AMCP | The data used in Chapter 11, Exercise 21 | data.frame | 42 | 3 |
C11E22 | AMCP | The data used in Chapter 11, Exercise 22 | data.frame | 19 | 7 |
C11E23 | AMCP | The data used in Chapter 11, Exercise 23 | data.frame | 183 | 3 |
C11E24 | AMCP | The data used in Chapter 11, Exercise 24 | data.frame | 90 | 3 |
C11E3 | AMCP | The data used in Chapter 11, Exercise 3 | data.frame | 5 | 4 |
C11E5 | AMCP | The data used in Chapter 11, Exercise 5 | data.frame | 5 | 3 |
C11T1 | AMCP | The data used in Chapter 11, Table 1 | data.frame | 6 | 2 |
C11T19 | AMCP | The data used in Chapter 11, Table 19 | data.frame | 24 | 3 |
C11T20 | AMCP | The data used in Chapter 11, Table 20 | data.frame | 15 | 3 |
C11T4 | AMCP | The data used in Chapter 11, Table 4 | data.frame | 10 | 4 |
C11T5 | AMCP | The data used in Chapter 11, Table 5 | data.frame | 12 | 4 |
C12E17 | AMCP | The data used in Chapter 12, Exercise 17 | data.frame | 14 | 5 |
C12E18 | AMCP | The data used in Chapter 12, Exercise 18 | data.frame | 10 | 3 |
C12E19 | AMCP | The data used in Chapter 12, Exercise 19 | data.frame | 47 | 6 |
C12E21 | AMCP | The data used in Chapter 12, Exercise 21 | data.frame | 36 | 4 |
C12E9 | AMCP | The data used in Chapter 12, Exercise 9 | data.frame | 10 | 4 |
C12T1 | AMCP | The data used in Chapter 12, Table 1 | data.frame | 10 | 6 |
C12T11 | AMCP | The data used in Chapter 12, Table 11 | data.frame | 10 | 3 |
C12T15 | AMCP | The data used in Chapter 12, Table 15 | data.frame | 10 | 3 |
C12T21 | AMCP | The data used in Chapter 12, Table 21 | data.frame | 18 | 5 |
C12T7 | AMCP | The data used in Chapter 12, Table 7 | data.frame | 10 | 3 |
C12T9 | AMCP | The data used in Chapter 12, Table 9 | data.frame | 10 | 2 |
C13E10 | AMCP | The data used in Chapter 13, Exercise 10 | data.frame | 14 | 4 |
C13E13 | AMCP | The data used in Chapter 13, Exercise 13 | data.frame | 14 | 4 |
C13E14 | AMCP | The data used in Chapter 13, Exercise 14 | data.frame | 13 | 3 |
C13E22 | AMCP | The data used in Chapter 13, Exercise 22 | data.frame | 5 | 3 |
C13E23 | AMCP | The data used in Chapter 13, Exercise 23 | data.frame | 19 | 7 |
C13E24 | AMCP | The data used in Chapter 13, Exercise 24 | data.frame | 183 | 3 |
C13E25 | AMCP | The data used in Chapter 13, Exercise 25 | data.frame | 30 | 3 |
C13E7 | AMCP | The data used in Chapter 13, Exercise 7 | data.frame | 5 | 4 |
C13T1 | AMCP | The data used in Chapter 13, Table 1 | data.frame | 5 | 2 |
C13T12 | AMCP | The data used in Chapter 13, Table 12 | data.frame | 8 | 2 |
C13T14 | AMCP | The data used in Chapter 13, Table 14 | data.frame | 8 | 2 |
C13T2 | AMCP | The data used in Chapter 13, Table 2 | data.frame | 8 | 3 |
C13T5 | AMCP | The data used in Chapter 13, Table 5 | data.frame | 12 | 4 |
C14E10 | AMCP | The data used in Chapter 14, Exercise 10 | data.frame | 10 | 4 |
C14E14 | AMCP | The data used in Chapter 14, Exercise 14 | data.frame | 30 | 5 |
C14E15 | AMCP | The data used in Chapter 14, Exercise 15 | data.frame | 10 | 3 |
C14E21 | AMCP | The data used in Chapter 14, Exercise 21 | data.frame | 14 | 5 |
C14E22 | AMCP | The data used in Chapter 14, Exercise 22 | data.frame | 47 | 6 |
C14T1 | AMCP | The data used in Chapter 14, Table 1 | data.frame | 10 | 4 |
C14T10 | AMCP | The data used in Chapter 14, Table 10 | data.frame | 20 | 4 |
C14T3 | AMCP | The data used in Chapter 14, Table 3 | data.frame | 10 | 3 |
C14T4 | AMCP | The data used in Chapter 14, Table 4 | data.frame | 10 | 6 |
C14T5 | AMCP | The data used in Chapter 14, Table 5 | data.frame | 10 | 5 |
C14T8 | AMCP | The data used in Chapter 14, Table 8 | data.frame | 20 | 3 |
C15E16 | AMCP | The data used in Chapter 15, Exercise 16 | data.frame | 14 | 4 |
C15E17 | AMCP | The data used in Chapter 15, Exercise 17 | data.frame | 56 | 4 |
C15E18 | AMCP | The data used in Chapter 15, Exercise 18 | data.frame | 24 | 4 |
C15E18U | AMCP | The data used in Chapter 15, Exercise 18 (Univariate) | data.frame | 72 | 3 |
C15E19 | AMCP | The data used in Chapter 15, Exercise 19 | data.frame | 24 | 4 |
C15E19U | AMCP | The data used in Chapter 15, Exercise 19 (Univariate) | data.frame | 72 | 3 |
C15T1 | AMCP | The data used in Chapter 15, Table 1 | data.frame | 12 | 4 |
C16E5 | AMCP | The data used in Chapter 16, Exercise 5 | data.frame | 24 | 3 |
C16E7 | AMCP | The data used in Chapter 16, Exercise 7 | data.frame | 29 | 6 |
C16E9 | AMCP | The data used in Chapter 16, Exercise 9 | data.frame | 29 | 6 |
C16T1 | AMCP | The data used in Chapter 16, Table 1 | data.frame | 24 | 3 |
C16T4 | AMCP | The data used in Chapter 16, Table 4 | data.frame | 29 | 6 |
C1E18 | AMCP | The data used in Chapter 1, Exercise 18 | data.frame | 4 | 3 |
C1E19 | AMCP | The data used in Chapter 1, Exercise 19 | data.frame | 30 | 2 |
C1E21 | AMCP | The data used in Chapter 1, Exercise 21 | data.frame | 12 | 2 |
C1E22 | AMCP | The data used in Chapter 1, Exercise 22 | data.frame | 11 | 3 |
C1E23 | AMCP | The data used in Chapter 1, Exercise 23 | data.frame | 12 | 3 |
C1T1 | AMCP | The data used in Chapter 1, Table 1 | data.frame | 10 | 3 |
C3E10 | AMCP | The data used in Chapter 3, Exercise 10 | data.frame | 36 | 3 |
C3E11 | AMCP | The data used in Chapter 3, Exercise 11 | data.frame | 24 | 2 |
C3E19 | AMCP | The data used in Chapter 3, Exercise 19 | data.frame | 155 | 3 |
C3E20 | AMCP | The data used in Chapter 3, Exercise 20 | data.frame | 72 | 2 |
C3E21 | AMCP | The data used in Chapter 3, Exercise 21 | data.frame | 192 | 2 |
C3E22 | AMCP | The data used in Chapter 3, Exercise 22 | data.frame | 310 | 5 |
C3E9 | AMCP | The data used in Chapter 3, Exercise 9 | data.frame | 12 | 2 |
C3T1 | AMCP | The data used in Chapter 3, Table 1 | data.frame | 6 | 1 |
C3T3 | AMCP | The data used in Chapter 3, Table 3 | data.frame | 30 | 2 |
C3T7R | AMCP | The data used for Chapter 3, Table 7 (raw data to produce the summary measures) | data.frame | 88 | 3 |
C3T9R | AMCP | The data used for Chapter 3, Table 9 (raw data to produce the summary measures) | data.frame | 88 | 3 |
C4E11 | AMCP | The data used in Chapter 4, Exercise 11 | data.frame | 24 | 2 |
C4E12 | AMCP | The data used in Chapter 4, Exercise 12 | data.frame | 18 | 2 |
C4E13 | AMCP | The data used in Chapter 4, Exercise 13 | data.frame | 20 | 2 |
C4T1 | AMCP | The data used in Chapter 4, Table 1 | data.frame | 20 | 2 |
C4T7 | AMCP | The data used in Chapter 4, Table 7 | data.frame | 15 | 2 |
C5E10 | AMCP | The data used in Chapter 5, Exercise 10 | data.frame | 24 | 2 |
C5E16 | AMCP | The data used in Chapter 5, Exercise 16 | data.frame | 18 | 2 |
C5E5 | AMCP | The data used in Chapter 5, Exercise 5 | data.frame | 20 | 2 |
C5T4 | AMCP | The data used in Chapter 5, Table 4 | data.frame | 24 | 2 |
C6E10 | AMCP | The data used in Chapter 6, Exercise 10 | data.frame | 45 | 2 |
C6E14 | AMCP | The data used in Chapter 6, Exercise 14 | data.frame | 48 | 2 |
C6E16 | AMCP | The data used in Chapter 6, Exercise 16 | data.frame | 91 | 5 |
C6T1 | AMCP | The data used in Chapter 6, Table 1 | data.frame | 24 | 2 |
C7E12 | AMCP | The data used in Chapter 7, Exercise 12 | data.frame | 32 | 3 |
C7E13 | AMCP | The data used in Chapter 7, Exercise 13 | data.frame | 48 | 3 |
C7E14 | AMCP | The data used in Chapter 7, Exercise 14 | data.frame | 28 | 3 |
C7E15 | AMCP | The data used in Chapter 7, Exercise 15 | data.frame | 36 | 3 |
C7E18 | AMCP | The data used in Chapter 7, Exercise 18 | data.frame | 22 | 3 |
C7E19 | AMCP | The data used in Chapter 7, Exercise 19 | data.frame | 40 | 3 |
C7E22 | AMCP | The data used in Chapter 7, Exercise 22 | data.frame | 28 | 4 |
C7E23 | AMCP | The data used in Chapter 7, Exercise 23 | data.frame | 68 | 4 |
C7E24 | AMCP | The data used in Chapter 7, Exercise 24 | data.frame | 56 | 4 |
C7E25 | AMCP | The data used in Chapter 7, Exercise 25 | data.frame | 60 | 4 |
C7E6 | AMCP | The data used in Chapter 7, Exercise 6 | data.frame | 45 | 3 |
C7E9 | AMCP | The data used in Chapter 7, Exercise 9 | data.frame | 48 | 3 |
C7T1 | AMCP | The data used in Chapter 7, Table 1 | data.frame | 20 | 2 |
C7T11 | AMCP | The data used in Chapter 7, Table 11 | data.frame | 45 | 3 |
C7T15 | AMCP | The data used in Chapter 7, Table 15 | data.frame | 22 | 3 |
C7T23 | AMCP | The data used in Chapter 7, Table 23 | data.frame | 45 | 3 |
C7T5 | AMCP | The data used in Chapter 7, Table 5 | data.frame | 30 | 3 |
C7T9 | AMCP | The data used in Chapter 7, Table 9 | data.frame | 36 | 3 |
C8E15 | AMCP | The data used in Chapter 8, Exercise 15 | data.frame | 48 | 4 |
C8E16 | AMCP | The data used in Chapter 8, Exercise 16 | data.frame | 96 | 4 |
C8E17 | AMCP | The data used in Chapter 8, Exercise 17 | data.frame | 54 | 4 |
C8E18 | AMCP | The data used in Chapter 8, Exercise 18 | data.frame | 80 | 5 |
C8E19 | AMCP | The data used in Chapter 8, Exercise 19 | data.frame | 80 | 5 |
C8T12 | AMCP | The data used in Chapter 8, Table 12 | data.frame | 72 | 4 |
C9E14 | AMCP | The data used in Chapter 9, Exercise 14 | data.frame | 155 | 4 |
C9E15 | AMCP | The data used in Chapter 9, Exercise 15 | data.frame | 310 | 6 |
C9E16 | AMCP | The data used in Chapter 9, Exercise 16 | data.frame | 310 | 6 |
C9E4 | AMCP | The data used in Chapter 9, Exercise 4 | data.frame | 10 | 3 |
C9ExtE1 | AMCP | The data used in Chapter 9 Extension, Exercise 1 | data.frame | 140 | 6 |
C9ExtE2 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 168 | 6 |
C9ExtE3 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 310 | 6 |
C9ExtFigs4and5 | AMCP | The data used in Chapter 9 Extension Figures 4 and 5 | data.frame | 310 | 10 |
C9ExtT1 | AMCP | The data used in Chapter 9, Extension Table 1 | data.frame | 6 | 3 |
C9T1 | AMCP | The data used in Chapter 9, Table 1 | data.frame | 6 | 3 |
C9T11 | AMCP | The data used in Chapter 9, Table 11 | data.frame | 18 | 4 |
C9T7 | AMCP | The data used in Chapter 9, Table 7 | data.frame | 30 | 3 |
T1T1 | AMCP | The data used in Tutorial 1, Table 1 | data.frame | 102 | 1 |
T2T1 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 2 |
T2T2 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 4 |
T3AT1 | AMCP | The data used in Tutorial 3A, Table 1 | data.frame | 8 | 2 |
T3AT2 | AMCP | The data used in Tutorial 3A, Table 2 | data.frame | 8 | 4 |
T3AT4 | AMCP | The data used in Tutorial 3A, Table 4 | data.frame | 10 | 6 |
T3AT5 | AMCP | The data used in Tutorial 3A, Table 5 | data.frame | 10 | 6 |
chapter_10_exercise_14 | AMCP | The data used in Chapter 10, Exercise 14 | data.frame | 63 | 4 |
chapter_10_exercise_7 | AMCP | The data used in Chapter 10, Exercise 7 | data.frame | 45 | 3 |
chapter_10_exercise_9 | AMCP | The data used in Chapter 10, Exercise 9 | data.frame | 72 | 4 |
chapter_10_table_5 | AMCP | The data used in Chapter 10, Table 5 | data.frame | 40 | 3 |
chapter_10_table_9 | AMCP | The data used in Chapter 10, Table 9 | data.frame | 24 | 3 |
chapter_11_exercise_17 | AMCP | The data used in Chapter 11, Exercise 17 | data.frame | 14 | 4 |
chapter_11_exercise_18 | AMCP | The data used in Chapter 11, Exercise 18 | data.frame | 12 | 3 |
chapter_11_exercise_19 | AMCP | The data used in Chapter 11, Exercise 19 | data.frame | 14 | 4 |
chapter_11_exercise_21 | AMCP | The data used in Chapter 11, Exercise 21 | data.frame | 42 | 3 |
chapter_11_exercise_22 | AMCP | The data used in Chapter 11, Exercise 22 | data.frame | 19 | 7 |
chapter_11_exercise_23 | AMCP | The data used in Chapter 11, Exercise 23 | data.frame | 183 | 3 |
chapter_11_exercise_24 | AMCP | The data used in Chapter 11, Exercise 24 | data.frame | 90 | 3 |
chapter_11_exercise_3 | AMCP | The data used in Chapter 11, Exercise 3 | data.frame | 5 | 4 |
chapter_11_exercise_5 | AMCP | The data used in Chapter 11, Exercise 5 | data.frame | 5 | 3 |
chapter_11_table_1 | AMCP | The data used in Chapter 11, Table 1 | data.frame | 6 | 2 |
chapter_11_table_19 | AMCP | The data used in Chapter 11, Table 19 | data.frame | 24 | 3 |
chapter_11_table_20 | AMCP | The data used in Chapter 11, Table 20 | data.frame | 15 | 3 |
chapter_11_table_4 | AMCP | The data used in Chapter 11, Table 4 | data.frame | 10 | 4 |
chapter_11_table_5 | AMCP | The data used in Chapter 11, Table 5 | data.frame | 12 | 4 |
chapter_12_exercise_17 | AMCP | The data used in Chapter 12, Exercise 17 | data.frame | 14 | 5 |
chapter_12_exercise_18 | AMCP | The data used in Chapter 12, Exercise 18 | data.frame | 10 | 3 |
chapter_12_exercise_19 | AMCP | The data used in Chapter 12, Exercise 19 | data.frame | 47 | 6 |
chapter_12_exercise_21 | AMCP | The data used in Chapter 12, Exercise 21 | data.frame | 36 | 4 |
chapter_12_exercise_9 | AMCP | The data used in Chapter 12, Exercise 9 | data.frame | 10 | 4 |
chapter_12_table_1 | AMCP | The data used in Chapter 12, Table 1 | data.frame | 10 | 6 |
chapter_12_table_11 | AMCP | The data used in Chapter 12, Table 11 | data.frame | 10 | 3 |
chapter_12_table_15 | AMCP | The data used in Chapter 12, Table 15 | data.frame | 10 | 3 |
chapter_12_table_21 | AMCP | The data used in Chapter 12, Table 21 | data.frame | 18 | 5 |
chapter_12_table_7 | AMCP | The data used in Chapter 12, Table 7 | data.frame | 10 | 3 |
chapter_12_table_9 | AMCP | The data used in Chapter 12, Table 9 | data.frame | 10 | 2 |
chapter_13_exercise_10 | AMCP | The data used in Chapter 13, Exercise 10 | data.frame | 14 | 4 |
chapter_13_exercise_13 | AMCP | The data used in Chapter 13, Exercise 13 | data.frame | 14 | 4 |
chapter_13_exercise_14 | AMCP | The data used in Chapter 13, Exercise 14 | data.frame | 13 | 3 |
chapter_13_exercise_22 | AMCP | The data used in Chapter 13, Exercise 22 | data.frame | 5 | 3 |
chapter_13_exercise_23 | AMCP | The data used in Chapter 13, Exercise 23 | data.frame | 19 | 7 |
chapter_13_exercise_24 | AMCP | The data used in Chapter 13, Exercise 24 | data.frame | 183 | 3 |
chapter_13_exercise_25 | AMCP | The data used in Chapter 13, Exercise 25 | data.frame | 30 | 3 |
chapter_13_exercise_7 | AMCP | The data used in Chapter 13, Exercise 7 | data.frame | 5 | 4 |
chapter_13_table_1 | AMCP | The data used in Chapter 13, Table 1 | data.frame | 5 | 2 |
chapter_13_table_12 | AMCP | The data used in Chapter 13, Table 12 | data.frame | 8 | 2 |
chapter_13_table_14 | AMCP | The data used in Chapter 13, Table 14 | data.frame | 8 | 2 |
chapter_13_table_2 | AMCP | The data used in Chapter 13, Table 2 | data.frame | 8 | 3 |
chapter_13_table_5 | AMCP | The data used in Chapter 13, Table 5 | data.frame | 12 | 4 |
chapter_14_exercise_10 | AMCP | The data used in Chapter 14, Exercise 10 | data.frame | 10 | 4 |
chapter_14_exercise_14 | AMCP | The data used in Chapter 14, Exercise 14 | data.frame | 30 | 5 |
chapter_14_exercise_15 | AMCP | The data used in Chapter 14, Exercise 15 | data.frame | 10 | 3 |
chapter_14_exercise_21 | AMCP | The data used in Chapter 14, Exercise 21 | data.frame | 14 | 5 |
chapter_14_exercise_22 | AMCP | The data used in Chapter 14, Exercise 22 | data.frame | 47 | 6 |
chapter_14_table_1 | AMCP | The data used in Chapter 14, Table 1 | data.frame | 10 | 4 |
chapter_14_table_10 | AMCP | The data used in Chapter 14, Table 10 | data.frame | 20 | 4 |
chapter_14_table_3 | AMCP | The data used in Chapter 14, Table 3 | data.frame | 10 | 3 |
chapter_14_table_4 | AMCP | The data used in Chapter 14, Table 4 | data.frame | 10 | 6 |
chapter_14_table_5 | AMCP | The data used in Chapter 14, Table 5 | data.frame | 10 | 5 |
chapter_14_table_8 | AMCP | The data used in Chapter 14, Table 8 | data.frame | 20 | 3 |
chapter_15_exercise_16 | AMCP | The data used in Chapter 15, Exercise 16 | data.frame | 14 | 4 |
chapter_15_exercise_17 | AMCP | The data used in Chapter 15, Exercise 17 | data.frame | 56 | 4 |
chapter_15_exercise_18 | AMCP | The data used in Chapter 15, Exercise 18 | data.frame | 24 | 4 |
chapter_15_exercise_18_univariate | AMCP | The data used in Chapter 15, Exercise 18 (Univariate) | data.frame | 72 | 3 |
chapter_15_exercise_19 | AMCP | The data used in Chapter 15, Exercise 19 | data.frame | 24 | 4 |
chapter_15_exercise_19_univariate | AMCP | The data used in Chapter 15, Exercise 19 (Univariate) | data.frame | 72 | 3 |
chapter_15_table_1 | AMCP | The data used in Chapter 15, Table 1 | data.frame | 12 | 4 |
chapter_16_exercise_5 | AMCP | The data used in Chapter 16, Exercise 5 | data.frame | 24 | 3 |
chapter_16_exercise_7 | AMCP | The data used in Chapter 16, Exercise 7 | data.frame | 29 | 6 |
chapter_16_exercise_9 | AMCP | The data used in Chapter 16, Exercise 9 | data.frame | 29 | 6 |
chapter_16_table_1 | AMCP | The data used in Chapter 16, Table 1 | data.frame | 24 | 3 |
chapter_16_table_4 | AMCP | The data used in Chapter 16, Table 4 | data.frame | 29 | 6 |
chapter_1_exercise_18 | AMCP | The data used in Chapter 1, Exercise 18 | data.frame | 4 | 3 |
chapter_1_exercise_19 | AMCP | The data used in Chapter 1, Exercise 19 | data.frame | 30 | 2 |
chapter_1_exercise_21 | AMCP | The data used in Chapter 1, Exercise 21 | data.frame | 12 | 2 |
chapter_1_exercise_22 | AMCP | The data used in Chapter 1, Exercise 22 | data.frame | 11 | 3 |
chapter_1_exercise_23 | AMCP | The data used in Chapter 1, Exercise 23 | data.frame | 12 | 3 |
chapter_1_table_1 | AMCP | The data used in Chapter 1, Table 1 | data.frame | 10 | 3 |
chapter_3_exercise_10 | AMCP | The data used in Chapter 3, Exercise 10 | data.frame | 36 | 3 |
chapter_3_exercise_11 | AMCP | The data used in Chapter 3, Exercise 11 | data.frame | 24 | 2 |
chapter_3_exercise_19 | AMCP | The data used in Chapter 3, Exercise 19 | data.frame | 155 | 3 |
chapter_3_exercise_20 | AMCP | The data used in Chapter 3, Exercise 20 | data.frame | 72 | 2 |
chapter_3_exercise_21 | AMCP | The data used in Chapter 3, Exercise 21 | data.frame | 192 | 2 |
chapter_3_exercise_22 | AMCP | The data used in Chapter 3, Exercise 22 | data.frame | 310 | 5 |
chapter_3_exercise_9 | AMCP | The data used in Chapter 3, Exercise 9 | data.frame | 12 | 2 |
chapter_3_table_1 | AMCP | The data used in Chapter 3, Table 1 | data.frame | 6 | 1 |
chapter_3_table_3 | AMCP | The data used in Chapter 3, Table 3 | data.frame | 30 | 2 |
chapter_3_table_7_raw | AMCP | The data used for Chapter 3, Table 7 (raw data to produce the summary measures) | data.frame | 88 | 3 |
chapter_3_table_9_raw | AMCP | The data used for Chapter 3, Table 9 (raw data to produce the summary measures) | data.frame | 88 | 3 |
chapter_4_exercise_11 | AMCP | The data used in Chapter 4, Exercise 11 | data.frame | 24 | 2 |
chapter_4_exercise_12 | AMCP | The data used in Chapter 4, Exercise 12 | data.frame | 18 | 2 |
chapter_4_exercise_13 | AMCP | The data used in Chapter 4, Exercise 13 | data.frame | 20 | 2 |
chapter_4_table_1 | AMCP | The data used in Chapter 4, Table 1 | data.frame | 20 | 2 |
chapter_4_table_7 | AMCP | The data used in Chapter 4, Table 7 | data.frame | 15 | 2 |
chapter_5_exercise_10 | AMCP | The data used in Chapter 5, Exercise 10 | data.frame | 24 | 2 |
chapter_5_exercise_16 | AMCP | The data used in Chapter 5, Exercise 16 | data.frame | 18 | 2 |
chapter_5_exercise_5 | AMCP | The data used in Chapter 5, Exercise 5 | data.frame | 20 | 2 |
chapter_5_table_4 | AMCP | The data used in Chapter 5, Table 4 | data.frame | 24 | 2 |
chapter_6_exercise_10 | AMCP | The data used in Chapter 6, Exercise 10 | data.frame | 45 | 2 |
chapter_6_exercise_14 | AMCP | The data used in Chapter 6, Exercise 14 | data.frame | 48 | 2 |
chapter_6_exercise_16 | AMCP | The data used in Chapter 6, Exercise 16 | data.frame | 91 | 5 |
chapter_6_table_1 | AMCP | The data used in Chapter 6, Table 1 | data.frame | 24 | 2 |
chapter_7_exercise_12 | AMCP | The data used in Chapter 7, Exercise 12 | data.frame | 32 | 3 |
chapter_7_exercise_13 | AMCP | The data used in Chapter 7, Exercise 13 | data.frame | 48 | 3 |
chapter_7_exercise_14 | AMCP | The data used in Chapter 7, Exercise 14 | data.frame | 28 | 3 |
chapter_7_exercise_15 | AMCP | The data used in Chapter 7, Exercise 15 | data.frame | 36 | 3 |
chapter_7_exercise_18 | AMCP | The data used in Chapter 7, Exercise 18 | data.frame | 22 | 3 |
chapter_7_exercise_19 | AMCP | The data used in Chapter 7, Exercise 19 | data.frame | 40 | 3 |
chapter_7_exercise_22 | AMCP | The data used in Chapter 7, Exercise 22 | data.frame | 28 | 4 |
chapter_7_exercise_23 | AMCP | The data used in Chapter 7, Exercise 23 | data.frame | 68 | 4 |
chapter_7_exercise_24 | AMCP | The data used in Chapter 7, Exercise 24 | data.frame | 56 | 4 |
chapter_7_exercise_25 | AMCP | The data used in Chapter 7, Exercise 25 | data.frame | 60 | 4 |
chapter_7_exercise_6 | AMCP | The data used in Chapter 7, Exercise 6 | data.frame | 45 | 3 |
chapter_7_exercise_9 | AMCP | The data used in Chapter 7, Exercise 9 | data.frame | 48 | 3 |
chapter_7_table_1 | AMCP | The data used in Chapter 7, Table 1 | data.frame | 20 | 2 |
chapter_7_table_11 | AMCP | The data used in Chapter 7, Table 11 | data.frame | 45 | 3 |
chapter_7_table_15 | AMCP | The data used in Chapter 7, Table 15 | data.frame | 22 | 3 |
chapter_7_table_23 | AMCP | The data used in Chapter 7, Table 23 | data.frame | 45 | 3 |
chapter_7_table_5 | AMCP | The data used in Chapter 7, Table 5 | data.frame | 30 | 3 |
chapter_7_table_9 | AMCP | The data used in Chapter 7, Table 9 | data.frame | 36 | 3 |
chapter_8_exercise_15 | AMCP | The data used in Chapter 8, Exercise 15 | data.frame | 48 | 4 |
chapter_8_exercise_16 | AMCP | The data used in Chapter 8, Exercise 16 | data.frame | 96 | 4 |
chapter_8_exercise_17 | AMCP | The data used in Chapter 8, Exercise 17 | data.frame | 54 | 4 |
chapter_8_exercise_18 | AMCP | The data used in Chapter 8, Exercise 18 | data.frame | 80 | 5 |
chapter_8_exercise_19 | AMCP | The data used in Chapter 8, Exercise 19 | data.frame | 80 | 5 |
chapter_8_table_12 | AMCP | The data used in Chapter 8, Table 12 | data.frame | 72 | 4 |
chapter_9_exercise_14 | AMCP | The data used in Chapter 9, Exercise 14 | data.frame | 155 | 4 |
chapter_9_exercise_15 | AMCP | The data used in Chapter 9, Exercise 15 | data.frame | 310 | 6 |
chapter_9_exercise_16 | AMCP | The data used in Chapter 9, Exercise 16 | data.frame | 310 | 6 |
chapter_9_exercise_4 | AMCP | The data used in Chapter 9, Exercise 4 | data.frame | 10 | 3 |
chapter_9_extension_exercise_1 | AMCP | The data used in Chapter 9 Extension, Exercise 1 | data.frame | 140 | 6 |
chapter_9_extension_exercise_2 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 168 | 6 |
chapter_9_extension_exercise_3 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 310 | 6 |
chapter_9_extension_figures_4_and_5 | AMCP | The data used in Chapter 9 Extension Figures 4 and 5 | data.frame | 310 | 10 |
chapter_9_extension_table_1 | AMCP | The data used in Chapter 9, Extension Table 1 | data.frame | 6 | 3 |
chapter_9_table_1 | AMCP | The data used in Chapter 9, Table 1 | data.frame | 6 | 3 |
chapter_9_table_11 | AMCP | The data used in Chapter 9, Table 11 | data.frame | 18 | 4 |
chapter_9_table_7 | AMCP | The data used in Chapter 9, Table 7 | data.frame | 30 | 3 |
tutorial_1_table_1 | AMCP | The data used in Tutorial 1, Table 1 | data.frame | 102 | 1 |
tutorial_2_table_1 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 2 |
tutorial_2_table_2 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 4 |
tutorial_3a_table_1 | AMCP | The data used in Tutorial 3A, Table 1 | data.frame | 8 | 2 |
tutorial_3a_table_2 | AMCP | The data used in Tutorial 3A, Table 2 | data.frame | 8 | 4 |
tutorial_3a_table_4 | AMCP | The data used in Tutorial 3A, Table 4 | data.frame | 10 | 6 |
tutorial_3a_table_5 | AMCP | The data used in Tutorial 3A, Table 5 | data.frame | 10 | 6 |
standgrowth | forestGYM | Data for construction of stand growth model. | data.frame | 330 | 16 |
cpSpecHpcMzXml | readBrukerFlexData | Mass spectrum generated by Bruker Daltonics CompassXport | list | | |
duke | bestridge | Duke breast cancer data | data.frame | 46 | 7130 |
patient.data | bestridge | Lymphoma patients data set | list | | |
trim32 | bestridge | The Bardet-Biedl syndrome Gene expression data | data.frame | 120 | 501 |
meta16S | aIc | 16S rRNA tag-sequencing data | data.frame | 860 | 359 |
metaTscome | aIc | meta-transcriptome data | data.frame | 3647 | 17 |
selex | aIc | Selection-based differential sequence variant abundance dataset | data.frame | 1600 | 14 |
singleCell | aIc | single cell transcriptome data | data.frame | 1508 | 2000 |
transcriptome | aIc | Saccharomyces cerevisiae transcriptome | data.frame | 5892 | 96 |
Tasmania | aPCoA | Tasmania Dataset | list | | |
DF2011 | stratEst | Data of Dal Bo and Frechette (2011) | data.frame | 7358 | 6 |
DFS2020 | stratEst | Data of Dvorak, Fischbacher and Schmelz (2020) | data.frame | 569 | 7 |
FRD2012 | stratEst | Data of Fudenberg, Rand, and Dreber (2012) | data.frame | 13126 | 9 |
WXZ2014 | stratEst | Data of the rock-paper-scissors game from Wang, Xu, and Zhou (2014) | data.frame | 21600 | 6 |
data.DF2011 | stratEst | Data of Dal Bo and Frechette (2011) | stratEst.data | 7358 | 7 |
data.DFS2020 | stratEst | Data of Dvorak, Fischbacher and Schmelz (2020) | stratEst.data | 569 | 8 |
data.FRD2012 | stratEst | Data of Fudenberg, Rand, and Dreber (2012) | stratEst.data | 13126 | 10 |
data.WXZ2014 | stratEst | Data of the rock-paper-scissors game from Wang, Xu, and Zhou (2014) | stratEst.data | 21600 | 7 |
strategies.DF2011 | stratEst | strategies.DF2011 | list | | |
strategies.DFS2020 | stratEst | strategies.DFS2020 | list | | |
strategies.FRD2012 | stratEst | strategies.FRD2012 | list | | |
strategies.PD | stratEst | strategies.PD | list | | |
strategies.RPS | stratEst | strategies.RPS | list | | |
colorPaletteNRIND | clinUtils | Color palette for a standard CDISC Normal/Reference Range Indicator. | character | | |
dataADaMCDISCP01 | clinUtils | Example of ADaM datasets from the CDISC original Pilot 01 study | list | | |
dataSDTMCDISCP01 | clinUtils | Example of SDTM datasets from the CDISC original Pilot 01 study | list | | |
shapePaletteNRIND | clinUtils | Shape palette for a standard CDISC Normal/Reference Range Indicator. | numeric | | |
sampleData | GIMMEgVAR | sampleData | list | | |
motcon | RSA | Data set on motive congruence. | data.frame | 84 | 3 |
motcon2 | RSA | Another data set on motive congruence. | data.frame | 362 | 3 |
selfother | RSA | A fake data set on self-other agreement | data.frame | 800 | 9 |
boundary | SurfaceTortoise | | SpatialPolygonsDataFrame | | |
runoff | PRSim | Sample runoff of a catchment | data.frame | 15706 | 4 |
runoff_multi_site_T | PRSim | Sample runoff and temperature data of two catchments with a similar discharge regime | list | | |
runoff_multi_sites | PRSim | Sample runoff of four catchments with a similar discharge regime | list | | |
simulations | PRSim | Simulated runoff | list | | |
simulations_multi_sites | PRSim | Simulated runoff for four catchments | list | | |
weather_multi_sites | PRSim | Sample temperature and precipitation of four catchments derived from the ERA5-Land gridded dataset | list | | |
weather_sim_multi_sites | PRSim | Simulated temperature and precipitation for two grid cells | list | | |
andes | tbea | Divergence-time estimation data for cis-trans-Andean pairs | data.frame | 54 | 3 |
laventa | tbea | Geochronology samples from the Honda Group in Colombia | data.frame | 87 | 7 |
Dogs_MimicData | CureDepCens | Dogs_MimicData data set | data.frame | 400 | 13 |
AirPassengers_ts | timeSeriesDataSets | Monthly Airline Passenger Numbers from 1949 to 1960. | ts | | |
BJsales_ts | timeSeriesDataSets | Sales Data with Leading Indicator. | ts | | |
JohnsonJohnson_ts | timeSeriesDataSets | Quarterly Earnings per Johnson & Johnson Share (1960-1981). | ts | | |
LakeHuron_ts | timeSeriesDataSets | Lake Huron Water Level (1875-1972). | ts | | |
Nile_ts | timeSeriesDataSets | Flow of the River Nile | ts | | |
USgas_ts | timeSeriesDataSets | US Monthly Natural Gas Consumption | ts | | |
WWWusage_ts | timeSeriesDataSets | Internet Usage per Minute | ts | | |
a10_ts | timeSeriesDataSets | Monthly Anti-Diabetic Drug Subsidy in Australia from 1991 to 2008. | ts | | |
airpass_ts | timeSeriesDataSets | Monthly Airline Passenger Numbers from 1949 to 1960. | ts | | |
ausbeer_ts | timeSeriesDataSets | Quarterly Australian Beer Production. | ts | | |
auscafe_ts | timeSeriesDataSets | Monthly Expenditure on Eating Out in Australia. | ts | | |
beer_ts | timeSeriesDataSets | Monthly Beer Production. | ts | | |
books_mts | timeSeriesDataSets | Sales of Paperback and Hardcover Books. | mts | 30 | 2 |
bricksq_ts | timeSeriesDataSets | Quarterly Clay Brick Production. | ts | | |
co2_ts | timeSeriesDataSets | Mauna Loa Atmospheric CO2 Concentration. | ts | | |
discoveries_ts | timeSeriesDataSets | Yearly Numbers of Important Discoveries. | ts | | |
economics_df_ts | timeSeriesDataSets | US Economic Time Series. | spec_tbl_df | 574 | 6 |
elec_ts | timeSeriesDataSets | Electricity Production. | ts | | |
elecdaily_mts | timeSeriesDataSets | Half-Hourly and Daily Electricity Demand for Victoria, Australia, in 2014. | mts | 365 | 3 |
elecdemand_msts | timeSeriesDataSets | Half-Hourly and Daily Electricity Demand for Victoria, Australia, in 2014. | msts | 17520 | 3 |
elecequip_ts | timeSeriesDataSets | Electrical Equipment Manufactured in the Euro Area. | ts | | |
euretail_ts | timeSeriesDataSets | Quarterly Retail Trade in the Euro Area. | ts | | |
goog200_ts | timeSeriesDataSets | Daily Closing Stock Prices of Google Inc. (200 Days). | ts | | |
gtemp_both_ts | timeSeriesDataSets | Global Mean Land and Open Ocean Temperature Deviations (1850-2023). | ts | | |
gtemp_land_ts | timeSeriesDataSets | Global Mean Land Temperature Deviations (1850-2023). | ts | | |
gtemp_ocean_ts | timeSeriesDataSets | Global Mean Ocean Temperature Deviations (1850-2023). | ts | | |
h02_ts | timeSeriesDataSets | Monthly Corticosteroid Drug Subsidy in Australia (1991-2008). | ts | | |
hsales2_ts | timeSeriesDataSets | Sales of New One-Family Houses (1987-1996). | ts | | |
hyndsight_ts | timeSeriesDataSets | Daily Pageviews for the Hyndsight Blog (April 2014 - April 2015). | ts | | |
ibm_mts | timeSeriesDataSets | IBM Sales and Profit Data. | mts | 42 | 4 |
ibmclose_ts | timeSeriesDataSets | Daily Closing Stock Prices of IBM. | ts | | |
jcars_ts | timeSeriesDataSets | Motor Vehicle Production (1947-1989). | ts | | |
jj_ts | timeSeriesDataSets | Johnson & Johnson Quarterly Earnings Per Share (1960-1981). | ts | | |
ldeaths_ts | timeSeriesDataSets | Monthly Deaths from Lung Diseases in the UK (1974-1980). | ts | | |
livestock_ts | timeSeriesDataSets | Livestock (Sheep) in Asia, 1961-2007. | ts | | |
marathon_ts | timeSeriesDataSets | Boston Marathon Winning Times Since 1897 | ts | | |
maxtemp_ts | timeSeriesDataSets | Maximum Annual Temperatures at Moorabbin Airport, Melbourne | ts | | |
mdeaths_ts | timeSeriesDataSets | Monthly Deaths from Lung Diseases in the UK | ts | | |
mens400_ts | timeSeriesDataSets | Winning Times in Olympic Men's 400m Track Final | ts | | |
milk_ts | timeSeriesDataSets | Monthly Milk Production per Cow | ts | | |
nail_ts | timeSeriesDataSets | Nail Prices | ts | | |
pedestrian_tbl_ts | timeSeriesDataSets | Pedestrian Counts in the City of Melbourne | tbl_ts | 66037 | 5 |
qauselec_ts | timeSeriesDataSets | Quarterly Australian Electricity Production | ts | | |
qcement_ts | timeSeriesDataSets | Quarterly Australian Portland Cement Production | ts | | |
qgas_ts | timeSeriesDataSets | Quarterly Australian Gas Production | ts | | |
sunspotarea_ts | timeSeriesDataSets | Annual Average Sunspot Area | ts | | |
taylor_30_min_df_ts | timeSeriesDataSets | Half-Hourly Electricity Demand | tbl_df | 4032 | 2 |
tourism_tbl_ts | timeSeriesDataSets | Australian Domestic Overnight Trips | tbl_ts | 24320 | 5 |
uschange_mts | timeSeriesDataSets | Growth Rates of Personal Consumption and Personal Income in the USA | mts | 187 | 5 |
usmelec_ts | timeSeriesDataSets | Monthly Total Net Electricity Generation in the USA | ts | | |
uspop_ts | timeSeriesDataSets | US Census Population Data | ts | | |
wineind_ts | timeSeriesDataSets | Australian Total Wine Sales | ts | | |
wmurders_ts | timeSeriesDataSets | Annual Female Murder Rate in the USA | ts | | |
woolyrnq_ts | timeSeriesDataSets | Quarterly Production of Woollen Yarn in Australia | ts | | |
rmadataset | DFP | A sample ExpressionSet object | ExpressionSet | | |
Altitude_Cluster | WallomicsData | Altitude Cluster | factor | | |
Ecotype | WallomicsData | Ecotype | factor | | |
Genetic_Cluster | WallomicsData | Genetic Cluster | factor | | |
Metabolomics_Rosettes | WallomicsData | Metabolomics Rosettes | data.frame | 30 | 6 |
Metabolomics_Stems | WallomicsData | Metabolomics Stems | data.frame | 30 | 6 |
Metadata | WallomicsData | Metadata | data.frame | 474 | 4 |
Phenomics_Rosettes | WallomicsData | Phenomics Rosettes | data.frame | 30 | 5 |
Phenomics_Stems | WallomicsData | Phenomics Stems | data.frame | 30 | 4 |
Proteomics_Rosettes_CW | WallomicsData | Proteomics Rosettes Cell Wall | data.frame | 30 | 364 |
Proteomics_Stems_CW | WallomicsData | Proteomics Stems Cell Wall | data.frame | 30 | 414 |
Temperature | WallomicsData | Temperature | factor | | |
Transcriptomics_Rosettes | WallomicsData | Transcriptomics Rosettes | data.frame | 30 | 19763 |
Transcriptomics_Rosettes_CW | WallomicsData | Transcriptomics Rosettes Cell Wall | data.frame | 30 | 364 |
Transcriptomics_Stems | WallomicsData | Transcriptomics Stems | data.frame | 30 | 22570 |
Transcriptomics_Stems_CW | WallomicsData | Transcriptomics Stems Cell Wall | data.frame | 30 | 414 |
exampleData | DiPALM | Example Data: Data for use with the DiPALM vignette | list | | |
testData | DiPALM | Test Data: Data for function testing | list | | |
mock | GenEst | A mock example data set | list | | |
solar_PV | GenEst | Photovoltaic Example Dataset | list | | |
solar_powerTower | GenEst | Power Tower Example Dataset | list | | |
solar_trough | GenEst | Trough-based solar thermal power simulated example | list | | |
wind_RP | GenEst | Wind Road and Pad (120m) Example | list | | |
wind_RPbat | GenEst | Wind Bat-Only Road and Pad (120m) Example | list | | |
wind_cleared | GenEst | Wind cleared plot (60m) Search Example | list | | |
alonso15 | island | Lakshadweep Archipelago coral fish community reassembly | list | | |
idaho | island | Mapped plant community time series, Dubois, ID | list | | |
lakshadweep | island | Lakshadweep Archipelago coral fish community reassembly data (expanded) | list | | |
lakshadweepPLUS | island | Lakshadweep Archipelago coral fish community reassembly data in a single data frame | list | | |
simberloff | island | Simberloff and Wilson original defaunation experiment data | list | | |
sex2 | logistf | Urinary Tract Infection in American College Students | data.frame | 239 | 7 |
sexagg | logistf | Urinary Tract Infection in American College Students | data.frame | 36 | 9 |
IVfeature | netSEM | IV features data | data.frame | 21 | 6 |
PVmodule | netSEM | Dataframe for PV module degradation under Damp Heat Exposure | spec_tbl_df | 16 | 4 |
acrylic | netSEM | A data frame of an acrylic polymer degradation experiment | data.frame | 357 | 6 |
backsheet | netSEM | Backsheet PET/PET/EVA Degradation | data.frame | 110 | 5 |
crack | netSEM | Crack Quantification for Photovoltaic Backsheets | tbl_df | 97 | 5 |
metal | netSEM | Aluminum Gradient Material for Metal's Design | data.frame | 72 | 6 |
pet | netSEM | A data frame of an degradation experiment of poly(ethylene-terephthalate) films | data.frame | 35 | 6 |
australiaGPCP | remote | Monthly GPCP precipitation data for Australia | RasterBrick | | |
pacificSST | remote | Monthly SSTs for the tropical Pacific Ocean | RasterBrick | | |
vdendool | remote | Mean seasonal (DJF) 700 mb geopotential heights | RasterBrick | | |
cities | nngeo | Point layer of the three largest cities in Israel | sf | 3 | 2 |
line | nngeo | Sample network dataset: lines | sf | 18 | 13 |
pnt | nngeo | Sample network dataset: points | sf | 6 | 7 |
towns | nngeo | Point layer of towns in Israel | sf | 193 | 5 |
water | nngeo | Polygonal layer of water bodies in Israel | sf | 4 | 2 |
DM | apmx | DM | data.frame | 22 | 12 |
EX | apmx | EX | data.frame | 42 | 19 |
LB | apmx | LB | data.frame | 2159 | 16 |
PC | apmx | PC | data.frame | 420 | 21 |
VL | apmx | VL | data.frame | 66 | 4 |
nelson_arrivals | treat.sim | Time dependent arrival profile from Nelson (2013) | data.frame | 18 | 3 |
nhanes_2010 | furniture | NHANES 2009-2010 | data.frame | 1417 | 24 |
LOPART.ROC | directlabels | ROC curve for LOPART algorithm and competitors | list | | |
LOPART100 | directlabels | Labeled Optimal Partitioning (LOPART) results | list | | |
SegCost | directlabels | Cost of segmentation models | data.frame | 560 | 5 |
iris.l1.cluster | directlabels | Clustering of the iris data with the l1 clusterpath | data.frame | 9643 | 8 |
normal.l2.cluster | directlabels | Clustering of some normal data in 2d with the l2 clusterpath | list | | |
odd_timings | directlabels | Odd timings | data.frame | 116 | 4 |
projectionSeconds | directlabels | Timings of projection algorithms | data.frame | 603 | 6 |
svmtrain | directlabels | False positive rates from several 1-SVM models | data.frame | 378 | 5 |
australia10 | comorbidity | Australian mortality data, 2010 | tbl_df | 3322 | 3 |
icd10_2009 | comorbidity | ICD-10 Diagnostic Codes, 2009 Version | tbl_df | 10817 | 4 |
icd10_2011 | comorbidity | ICD-10 Diagnostic Codes, 2011 Version | tbl_df | 10856 | 4 |
icd10cm_2017 | comorbidity | ICD-10-CM Diagnostic Codes, 2017 Version | spec_tbl_df | 71486 | 2 |
icd10cm_2018 | comorbidity | ICD-10-CM Diagnostic Codes, 2018 Version | spec_tbl_df | 71704 | 2 |
icd10cm_2022 | comorbidity | ICD-10-CM Diagnostic Codes, 2022 Version | data.frame | 72750 | 2 |
icd9_2015 | comorbidity | ICD-9 Diagnostic Codes, 2015 Version (v32) | tbl_df | 14567 | 3 |
nhds2010 | comorbidity | Adult same-day discharges, 2010 | tbl_df | 2210 | 15 |
flucyl | mem | Castilla y Leon influenza crude rates | data.frame | 33 | 8 |
flucylraw | mem | Castilla y Leon influenza standarised rates | data.frame | 267 | 3 |
cirdata | isocir | Random Circular Data. | numeric | | |
cirgenes | isocir | A set of angular measurements from cell-cycle experiments with genes. | matrix | 10 | 16 |
datareplic | isocir | Random Circular Data with Replications. | matrix | 8 | 10 |
simtheopp | npde | Simulated data for the computation of normalised prediction distribution errors in the theophylline dataset | data.frame | 13200 | 3 |
simvirload | npde | Simulated HIV viral loads in HIV patients | data.frame | 150000 | 3 |
simwarfarinCov | npde | Pharmacokinetics of warfarin | data.frame | 247000 | 3 |
theopp | npde | Pharmacokinetics of theophylline | data.frame | 132 | 5 |
virload | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 5 |
virload20 | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 5 |
virload50 | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 5 |
virloadMDV20 | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 6 |
warfarin | npde | Pharmacokinetics of warfarin | data.frame | 247 | 8 |
ami | BNSP | Amitriptyline dataset from Johnson and Wichern | data.frame | 17 | 7 |
simD | BNSP | Simulated dataset | data.frame | 300 | 3 |
simD2 | BNSP | Simulated dataset | data.frame | 300 | 4 |
sim_sce_test | smartid | scRNA-seq test data of 4 groups simulated by 'splatter'. | SingleCellExperiment | | |
Data_sample | compindPCA | Sample data for the PCA based compositive index. | data.frame | 30 | 11 |
ghibli_palettes | ghibli | Complete list of available ghibli palettes | list | | |
testAudioData | voiceR | voiceR test Audio Data | data.frame | 90 | 11 |
testAudioList | voiceR | voiceR test Audio List | list | | |
PANAS_november | cosinor2 | Self-reported mood | data.frame | 19 | 30 |
PANAS_time | cosinor2 | Measurement times of self-reported mood | numeric | | |
PA_extraverts | cosinor2 | Self-reported positive affect of extraverts | data.frame | 24 | 6 |
PA_introverts | cosinor2 | Self-reported positive affect of introverts | data.frame | 29 | 6 |
PA_time | cosinor2 | Measurement times of self-reported positive affect | numeric | | |
temperature_zg | cosinor2 | Daily air temperature in Zagreb | data.frame | 48 | 2 |
BES_panel | OrthoPanels | Responses from the 2010 British Election Study | data.frame | 5535 | 11 |
abond_panel | OrthoPanels | UK Company Data Panel | data.frame | 813 | 16 |
dass | pleLMA | Dateframe of responses to items from depression, anxiety, and stress scales | data.frame | 1000 | 42 |
vocab | pleLMA | Dataframe of response to vocabulary items from the 2018 General Social Survey | data.frame | 1309 | 10 |
icils_conf | intsvy | Config files for intsvy studies | list | | |
llece_conf | intsvy | Config files for intsvy studies | list | | |
pasec_conf | intsvy | Config files for intsvy studies | list | | |
piaac_conf | intsvy | Config files for intsvy studies | list | | |
pirls_conf | intsvy | Config files for intsvy studies | list | | |
pisa_conf | intsvy | Config files for intsvy studies | list | | |
sea_conf | intsvy | Config files for intsvy studies | list | | |
timss4_conf | intsvy | Config files for intsvy studies | list | | |
timss8_conf | intsvy | Config files for intsvy studies | list | | |
BTflow | exdqlm | Monthly time-series of water flow at Big Tree water gauge. | ts | | |
ELIanoms | exdqlm | Daily time-series of ELI anomalies. | ts | | |
nino34 | exdqlm | Monthly Niño 3.4 Index. | ts | | |
scIVTmag | exdqlm | Time series of daily average magnitude IVT in Santa Cruz, CA. | ts | | |
NSDUH_female | twangMediation | A dataset containing the substance use condition and sexual orientation of 40293 women respondents to the 2017 & 2018 National Survey of Drug Use and Health. | data.frame | 40293 | 14 |
tMdat | twangMediation | Simulated data for twangMediation | data.frame | 500 | 7 |
example_performance_results | eiExpand | Example performance analysis results | data.frame | 12 | 7 |
example_rpvDF | eiExpand | Example RPV analysis results in Washington State | tbl_df | 72 | 13 |
mt_block_data | eiExpand | Example block-level population data from Montana for split precinct analysis | sf | 3880 | 2 |
planShp | eiExpand | Example district plan shape for split precinct analysis | sf | 1 | 18 |
south_carolina | eiExpand | Example election and demographic data from South Carolina 2020 General Elections | data.frame | 750 | 42 |
vtd | eiExpand | Example vtd-level sf dataframe with election results for split precinct analysis | sf | 19 | 12 |
wa_block_data | eiExpand | Example block-level population data from Washington for BISG | sf | 821 | 8 |
wa_geocoded | eiExpand | Example geocoded voter file from Washington for BISG | data.frame | 1000 | 6 |
washington | eiExpand | Example election data with BISG demographics from Washington 2020 General Presidential Election | data.frame | 637 | 27 |
subtype_data | riskclustr | Simulated subtype data | data.frame | 2000 | 38 |
model.output | RLumModel | Example data (TL curve) simulated with parameter set from Pagonis 2007 | RLum.Analysis | | |
BNhold | EPX | AID348 hold-out data using Burden Numbers for testing the EPX package | data.frame | 3946 | 25 |
BNsample | EPX | AID348 sample (training) data with Burden Numbers for testing the EPX package | data.frame | 1000 | 25 |
harvest | EPX | Simulated dataset for testing the EPX package | data.frame | 190 | 4 |
breastcancer | PPCI | Discrimination of Cancerous and Non-Cancerous Breast Masses | list | | |
dermatology | PPCI | Eryhemato-Squamous Disease Identification | list | | |
optidigits | PPCI | Optical Recognition of Handwritten Digits | list | | |
pendigits | PPCI | Pen-based Recognition of Handwritten Digits | list | | |
phoneme | PPCI | Speech Recognition through Phoneme Identification | list | | |
yale | PPCI | Face Recognition | list | | |
area_titles | blscrapeR | Dataset containing FIPS codes for counties, states and MSAs. | data.frame | 4723 | 2 |
county_fips | blscrapeR | Return a dataframe of county FIPS codes by state. | data.frame | 3235 | 5 |
cu_main | blscrapeR | Dataset containing All items in U.S. city average, all urban consumers, seasonally adjusted CUSR0000SA0. | tbl_df | 924 | 4 |
naics | blscrapeR | Dataset containing NIACS codes for industry lookups. | data.frame | 2469 | 2 |
series_ids | blscrapeR | Dataset containing BLS series ids and descriptions. | data.frame | 97813 | 4 |
size_titles | blscrapeR | Dataset containing size codes for US industries by size. | data.frame | 10 | 2 |
state_fips | blscrapeR | Dataset with the lat. / long. of county FIPS codes used for mapping. | data.frame | 57 | 4 |
simulation | SimCorMultRes | Simulated Correlation Parameters | data.frame | 100 | 4 |
cisbpTFcat | TFutils | cisbpTFcat: data.frame with information on CISBP TFs for human, retained for reproducibility support; see cisbpTFcat_2.0 for a more recent catalog | data.frame | 7592 | 28 |
cisbpTFcat_2.0 | TFutils | cisbpTFcat_2.0: data.frame with information on CISBP TFs for human, described in PMID 31133749 | data.frame | 5861 | 28 |
demo_fimo_granges | TFutils | a list of GRanges instances with TF FIMO scores returned by 'fimo_granges' | list | | |
encode690 | TFutils | encode690: DataFrame extending AnnotationHub metadata about ENCODE cell line x TF ranges | DFrame | | |
fimo16 | TFutils | fimo16: GenomicFiles instance to AWS S3-resident FIMO bed for 16 TFs | GenomicFiles | | |
fimoMap | TFutils | fimoMap: table with Mnnnn (motif PWM tags) and HGNC symbols for TFs | data.frame | 689 | 2 |
gwascat_hg19_chr17 | TFutils | gwascat_hg19: GRanges of march 21 2018 EBI gwascat, limit to chr17 | GRanges | | |
hocomoco.mono | TFutils | hocomoco.mono: data.frame with information on HOCOMOCO TFs for human | data.frame | 771 | 9 |
hocomoco.mono.sep2018 | TFutils | hocomoco.mono.sep2018: data.frame with information on HOCOMOCO TFs for human, Sept 2018 download | data.frame | 769 | 9 |
lambert_snps | TFutils | lambert_snps is Table S3 of Lambert et al PMID 29425488 | data.frame | 143864 | 6 |
metadata_tf | TFutils | metadata_tf: list with metadata (motif_if and hgnc_symbol) about all the CISBP FIMO scan TF bed files | list | | |
named_tf | TFutils | named_tf: named list with the names being the hgnc_symbol of the motif_id | list | | |
seqinfo_hg19_chr17 | TFutils | a Seqinfo instance for a chr17 in hg19 | Seqinfo | | |
tfhash | TFutils | tfhash: data.frame with MSigDb TFs, TF targets as symbol or ENTREZ | data.frame | 164130 | 3 |
tftColl | TFutils | tftColl: GSEABase GeneSetCollection for transcription factor targets | GeneSetCollection | | |
tftCollMap | TFutils | tftCollMap: data.frame with information on MSigDb TFs for human | data.frame | 615 | 3 |
ECG | Rlibeemd | Electrocardiogram Data Example ECG data from MIT-BIH Normal Sinus Rhythm Database, ECG1 of record 16265, first 2049 observations (0 to 16 seconds with sampling interval of 0.0078125 seconds) | ts | | |
float | Rlibeemd | Float Data The data are a position record from an acoustically tracked subsurface oceanographic float, used as an example data in Rilling et al (2007). | data.frame | 549 | 2 |
lorenz.ts | tseriesChaos | Lorenz simulated time series, without noise | ts | | |
rossler.ts | tseriesChaos | Roessler simulated time series, without noise | ts | | |
web | network.tools | Bipartite network | data.frame | 32 | 3 |
game_of_thrones_network | igraphwalshdata | Game of Thrones Social Network Data | igraph | | |
marvel_bimodal_network | igraphwalshdata | Marvel Bimodal Network Data | igraph | | |
marvel_network | igraphwalshdata | Marvel Unimodal Network Data | igraph | | |
mjp_crisis_network | igraphwalshdata | Modernist Journals Project *The Crisis* (1910-1922) Social Network Data | igraph | | |
mjp_marsden_network | igraphwalshdata | *The Freewoman* (1911-1912), *The New Freewoman* (1913), and *The Egoist* (1914-1919) Social Network Data | igraph | | |
mjp_plr_network | igraphwalshdata | Modernist Journals Project *Poetry* (1912-1922) and *The Little Review* (1914-1922) Social Network Data | igraph | | |
political_books_network | igraphwalshdata | Political Books Social Network Data | igraph | | |
quaker_network | igraphwalshdata | 17th Century Quakers Social Network Data | igraph | | |
trump_network | igraphwalshdata | Trump Social Network Data | igraph | | |
example.data | hettest | Example data | list | | |
BD_Insitu | RFplus | Precipitation Station Measurement Dataset | data.table | 120 | 11 |
Cords_Insitu | RFplus | Precipitation Station Coordinates Dataset | data.table | 10 | 4 |
edhec | PerformanceAnalytics | EDHEC-Risk Hedge Fund Style Indices | xts | 293 | 13 |
managers | PerformanceAnalytics | Hypothetical Alternative Asset Manager and Benchmark Data | xts | 132 | 10 |
portfolio_bacon | PerformanceAnalytics | Bacon(2008) Data | xts | 24 | 2 |
prices | PerformanceAnalytics | Selected Price Series Example Data | zoo | 2011 | 1 |
test_returns | PerformanceAnalytics | Sample sector returns for use by unit tests | xts | 5 | 10 |
test_weights | PerformanceAnalytics | Sample sector weights for use by unit tests | xts | 5 | 10 |
weights | PerformanceAnalytics | Selected Portfolio Weights Data | xts | 8 | 11 |
ext_linear | xrnet | Simulated external data | matrix | 50 | |
x_linear | xrnet | Simulated example data for hierarchical regularized linear regression | matrix | 200 | |
y_linear | xrnet | Simulated outcome data | numeric | | |
pplace | BoSSA | A placement object as obtained with the read_sqlite function | pplace | | |
meta_meat | multibiasmeta | Meta-analysis about meat consumption | tbl_df | 100 | 4 |
centers | spDates | Coordinates of 9 sites considered as potential centers of origin of the Neolithic expansion. Modified from Pinhasi et al. (2005). | SpatialPointsDataFrame | | |
land | spDates | Land polygons. | SpatialPolygonsDataFrame | | |
neof | spDates | Radiocarbon dates and coordinates of 717 Neolithic sites in the Near East and Europe. Modified from Pinhasi et al. (2005). Only the earliest dates per site are included. | SpatialPointsDataFrame | | |
gdp_2014_admin_districts | leafdown | GPD for administrative districts of Germany for 2014. | data.frame | 402 | 2 |
gdp_2014_federal_states | leafdown | GPD for federal states of Germany for 2014. | data.frame | 16 | 2 |
us_election_counties | leafdown | Results of the 2016 US Presidential Election - County Level | tbl_df | 3143 | 17 |
us_election_states | leafdown | Results of the 2016 US Presidential Election - State Level | tbl_df | 51 | 15 |
ACTG175 | LongCART | Converted AIDS Clinical Trials Group Study 175 (source: speff2trial package) | data.frame | 6417 | 24 |
GBSG2 | LongCART | German Breast Cancer Study Group 2 (source: TH.data package) | data.frame | 686 | 10 |
acidata1 | plantecophys | An example A-Ci curve | data.frame | 10 | 5 |
manyacidat | plantecophys | An example dataset with multiple A-Ci curves | data.frame | 390 | 6 |
wine | bootcluster | Wine Data Set | data.frame | 178 | 14 |
cabrera | MetaLandSim | Modified patch occupancy data of Cabrera vole | metapopulation | | |
landscape_change | MetaLandSim | Landscape loosing 5% of patches per time step | list | | |
mc_df | MetaLandSim | Modified patch occupancy data of Cabrera vole as a data frame | data.frame | 685 | 5 |
occ.landscape | MetaLandSim | Sample landscape with one simulated occupancy snapshot | metapopulation | | |
occ.landscape2 | MetaLandSim | Sample landscape with 10 simulated occupancy snapshots | metapopulation | | |
param1 | MetaLandSim | Sample parameter data frame number 1 | data.frame | 4 | 1 |
param2 | MetaLandSim | Sample parameter data frame number 2 | data.frame | 4 | 1 |
rg_exp | MetaLandSim | List with range.expansion output | data.frame | 100 | 4 |
rland | MetaLandSim | Random landscape | landscape | | |
sim.area | MetaLandSim | Vector of the areas for each site; here, 100 sites | numeric | | |
sim.det.20 | MetaLandSim | Array corresponding to nsites x nyears x nvisits | array | | |
sim.distance | MetaLandSim | Distance matrix between sampling sites (nsite x nsite). | matrix | 100 | |
z.sim | MetaLandSim | Occupancy data generated with perfect detection. | matrix | 100 | |
z.sim.20 | MetaLandSim | Occupancy data generated with perfect detection with approximately 20% of data missing at random. | matrix | 100 | |
z.sim.20.fa | MetaLandSim | Occupancy data containing false absences | matrix | 100 | |
countries | PWIR | Index of Countries. | data.frame | 200 | 1 |
toyexample | wcep | Toy example | data.frame | 104 | 4 |
europe_100km | rN2000 | 100-km grid of Europe | sf | 5712 | 4 |
europe_10km | rN2000 | 10-km grid of Europe | sf | 5712 | 4 |
europe_countries_lowres | rN2000 | European country administrative boundaries | sf | 67 | 64 |
europe_countries_midres | rN2000 | European country administrative boundaries | sf | 80 | 64 |
test_data | PSW | Test data | data.frame | 500 | 7 |
lipcancer | eCAR | Number of recorded lip cancer cases in the 56 districts of Scotland. | list | | |
alphaltc | gpbStat | Line x Tester data (only Crosses) in Alpha Lattice design. | data.frame | 60 | 5 |
alphaltcchk | gpbStat | Line x Tester data (Crosses and Checks) in Alpha Lattice | data.frame | 54 | 6 |
alphaltcmt | gpbStat | Line x Tester data (only Crosses) in Alpha Lattice design. | spec_tbl_df | 60 | 7 |
alphaltcs | gpbStat | Line x Tester data (only Crosses) with single plant observations laid in Alpha Lattice design. | tbl_df | 240 | 6 |
datdti | gpbStat | Data of estimating drought tolerance indices without replication | tbl_df | 30 | 8 |
datrdti | gpbStat | Data of estimating drought tolerance indices with replication | tbl_df | 60 | 9 |
dm2alpha | gpbStat | Diallel Method 2 data in Alpha Lattice. | data.frame | 240 | 5 |
dm2rcbd | gpbStat | Diallel Method 2 data in RCBD | tbl_df | 240 | 4 |
rcbdltc | gpbStat | Line x Tester data in RCBD | tbl_df | 60 | 4 |
rcbdltcchk | gpbStat | Line x Tester data (Crosses and Checks) in RCBD | tbl_df | 72 | 5 |
rcbdltcmt | gpbStat | Line x Tester data (only Crosses) in Randomized Complete Block design. | spec_tbl_df | 60 | 5 |
rcbdltcs | gpbStat | Line x Tester data (only Crosses) with single plant observations laid in RCBD design. | spec_tbl_df | 240 | 5 |
Bundesliga | perryExamples | Austrian Bundesliga football player data | data.frame | 123 | 20 |
TopGearMPG | perryExamples | Top Gear fuel consumption data | data.frame | 255 | 11 |
pakistan | endorse | Pakistan Survey Experiment on Support for Militant Groups | data.frame | 5212 | 25 |
data_abundance | specieschrom | Abundance of 14 pseudo-species. A dataset containing the abundance values of 14 pseudo-species in 100 samples. The environmental conditions associated with each samples are described in the environment dataset. Pseudo-species v1 and v2, v3 and v4, v5 and v6, v7 and v8, v9 and v10, v11 and v12, v13 and v14 have the same niche. | data.frame | 100 | 14 |
environment | specieschrom | Three fictive environmental variables A dataset containing the values of three fictive envionemental variables in 100 samples. The corresponding pseudo-species abundance in each sample are available in the data_abundance dataset. | data.frame | 100 | 3 |
geninvitro | NMTox | Genetic toxicity in vitro dataset | tbl_df | 4161 | 17 |
nm400 | NMTox | NM-400 in vitro dataset | tbl_df | 253 | 17 |
SEXE | mmeln | A two mixture example | factor | | |
Y | mmeln | A two mixture example | matrix | 30 | |
wafer40 | npregderiv | The data set on the n=7755 substrate's deflection measurements before and after the thin film deposition in two radial directions. | data.frame | 7755 | 5 |
Data_potato | stlARIMA | Normalized Monthly Average Potato Price of India | ts | 79 | 1 |
exp.m | SightabilityModel | Experimental (test trials) data set used to estimate detection probabilities for moose in MN | data.frame | 124 | 4 |
g.fit | SightabilityModel | Mountain Goat Sightability Model Information | list | | |
gdat | SightabilityModel | Mountain Goat Survey Data from Olympic National park | data.frame | 77 | 6 |
obs.m | SightabilityModel | MN moose survey data | data.frame | 805 | 10 |
sampinfo.m | SightabilityModel | Data set containing sampling information for observation survey of moose in MN | data.frame | 12 | 4 |
lambs | kfino | a dataset containing the WoW weighing for 4 animals of 1296 observations, https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 1296 | 5 |
merinos1 | kfino | a dataset containing the WoW weighing for one animal (merinos lamb) of 397 observations. https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 397 | 5 |
merinos2 | kfino | a dataset containing the WoW weighing for one animal (merinos lamb) of 345 observations, difficult to model. https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 345 | 5 |
spring1 | kfino | a dataset containing the WoW weighing for one animal of 203 observations. https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 203 | 5 |
CuHallData | complexlm | AC (Complex) Hall effect data measured from a thin-film copper sample. | data.frame | 240 | 12 |
AddHealth | gscaLCA | Add Health data about substance use | data.frame | 5114 | 8 |
TALIS | gscaLCA | Teaching and Learning International Survey | data.frame | 2560 | 6 |
data_fake_county | attrib | Fake data for mortality in Norway | data.table | 6314 | 12 |
data_fake_nation | attrib | Fake data for mortality in Norway nationally | data.table | 574 | 11 |
press | jcext | Mean Sea Level pressure files | list | | |
daily_data | dsa | Exemplary time series | xts | 3430 | 6 |
dsa_examples | dsa | Exemplary dsa outputs | list | | |
holidays | dsa | Data set for frequently used regressors | xts | 46021 | 131 |
data_gaussian | saturnin | Gaussian data. | matrix | 50 | |
data_multinomial | saturnin | Multinomial data. | matrix | 100 | |
freq_study | freqtables | Simulated study data. | tbl_df | 100 | 6 |
linkdata | CropScapeR | A dataset documenting the correspondence between crop names and crop names for CDL | data.table | 256 | 2 |
jan | pa | Edited Barra data set in Jan. 2010. | data.frame | 3000 | 15 |
quarter | pa | Edited Barra data set for Q1, 2010. | data.frame | 9000 | 15 |
test | pa | A sample portfolio edited based on Barra data set in Jan. 2010. | data.frame | 3000 | 6 |
year | pa | Edited Barra data set in year 2010. | data.frame | 36000 | 15 |
pmcode_00041 | ie2miscdata | 00041 Weather | data.frame | 54 | 3 |
pmcode_00067 | ie2miscdata | 00067 Tide stage, code | data.frame | 315 | 3 |
pmcode_00115 | ie2miscdata | 00115 Sample treatment | data.frame | 4 | 3 |
pmcode_01300 | ie2miscdata | 01300 Oil-Grease (Severity) | data.frame | 5 | 3 |
pmcode_01305 | ie2miscdata | 01305 Detergent Suds (Severity) | data.frame | 5 | 3 |
pmcode_01310 | ie2miscdata | 01310 Gas Bubbles (Severity) | data.frame | 5 | 3 |
pmcode_01315 | ie2miscdata | 01315 Sludge: Floating (Severity) | data.frame | 5 | 3 |
pmcode_01320 | ie2miscdata | 01320 Garbage, Floating (Severity) | data.frame | 5 | 3 |
pmcode_01325 | ie2miscdata | 01325 Algae, Floating Mats (Severity) | data.frame | 5 | 3 |
pmcode_01330 | ie2miscdata | 01330 Odor, Atmospheric (Severity) | data.frame | 5 | 3 |
pmcode_01335 | ie2miscdata | 01335 Sewage Solids, Fresh, Floating (Severity) | data.frame | 5 | 3 |
pmcode_01340 | ie2miscdata | 01340 Fish, Dead (Severity) | data.frame | 5 | 3 |
pmcode_01345 | ie2miscdata | 01345 Debris, Floating (Severity) | data.frame | 5 | 3 |
pmcode_01350 | ie2miscdata | 01350 Turbidity (Severity) | data.frame | 5 | 3 |
pmcode_01351 | ie2miscdata | 01351 Streamflow (Severity) | data.frame | 5 | 3 |
pmcode_01355 | ie2miscdata | 01355 Ice Cover, Floating Or Solid (Severity) | data.frame | 5 | 3 |
pmcode_04117 | ie2miscdata | 04117 Tether Line Used For Collecting Sample (Yes=1) Codes | data.frame | 2 | 3 |
pmcode_31678 | ie2miscdata | 31678 Streptocci, Fecal, Tube Configuration | data.frame | 8 | 3 |
pmcode_49986 | ie2miscdata | 49986 Degree Of Decomposition, Soil, Code | data.frame | 3 | 3 |
pmcode_50276 | ie2miscdata | 50276 Filter Type, Code | data.frame | 14 | 3 |
pmcode_50280 | ie2miscdata | 50280 Water Samples, Code | data.frame | 28 | 3 |
pmcode_62955 | ie2miscdata | 62955 Sample Matrix, Code | data.frame | 3 | 3 |
pmcode_71995 | ie2miscdata | 71995 Water Use, Primary (Codes) | data.frame | 131 | 3 |
pmcode_71996 | ie2miscdata | 71996 Water Use, Secondary (Codes) | data.frame | 131 | 3 |
pmcode_71997 | ie2miscdata | 71997 Water Use, Tertiary (Codes) | data.frame | 131 | 3 |
pmcode_71998 | ie2miscdata | 71998 Water Use, Quaternary (Codes) | data.frame | 131 | 3 |
pmcode_71999 | ie2miscdata | 71999 Sample Purpose (Codes) | data.frame | 28 | 3 |
pmcode_72005 | ie2miscdata | 72005 Sample Source (Codes) | data.frame | 101 | 3 |
pmcode_72006 | ie2miscdata | 72006 Sampling Condition (Codes) | data.frame | 46 | 3 |
pmcode_74200 | ie2miscdata | 74200 Sample Preservation Method, (Codes) | data.frame | 119 | 3 |
pmcode_82305 | ie2miscdata | 82305 Atmospheric Deposition Type Bulk, (Codes) | data.frame | 5 | 3 |
pmcode_82309 | ie2miscdata | 82309 Contamination Source, Possible (Codes) | data.frame | 22 | 3 |
pmcode_82398 | ie2miscdata | 82398 Sampling Method (Codes) | data.frame | 63 | 3 |
pmcode_82923 | ie2miscdata | 82923 Atmospheric Deposition Type Wet, (Codes) | data.frame | 7 | 3 |
pmcode_84060 | ie2miscdata | 84060 Topography, Physiographic Setting (Codes) | data.frame | 18 | 3 |
pmcode_84143 | ie2miscdata | 84143 Well Purging Condition (Codes) | data.frame | 12 | 3 |
pmcode_84144 | ie2miscdata | 84144 Well Selection Criteria (Codes) | data.frame | 2 | 3 |
pmcode_84145 | ie2miscdata | 84145 Project Component (Codes) | data.frame | 7 | 3 |
pmcode_84146 | ie2miscdata | 84146 Land Use, Predominant, Within 200 Feet Of Well, (Codes) | data.frame | 15 | 3 |
pmcode_84147 | ie2miscdata | 84147 Land Use, Predominant, Within 0.25 Mile Of Well (Codes) | data.frame | 15 | 3 |
pmcode_84148 | ie2miscdata | 84148 Land Use, Predominant Fraction, Within 0.25 Mile Of Well (Codes) | data.frame | 4 | 3 |
pmcode_84149 | ie2miscdata | 84149 Land-Use Changes Within Last 10 Years, Within 0.25 Mile Of Well (Codes) | data.frame | 4 | 3 |
pmcode_84164 | ie2miscdata | 84164 Sampler Type, (Codes) | data.frame | 121 | 3 |
pmcode_84171 | ie2miscdata | 84171 Sample Splitter Type, Field Code | data.frame | 12 | 3 |
pmcode_84172 | ie2miscdata | 84172 Air Sampler Filter Type, Code | data.frame | 4 | 3 |
pmcode_84173 | ie2miscdata | 84173 Air Sample Trap Sorbent Type, Code | data.frame | 6 | 3 |
pmcode_91112 | ie2miscdata | 91112 Latitude/Longitude Horizontal Datum | data.frame | 7 | 3 |
pmcode_91113 | ie2miscdata | 91113 Latitude/Longitude Measurement Method | data.frame | 9 | 3 |
pmcode_91114 | ie2miscdata | 91114 Latitude/Longitude Coordinate Accuracy | data.frame | 6 | 3 |
pmcode_99100 | ie2miscdata | 99100 Blank, Type Of Solution, Fixed Value Code | data.frame | 13 | 3 |
pmcode_99101 | ie2miscdata | 99101 Blank, Source Of Solution, Fixed Value Code | data.frame | 25 | 3 |
pmcode_99102 | ie2miscdata | 99102 Blank, Type Of Sample, Fixed Value Code | data.frame | 13 | 3 |
pmcode_99103 | ie2miscdata | 99103 Reference Material, Source, Fixed Value Code | data.frame | 12 | 3 |
pmcode_99105 | ie2miscdata | 99105 Replicate, Type, Fixed Value Code | data.frame | 6 | 3 |
pmcode_99106 | ie2miscdata | 99106 Spike, Type, Fixed Value Code | data.frame | 5 | 3 |
pmcode_99107 | ie2miscdata | 99107 Spike, Source, Fixed Value Code | data.frame | 14 | 3 |
pmcode_99111 | ie2miscdata | 99111 Quality Assurance Data Type Associated With Sample, Code | data.frame | 9 | 3 |
pmcode_99112 | ie2miscdata | 99112 Sulfide, Water, Filtered, Field, milligrams Per Liter | data.frame | 10 | 3 |
pmcode_99329 | ie2miscdata | 99329 Coliphage, somatic, E. coli C-host, 2-stepenrichment presence/absence per 1 liter | data.frame | 2 | 3 |
pmcode_99335 | ie2miscdata | 99335 ColipgeF-spec, famp, 2-step,pres(1) abs(2)/ 1 L | data.frame | 2 | 3 |
pmcode_99595 | ie2miscdata | 99595 Total coliform, ColilrtPA, wtrprs/abs /1L | data.frame | 2 | 3 |
pmcode_99596 | ie2miscdata | 99596 E. coliColilrt PA, water prs/abs /1 L | data.frame | 2 | 3 |
pmcode_99766 | ie2miscdata | 99766 Entero- virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99767 | ie2miscdata | 99767 Reo- virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99768 | ie2miscdata | 99768 Rota virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99769 | ie2miscdata | 99769 Hepatitis-A virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99770 | ie2miscdata | 99770 Norwalk virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99771 | ie2miscdata | 99771 Calici- virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
tz_codes | ie2miscdata | Timezone (tz) codes | data.table | 54 | 7 |
weather_results | ie2miscdata | Global Engineering Weather Data | data.table | 805 | 7 |
prodelec | dynBiplotGUI | Electric production | data.frame | 55 | 7 |
content | fcr | Example dataset | data.frame | 3967 | 9 |
amd | clusrank | CARMS scores | data.frame | 283 | 7 |
crd | clusrank | Clustered Non-Stratified Data for Testing Clustered Rank Sum Test | data.frame | 340 | 3 |
crdStr | clusrank | Clustered Stratified Data for Testing the Clustered Rank Sum Test | data.frame | 956 | 4 |
crsd | clusrank | Difference Between Pre and Post Treatment Scores with Clustering Structure: Balanced | data.frame | 40 | 2 |
crsdUnb | clusrank | Difference between Pre and Post Treatment Scores with Clustering Structure: Unbalanced | data.frame | 748 | 2 |
CmonsData | PAICE | Occurrence matrix of _Cistus monspeliensis_ in the Canary Islands | data.frame | 37 | 20 |
CmonsNetwork | PAICE | Genealogical relationship of _Cistus monspeliensis_ haplotypes | data.frame | 18 | 3 |
CmonsRare | PAICE | Simulated rarefaction curves of _Cistus monspeliensis_ | rarecol | | |
colon | sdwd | simplified gene expression data from Alon et al. (1999) | list | | |
tree_amphibian_n100 | megatrees | 100 randomly selected Mega-tree of Amphibians from VertLife. | list | | |
tree_bee | megatrees | The Maximum Likely Mega-tree of Bees from Bee Tree of Life | phylo | | |
tree_bee_n100 | megatrees | 100 randomly selected Mega-tree of Bees from Bee Tree of Life | list | | |
tree_bird_n100 | megatrees | 100 randomly selected Mega-tree of Birds from Bird Tree. | list | | |
tree_butterfly | megatrees | Tree of 2244 butterfly species | phylo | | |
tree_fish_12k | megatrees | Mega-tree of 11638 Fish from The Fish Tree of Life | phylo | | |
tree_fish_32k_n50 | megatrees | 50 Mega-tree of 31516 Fish from The Fish Tree of Life | list | | |
tree_mammal_n100_phylacine | megatrees | 100 randomly selected Mega-tree of Mammals from PHYLACINE V1.2. | list | | |
tree_mammal_n100_vertlife | megatrees | 100 randomly selected Mega-tree of Mammals from VertLife. | list | | |
tree_plant_otl | megatrees | Mega-tree of Plants based on Open Tree of Life | phylo | | |
tree_reptile_n100 | megatrees | 100 randomly selected Mega-tree of Reptiles (Squamates) from VertLife. | list | | |
tree_shark_ray_n100 | megatrees | 100 randomly selected Mega-tree of Sharks, Rays, and Chimaeras from VertLife. | list | | |
ranks_antifragility | MSmix | Antifragility Data (complete rankings with covariates) | data.frame | 99 | 17 |
ranks_beers | MSmix | Beers Data (partial rankings with covariate) | data.frame | 105 | 21 |
ranks_horror | MSmix | Arkham Horror Data (complete rankings) | data.frame | 421 | 5 |
ranks_read_genres | MSmix | Reading Genres Data (partial rankings with covariates) | data.frame | 507 | 18 |
ranks_sports | MSmix | Sports Data (complete rankings with covariates) | data.frame | 647 | 39 |
fit_US_cities | distributionsrd | Fitted distributions to the US Census 2000 city size distribution. | tbl_df | 52 | 7 |
USCrimes | TeachingDemos | US Crime Statistics | array | | |
ccc | TeachingDemos | Sample data downloaded and converted from a GPS unit | data.frame | 89 | 13 |
coin.faces | TeachingDemos | Designs for coin faces for use with plot.rgl.coin | list | | |
evap | TeachingDemos | Data on soil evaporation. | data.frame | 46 | 14 |
h2h | TeachingDemos | Sample data downloaded and converted from a GPS unit | data.frame | 131 | 16 |
ldsgrowth | TeachingDemos | Growth of The Church of Jesus Christ of Latter-day Saints. | data.frame | 179 | 6 |
outliers | TeachingDemos | Outliers data | numeric | | |
steps | TeachingDemos | Steps data | data.frame | 331 | 79 |
stork | TeachingDemos | Neyman's Stork data | data.frame | 54 | 6 |
towork | TeachingDemos | Sample data downloaded and converted from a GPS unit | data.frame | 211 | 16 |
IV_4K | ddiv | Damp Heat Plus Dynamic Mechanical Load Indoor Accelerated Test I-V Curve. | data.frame | 3637 | 2 |
IV_5M_1 | ddiv | I-V Curves from External Solar Testing Laboratory. | data.frame | 478 | 2 |
IV_5M_2 | ddiv | I-V Curves from External Solar Testing Laboratory. | data.frame | 476 | 2 |
IV_daystar | ddiv | Outdoor Time Series I-V Curve Data from SDLE SunFarm. | data.frame | 48 | 2 |
IV_step1 | ddiv | A data frame of IV curve with 1 step. | data.frame | 41 | 2 |
IV_step2 | ddiv | A data frame of IV curve with 2 step. | data.frame | 41 | 2 |
IV_step3 | ddiv | A data frame of IV curve with 3 step. | data.frame | 41 | 2 |
IV_timeseries | ddiv | Outdoor Time Series I-V Curve Data from SDLE SunFarm. | data.frame | 60 | 2 |
anmu | quickNmix | Ancient Murrelet Chick Counts | matrix | 6 | |
eagles | quickNmix | Golden Eagle Counts Data | data.frame | 28 | 11 |
wav | signal | Example wav file | Sample | | |
HPK_SampleData | HEDA | HPK_SampleData | data.frame | 30000 | 3 |
Brennan.3.2 | gtheory | Brennan's (2001) Table 3.2 | data.frame | 120 | 4 |
Rajaratnam.2 | gtheory | Rajaratnam, Cronbach and Gleser's (1965) Table 2 | data.frame | 64 | 4 |
Enron | SBMSplitMerge | The Enron data set as extracted from 'igraph' using the script in data-raw | list | | |
Macaque | SBMSplitMerge | The Macaque data set as extracted from 'igraph' using the script in data-raw | edges | | |
StackOverflow | SBMSplitMerge | The Stack-Overflow data set as extracted from 'igraph' using the script in data-raw Extracted on 27/8/2019 from Kaggle (login required) using: 'library(rvest)' 'read_html("https://www.kaggle.com/stackoverflow/stack-overflow-tag-network/downloads/stack_network_links.csv/1")' | edges | | |
Example_IDEAM | ideamdb | A dataset with fictitious values of no real IDEAM's Stations. The text file keeps IDEAM's text format. | character | | |
line73 | r4lineups | line73 | data.frame | 42 | 1 |
mickwick | r4lineups | Confidence & Accuracy data (Mickes & Wixted) | tbl_df | 100 | 2 |
mockdata | r4lineups | mockdata | tbl_df | 94 | 3 |
nortje2012 | r4lineups | nortje2012 | tbl_df | 133 | 3 |
example_markblue | AgroTech | Dataset: Example markblue | tbl_df | 20 | 5 |
example_markbluecurve | AgroTech | Dataset: Example markbluecurve | tbl_df | 26 | 4 |
example_markmet | AgroTech | Dataset: Example markmet | tbl_df | 60 | 3 |
example_meteorological | AgroTech | Dataset: Example meteorological | tbl_df | 152 | 4 |
nhanes | pomcheckr | National Health and Nutrition Examination Survey 2011-2012 | tbl_df | 9756 | 16 |
ologit | pomcheckr | Simulated data for ordinal logistic regression example. | tbl_df | 400 | 4 |
bathymetry | TrackReconstruction | Bathymetry data for the Eastern Bering Sea | data.frame | 1814400 | 3 |
georef1min01 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 6681 | 6 |
georef1min02 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 9147 | 6 |
georef1min03 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 6724 | 6 |
georef1min26 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 430 | 6 |
georef1min95 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 6566 | 6 |
gpsdata01 | TrackReconstruction | GPS raw data | data.frame | 233 | 3 |
gpsdata02 | TrackReconstruction | GPS raw data | data.frame | 276 | 3 |
gpsdata03 | TrackReconstruction | GPS raw data | data.frame | 57 | 3 |
gpsdata26 | TrackReconstruction | GPS raw data | data.frame | 9 | 3 |
gpsdata95 | TrackReconstruction | GPS raw data | data.frame | 93 | 3 |
rawdata | TrackReconstruction | Raw triaxial magnetomater and accelerometer data | data.frame | 133100 | 9 |
rawdatagap | TrackReconstruction | Raw biologger data with a gap | data.frame | 13738 | 9 |
square | TrackReconstruction | Raw triaxial magnetomater and accelerometer data | data.frame | 100 | 10 |
data_TRY_15160 | rtry | Sample TRY data (Request 15160) | data.table | 1782 | 28 |
data_TRY_15161 | rtry | Sample TRY data (Request 15161) | data.table | 4627 | 28 |
data_coordinates | rtry | Sample coordinates data | data.table | 20 | 2 |
data_locations | rtry | Sample locations data | data.table | 20 | 3 |
Abstracts | deepMOU | Abstracts dataset | matrix | 379 | 606 |
CNAE2 | deepMOU | CNAE dataset on classes 4 and 9 | matrix | 240 | 357 |
cl_CNAE | deepMOU | Classification labels of the CNAE2 data set | integer | | |
aspre_emulation | OptHoldoutSize | Emulation-based OHS estimation for ASPRE | list | | |
aspre_parametric | OptHoldoutSize | Parametric-based OHS estimation for ASPRE | list | | |
ci_cover_a_yn | OptHoldoutSize | Data for example on asymptotic confidence interval for OHS. | matrix | 11 | |
ci_cover_cost_a_yn | OptHoldoutSize | Data for example on asymptotic confidence interval for min cost. | matrix | 11 | |
ci_cover_cost_e_yn | OptHoldoutSize | Data for example on empirical confidence interval for min cost. | matrix | 11 | |
ci_cover_e_yn | OptHoldoutSize | Data for example on empirical confidence interval for OHS. | matrix | 11 | |
data_example_simulation | OptHoldoutSize | Data for vignette showing general example | list | | |
data_nextpoint_em | OptHoldoutSize | Data for 'next point' demonstration vignette on algorithm comparison using emulation algorithm | list | | |
data_nextpoint_par | OptHoldoutSize | Data for 'next point' demonstration vignette on algorithm comparison using parametric algorithm | list | | |
ohs_array | OptHoldoutSize | Data for vignette on algorithm comparison | array | | |
ohs_resample | OptHoldoutSize | Data for vignette on algorithm comparison | matrix | 1000 | 4 |
params_aspre | OptHoldoutSize | Parameters of reported ASPRE dataset | list | | |
symptom | simfit | Responses to symptoms from a sample of the general population of Pakistan. | data.frame | 151 | 10 |
German_Credit | CollapseLevels | German Credit data set | data.frame | 1000 | 21 |
bread_mixture | stepjglm | Bread-making problem data | data.frame | 90 | 6 |
injection_molding | stepjglm | Data from Injection molding experiment | data.frame | 32 | 11 |
GROAN.AI | GROAN | Example data for pea AI lines | list | | |
GROAN.KI | GROAN | Example data for pea KI lines | list | | |
GROAN.pea.SNPs | GROAN | [DEPRECATED] | data.frame | 103 | 647 |
GROAN.pea.kinship | GROAN | [DEPRECATED] | data.frame | 103 | 103 |
GROAN.pea.yield | GROAN | [DEPRECATED] | numeric | | |
transmat | DRaWR | Sample transition matrix. | dgCMatrix | | |
simdata | mme | Dataset for Model 1 | data.frame | 15 | 9 |
simdata2 | mme | Dataset for Model 2 | data.frame | 20 | 9 |
simdata3 | mme | Dataset for Model 3 | data.frame | 40 | 9 |
MA0003.2 | TFBSTools | Some example PFM matrices. | PFMatrix | | |
MA0004.1 | TFBSTools | Some example PFM matrices. | PFMatrix | | |
MA0043 | TFBSTools | Some example PFM matrices. | PFMatrix | | |
MA0048 | TFBSTools | Some example PFM matrices. | PFMatrix | | |
Edgescore | EGRNi | Edge score obtained from 4 different methods for Ensemble Gene Regulatory Network Inference | data.frame | 4950 | 6 |
gene_exp | EGRNi | Gene expression data for Ensemble Gene Regulatory Network Inference | spec_tbl_df | 100 | 47 |
pvalue | EGRNi | Probability values for Ensemble Gene Regulatory Network Inference | data.frame | 4950 | 6 |
weight | EGRNi | Weights for Ensemble Gene Regulatory Network Inference | data.frame | 1 | 4 |
cccma | MBC | Sample CanESM2 and CanRCM4 data | list | | |
x_multiPop | popPCR | dPCR sample w/ >=3 populations | numeric | | |
x_onePop | popPCR | dPCR sample w/ 1 population | numeric | | |
x_twoPop | popPCR | dPCR sample w/ 2 populations | numeric | | |
ccData | ICODS | Toy Example for Case-Cohort Design with Interval-Censored Data | data.frame | 500 | 6 |
odsData | ICODS | Toy Example for ODS Design with Interval-Censored Data | data.frame | 501 | 6 |
rain | ensemblepp | Precipitation Observations and Forecasts for Innsbruck | data.frame | 2749 | 12 |
temp | ensemblepp | Minimum Temperature Observations and Forecasts for Innsbruck | data.frame | 2749 | 12 |
milazzese | TaxicabCA | Counts of archeological objects | data.frame | 31 | 19 |
rodent | TaxicabCA | Rodent species abundance | data.frame | 28 | 9 |
ktx | kidney.epi | Sample dataset with kidney transplant patients. | data.frame | 10 | 13 |
background_df | scPCA | Simulated Background Data for cPCA and scPCA | data.frame | 400 | 30 |
toy_df | scPCA | Simulated Target Data for cPCA and scPCA | data.frame | 400 | 31 |
UScrime_data | PEPBVS | US Crime Data | data.frame | 47 | 15 |
data_example1 | SLEMI | Exemplary data set I | data.frame | 1500 | 3 |
data_example2 | SLEMI | Exemplary data set II | data.frame | 1500 | 4 |
data_nfkb | SLEMI | Data from experiment with NFkB pathway | data.frame | 15632 | 6 |
arctic_2019 | puls | NOAA's Arctic Sea Daily Ice Extend Data | spec_tbl_df | 13391 | 6 |
smoothed_arctic | puls | Discrete Form of Smoothed Functional Form of Arctic Data | tbl_df | 39 | 366 |
nsw | causalSLSE | Lalonde Subsample of the National Supported Work Demonstration Data (NSW) | data.frame | 722 | 9 |
simDat1 | causalSLSE | Simulated Data | data.frame | 300 | 9 |
simDat2 | causalSLSE | Simulated Data | data.frame | 300 | 11 |
simDat3 | causalSLSE | Simulated Data | data.frame | 300 | 16 |
simDat4 | causalSLSE | Simulated Data. | data.frame | 500 | 7 |
simDat5 | causalSLSE | Simulated Data | data.frame | 300 | 6 |
Bean | ZeBook | Bean gene-based models dataset | list | | |
Sunflower_Phomopsis | ZeBook | Phomopsis stem canker observations for Sunflower | data.frame | 43 | 2 |
WheatYieldGreece | ZeBook | National Wheat Yield evolution for Greece from FAO | data.frame | 50 | 2 |
Wheat_GPC | ZeBook | Grain Protein Contents in Wheat Grains | data.frame | 43 | 13 |
carcass_data | ZeBook | Data of growth of beef cattle for Carcass model | list | | |
chicks_data | ZeBook | Data of growth of chicks | data.frame | 600 | 4 |
maize.data_EuropeEU | ZeBook | maize biomass and leaf area data | data.frame | 40 | 6 |
maize.data_MetaModelling | ZeBook | dataset of simulation for maize final biomass | data.frame | 680 | 9 |
seedweight.data | ZeBook | Wheat grain weight measurements after anthesis | data.frame | 31 | 3 |
watbal.simobsdata | ZeBook | Soil water content measurements and associated simulations with WaterBalance model | data.frame | 123 | 10 |
weather_EuropeEU | ZeBook | Weather serie for Europe EU from NASA POWER agroclimatology | data.frame | 292160 | 8 |
weather_FranceWest | ZeBook | Weather series for western France from NASA POWER agroclimatology | data.frame | 248360 | 10 |
weather_GNS | ZeBook | Weather series for Gainesville (FL, USA) years 1982 and 1983 | data.frame | 730 | 11 |
weather_SouthAsia | ZeBook | Weather series for southern Asia from NASA POWER agroclimatology | data.frame | 496808 | 9 |
UKmortality | rprev | General population survival data. | data.table | 109575 | 3 |
prevsim | rprev | Simulated patient dataset. | data.frame | 1000 | 6 |
MLB2016 | pinnacle.data | MLB2016. | tbl_df | 2462 | 11 |
USA_Election_2016 | pinnacle.data | USA_Election_2016 | tbl_df | 1443 | 5 |
emp_agr | TSsmoothing | Employment in agriculture | ts | | |
ltable | TSsmoothing | Lambda values table. | array | | |
trade | TSsmoothing | Annual Trade for USA and Mexico | matrix | 49 | 2 |
FC | ThurMod | Paired comparisons of $N=15$ items from one factor/trait (Thurstonian modeling) | data.frame | 1000 | 105 |
FC12 | ThurMod | Paired comparisons of $N=12$ items from one factor/trait (Thurstonian modeling) | data.frame | 1000 | 66 |
FC_raw | ThurMod | Raw ranking data of $N=15$ items from three factors/traits (Thurstonian modeling) | matrix | 1000 | 15 |
FC_scores | ThurMod | Scores of the data set 'FC' from Mplus. | data.frame | 1000 | 111 |
Earthquake | spherepc | Earthquake | data.frame | 77 | 22 |
nlsy27 | LAWBL | National Longitudinal Survey of Youth 1997 | list | | |
sim18ccfa40 | LAWBL | Simulated CCFA data with LI and missingness | list | | |
sim18ccfa41 | LAWBL | Simulated CCFA data with LD and missingness | list | | |
sim18cfa0 | LAWBL | Simulated CFA data with LI | list | | |
sim18cfa1 | LAWBL | Simulated CFA data with LD | list | | |
sim18mcfa41 | LAWBL | Simulated MCFA data with LD and Missingness | list | | |
sim24ccfa21 | LAWBL | Simulated CCFA data (dichotomous) with LD and a minor factor/trait | list | | |
WineData | NetDA | Network-Based Discriminant Analysis Subject to Multi-Label Classes | data.frame | 178 | 14 |
cardealers1 | adea | A data set about car dealers, 1 of 4, to be used in DEA | data.frame | 4 | 2 |
cardealers2 | adea | A data set about cardealers, 2 of 4, to be used in DEA | data.frame | 6 | 3 |
cardealers3 | adea | A data set about car dealers, 3 of 4, to be used in DEA | data.frame | 6 | 3 |
cardealers4 | adea | A data set about car dealers, 4 of 4, to be used in DEA | data.frame | 6 | 4 |
spanishuniversities2018 | adea | A data set of Spanish public universities | data.frame | 47 | 9 |
tokyo_libraries | adea | A data set of Tokyo libraries | data.frame | 23 | 6 |
especies | cncaGUI | Species data | data.frame | 28 | 12 |
variables | cncaGUI | Environmental variables data | data.frame | 28 | 6 |
sicri2018 | LearningStats | SICRI: information system on risk-taking behaviour | data.frame | 7853 | 18 |
margex | prediction | Artificial data for margins, copied from Stata | tbl_df | 3000 | 11 |
lalonde.exp | causalsens | Experimental data from the job training program first studied by LaLonde (1986) | data.frame | 445 | 12 |
lalonde.psid | causalsens | Non-experimental data from Lalonde (1986) | data.frame | 2675 | 12 |
col | RpeakChrom | Parameters data frame for columnar measurements. | data.frame | 50 | 9 |
parameters_col_metoxi | RpeakChrom | Parameters data frame for sulphadimetoxine columnar measurements. | data.frame | 12 | 9 |
parameters_dead | RpeakChrom | Parameters data frame for dead marker measurements. | data.frame | 13 | 9 |
parameters_ext | RpeakChrom | Parameters data frame for Kbr extracolumnar measurements. | data.frame | 13 | 9 |
peak | RpeakChrom | Peak read using readChrom function. | data.frame | 12000 | 2 |
ex2PL | PsyControl | Example data set based on a simulated 2PL model. | matrix | 200 | |
exGRM | PsyControl | Example data set based on a simulated GRM model. | matrix | 100 | |
gh | PsyControl | Example data set based on a simulated GRM model. | list | | |
anole | patternator | Dorsal pattern image of a female brown anole lizard | data.table | 1675 | 2 |
dFactors | nFactors | Eigenvalues from classical studies | list | | |
AP | TopicScore | Associated Press data | simple_triplet_matrix | | |
disabData | addhaz | Example of disability data | data.frame | 6294 | 7 |
Democratization | qcauchyreg | Estimation of Democratization Index | data.frame | 138 | 4 |
Poverty | qcauchyreg | Percentage of extremely poor. | data.frame | 5501 | 4 |
carabidae | sinar | Counts of arthropods in a grid-sampled wheat field | matrix | 9 | 7 |
nematodes | sinar | A matrix of counting data with 15 rows and 15 columns. | matrix | 15 | 15 |
data_Bauer_Curran_2005 | SIMPLE.REGRESSION | data_Bauer_Curran_2005 | data.frame | 7185 | 14 |
data_Bodner_2016 | SIMPLE.REGRESSION | data_Bodner_2016 | data.frame | 956 | 8 |
data_Chapman_Little_2016 | SIMPLE.REGRESSION | data_Chapman_Little_2016 | data.frame | 211 | 5 |
data_Cohen_Aiken_West_2003_7 | SIMPLE.REGRESSION | data_Cohen_Aiken_West_2003_7 | data.frame | 245 | 4 |
data_Cohen_Aiken_West_2003_9 | SIMPLE.REGRESSION | data_Cohen_Aiken_West_2003_9 | data.frame | 150 | 6 |
data_Green_Salkind_2014 | SIMPLE.REGRESSION | data_Green_Salkind_2014 | data.frame | 100 | 8 |
data_Halvorson_2022_log | SIMPLE.REGRESSION | data_Halvorson_2022_log | data.frame | 500 | 4 |
data_Halvorson_2022_pois | SIMPLE.REGRESSION | data_Halvorson_2022_pois | data.frame | 423 | 8 |
data_Huitema_2011 | SIMPLE.REGRESSION | data_Huitema_2011 | data.frame | 30 | 4 |
data_Kremelburg_2011 | SIMPLE.REGRESSION | data_Kremelburg_2011 | data.frame | 46510 | 12 |
data_Lorah_Wong_2018 | SIMPLE.REGRESSION | data_Lorah_Wong_2018 | data.frame | 293 | 4 |
data_Meyers_2013 | SIMPLE.REGRESSION | data_Meyers_2013 | data.frame | 410 | 4 |
data_OConnor_Dvorak_2001 | SIMPLE.REGRESSION | data_OConnor_Dvorak_2001 | data.frame | 131 | 3 |
data_Orme_2009_2 | SIMPLE.REGRESSION | data_Orme_2009_2 | data.frame | 131 | 6 |
data_Orme_2009_5 | SIMPLE.REGRESSION | data_Orme_2009_5 | data.frame | 285 | 10 |
data_Pedhazur_1997 | SIMPLE.REGRESSION | data_Pedhazur_1997 | data.frame | 40 | 3 |
data_Pituch_Stevens_2016 | SIMPLE.REGRESSION | data_Pituch_Stevens_2016 | data.frame | 200 | 3 |
baltimore | dyn | Baltimore energy data | zoo | 96 | 6 |
cantilever | mistral | A function calculating the deviation of a cantilever beam. | function | | |
kiureghian | mistral | A limit-state-function defined by Der Kiureghian | function | | |
oscillator_d6 | mistral | A limit-state-function defined with a non-linear oscillator in dimension 6. | function | | |
oscillator_d8 | mistral | A limit-state-function defined with a two degrees of freedom damped oscillator | function | | |
rackwitz | mistral | A limit-state-function defined by Rackwitz | function | | |
twodof | mistral | A limit-state-function defined with a two degrees of freedom damped oscillator | function | | |
waarts | mistral | A limit-state-function defined by Waarts | function | | |
academic_awards | gorica | Academic awards data | data.frame | 200 | 4 |
hox_2010 | gorica | Sesame Street data based on Hox (2010) | data.frame | 2000 | 6 |
nederhof_2014 | gorica | Data based on Nederhof, Ormel, and Oldehinkel (2014) | data.frame | 310 | 4 |
reading_ach | gorica | Reading achievement data | data.frame | 10320 | 5 |
school_admissions | gorica | High School Admissions Data | data.frame | 30 | 3 |
stevens_1999 | gorica | Sesame Street data based on Stevens (1999) | data.frame | 240 | 14 |
wechsler | gorica | Wechsler intelligence test data | data.frame | 1680 | 10 |
chldat | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay | data.frame | 452 | 4 |
daydat | WRTDStidal | Daily chlorophyll, salinity, and discharge time series for the Upper Patuxent River Estuary | data.frame | 3506 | 9 |
tidfit | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object | tidal | 452 | 15 |
tidfitmean | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object for the conditional mean model | tidalmean | 452 | 13 |
tidobj | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object | tidal | 452 | 9 |
tidobjmean | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object, conditional mean model | tidalmean | 452 | 9 |
SMBassWB1 | RFishBC | Fish-specific data for West Bearskin Lake Smallmouth Bass. | data.frame | 445 | 6 |
SMBassWB2 | RFishBC | Radial measurements for for West Bearskin Lake Smallmouth Bass. | data.frame | 181 | 13 |
StdIntLit | RFishBC | Standard intercepts for Fraser-Lee model by species. | data.frame | 12 | 3 |
Eyam | MultiBD | Eyam plague. | data.frame | 8 | 4 |
Wheat_IBCF | IBCF.MTME | Wheat Data | data.frame | 3000 | 4 |
Year_IBCF | IBCF.MTME | Year_IBCF Data | data.frame | 720 | 4 |
Brazil_epiflows | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | epiflows | | |
YF_Brazil | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | list | | |
YF_coordinates | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | data.frame | 15 | 3 |
YF_flows | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | data.frame | 100 | 3 |
YF_locations | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | data.frame | 15 | 6 |
cells_citeseq_mtx | dsb | small example CITE-seq protein dataset for 87 surface protein in 2872 cells | matrix | 87 | 2872 |
empty_drop_citeseq_mtx | dsb | small example CITE-seq protein dataset for 87 surface protein in 8005 empty droplets | matrix | 87 | 8005 |
boxly_adeg | boxly | An example ADEG dataset | tbl_df | 32139 | 35 |
boxly_adlb | boxly | An example ADLB dataset | tbl_df | 24746 | 24 |
boxly_adsl | boxly | A Subject Level Demographic Dataset | data.frame | 254 | 49 |
boxly_advs | boxly | An example ADVS dataset | tbl_df | 32139 | 34 |
VADIR_fake | sampleVADIR | Fake VADIR data | data.frame | 200000 | 10 |
rankDat | sampleVADIR | Rank to pay grade data | data.frame | 114 | 6 |
school23 | influence.ME | Math test performance in 23 schools | data.frame | 519 | 15 |
amd | eyedata | Twelve years neovascular AMD survival data | tbl_df | 118255 | 11 |
amd2 | eyedata | Real life data of patients with neovascular AMD | tbl_df | 40764 | 7 |
amd3 | eyedata | Ten year neovascular AMD survival data | tbl_df | 6696 | 23 |
amdoct | eyedata | Real life OCT segmentation data of patients with AMD | tbl_df | 2966 | 24 |
dme | eyedata | Real life data of patients with diabetic macular edema | tbl_df | 40281 | 8 |
stations | h3r | Stations | data.frame | 219 | 4 |
gs.data | odk | 'Google Sheets' Data for 'odk.frame' | Workbook | | |
odk.frame | odk | 'Google Sheets' or 'XLSForm' Dummy 'ODK' Frame | Workbook | | |
pd1.count.matrix | robustrao | pubdata1 | matrix | 249 | 5 |
pd1.similarity | robustrao | pubdata1 | matrix | 249 | 249 |
pd1.uncat.refs | robustrao | pubdata1 | numeric | | |
pd2.count.matrix | robustrao | pubdata2 | matrix | 249 | 2 |
pd2.similarity | robustrao | pubdata2 | matrix | 249 | 249 |
pd2.uncat.refs | robustrao | pubdata2 | numeric | | |
Crossdata | ComparisonSurv | The Data with Survival Curves Crossed | data.frame | 200 | 3 |
PHdata | ComparisonSurv | The Data Satisfied Proportional Hazard Assumption | data.frame | 200 | 3 |
synthetic.sub35 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2858 | 10 |
synthetic.sub40 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2500 | 10 |
synthetic.sub42 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2381 | 10 |
synthetic.sub50 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2000 | 10 |
data2 | RPEXE.RPEXT | RPEXE_fitting | data.frame | 118 | 3 |
df | RPEXE.RPEXT | JAMA Breast cancer | data.frame | 508 | 10 |
loopcut_onestep_data | RPEXE.RPEXT | Example data for loopcut_onestep | matrix | 178 | 2 |
loopcuts_cut | RPEXE.RPEXT | Example data for loopcuts_cuttimes | numeric | | |
loopcuts_t_c | RPEXE.RPEXT | Example data for loopcut_times_censoring | matrix | 178 | 2 |
loopcuts_umbrella_cuttimes_mono | RPEXE.RPEXT | Example data for loopcut_umbrella | matrix | 11 | 2 |
pava_dfrd | RPEXE.RPEXT | Example data for pava | matrix | 139 | 3 |
pexeest_times_censoring | RPEXE.RPEXT | Example data for pexeest_times_censoring | matrix | 178 | 2 |
simple | RPEXE.RPEXT | None Small Cell Lung cancer data | data.frame | 178 | 2 |
t100 | RPEXE.RPEXT | Example data for pexeest_tx | numeric | | |
CL_ACTIVITY_ANZSIC06 | statcodelists | Codelist Activity - ISIC, Revision 4 | data.frame | 825 | 5 |
CL_ACTIVITY_ISIC4 | statcodelists | Codelist Activity - ISIC, Revision 4 | data.frame | 766 | 5 |
CL_ACTIVITY_NACE2 | statcodelists | Codelist Activity - NACE, Revision 2 | data.frame | 996 | 5 |
CL_AGE | statcodelists | Codelist Age | data.frame | 5 | 5 |
CL_AREA | statcodelists | Reference Area Code List | data.frame | 899 | 5 |
CL_CIVIL_STATUS | statcodelists | Codelist Civil (or Marital) Status | data.frame | 8 | 5 |
CL_COFOG_1999 | statcodelists | Classification of the Functions of Government | data.frame | 188 | 5 |
CL_CONF_STATUS | statcodelists | Codelist Confidentiality Status | data.frame | 11 | 5 |
CL_COPNI_1999 | statcodelists | Classification of the Purposes of Non-Profit Institutions Serving Households | data.frame | 65 | 5 |
CL_COPP_1999 | statcodelists | Classification of the Outlays of Producers According to Purpose | data.frame | 51 | 5 |
CL_DECIMALS | statcodelists | Codelist Decimals | data.frame | 16 | 5 |
CL_DEG_URB | statcodelists | Codelist Degree of Urbanization | data.frame | 14 | 5 |
CL_FREQ | statcodelists | Codelist Frequency | data.frame | 34 | 5 |
CL_OBS_STATUS | statcodelists | Codelist Observation status | data.frame | 20 | 5 |
CL_OCCUPATION | statcodelists | Codelist Occupation | data.frame | 619 | 5 |
CL_SEASONAL_ADJUST | statcodelists | Codelist Seasonal Adjustment | data.frame | 11 | 5 |
CL_SEX | statcodelists | Codelist Sex | data.frame | 7 | 5 |
CL_TIMETRANS | statcodelists | Codelist Time Transformation | data.frame | 47 | 5 |
CL_TIMETRANS_PER | statcodelists | Codelist Time Transformation Period | data.frame | 12 | 5 |
CL_TIMETRANS_TYPE | statcodelists | Codelist Time Transformation Type | data.frame | 13 | 5 |
CL_TIME_FORMAT | statcodelists | Codelist Time Format | data.frame | 21 | 5 |
CL_TIME_PER_COLLECT | statcodelists | Codelist Time Period - Collection | data.frame | 8 | 5 |
CL_UNIT_MULT | statcodelists | Codelist Unit multiplier | data.frame | 31 | 5 |
codebooks | statcodelists | Available codelists by concept | data.frame | 21 | 3 |
world | MoLE | Model parameters | list | | |
disorders | configural | Meta-analytic correlations among Big Five personality traits and psychological disorders | list | | |
gre | configural | Meta-analytic correlations of Graduate Record Examination subtests with graduate grade point average | list | | |
hrm | configural | Meta-analytic correlations of HRM practices with organizational financial performance | list | | |
jobchar | configural | Meta-analytic correlations of job characteristics with performance and satisfaction | list | | |
mindfulness | configural | Meta-analytic correlations among Big Five personality traits and trait mindfulness | list | | |
prejudice | configural | Correlations between study design moderators and effect sizes for prejudice reduction following intergroup contact | list | | |
team | configural | Meta-analytic correlations among team processes and team effectiveness | list | | |
newdata | fpa | Fixation probability data generated by ft2fp() function | data.frame | 3264 | 8 |
pattern | fpa | Summary of fixation pattern generated by get_pattern() function | cast_df | 32 | 53 |
rawdata | fpa | Fixation time data of an eye movement experiment | data.frame | 1603 | 7 |
CCU12_Precip | UStatBookABSC | Precipitation for June-September 2012 recorded in Kolkata | data.frame | 51 | 4 |
sucra | OrigamiPlot | SUCRA | data.frame | 8 | 5 |
Ex1 | APFr | Example dataset 1 | numeric | | |
Ex2 | APFr | Example 2 | list | | |
cement | ImpShrinkage | Hald's Cement Data | data.frame | 13 | 5 |
BaetenEtAl2013 | bayesmeta | Ankylosing spondylitis example data | data.frame | 8 | 4 |
BucherEtAl1997 | bayesmeta | Direct and indirect comparison example data | data.frame | 22 | 7 |
Cochran1954 | bayesmeta | Fly counts example data | data.frame | 7 | 3 |
CrinsEtAl2014 | bayesmeta | Pediatric liver transplant example data | data.frame | 6 | 19 |
GoralczykEtAl2011 | bayesmeta | Liver transplant example data | data.frame | 19 | 17 |
HinksEtAl2010 | bayesmeta | JIA example data | data.frame | 3 | 8 |
KarnerEtAl2014 | bayesmeta | COPD example data | data.frame | 22 | 27 |
NicholasEtAl2019 | bayesmeta | Multiple sclerosis disability progression example data | data.frame | 28 | 4 |
Peto1980 | bayesmeta | Aspirin after myocardial infarction example data | data.frame | 6 | 11 |
RobergeEtAl2017 | bayesmeta | Aspirin during pregnancy example data | data.frame | 45 | 14 |
Rubin1981 | bayesmeta | 8-schools example data | data.frame | 8 | 4 |
SchmidliEtAl2017 | bayesmeta | Historical variance example data | data.frame | 6 | 4 |
SidikJonkman2007 | bayesmeta | Postoperative complication odds example data | data.frame | 29 | 7 |
SnedecorCochran | bayesmeta | Artificial insemination of cows example data | data.frame | 6 | 4 |
advert | fma | Sales and advertising expenditure | mts | 24 | 2 |
advsales | fma | Sales volume and advertising expenditure | mts | 36 | 2 |
airpass | fma | Monthly Airline Passenger Numbers 1949-1960 | ts | | |
auto | fma | Attributes of some US and Japanese automobiles | data.frame | 45 | 4 |
bank | fma | Mutual savings bank deposits | data.frame | 60 | 3 |
beer | fma | Monthly beer production | ts | | |
bicoal | fma | Annual bituminous coal production | ts | | |
books | fma | Sales of paperback and hardcover books | mts | 30 | 2 |
boston | fma | Monthly dollar volume of sales | mts | 35 | 2 |
bricksq | fma | Quarterly clay brick production | ts | | |
canadian | fma | Canadian unemployment rate | ts | | |
capital | fma | Quarterly capital expenditure and appropriations | mts | 88 | 2 |
cement | fma | Cement composition and heat data | data.frame | 10 | 4 |
chicken | fma | Price of chicken | ts | | |
condmilk | fma | Condensed milk | ts | | |
copper | fma | Copper price | ts | | |
copper1 | fma | Copper prices | ts | | |
copper2 | fma | Copper prices | ts | | |
copper3 | fma | Copper prices | ts | | |
cowtemp | fma | Temperature of a cow | ts | | |
cpimel | fma | Consumer price index | ts | | |
dexter | fma | Dexterity test and production ratings | data.frame | 20 | 2 |
dj | fma | Dow-Jones index | ts | | |
dole | fma | Unemployment benefits in Australia | ts | | |
dowjones | fma | Dow-Jones index | ts | | |
econsumption | fma | Electricity consumption and temperature | data.frame | 12 | 2 |
eggs | fma | Price of eggs | ts | | |
eknives | fma | Sales of electric knives | ts | | |
elco | fma | Sales of Elco's laser printers | ts | | |
elec | fma | Electricity production | ts | | |
expenditure | fma | Expenditure | numeric | | |
fancy | fma | Sales for a souvenir shop | ts | | |
french | fma | Industry index | ts | | |
housing | fma | Housing data | mts | 82 | 3 |
hsales | fma | Sales of one-family houses | ts | | |
hsales2 | fma | Sales of new one-family houses | ts | | |
huron | fma | Level of Lake Huron | ts | | |
ibm | fma | IBM sales and profit | mts | 42 | 4 |
ibmclose | fma | Closing IBM stock price | ts | | |
input | fma | Input series | ts | | |
internet | fma | Number of internet users | ts | | |
invent15 | fma | Inventory demand | ts | | |
jcars | fma | Motor vehicle production | ts | | |
kkong | fma | King Kong data | data.frame | 21 | 2 |
labour | fma | Civilian labour force | ts | | |
lynx | fma | Annual Canadian Lynx trappings 1821-1934 | ts | | |
milk | fma | Monthly milk production per cow | ts | | |
mink | fma | Number of minks trapped | ts | | |
mortal | fma | Mortality | data.frame | 156 | 2 |
motel | fma | Total accommodation at hotel, motel and guest house | mts | 186 | 2 |
motion | fma | Employment figures in the motion picture industry | ts | | |
nail | fma | Nail prices | ts | | |
oilprice | fma | Oil prices | ts | | |
olympic | fma | Men's 400 m final winning times in each Olympic Games | data.frame | 23 | 2 |
ozone | fma | Ozone depletion and melanoma rates | data.frame | 11 | 2 |
paris | fma | Average temperature | ts | | |
pcv | fma | GDP | data.frame | 19 | 2 |
petrol | fma | Sales of petroleum and related product | mts | 252 | 4 |
pigs | fma | Number of pigs slaughtered | ts | | |
plastics | fma | Sales of plastic product | ts | | |
pollution | fma | Shipment of pollution equipment | ts | | |
productC | fma | Sales of product C | ts | | |
pulpprice | fma | Pulp price and shipments | data.frame | 25 | 2 |
qelec | fma | Electricity production | ts | | |
qsales | fma | Sales data | ts | | |
running | fma | Running times and maximal aerobic capacity | data.frame | 14 | 2 |
sales | fma | Sales data | ts | | |
schizo | fma | Perceptual speed scores | ts | | |
shampoo | fma | Sales of shampoo | ts | | |
sheep | fma | Sheep population | ts | | |
ship | fma | Electric can opener shipments | ts | | |
shipex | fma | Shipments | ts | | |
strikes | fma | Number of strikes | ts | | |
telephone | fma | Telephone cost | ts | | |
texasgas | fma | Price and consumption of natural gas | data.frame | 20 | 2 |
ukdeaths | fma | Total deaths and serious injuries | ts | | |
usdeaths | fma | Accidental deaths in USA | ts | | |
uselec | fma | Total generation of electricity | ts | | |
ustreas | fma | Treasury bill contracts | ts | | |
wagesuk | fma | Real daily wages | ts | | |
wheat | fma | Wheat prices | ts | | |
wn | fma | White noise series | ts | | |
wnoise | fma | White noise time series | ts | | |
writing | fma | Sales of printing and writing paper | ts | | |
ppendemic_tab13 | ppendemic | ppendemic_tab: Endemic Plant Database of Peru | tbl_df | 7815 | 14 |
Pistoia | LPM | Dataset of Pistoia (Italy) | data.frame | 744 | 2 |
hourly.rainfall.series | LPM | hourly rainfall series | data.frame | 41094 | 1 |
milano | LPM | Maximum annual rainfall series for different durations | data.frame | 30 | 11 |
series.rainfall | LPM | Daily Rainfall Series | matrix | 5475 | 5 |
series.runoff | LPM | Daily Runoff Series | numeric | | |
BMI | wec | Data on BMI of Dutch citizens | data.frame | 3314 | 7 |
PUMS | wec | Public Use Microdata Sample files (PUMS) 2013 | data.table | 10000 | 4 |
WachusettReservoir | qqtest | Storage, in millions of gallons daily per square mile of net land area, at the Wachusett Reservoir in Massacusetts - storage computed for each of several rates of draft (draft being a determined maintainable flow in 1,000s of gallons per square mile daily). | data.frame | 15 | 6 |
bacteria | qqtest | Bacteria from Delaware River water entering the Torresdale Filter of the Philadelphia water supply 1913. | data.frame | 22 | 2 |
penicillin | qqtest | 31 contrast sums from a 32 run 2^(5-0) factorial experiment on penicillin production. | data.frame | 31 | 1 |
primer | qqtest | Automobile primer paint thickness quality control measurements. | data.frame | 20 | 14 |
pullstrength | qqtest | Strength of pull for 519 males aged 23-26. | data.frame | 7 | 5 |
sittingHeights | qqtest | Sitting height in inches of female adults (aged 23-50). | data.frame | 9 | 8 |
stacklossDistances | qqtest | Mahalanobis squared distances of Brownlee's stack loss plant operation data based only on the explanatory variates (air flow, water temperature, and acid concentration). | data.frame | 21 | 2 |
mayo | APtools | Mayo Marker data | data.frame | 312 | 4 |
LR_dataset | TDCor | Lateral root transcriptomic dataset | matrix | 15240 | 18 |
TF | TDCor | Table of 1834 Arabidopsis Transcription factors | data.frame | 1834 | 2 |
l_genes | TDCor | l_genes | character | | |
l_names | TDCor | l_names | character | | |
l_prior | TDCor | l_prior | integer | | |
times | TDCor | The 'times' vector to use with the lateral root dataset | numeric | | |
Farms | sfadv | Data set of farm accountancy data | data.frame | 2500 | 14 |
petersen | multiwayvcov | Simulation of clustering with firm and time effects. | data.frame | 5000 | 4 |
rice_normal | dhga | The gene expression data of rice under control or normal condition | data.frame | 200 | 20 |
rice_salt | dhga | The gene expression data of rice under salinity stress condition | data.frame | 200 | 20 |
lung | pvclust | DNA Microarray Data of Lung Tumors | data.frame | 916 | 73 |
CHROM | ADPF | Data frame of Chromatogram values | data.frame | 201 | 6 |
BWHCitationReport | hindexcalculator | WoS exported citation report for search AD=(brigham same anes*) OR AD=(brigham same anaes*) | data.frame | 3075 | 92 |
data | NegativeControlOutcomeAdjustment | Data for examples | data.frame | 1000 | 5 |
data_cls | etree | Classification toy dataset | list | | |
data_reg | etree | Regression toy dataset | list | | |
db1rl | LearnSL | Test Database 1 | data.frame | 20 | 5 |
db2 | LearnSL | Test Database 6 | data.frame | 10 | 4 |
db3 | LearnSL | Test Database 7 | data.frame | 12 | 4 |
db_flowers | LearnSL | Test Database 5 | data.frame | 20 | 5 |
db_per_and | LearnSL | Test Database 2 | data.frame | 8 | 4 |
db_per_or | LearnSL | Test Database 3 | data.frame | 8 | 4 |
db_per_xor | LearnSL | Test Database 4 | data.frame | 8 | 4 |
db_tree_struct | LearnSL | Test Database 8 | tree_struct | | |
BR_LatSq | scidesignR | BR_LatSq | spec_tbl_df | 16 | 4 |
CSectdat | scidesignR | CSectdat | tbl_df | 7779 | 2 |
agedata | scidesignR | agedata | tbl_df | 50 | 1 |
chemplant | scidesignR | chemplant | data.frame | 16 | 5 |
cookies | scidesignR | cookies | data.frame | 8 | 5 |
covid19_trial | scidesignR | covid19_trial | tbl_df | 20 | 4 |
fertdat | scidesignR | fertdat | tbl_df | 12 | 2 |
hsvdat | scidesignR | hsvdat | data.frame | 32 | 8 |
leafspring | scidesignR | leafspring | data.frame | 16 | 8 |
lifesat_childmort | scidesignR | lifesat_childmort | spec_tbl_df | 22595 | 7 |
nhefs9282 | scidesignR | nhefs9282 | spec_tbl_df | 9281 | 34 |
painstudy | scidesignR | painstudy | data.frame | 150 | 2 |
painstudy2 | scidesignR | painstudy2 | data.frame | 120 | 2 |
rtdat | scidesignR | rtdat | tbl_df | 100 | 2 |
shoedat_obs | scidesignR | shoedat_obs | data.frame | 10 | 5 |
silkdat | scidesignR | silkdat | tbl_df | 48 | 11 |
wtlossdat | scidesignR | wtlossdat | spec_tbl_df | 8 | 5 |
abdom | lmls | Abdominal circumference data | data.frame | 610 | 2 |
friends | friends | The transcript of Friends | tbl_df | 67359 | 6 |
friends_emotions | friends | Emotions for transcript of Friends | tbl_df | 12606 | 5 |
friends_entities | friends | Character Entities for transcript of Friends | tbl_df | 10557 | 5 |
friends_info | friends | Episode Information | tbl_df | 236 | 8 |
catage.long | TAF | Catch at Age in Long Format | data.frame | 32 | 3 |
catage.taf | TAF | Catch at Age in TAF Format | data.frame | 8 | 5 |
catage.xtab | TAF | Catch at Age in Crosstab Format | data.frame | 8 | 4 |
summary.taf | TAF | Summary Results in TAF Format | data.frame | 3 | 16 |
taf.blue | TAF | TAF Colors | character | | |
taf.dark | TAF | TAF Colors | character | | |
taf.green | TAF | TAF Colors | character | | |
taf.light | TAF | TAF Colors | character | | |
taf.orange | TAF | TAF Colors | character | | |
E2grades | ProfessR | Examination grades from Test 2 in 2007 | numeric | | |
QBANK1 | ProfessR | Example Question Bank | list | | |
QBANK2 | ProfessR | Example Question Bank | list | | |
jackolantern_surreal_data | surreal | Jack-o'-Lantern Surreal Data | data.frame | 5395 | 7 |
r_logo_image_data | surreal | R Logo Pixel Data | data.frame | 2000 | 2 |
diabetes | glmxdiag | Diabetes | data.frame | 43 | 3 |
moons | glmxdiag | Moons of the 13 planets of the Solar System | data.frame | 13 | 5 |
stopping | glmxdiag | Stopping | data.frame | 63 | 2 |
bottle.df | Hotelling | Bottle data | data.frame | 120 | 6 |
container.df | Hotelling | Container data | data.frame | 20 | 10 |
manova1.df | Hotelling | manova1 data | data.frame | 18 | 4 |
CCTable11.1a | AlgDesign | Cochran and Cox design | data.frame | 27 | 3 |
GVTable1 | AlgDesign | Goos Vandebroek Table 1 | matrix | 42 | 5 |
GVTable3 | AlgDesign | Goos Vandebroek Table 3 | matrix | 27 | 3 |
TGTable3 | AlgDesign | Trinca Gilmour Table 3 | data.frame | 45 | 5 |
TGTable5 | AlgDesign | Trinca Gilmour Table 5 | matrix | 42 | 5 |
Doubs.env | codep | The Doubs Fish Data | matrix | 30 | 9 |
Doubs.fish | codep | The Doubs Fish Data | matrix | 30 | 27 |
Doubs.geo | codep | The Doubs Fish Data | matrix | 30 | 4 |
LGDat | codep | Legendre and Gallagher Synthetic Example | data.frame | 19 | 10 |
mite.env | codep | The Oribatid Mite Data Set | matrix | 70 | 14 |
mite.geo | codep | The Oribatid Mite Data Set | matrix | 70 | 2 |
mite.species | codep | The Oribatid Mite Data Set | matrix | 70 | 35 |
salmon | codep | The St. Marguerite River Altantic Salmon Parr Transect | data.frame | 76 | 5 |
caribou | spmodel | A caribou forage experiment | tbl_df | 30 | 5 |
lake | spmodel | National Lakes Assessment Data | sf | 102 | 9 |
lake_preds | spmodel | Lakes Prediction Data | sf | 10 | 8 |
moose | spmodel | Moose counts and presence in Alaska, USA | sf | 218 | 5 |
moose_preds | spmodel | Locations at which to predict moose counts and presence in Alaska, USA | sf | 100 | 3 |
moss | spmodel | Heavy metals in mosses near a mining road in Alaska, USA | sf | 365 | 8 |
seal | spmodel | Estimated harbor-seal trends from abundance data in southeast Alaska, USA | sf | 149 | 3 |
sulfate | spmodel | Sulfate atmospheric deposition in the conterminous USA | sf | 197 | 2 |
sulfate_preds | spmodel | Locations at which to predict sulfate atmospheric deposition in the conterminous USA | sf | 100 | 1 |
texas | spmodel | Texas Turnout Data | sf | 254 | 4 |
left_hippocampus_mask | ravetools | Left 'Hippocampus' of 'N27-Collin' brain | array | | |
cran_to_spdx | cffr | Mapping between 'License' fields and SPDX | data.frame | 94 | 2 |
cc_empty_fb_ad_actions | cornucopia | | tbl_df | | 4 |
cc_empty_fb_ad_campaign | cornucopia | | tbl_df | | 31 |
cc_empty_fb_ad_creatives_id | cornucopia | | tbl_df | | 3 |
cc_empty_fb_page_insights | cornucopia | | tbl_df | | 6 |
cc_empty_fb_page_post_df | cornucopia | | tbl_df | | 21 |
cc_empty_fb_page_video_df | cornucopia | | tbl_df | | 17 |
cc_empty_instagram_ig_bd_users_df | cornucopia | | tbl_df | | 2 |
cc_empty_instagram_media_df | cornucopia | | tbl_df | | 17 |
cc_empty_instagram_media_id_df | cornucopia | | tbl_df | | 1 |
cc_empty_instagram_media_insights | cornucopia | | list | | |
cc_valid_fields_ad_campaign_group_v | cornucopia | | character | | |
cc_valid_fields_ad_insights | cornucopia | A list with all valid fields for the Ad Insights Marketing API | list | | |
cc_valid_fields_ad_insights_v | cornucopia | | character | | |
cc_valid_fields_fb_post_insights | cornucopia | | character | | |
cc_valid_fields_fb_product | cornucopia | | character | | |
cc_valid_fields_fb_video_insights | cornucopia | | character | | |
cc_valid_fields_instagram_media_v | cornucopia | | character | | |
cc_valid_metrics_ig_media_insights | cornucopia | | list | | |
benchmark64.data | bit64 | Results of performance measurement on a Core i7 Lenovo T410 8 GB RAM under Windows 7 64bit | matrix | 16 | 6 |
optimizer64.data | bit64 | Results of performance measurement on a Core i7 Lenovo T410 8 GB RAM under Windows 7 64bit | matrix | 8 | 2 |
sppEquivalencies_CA | LandR | Table of species name equivalencies for Canadian trees | data.table | 203 | 23 |
sunspots_births | rotasym | Recorded sunspots births during 1872-2018 | data.frame | 51303 | 6 |
agriculture | cluster | European Union Agricultural Workforces | data.frame | 12 | 2 |
animals | cluster | Attributes of Animals | data.frame | 20 | 6 |
chorSub | cluster | Subset of C-horizon of Kola Data | matrix | 61 | 10 |
flower | cluster | Flower Characteristics | data.frame | 18 | 8 |
plantTraits | cluster | Plant Species Traits Data | data.frame | 136 | 31 |
pluton | cluster | Isotopic Composition Plutonium Batches | data.frame | 45 | 4 |
ruspini | cluster | Ruspini Data | data.frame | 75 | 2 |
votes.repub | cluster | Votes for Republican Candidate in Presidential Elections | data.frame | 50 | 31 |
xclara | cluster | Bivariate Data Set with 3 Clusters | data.frame | 3000 | 2 |
apollo_drugChoiceData | apollo | Simulated dataset of medication choice. | data.frame | 10000 | 33 |
apollo_modeChoiceData | apollo | Simulated dataset of mode choice. | data.frame | 8000 | 26 |
apollo_swissRouteChoiceData | apollo | Dataset of route choice. | data.frame | 3492 | 16 |
apollo_timeUseData | apollo | Dataset of time use. | data.frame | 2826 | 20 |
CHARM | BuyseTest | RCT In Chronic Heart Failure Assessing an Inhibitor of the Renin-Angiotensin System. | data.frame | 3023 | 8 |
EB | BuyseTest | Rare disease trial | data.frame | 30 | 7 |
prodige | BuyseTest | RCT In Metastatic Pancreatic Cancer Comparing Two Chemoterapy. | data.table | 823 | 8 |
occurrences | ppgm | Sceloporus occurrence data | data.frame | 18658 | 22 |
paleoclimate | ppgm | Paleoclimate Data for ppgm examples | list | | |
sampletrees | ppgm | Sample of Sceloporus phylogenies | multiPhylo | | |
scel_fossils | ppgm | Sceloporus fossil data | matrix | 45 | 4 |
indData | nbTransmission | Individual-level simulated outbreak dataset | data.frame | 100 | 8 |
nbResults | nbTransmission | Dataset with results of 'nbProbabilities' | data.frame | 4949 | 24 |
pairData | nbTransmission | Pair-level simulated outbreak dataset | data.frame | 9900 | 17 |
model_db | parsnip | parsnip model specification database | tbl_df | 105 | 7 |
Facet_group | LorMe | Tax summary object with Facet 2x2 Groups | list | | |
Three_group | LorMe | Tax summary object with three groups | list | | |
Two_group | LorMe | Tax summary object with two groups | list | | |
testotu | LorMe | test otudata | data.frame | 1000 | 22 |
metals | qgcomp | Well water data | data.frame | 452 | 26 |
hpc_cv | yardstick | Multiclass Probability Predictions | data.frame | 3467 | 7 |
lung_surv | yardstick | Survival Analysis Results | tbl_df | 228 | 3 |
pathology | yardstick | Liver Pathology Data | data.frame | 344 | 2 |
solubility_test | yardstick | Solubility Predictions from MARS Model | data.frame | 316 | 2 |
two_class_example | yardstick | Two Class Predictions | data.frame | 500 | 4 |
hub_con_output | hubUtils | Example Hub model output data | tbl_df | 92 | 8 |
std_colnames | hubUtils | Hubverse model output standard column names | character | | |
available_price_indexes | realtalk | Price indexes available in the 'realtalk' package | tbl_df | 20 | 6 |
c_cpi_u_annual | realtalk | Chained Consumer Price Index for Urban Consumers (C-CPI-U) data | tbl_df | 25 | 2 |
c_cpi_u_extended_annual | realtalk | Extended Chained Consumer Price Index for Urban Consumers (C-CPI-U) | tbl_df | 88 | 2 |
c_cpi_u_extended_monthly_nsa | realtalk | Extended Chained Consumer Price Index for Urban Consumers (C-CPI-U) | tbl_df | 1058 | 3 |
c_cpi_u_extended_monthly_sa | realtalk | Extended Chained Consumer Price Index for Urban Consumers (C-CPI-U) | tbl_df | 938 | 3 |
c_cpi_u_extended_quarterly_nsa | realtalk | Extended Chained Consumer Price Index for Urban Consumers (C-CPI-U) | tbl_df | 352 | 3 |
c_cpi_u_extended_quarterly_sa | realtalk | Extended Chained Consumer Price Index for Urban Consumers (C-CPI-U) | tbl_df | 312 | 3 |
c_cpi_u_monthly_nsa | realtalk | Chained Consumer Price Index for Urban Consumers (C-CPI-U) data | tbl_df | 303 | 3 |
c_cpi_u_quarterly_nsa | realtalk | Chained Consumer Price Index for Urban Consumers (C-CPI-U) data | tbl_df | 100 | 3 |
cpi_u_annual | realtalk | Consumer Price Index for Urban Consumers (CPI-U) data | tbl_df | 88 | 2 |
cpi_u_monthly_nsa | realtalk | Consumer Price Index for Urban Consumers (CPI-U) data | tbl_df | 1058 | 3 |
cpi_u_monthly_sa | realtalk | Consumer Price Index for Urban Consumers (CPI-U) data | tbl_df | 938 | 3 |
cpi_u_quarterly_nsa | realtalk | Consumer Price Index for Urban Consumers (CPI-U) data | tbl_df | 352 | 3 |
cpi_u_quarterly_sa | realtalk | Consumer Price Index for Urban Consumers (CPI-U) data | tbl_df | 312 | 3 |
cpi_u_rs_annual | realtalk | Consumer Price Index for Urban Consumers Research Series (CPI-U-RS) data | tbl_df | 46 | 2 |
cpi_u_rs_monthly_nsa | realtalk | Consumer Price Index for Urban Consumers Research Series (CPI-U-RS) data | tbl_df | 553 | 3 |
cpi_u_x1_annual | realtalk | Experimental Consumer Price Index for Urban Consumers X1 (CPI-U-X1) data | tbl_df | 16 | 2 |
cpi_u_x1_monthly_nsa | realtalk | Experimental Consumer Price Index for Urban Consumers X1 (CPI-U-X1) data | tbl_df | 192 | 3 |
pce_annual | realtalk | Personal Consumption Expenditures (PCE) price index | tbl_df | 96 | 2 |
pce_monthly_sa | realtalk | Personal Consumption Expenditures (PCE) price index | tbl_df | 793 | 3 |
pce_quarterly_sa | realtalk | Personal Consumption Expenditures (PCE) price index | tbl_df | 312 | 3 |
us_minimum_wage_annual | realtalk | Example nominal US federal minimum wage data | tbl_df | 88 | 2 |
us_minimum_wage_monthly | realtalk | Example nominal US federal minimum wage data | tbl_df | 1047 | 3 |
Pigs | cv | Body Weights of 48 Pigs in 9 Successive Weeks | data.frame | 432 | 3 |
woodiv_categories | funbiogeo | Species x Categories of some Mediterranean Conifers | data.frame | 24 | 6 |
woodiv_locations | funbiogeo | Sites x Locations of some Mediterranean Conifers | sf | 5366 | 3 |
woodiv_site_species | funbiogeo | Sites x Species of some Mediterranean Conifers | data.frame | 5366 | 25 |
woodiv_traits | funbiogeo | Species x Traits of some Mediterranean Conifers | data.frame | 24 | 5 |
hg19pvalues | metaseqR2 | p-values from human RNA-Seq data with two conditions, four samples | matrix | 1000 | 9 |
libsizeListMm9 | metaseqR2 | Mouse RNA-Seq data with two conditions, four samples | list | | |
mm9GeneCounts | metaseqR2 | Mouse RNA-Seq data with two conditions, four samples | data.frame | 3787 | 12 |
sampleListMm9 | metaseqR2 | Mouse RNA-Seq data with two conditions, four samples | list | | |
HuntingSpiders | partykit | Abundance of Hunting Spiders | data.frame | 28 | 18 |
WeatherPlay | partykit | Weather Conditions and Playing a Game | data.frame | 14 | 5 |
GrowthNUTS2 | lagsarlmtree | Determinants of Regional Economic Growth | data.frame | 255 | 58 |
WeightsNUTS2 | lagsarlmtree | Spatial Weights for European Union NUTS2 Regions | list | | |
akan | cofad | Data from Akan et al. (2018), experiment 2B | tbl_df | 270 | 3 |
furr_p4 | cofad | Empathy data set by Furr (2004) | data.frame | 20 | 2 |
haans_within1by4 | cofad | Haans within data example | tbl_df | 20 | 3 |
maraver | cofad | Data from Maraver et al. (2021) | tbl_df | 120 | 3 |
rosenthal_chap5_q2 | cofad | Complexity data set by Rosenthal and Rosnow (2000) | data.frame | 18 | 4 |
rosenthal_p141 | cofad | Data set by Rosenthal and Rosnow (2000) | data.frame | 14 | 4 |
rosenthal_tbl31 | cofad | Data set by Rosenthal and Rosnow (2000) | data.frame | 20 | 2 |
rosenthal_tbl53 | cofad | Children data set by Rosenthal and Rosnow (2000) | data.frame | 36 | 4 |
rosenthal_tbl59 | cofad | Therapy data set by Rosenthal and Rosnow (2000) | data.frame | 12 | 4 |
rosenthal_tbl68 | cofad | Data set by Rosenthal and Rosnow (2000) | data.frame | 32 | 4 |
schwoebel | cofad | Data from Schwoebel et al. (2018) | tbl_df | 64 | 2 |
sedlmeier_p525 | cofad | Problem solving data set by Sedlmeier & Renkewitz (2018) | data.frame | 15 | 3 |
sedlmeier_p537 | cofad | Music data set by Sedlmeier & Renkewitz (2018) | data.frame | 32 | 3 |
testing_effect | cofad | Testing Effect data | data.frame | 60 | 3 |
mdsChr22ExObj | IntEREst | Object of SummarizedExperiment type for exon-exon junction of MDS data | SummarizedExperiment | | |
mdsChr22IntSpObj | IntEREst | Object of SummarizedExperiment type for intron spanning reads of MDS data | SummarizedExperiment | | |
mdsChr22Obj | IntEREst | Object of SummarizedExperiment type for intron retention MDS data | SummarizedExperiment | | |
pwmU12db | IntEREst | PWM of U12 and U2-type introns splice sites | list | | |
u12 | IntEREst | U12 data | data.frame | 22713 | 16 |
rep1_clusters | jazzPanda | Rep1 selected cells | data.frame | 1705 | 6 |
rep1_neg | jazzPanda | Rep1 negative control genes within the selected region. | SpatialExperiment | | |
rep1_sub | jazzPanda | A small section of Xenium human breast cancer rep1. | SpatialExperiment | | |
rep2_clusters | jazzPanda | Rep2 selected cells | data.frame | 1815 | 6 |
rep2_neg | jazzPanda | Rep2 negative control genes within the selected region. | SpatialExperiment | | |
rep2_sub | jazzPanda | A small section of Xenium human breast cancer rep2. | SpatialExperiment | | |
pbmc_facs | fastglmpca | Mixture of 10 FACS-purified PBMC Single-Cell RNA-seq data | list | | |
Carcinoma | ggpcp | Data set: Assessment of Carcinoma slides | tbl_df | 118 | 9 |
nasa | ggpcp | Data set: NASA - Data Expo 2006 | data.frame | 41472 | 15 |
covid19_sa | epichains | COVID-19 Data Repository for South Africa | tbl_df | 14 | 2 |
hearts | tfdatasets | Heart Disease Data Set | tbl_df | 303 | 14 |
HS20imps | lavaan.mi | List of imputed Holzinger & Swineford (1939) datasets | mi | | |
binHS5imps | lavaan.mi | List of imputed Holzinger & Swineford (1939) dichotomized data | list | | |
FORCE | forrel | FORCE panel kinship SNPs | data.frame | 3915 | 6 |
NorwegianFrequencies | forrel | Norwegian STR frequencies | list | | |
CRM001 | eCerto | An example set of data collected for a CRM. | list | | |
LTS001 | eCerto | An example set of data collected for a LTS monitoring. | list | | |
cvals_Dixon | eCerto | Dixon critical values table. | matrix | 29 | 16 |
cvals_Grubbs2 | eCerto | Grubbs2 critical values table. | matrix | 97 | 13 |
data_prospect5 | ccrtm | refractive index and specific absorption coefficients for PROSPECT 5 | matrix | 2101 | 7 |
data_prospectd | ccrtm | refractive index and specific absorption coefficients for PROSPECT D | matrix | 2101 | 8 |
soil | ccrtm | soil reflectance | matrix | 2101 | 2 |
solar | ccrtm | direct and diffuse light | matrix | 2101 | 2 |
NLD_dist | tmap | Netherlands datasets | sf | 3340 | 19 |
NLD_muni | tmap | Netherlands datasets | sf | 345 | 19 |
NLD_prov | tmap | Netherlands datasets | sf | 12 | 3 |
World | tmap | World dataset | sf | 177 | 18 |
World_rivers | tmap | Spatial data of rivers | sf | 1632 | 5 |
land | tmap | Spatial data of global land cover | stars | | |
metro | tmap | Spatial data of metropolitan areas | sf | 436 | 13 |
events_SISe3 | SimInf | Example data to initialize events for the 'SISe3' model | data.frame | 783773 | 8 |
nodes | SimInf | Example data with spatial distribution of nodes | data.frame | 1600 | 2 |
u0_SISe3 | SimInf | Example data to initialize the 'SISe3' model | data.frame | 1600 | 6 |
Hadza | SQMtools | Hadza hunter-gatherer gut metagenomes | SQM | | |
MGKOs | SQMtools | Single Copy Phylogenetic Marker Genes from Sunagawa's group (KOs) | character | | |
MGOGs | SQMtools | Single Copy Phylogenetic Marker Genes from Sunagawa's group (OGs) | character | | |
RecA | SQMtools | RecA/RadA recombinase | character | | |
USiCGs | SQMtools | Universal Single-Copy Genes | character | | |
api29 | OTrecod | Student performance in California schools: the results of the county 29 | data.frame | 418 | 12 |
api35 | OTrecod | Student performance in California schools: the results of the county 35 | data.frame | 362 | 12 |
ncds_14 | OTrecod | National Child Development Study: a sample of the first four waves of data collection | data.frame | 5476 | 6 |
ncds_5 | OTrecod | National Child Development Study: a sample of the fifth wave of data collection | data.frame | 365 | 6 |
simu_data | OTrecod | A simulated dataset to test the functions of the OTrecod package | data.frame | 700 | 8 |
tab_test | OTrecod | A simulated dataset to test the library | data.frame | 10000 | 6 |
pbmc.rna.mat | SPECK | Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample. | dgCMatrix | | |
LDL | Markovchart | Aggregated low-density-lipoprotein patient data for control chart applications | data.frame | 1 | 12 |
diabetes | Markovchart | Pseudonymised and randomised time series dataset of diabetes patients for control chart applications | data.frame | 87598 | 11 |
schools | CMatching | Schools data set (NELS-88) | data.frame | 260 | 19 |
CpG.gene.map.for.DEG | InterSIM | InterSIM | data.frame | 367 | 2 |
cov.M | InterSIM | InterSIM | matrix | 367 | 367 |
cov.expr | InterSIM | InterSIM | matrix | 131 | 131 |
cov.protein | InterSIM | InterSIM | matrix | 160 | 160 |
mean.M | InterSIM | InterSIM | numeric | | |
mean.expr | InterSIM | InterSIM | numeric | | |
mean.expr.with.mapped.protein | InterSIM | InterSIM | numeric | | |
mean.protein | InterSIM | InterSIM | numeric | | |
methyl.gene.level.mean | InterSIM | InterSIM | numeric | | |
protein.gene.map.for.DEP | InterSIM | InterSIM | data.frame | 160 | 2 |
rho.expr.protein | InterSIM | InterSIM | numeric | | |
rho.methyl.expr | InterSIM | InterSIM | numeric | | |
simdata1 | markophylo | Simulated data. | list | | |
simdata2 | markophylo | Simulated data. | list | | |
simdata3 | markophylo | Simulated data. | list | | |
simdata4 | markophylo | Simulated data. | list | | |
simdata5 | markophylo | Simulated data. | list | | |
lognormAssay | rADA | Simulated Lognormal Dataset | data.frame | 100 | 20 |
trauma_data | CDsampling | Trauma data with multinomial response | data.frame | 802 | 5 |
trial_data | CDsampling | Generated clinical trial data with binary response | data.frame | 500 | 6 |
dd | EHR | dd | data.frame | 10000 | 1505 |
dd.baseline | EHR | dd.baseline | data.frame | 10000 | 1505 |
dd.baseline.small | EHR | dd.baseline.small | data.frame | 2000 | 55 |
dd.small | EHR | dd.small | data.frame | 2000 | 55 |
lam_metadata | EHR | Example of Metadata for Lamotrigine Data | data.frame | 5 | 4 |
lam_mxr_parsed | EHR | Example of Lamotrigine Output from 'parseMedExtractR' | data.table | 10 | 9 |
tac_lab | EHR | Example of Lab Time Data for Tacrolimus | data.frame | 2 | 3 |
tac_metadata | EHR | Example of Metadata for Tacrolimus Data | data.frame | 3 | 4 |
tac_mxr_parsed | EHR | Example of Tacrolimus Output from 'parseMedExtractR' | data.table | 7 | 9 |
Australia | TestDimorph | Australia | data.frame | 94 | 9 |
Cremains_measurements | TestDimorph | Measurements from calcined postcranial materials. | data.frame | 21 | 8 |
FT | TestDimorph | Heuristic data | data.frame | 24 | 3 |
Howells | TestDimorph | The Howells' craniometric data | data.frame | 441 | 10 |
Howells_R | TestDimorph | Pooled within group correlation matrix for Howells' data | matrix | 8 | 8 |
Howells_V | TestDimorph | Pooled within-group variance-covariance matrix for Howells' data | matrix | 8 | 8 |
Howells_summary | TestDimorph | Summary of the Howells' craniometric data | data.frame | 32 | 8 |
Howells_summary_list | TestDimorph | List format of Howells_summary for multivariate analysis | list | | |
NHANES_1999 | TestDimorph | NHANES 1999 | data.frame | 1430 | 5 |
SMO | TestDimorph | Hypothetical set of unbalanced data | data.frame | 11 | 3 |
baboon.parms_R | TestDimorph | Pooled within group correlation matrix for baboon data | matrix | 4 | |
baboon.parms_df | TestDimorph | data frame format for the baboon.parms_df for multivariate analysis | data.frame | 12 | 8 |
baboon.parms_list | TestDimorph | List format for the baboon.parms_df for multivariate analysis | list | | |
adapt | borrowr | Data set used in the package vignette | data.frame | 180 | 5 |
ExCWM | flexCWM | dataset ExCWM | data.frame | 200 | 8 |
students | flexCWM | dataset students | data.frame | 270 | 4 |
demo_DMRfinder_DMRs | metevalue | DMRfinder Output Demo Dataset | data.frame | 757 | 8 |
demo_DMRfinder_rate_combine | metevalue | DMRfinder Methyrate Demo Dataset | data.frame | 46073 | 6 |
demo_biseq_DMR | metevalue | BiSeq Output Demo Dataset | data.frame | 14 | 9 |
demo_biseq_methyrate | metevalue | BiSeq Methyrate Demo Dataset | data.frame | 10502 | 12 |
demo_desq_out | metevalue | DESeq Output Dataset | matrix | 8166 | 7 |
demo_methylkit_met_all | metevalue | Methyrate output dataset from methylKit | data.frame | 24 | 7 |
demo_methylkit_methyrate | metevalue | Methyrate Dataset | data.frame | 963 | 6 |
demo_metilene_input | metevalue | Metilene Methyrate Demo Dataset | data.frame | 86723 | 18 |
demo_metilene_out | metevalue | Metilene Demo Output Dataset | data.frame | 723 | 10 |
higlasso.df | higlasso | Synthetic Example Data For Higlasso | data.frame | 300 | 8 |
all_genes | TPEA | All human protein coding genes | data.frame | 20949 | 1 |
gene2ec | TPEA | The relationship of genes and EC | data.frame | 3521 | 2 |
gene2ko | TPEA | The relationship of genes and KO | data.frame | 9730 | 2 |
keggGene2gene | TPEA | KeggGene to genes | data.frame | 21796 | 2 |
node_gene | TPEA | The relationship between nodes and genes | list | | |
num_node_gene_score | TPEA | The score of each node in a certain pathway | list | | |
pathway_names | TPEA | Pathway names in KEGG Database | data.frame | 109 | 2 |
Lung | compound.Cox | Survival data for patients with non-small-cell lung cancer. | data.frame | 125 | 100 |
PBC | compound.Cox | Primary biliary cirrhosis (PBC) of the liver data | data.frame | 276 | 19 |
Samusik_01_subset | hypergate | 2000 events randomly sampled from the 'Samusik_01' dataset | list | | |
gsz | ivDiag | Data from GSZ (2016) | data.frame | 5357 | 11 |
gsz_south | ivDiag | Data from GSZ (2016): Subsample | data.frame | 2175 | 11 |
rueda | ivDiag | Data from Rueda (2017) | data.frame | 4352 | 6 |
ML_ex_dat | GPSeqClus | Sample Data for Sequential Clustering Routine | data.frame | 2138 | 4 |
alligatorDiet | syllogi | Study of Diets in Alligators | data.frame | 16 | 8 |
alligatorLength | syllogi | Study of Diets in Alligators at Lake George, Florida | data.frame | 63 | 3 |
annualSales | syllogi | Fictitious Data Set of Annual Sales | data.frame | 12 | 3 |
beer | syllogi | Beer | data.frame | 86 | 5 |
bighornSheep | syllogi | Bighorn Sheep | data.frame | 8000 | 11 |
bladderCancer | syllogi | Study of Recurrence of Bladder Cancer | data.frame | 31 | 3 |
butterflyPlot | syllogi | Fictitious Data Set of Butterfly Counts | data.frame | 40 | 2 |
depression | syllogi | Self Reported Depression | data.frame | 50 | 13 |
dogFood | syllogi | Fictitious Data Set Comparing Dog Food Brands | data.frame | 25 | 2 |
federalistPapers | syllogi | Federalist Papers | list | | |
genericData | syllogi | Generic Data Set | data.frame | 60 | 7 |
golf | syllogi | Golfing | data.frame | 18 | 3 |
nutritionCancer | syllogi | Nutrition Cancer Study | data.frame | 50 | 6 |
osteosarcoma | syllogi | Study of Nonmetastatic Osteosarcoma | data.frame | 8 | 5 |
patientSatisfaction | syllogi | Patient Satisfaction | data.frame | 46 | 4 |
politicalIdeology | syllogi | Political Ideology | data.frame | 20 | 4 |
schoolProgram | syllogi | High School and Beyond Survey | data.frame | 200 | 11 |
shipDamage | syllogi | Wave Damage of Ships | data.frame | 20 | 5 |
shipGold | syllogi | Ships and Gold | data.frame | 20 | 2 |
ski | syllogi | Ski Resort | data.frame | 9 | 4 |
weightLoss | syllogi | Weight Loss Study | data.frame | 60 | 2 |
wheat | syllogi | Wheat Kernels | data.frame | 275 | 7 |
coc | isni | A data set for Psychiatric Drug Treatment | data.frame | 869 | 10 |
qolef | isni | A data set for Quality of Life Emontional Functioning outcome. | data.frame | 2860 | 10 |
skquit | isni | A randomzied trial data set for Smoking cessation | data.frame | 1861 | 6 |
sos | isni | Dataset for a survey of sexual behavior | data.frame | 6136 | 3 |
SimulatedData | NVCSSL | Simulated data for illustration | data.frame | 436 | 103 |
srilanka | NiLeDAM | An example data set: electron microprobe data. | data.frame | 32 | 6 |
chirps_monthly | SeaVal | Monthly mean precipitation | data.table | 209040 | 6 |
ecmwf_monthly | SeaVal | Monthly mean precipitation forecast example dataset | data.table | 37224 | 9 |
Tropheus | vcvComp | Tropheus dataset | data.frame | 723 | 57 |
Tropheus.IK.coord | vcvComp | Tropheus IK coord dataset | data.frame | 511 | 58 |
indSex | MoNAn | Example Data for the MoNAn Package | numeric | | |
mobilityEdgelist | MoNAn | Example Data for the MoNAn Package | matrix | 742 | |
myAlg | MoNAn | Exemplary Outcome Objects for the MoNAn Package | algorithm.monan | | |
myEffects | MoNAn | Exemplary Outcome Objects for the MoNAn Package | effectsList.monan | | |
myResDN | MoNAn | Exemplary Outcome Objects for the MoNAn Package | result.monan | | |
mySimDN | MoNAn | Exemplary Outcome Objects for the MoNAn Package | sims.monan | | |
myState | MoNAn | Exemplary Outcome Objects for the MoNAn Package | processState.monan | | |
orgRegion | MoNAn | Example Data for the MoNAn Package | numeric | | |
orgSize | MoNAn | Example Data for the MoNAn Package | numeric | | |
events.fishes | BAMMtools | BAMMtools datasets | data.frame | 2313 | 8 |
events.primates | BAMMtools | BAMMtools datasets | data.frame | 16860 | 6 |
events.whales | BAMMtools | BAMMtools datasets | data.frame | 4509 | 8 |
fishes | BAMMtools | BAMMtools datasets | phylo | | |
mass.primates | BAMMtools | BAMMtools datasets | data.frame | 233 | 2 |
mcmc.primates | BAMMtools | BAMMtools datasets | data.frame | 4000 | 6 |
mcmc.whales | BAMMtools | BAMMtools datasets | data.frame | 2000 | 6 |
primates | BAMMtools | BAMMtools datasets | phylo | | |
traits.fishes | BAMMtools | BAMMtools datasets | numeric | | |
whales | BAMMtools | BAMMtools datasets | phylo | | |
med_dat | hdmed | Mediation Example Dataset | list | | |
FlowcytometricData | opdisDownsampling | Example data of hematologic marker expression. | data.frame | 111686 | 7 |
GMMartificialData | opdisDownsampling | Example data an artificial Gaussian mixture. | data.frame | 30000 | 11 |
iccdata1 | irrICC | Scores assigned by 4 judges to 5 targets/subjects. | data.frame | 12 | 5 |
iccdata2 | irrICC | Scores assigned by 4 judges to 5 targets/subjects distributed in 2 groups A and B. | data.frame | 15 | 6 |
iccdata3 | irrICC | Scores assigned by 3 raters to 4 subjects. | data.frame | 4 | 4 |
LASERI | ICSNP | Cardiovascular Responses to Head-up Tilt | data.frame | 223 | 32 |
pulmonary | ICSNP | Change in Pulmonary Response after Exposure to Cotton Dust | data.frame | 12 | 3 |
toy_data | TrumpetPlots | Toy dataset | data.table | 8000 | 7 |
plasma | qrjoint | Plasma Concentration of Beta-Carotene and Retinol | data.frame | 315 | 14 |
redmaple | qrjoint | Basal Areas of Red Maple Trees | data.frame | 608 | 8 |
simX | FourWayHMM | Simulated Data | array | | |
biasMatrix | falconx | Bias Matrix | data.frame | 1000 | 2 |
pos | falconx | Position (bp) | integer | | |
readMatrix | falconx | Reads Matrix | data.frame | 1000 | 4 |
tauhat | falconx | Estimated Break Points | numeric | | |
I_sig | SLIDE | Protein Expression Levels in an Infected Cell Sample | data.frame | 38 | 7 |
UN_sig | SLIDE | Protein Expression Levels in an Uninfected Cell Population | data.frame | 781 | 7 |
Q | LTCDM | Data Set Q | data.frame | 40 | 4 |
cep | LTCDM | Data Set cep | list | | |
dat0 | LTCDM | Data Set dat0 | data.frame | 719 | 40 |
dat1 | LTCDM | Data Set dat1 | data.frame | 2005 | 82 |
step3.output | LTCDM | Data Set step3.output | list | | |
Simulated_data | GSSE | Simulated Parkinson's disease data | data.frame | 268 | 4 |
p0G_data | GSSE | Data Set for Illustration of the 'p0G' Calculation | data.frame | 20 | 2 |
dataset | CeRNASeek | Data for Examples | list | | |
CPPdata | JADE | Cocktail Party Problem Data | data.frame | 50000 | 4 |
bologna | SpatEntropy | Bologna urban data. | matrix | 135 | |
bolognaTess | SpatEntropy | Municipalities' administrative borders for Bologna urban data. | list | | |
bolognaW | SpatEntropy | Observation window for Bologna urban data. | owin | | |
raintrees | SpatEntropy | Rainforest tree data 1. | ppp | | |
raintrees2 | SpatEntropy | Rainforest tree data 2. | ppp | | |
raintreesCOV | SpatEntropy | Covariates for the rainforest tree data. | imlist | | |
turin | SpatEntropy | Turin urban data. | matrix | 111 | |
turinTess | SpatEntropy | Municipalities' administrative borders for Turin urban data. | list | | |
turinW | SpatEntropy | Observation window for Turin urban data. | owin | | |
EPA.ref | httk | Reference for EPA Physico-Chemical Data | character | | |
Frank2018invivo | httk | Literature In Vivo Data on Doses Causing Neurological Effects | data.frame | 14 | 16 |
Obach2008 | httk | Published Pharmacokinetic Parameters from Obach et al. 2008 | data.frame | 670 | 7 |
Tables.Rdata.stamp | httk | A timestamp of table creation | character | | |
Wetmore2012 | httk | Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012. | data.frame | 13 | 15 |
armitage_input | httk | Armitage et al. (2014) Model Inputs from Honda et al. (2019) | data.table | 1194 | 7 |
aylward2014 | httk | Aylward et al. 2014 | matrix | 26 | |
bmiage | httk | CDC BMI-for-age charts | data.table | 434 | 5 |
chem.invivo.PK.aggregate.data | httk | Parameter Estimates from Wambaugh et al. (2018) | data.frame | 48 | 30 |
chem.invivo.PK.data | httk | Published toxicokinetic time course measurements | data.frame | 2454 | 19 |
chem.invivo.PK.summary.data | httk | Summary of published toxicokinetic time course experiments | data.frame | 98 | 18 |
chem.physical_and_invitro.data | httk | Physico-chemical properties and in vitro measurements for toxicokinetics | data.frame | 11204 | 66 |
concentration_data_Linakis2020 | httk | Concentration data involved in Linakis 2020 vignette analysis. | data.frame | 2142 | 16 |
dawson2021 | httk | Dawson et al. 2021 data | data.frame | 10452 | 6 |
example.seem | httk | SEEM Example Data We can grab SEEM daily intake rate predictions already in RData format from https://github.com/HumanExposure/SEEM3RPackage/tree/main/SEEM3/data Download the file Ring2018Preds.RData | data.frame | 43 | 39 |
example.toxcast | httk | ToxCast Example Data The main page for the ToxCast data is here: https://www.epa.gov/comptox-tools/exploring-toxcast-data Most useful to us is a single file containing all the hits across all chemcials and assays: https://clowder.edap-cluster.com/datasets/6364026ee4b04f6bb1409eda?space=62bb560ee4b07abf29f88fef | data.frame | 38531 | 9 |
fetalpcs | httk | Fetal Partition Coefficients | data.frame | 87 | 11 |
hct_h | httk | KDE bandwidths for residual variability in hematocrit | list | | |
honda2023.data | httk | Measured Caco-2 Apical-Basal Permeability Data | data.frame | 634 | 5 |
honda2023.qspr | httk | Predicted Caco-2 Apical-Basal Permeabilities | data.frame | 14033 | 5 |
howgate | httk | Howgate 2006 | data.table | 24 | 11 |
httk.performance | httk | Historical Performance of R Package httk | data.frame | 26 | 18 |
hw_H | httk | KDE bandwidth for residual variability in height/weight | list | | |
johnson | httk | Johnson 2006 | data.table | 60 | 11 |
kapraun2019 | httk | Kapraun et al. 2019 data | list | | |
mcnally_dt | httk | Reference tissue masses and flows from tables in McNally et al. 2014. | data.table | 36 | 10 |
mecdt | httk | Pre-processed NHANES data. | data.table | 23620 | 14 |
metabolism_data_Linakis2020 | httk | Metabolism data involved in Linakis 2020 vignette analysis. | data.frame | 83 | 34 |
onlyp | httk | NHANES Exposure Data | data.table | 1060 | 5 |
pc.data | httk | Partition Coefficient Data | data.frame | 1221 | 8 |
pearce2017regression | httk | Pearce et al. 2017 data | matrix | 12 | 4 |
pharma | httk | DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Data | matrix | 14 | |
physiology.data | httk | Species-specific physiology parameters | data.frame | 18 | 8 |
pksim.pcs | httk | Partition Coefficients from PK-Sim | data.frame | 72 | 8 |
pradeep2020 | httk | Pradeep et al. 2020 | data.frame | 8573 | 4 |
pregnonpregaucs | httk | AUCs for Pregnant and Non-Pregnant Women | data.frame | 12 | 34 |
scr_h | httk | KDE bandwidths for residual variability in serum creatinine | list | | |
sipes2017 | httk | Sipes et al. 2017 data | data.frame | 8719 | 3 |
supptab1_Linakis2020 | httk | Supplementary output from Linakis 2020 vignette analysis. | data.frame | 50 | 13 |
supptab2_Linakis2020 | httk | More supplementary output from Linakis 2020 vignette analysis. | data.frame | 42 | 12 |
tissue.data | httk | Tissue composition and species-specific physiology parameters | data.frame | 406 | 5 |
wambaugh2019 | httk | in vitro Toxicokinetic Data from Wambaugh et al. (2019) | data.frame | 496 | 17 |
wambaugh2019.nhanes | httk | NHANES Chemical Intake Rates for chemicals in Wambaugh et al. (2019) | data.frame | 20 | 4 |
wambaugh2019.raw | httk | Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al. (2019) | data.frame | 530 | 28 |
wambaugh2019.seem3 | httk | ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rates | data.table | 385 | 38 |
wambaugh2019.tox21 | httk | Tox21 2015 Active Hit Calls (EPA) | data.frame | 401 | 6 |
wang2018 | httk | Wang et al. 2018 Wang et al. (2018) screened the blood of 75 pregnant women for the presence of environmental organic acids (EOAs) and identified mass spectral features corresponding to 453 chemical formulae of which 48 could be mapped to likely structures. Of the 48 with tentative structures the identity of six were confirmed with available chemical standards. | matrix | 20 | |
well_param | httk | Microtiter Plate Well Descriptions for Armitage et al. (2014) Model | data.table | 11 | 8 |
wfl | httk | WHO weight-for-length charts | data.table | 262 | 4 |
drugdata | WCE | Simulated dataset to illustrate the use of WCE models | data.frame | 77038 | 7 |
dental | geesmv | A Data Set of Orthodontic Measurements on Children | data.frame | 27 | 6 |
seizure | geesmv | Epiliptic seizure counts from the Randomized Progabide Trial | data.frame | 59 | 7 |
toenail | geesmv | Toenail infection data from a multicenter study | data.frame | 1908 | 5 |
strength | mdscore | Impact Strength an Insulating Material | data.frame | 30 | 3 |
Xmat | icRSF | A covariate matrix | matrix | 300 | 1000 |
pheno | icRSF | A longitudinal data with diagnostic results for pre-determined time | data.frame | 629 | 3 |
khan2001 | sda | Childhood Cancer Study of Khan et al. (2001) | list | | |
singh2002 | sda | Prostate Cancer Study of Singh et al. (2002) | list | | |
Adult | arules | Adult Data Set | transactions | | |
AdultUCI | arules | Adult Data Set | data.frame | 48842 | 15 |
Epub | arules | The Epub Transactions Data Set | transactions | | |
Groceries | arules | The Groceries Transactions Data Set | transactions | | |
Income | arules | The Income Data Set | transactions | | |
IncomeESL | arules | The Income Data Set | data.frame | 8993 | 14 |
Mushroom | arules | The Mushroom Data Set as Transactions | transactions | | |
SunBai | arules | The SunBai Weighted Transactions Data Set | transactions | | |
data_corpus_EPcoaldebate | quanteda.textmodels | Crowd-labelled sentence corpus from a 2010 EP debate on coal subsidies | corpus | | |
data_corpus_dailnoconf1991 | quanteda.textmodels | Confidence debate from 1991 Irish Parliament | corpus | | |
data_corpus_irishbudget2010 | quanteda.textmodels | Irish budget speeches from 2010 | corpus | | |
data_corpus_moviereviews | quanteda.textmodels | Movie reviews with polarity from Pang and Lee (2004) | corpus | | |
gutten | FAwR | von Guttenberg's Norway spruce (Picea abies [L.] Karst) tree measurement data. | data.frame | 1200 | 9 |
herbdata | FAwR | Herbicide trial seedling data | data.frame | 960 | 8 |
leuschner | FAwR | Leuschner harvest schedule yield data | data.frame | 48 | 4 |
stage | FAwR | Stage's Grand fir (Abies grandis (Dougl) Lindl.) tree measurement data | data.frame | 542 | 11 |
sweetgum | FAwR | Lenhart's sweetgum (Liquidambar styraciflua L.) tree measurement data. | data.frame | 39 | 8 |
ufc | FAwR | Upper Flat Creek forest cruise tree data | data.frame | 336 | 5 |
jam_values | getACS | ACS Jam Values for Medians | tbl_df | 20 | 6 |
race_iteration | getACS | Race or Latino Origin Table Codes | tbl_df | 9 | 3 |
tigerweb_geo_index | getACS | U.S. Census Bureau ArcGIS Services Index | tbl_df | 7750 | 15 |
usa_states | getACS | U.S. States Reference Data | data.frame | 56 | 7 |
sb_cat | LPDynR | Standing Biomass | RasterBrick | | |
sbd_cat | LPDynR | Season Beginning Day | RasterBrick | | |
sl_cat | LPDynR | Season Length | RasterBrick | | |
AutumnLab | MLDS | Difference Scale Judgement Data Set | mlds.df | 210 | 5 |
Transparency | MLDS | Difference Scaling of Transparency | data.frame | 2520 | 6 |
kk1 | MLDS | Difference Scale Judgment Data Sets | mlds.df | 330 | 5 |
kk2 | MLDS | Difference Scale Judgment Data Sets | mlds.df | 330 | 5 |
kk3 | MLDS | Difference Scale Judgment Data Sets | mlds.df | 330 | 5 |
kktriad | MLDS | Difference Scale Judgment Data Sets | mlbs.df | 165 | 4 |
mark0 | SOFIA | Genetic maps to be used as examples for generating Circos figures | data.frame | 10000 | 6 |
N41.G.ruber.seasonality | sedproxy | Seasonality of Globigerinoides ruber at core MD97-2141 | numeric | | |
N41.proxy | sedproxy | Mg/Ca proxy based temperature reconstruction for core MD97-2141 | tbl_df | 216 | 4 |
N41.proxy.details | sedproxy | Metadata for datset 'N41.proxy' | tbl_df | 1 | 17 |
N41.t21k.climate | sedproxy | Climate (surface temperature) at core MD97-2141 from TraCE-21ka | matrix | 22040 | 12 |
gisp2.ann | sedproxy | gisp2 ice core data at annual resolution | tbl_df | 49885 | 3 |
param.tab | sedproxy | sedproxy parameters | spec_tbl_df | 14 | 4 |
scussolini.tab1 | sedproxy | Scussolini et al. (2013) Table 1 | tbl_df | 22 | 6 |
stage.labels | sedproxy | Labels for proxy stages | character | | |
stages.key | sedproxy | Description of proxy stages | tbl_df | 16 | 7 |
Abrasion | MLGdata | Abrasion loss | data.frame | 30 | 3 |
Aids | MLGdata | Aids mortality | data.frame | 14 | 2 |
Alligators | MLGdata | Alligator food choice data | data.frame | 40 | 4 |
Ants | MLGdata | Ants and sandwiches | data.frame | 48 | 5 |
Aziende | MLGdata | Number of closed businesses | data.frame | 16 | 4 |
Bartlett | MLGdata | Bartlett data on plum root cuttings | table | | |
Bartlett2 | MLGdata | Bartlett data on plum root cuttings | data.frame | 4 | 4 |
Beetles | MLGdata | Deaths of flour beetles | data.frame | 8 | 3 |
Beetles10 | MLGdata | Deaths of flour beetles | data.frame | 481 | 2 |
Bioassay | MLGdata | Biological experiment | data.frame | 10 | 3 |
Biochemists | MLGdata | article production by graduate students in biochemistry Ph.D. programs | data.frame | 915 | 6 |
Britishdoc | MLGdata | British doctors study | data.frame | 10 | 4 |
Calcium | MLGdata | Calcium Uptake Data | data.frame | 27 | 2 |
Cement | MLGdata | Tensile strength of cement | data.frame | 21 | 2 |
Chimps | MLGdata | Chimpanzee Learning Data | data.frame | 40 | 3 |
Chlorsulfuron | MLGdata | Chlorsulfuron Data | data.frame | 51 | 3 |
Clotting | MLGdata | Blood clotting times | data.frame | 18 | 3 |
Credit | MLGdata | Credit Score Data From a South German Bank | data.frame | 1000 | 8 |
Customer | MLGdata | Bus customer satisfaction | data.frame | 12231 | 2 |
Customer3 | MLGdata | Bus customer satisfaction | data.frame | 4 | 6 |
Dogs | MLGdata | Dogs data | data.frame | 40 | 4 |
Drugs | MLGdata | Student Substance Use | data.frame | 8 | 4 |
Drugs2 | MLGdata | Student Substance Use | data.frame | 4 | 5 |
Drugs3 | MLGdata | Student Substance Use | data.frame | 32 | 6 |
Esito | MLGdata | Recreational activities and university performance | data.frame | 18 | 4 |
Germination | MLGdata | Seed Germination | data.frame | 21 | 4 |
Heart | MLGdata | Creatinine kinase and heart attacks | data.frame | 13 | 4 |
Homicide | MLGdata | Homicide data | data.frame | 1308 | 2 |
Infant | MLGdata | Infant survival | data.frame | 16 | 5 |
Kyphosis | MLGdata | Data on Children who have had Corrective Spinal Surgery | data.frame | 81 | 4 |
Malaria | MLGdata | Malaria Transmission in the Western Kenyan Highlands | data.frame | 8204 | 13 |
Mental | MLGdata | Mental impairment | data.frame | 40 | 3 |
Neonati | MLGdata | Weight at birth | data.frame | 32 | 3 |
Ohio | MLGdata | Ohio Children Wheeze Status | data.frame | 2148 | 4 |
Orthodont | MLGdata | Growth curve data on an orthdontic measurement | data.frame | 27 | 5 |
Orthodont1 | MLGdata | Growth curve data on an orthdontic measurement | data.frame | 108 | 4 |
Pneu | MLGdata | Pneumoconiosis amongst Coalminers | data.frame | 8 | 4 |
Rats | MLGdata | Teratology study | data.frame | 58 | 5 |
Seed | MLGdata | Seed germination | data.frame | 20 | 2 |
Snore | MLGdata | Snoring and heart disease | data.frame | 8 | 3 |
Spending | MLGdata | Opinions about government spending | data.frame | 81 | 5 |
Stroke | MLGdata | Stroke data | data.frame | 24 | 10 |
Stroke1 | MLGdata | Stroke data | data.frame | 192 | 4 |
Testingresso | MLGdata | University admission test | data.frame | 630 | 3 |
Vehicle | MLGdata | Preferred vehicle | data.frame | 2067 | 4 |
Wool | MLGdata | Wool data | data.frame | 27 | 4 |
pcd_distributions | primarycensored | Supported delay distributions | data.frame | 17 | 4 |
pcd_primary_distributions | primarycensored | Supported primary event distributions | data.frame | 2 | 4 |
dkd_spe_subset | standR | Description of the standR example datasets | SpatialExperiment | | |
beliefs | batchLLM | Beliefs Dataset | data.frame | 20 | 1 |
my_winputall_data | winputall | Example data | tbl_df | 175 | 9 |
MacroTS | bootUR | Macroeconomic Time Series | mts | 100 | 20 |
spe | hoodscanR | Example test spatial transcriptomics data | SpatialExperiment | | |
countries | penppml | Country ISO Codes | data.frame | 249 | 4 |
trade | penppml | International trade agreements data set | data.frame | 316317 | 22 |
returns | svines | Stock returns of 20 companies | data.frame | 1296 | 20 |
bowl_offers | politeness | Purchase offers for bowl | data.frame | 70 | 2 |
feature_table | politeness | Table of Politeness Features | data.frame | 40 | 4 |
phone_offers | politeness | #' Positive Emotions List #' #' Positive words. #' #' @format A list of 2006 positively-valenced words #' "positive_list" | data.frame | 355 | 2 |
polite_train | politeness | Pre-Trained Politeness | list | | |
receptive_model | politeness | A pre-trained model for detecting conversational receptiveness. Estimated with glmnet using annotated data from a previous paper. Primarily for use within the receptiveness() function. | cv.glmnet | | |
receptive_names | politeness | This is the list of variables to be extracted for the receptiveness algorithm For internal use only, within the receptiveness() function. | character | | |
receptive_polite | politeness | Pre-Trained Receptiveness Data | data.frame | 2860 | 39 |
receptive_train | politeness | Pre-Trained Receptiveness Data | data.frame | 2860 | 2 |
uk2us | politeness | UK to US Conversion dictionary | dictionary2 | | |
bartmodel1 | tidytreatment | Example model 1 | wbart | | |
bartmodel1_modelmatrix | tidytreatment | Model matrix used for 'bartmodel1' | matrix | 100 | 15 |
highDim_testdataset3 | tidytreatment | ACIC2019 High Dimensional Test Dataset | data.frame | 2000 | 187 |
suhillsim1 | tidytreatment | Example simulated dataset 1 | suhillsim | | |
suhillsim2_ranef | tidytreatment | Example simulated dataset 2: with subject specific random effects | suhillsim | | |
us_fiscal_cond_forecasts | bsvars | A matrix to be used in a conditional forecasting example including the projected values of total tax revenue that are projected to increase at an average quarterly sample growth rate. The other two columns are filled with 'NA' values, which implies that the future values of the corresponding endogenous variables, namely government spending and GDP, will be forecasted given the provided projected values of total tax revenue. The matrix includes future values for the forecast horizon of two years for the US fiscal model for the period 2024 Q3 - 2026 Q2. | mts | 8 | 3 |
us_fiscal_ex | bsvars | A 3-variable system of exogenous variables for the US fiscal model for the period 1948 Q1 - 2024 Q2 | mts | 306 | 3 |
us_fiscal_ex_forecasts | bsvars | A 3-variable system of exogenous variables' future values for the forecast horizon of two years for the US fiscal model for the period 2024 Q3 - 2026 Q2 | mts | 8 | 3 |
us_fiscal_lsuw | bsvars | A 3-variable US fiscal system for the period 1948 Q1 - 2024 Q2 | mts | 306 | 3 |
flowers | pavo | Reflectance spectra from a suite of native Australian flowers, collected around Cairns, Queensland. | rspec | 401 | 37 |
sicalis | pavo | Spectral curves from three body regions of stripe-tailed yellow finch (_Sicalis citrina_) males | rspec | 401 | 22 |
teal | pavo | Angle-resolved reflectance data for the iridescent wing patch of a male green-winged teal (_Anas carolinensis_) | rspec | 401 | 13 |
BriggsEx47 | rdecision | Probabilistic results of HIV model | data.frame | 1000 | 7 |
Gupta2013 | NMAoutlier | Network meta-analysis comparing interventions for actinic keratosis | data.frame | 41 | 5 |
Schoenberg2013 | NMAoutlier | Network meta-analysis comparing the effects after Laparoscopic Heller myotomy. | data.frame | 16 | 5 |
srdata | pvm | A Simulated Spontaneous Reporting System | list | | |
rawdat_mvmr | MVMR | Raw multivariable MR summary data using lipid fractions as exposures and systolic blood pressure as an outcome. | data.frame | 145 | 9 |
Namib | provenance | An example dataset | list | | |
SNSM | provenance | varietal data example | list | | |
densities | provenance | A list of rock and mineral densities | data.frame | 1 | 47 |
endmembers | provenance | Petrographic end-member compositions | compositional | | |
dji30retw | tsmarch | Dow Jones 30 Constituents Closing Value log Weekly Return | data.frame | 1141 | 30 |
globalindices | tsmarch | Global Financial Indices Closing Value log Weekly Return | data.frame | 1698 | 34 |
dmbp | tsgarch | Deutschemark/British pound Exchange Rate | data.frame | 1974 | 2 |
nikkei | tsgarch | Japanese NIKKEI Stock Index | data.frame | 4246 | 2 |
regional_groupings | misuvi | Regional Groupings for Michigan | tbl_df | 83 | 10 |
bio_NT_demo | BerkeleyForestsAnalytics | Data for biomass demonstrations | data.frame | 10 | 8 |
bio_demo_data | BerkeleyForestsAnalytics | Data for biomass demonstrations | data.frame | 9 | 8 |
compilation_cwd_demo | BerkeleyForestsAnalytics | Coarse woody debris data for compilation demonstrations | data.frame | 9 | 9 |
compilation_ffs_demo | BerkeleyForestsAnalytics | Data for general Fire and Fire Surrogate demonstrations | data.frame | 9 | 8 |
compilation_fpc_demo | BerkeleyForestsAnalytics | FPC data for general simple random sampling demonstrations | data.frame | 2 | 3 |
compilation_fwd_demo | BerkeleyForestsAnalytics | Fine woody debris data for compilation demonstrations | data.frame | 9 | 11 |
compilation_srs_demo | BerkeleyForestsAnalytics | Data for general simple random sampling demonstrations | data.frame | 9 | 7 |
compilation_srs_sp_demo | BerkeleyForestsAnalytics | Data for general simple random sampling demonstrations | data.frame | 8 | 5 |
compilation_strs_demo | BerkeleyForestsAnalytics | Data for general stratified random sampling demonstrations | data.frame | 9 | 8 |
compilation_wt_demo | BerkeleyForestsAnalytics | Weight data for stratified random sampling demonstrations | data.frame | 4 | 3 |
cwd_NS_demo | BerkeleyForestsAnalytics | Data for coarse woody debris demonstrations | data.frame | 16 | 8 |
cwd_YS_demo | BerkeleyForestsAnalytics | Data for coarse woody debris demonstrations | data.frame | 12 | 8 |
for_NT_demo | BerkeleyForestsAnalytics | Data for forest composition and structure demonstrations | data.frame | 10 | 7 |
for_demo_data | BerkeleyForestsAnalytics | Data for forest composition and structure demonstrations | data.frame | 9 | 7 |
fwd_demo | BerkeleyForestsAnalytics | Data for fine woody debris demonstrations | data.frame | 12 | 11 |
lit_duff_avg_demo | BerkeleyForestsAnalytics | Data for duff and litter demonstrations | data.frame | 12 | 6 |
lit_duff_demo | BerkeleyForestsAnalytics | Data for duff and litter demonstrations | data.frame | 24 | 6 |
nsvb_demo | BerkeleyForestsAnalytics | Data for NSVB framework biomass and carbon demonstrations | data.frame | 16 | 14 |
overstory_demo | BerkeleyForestsAnalytics | Overstory data for surface and ground fuel demonstrations | data.frame | 14 | 6 |
vign_fuels_1 | BerkeleyForestsAnalytics | Fuel data for vignette, version 1 | data.frame | 236 | 16 |
vign_fuels_2 | BerkeleyForestsAnalytics | Fuel data for vignette, version 2 | data.frame | 236 | 16 |
vign_fuels_3 | BerkeleyForestsAnalytics | Fuel data for vignette, version 3 | data.frame | 236 | 16 |
vign_fuels_4 | BerkeleyForestsAnalytics | Fuel data for vignette, version 4 | data.frame | 236 | 16 |
vign_fuels_5 | BerkeleyForestsAnalytics | Fuel data for vignette, version 5 | data.frame | 236 | 16 |
vign_trees_1 | BerkeleyForestsAnalytics | Tree data for vignette, version 1 | data.frame | 2250 | 10 |
vign_trees_2 | BerkeleyForestsAnalytics | Tree data for vignette, version 2 | data.frame | 2250 | 10 |
vign_trees_3 | BerkeleyForestsAnalytics | Tree data for vignette, version 3 | data.frame | 2250 | 10 |
vign_trees_4 | BerkeleyForestsAnalytics | Tree data for vignette, version 4 | data.frame | 2250 | 10 |
vign_trees_5 | BerkeleyForestsAnalytics | Tree data for vignette, version 5 | data.frame | 2250 | 10 |
aaer_dates | farr | AAER dates from SEC | tbl_df | 2920 | 4 |
aaer_firm_year | farr | AAERs from Bao et al. (2020) | tbl_df | 415 | 4 |
apple_events | farr | Dates for Apple Events | tbl_df | 47 | 3 |
aus_bank_funds | farr | Australian bank fundamental data | tbl_df | 283 | 7 |
aus_bank_rets | farr | Australian bank stock market data | tbl_df | 3047 | 4 |
aus_banks | farr | Australian banks | tbl_df | 10 | 3 |
bloomfield_2021 | farr | Firm-years in RDD analysis of Bloomfield (2021) | spec_tbl_df | 1855 | 2 |
by_tag_year | farr | Tags on StackOverflow | spec_tbl_df | 40518 | 4 |
camp_attendance | farr | Camp attendance | tbl_df | 1000 | 2 |
cmsw_2018 | farr | Data for CMSW | tbl_df | 1133 | 31 |
comp | farr | Data on accruals and auditor choice | tbl_df | 16237 | 14 |
fhk_firm_years | farr | Firm-years for replication of Fang, Huang and Karpoff (2016) | tbl_df | 60272 | 2 |
fhk_pilot | farr | Treatment indicators for SHO pilot firms | tbl_df | 3030 | 4 |
gvkey_ciks | farr | GVKEY-CIK links | tbl_df | 78339 | 5 |
idd_dates | farr | Dates for Inevitable Disclosure Doctrine (IDD) | tbl_df | 24 | 3 |
iliev_2010 | farr | Data on public float | tbl_df | 7214 | 9 |
llz_2018 | farr | GVKEYs used in Li, Lin and Zhang (2018) | tbl_df | 5830 | 1 |
michels_2017 | farr | Data on firms suffering natural disasters | tbl_df | 423 | 12 |
sho_r3000 | farr | Russell 3000 stocks at time of SEC Reg SHO sample formation. | tbl_df | 3000 | 2 |
sho_r3000_gvkeys | farr | Russell 3000 sample used by SEC with GVKEYs | tbl_df | 2951 | 4 |
sho_r3000_sample | farr | Russell 3000 sample used by SEC | tbl_df | 2954 | 3 |
sho_tickers | farr | Tickers of pilot firms for Reg SHO. | tbl_df | 986 | 2 |
state_hq | farr | Data on firm headquarters based on SEC EDGAR filings | tbl_df | 53133 | 4 |
test_scores | farr | Test scores | tbl_df | 4000 | 3 |
undisclosed_names | farr | Customer names that represent non-disclosures. | tbl_df | 460 | 2 |
zhang_2007_events | farr | Event dates from Zhang (2007) | tbl_df | 30 | 3 |
zhang_2007_windows | farr | Event windows from Zhang (2007) | tbl_df | 17 | 3 |
T47D | RNAseqQC | The T47D cell line data of RNA-seq experiment GSE89888 | DESeqDataSet | | |
T47D_diff_testing | RNAseqQC | Differential expression results corresponding to the T47D data set. | DESeqResults | | |
annotation.spa | BulkSignalR | A skinny dataframe used in the spatial workflow | DFrame | | |
bodyMap.mouse | BulkSignalR | Mouse transcriptomes across tissues | data.frame | 24543 | 8 |
bsrdm | BulkSignalR | A skinny BSR-dataModel object related to sdc. | BSRDataModel | | |
bsrdm.comp | BulkSignalR | A skinny BSR-dataModelComp object related to sdc. | BSRDataModelComp | | |
bsrdm.spa | BulkSignalR | A skinny BSR-dataModel object related to spatial dataset | BSRDataModel | | |
bsrinf | BulkSignalR | A skinny BSR-Inference object related to sdc. | BSRInference | | |
bsrinf.comp | BulkSignalR | A skinny BSR-InferenceComp object related to sdc. | BSRInferenceComp | | |
bsrinf.mouse | BulkSignalR | A skinny BSR-inference object related to bodyMap.mouse | BSRInference | | |
bsrinf.spa | BulkSignalR | A skinny BSR-inference object related to spatial dataset | BSRInference | | |
immune.signatures | BulkSignalR | Immune cell gene signatures | data.frame | 1541 | 2 |
ortholog.dict | BulkSignalR | A skinny dataframe used in the mouse workflow | data.frame | 4572 | 1 |
p.EMT | BulkSignalR | Partial EMT gene signature | data.frame | 100 | 1 |
sdc | BulkSignalR | Salivary duct carcinoma transcriptomes | data.frame | 19764 | 26 |
tme.signatures | BulkSignalR | Tumor microenvironment gene signatures | data.frame | 213 | 2 |
egk_destinations | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_fire | steps | Eastern Grey Kangaroo example data | RasterBrick | | |
egk_hab | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_k | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_mat | steps | Eastern Grey Kangaroo example data | matrix | 3 | 3 |
egk_mat_stoch | steps | Eastern Grey Kangaroo example data | matrix | 3 | 3 |
egk_origins | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_pop | steps | Eastern Grey Kangaroo example data | RasterStack | | |
egk_road | steps | Eastern Grey Kangaroo example data | RasterBrick | | |
egk_sf | steps | Eastern Grey Kangaroo example data | RasterStack | | |
eva | aws.wrfsmn | Evaporation data (observation and model) | data.frame | 1096 | 9 |
BPV | Polytect | BPV data | data.frame | 25461 | 3 |
CA | Polytect | CA data | data.frame | 23863 | 2 |
CNV5plex | Polytect | CNV 5-plex data | data.frame | 24827 | 5 |
CNV6plex | Polytect | CNV 6-plex data | data.frame | 24483 | 6 |
HIV | Polytect | HIV data | data.frame | 16946 | 4 |
HR | Polytect | HR data | data.frame | 18233 | 2 |
LR | Polytect | LR data | data.frame | 22723 | 2 |
MM | Polytect | MM data | data.frame | 14203 | 2 |
Ames | coursekata | Ames, Iowa housing data | tbl_df | 185 | 21 |
Fingers | coursekata | Data from introductory statistics students at a university. | data.frame | 157 | 17 |
FingersMessy | coursekata | Raw data from introductory statistics students at a university. | data.frame | 210 | 17 |
Smallville | coursekata | Simulated housing data | data.table | 32 | 4 |
Survey | coursekata | Students at a university were asked to enter a random number between 1-20 into a survey. | data.frame | 211 | 1 |
Tables | coursekata | Tables data | data.frame | 44 | 2 |
TipExperiment | coursekata | Data from an experiment about smiley faces and tips | data.frame | 44 | 5 |
World | coursekata | Data on countries from the Happy Planet Index project. | tbl_df | 130 | 14 |
class_data | coursekata | Generated "class data" for exploring pairwise tests | tbl_df | 105 | 2 |
er | coursekata | Emergency room canine therapy | tbl_df | 84 | 53 |
fevdata | coursekata | Forced Expiratory Volume (FEV) Data | tbl_df | 654 | 5 |
game_data | coursekata | Simulated math game data. | data.frame | 105 | 2 |
penguins | coursekata | A modified form of the 'palmerpenguins::penguins' data set. | tbl_df | 333 | 7 |
tip_exp | coursekata | Simulated data for an experiment about smiley faces and tips | tbl_df | 89 | 3 |
scc_rain | epicmodel | Rain example SCC model | epicmodel_scc | | |
steplist_party | epicmodel | Birthday party example steplist | epicmodel_steplist | | |
steplist_rain | epicmodel | Rain example steplist | epicmodel_steplist | | |
OBDAPpoint | PublicWorksFinanceIT | Soil Defense Public Work for the Molise. | data.frame | 550 | 22 |
OCpoint | PublicWorksFinanceIT | Soil Defense Public works for the Umbria Region | data.frame | 82 | 44 |
RENDISpoint | PublicWorksFinanceIT | Soil Defense Public Works for the Basilicata Region. | data.frame | 210 | 27 |
Abortion | vcdExtra | Abortion Opinion Data | table | | |
Accident | vcdExtra | Traffic Accident Victims in France in 1958 | data.frame | 80 | 5 |
AirCrash | vcdExtra | Air Crash Data | data.frame | 439 | 5 |
Alligator | vcdExtra | Alligator Food Choice | data.frame | 80 | 5 |
Asbestos | vcdExtra | Effect of Exposure to Asbestos | matrix | 5 | 4 |
Bartlett | vcdExtra | Bartlett Data on Plum Root Cuttings | table | | |
Burt | vcdExtra | Burt (1950) Data on Hair, Eyes, Head and Stature | data.frame | 36 | 5 |
Caesar | vcdExtra | Risk Factors for Infection in Caesarian Births | table | | |
Cancer | vcdExtra | Survival of Breast Cancer Patients | table | | |
Cormorants | vcdExtra | Advertising Behavior by Males Cormorants | data.frame | 343 | 8 |
CyclingDeaths | vcdExtra | London Cycling Deaths | data.frame | 208 | 2 |
DaytonSurvey | vcdExtra | Dayton Student Survey on Substance Use | data.frame | 32 | 6 |
Depends | vcdExtra | Dependencies of R Packages | table | | |
Detergent | vcdExtra | Detergent preference data | table | | |
Donner | vcdExtra | Survival in the Donner Party | data.frame | 90 | 5 |
Draft1970 | vcdExtra | USA 1970 Draft Lottery Data | data.frame | 366 | 3 |
Draft1970table | vcdExtra | USA 1970 Draft Lottery Table | table | 12 | 3 |
Dyke | vcdExtra | Sources of Knowledge of Cancer | table | | |
Fungicide | vcdExtra | Carcinogenic Effects of a Fungicide | array | | |
GSS | vcdExtra | General Social Survey- Sex and Party affiliation | data.frame | 6 | 3 |
Geissler | vcdExtra | Geissler's Data on the Human Sex Ratio | data.frame | 90 | 4 |
Gilby | vcdExtra | Clothing and Intelligence Rating of Children | table | 6 | 4 |
Glass | vcdExtra | British Social Mobility from Glass(1954) | data.frame | 25 | 3 |
HairEyePlace | vcdExtra | Hair Color and Eye Color in Caithness and Aberdeen | array | | |
Hauser79 | vcdExtra | Hauser (1979) Data on Social Mobility | data.frame | 25 | 3 |
Heart | vcdExtra | Sex, Occupation and Heart Disease | table | | |
Heckman | vcdExtra | Labour Force Participation of Married Women 1967-1971 | table | | |
HospVisits | vcdExtra | Hospital Visits Data | table | 3 | 3 |
HouseTasks | vcdExtra | Household Tasks Performed by Husbands and Wives | table | 13 | 4 |
Hoyt | vcdExtra | Minnesota High School Graduates | table | | |
ICU | vcdExtra | ICU data set | data.frame | 200 | 22 |
JobSat | vcdExtra | Cross-classification of job satisfaction by income | table | 4 | 4 |
Mammograms | vcdExtra | Mammogram Ratings | matrix | 4 | 4 |
Mental | vcdExtra | Mental Impairment and Parents SES | data.frame | 24 | 3 |
Mice | vcdExtra | Mice Depletion Data | data.frame | 30 | 4 |
Mobility | vcdExtra | Social Mobility data | table | 5 | 5 |
PhdPubs | vcdExtra | Publications of PhD Candidates | data.frame | 915 | 6 |
ShakeWords | vcdExtra | Shakespeare's Word Type Frequencies | data.frame | 100 | 2 |
TV | vcdExtra | TV Viewing Data | array | | |
Titanicp | vcdExtra | Passengers on the Titanic | data.frame | 1309 | 6 |
Toxaemia | vcdExtra | Toxaemia Symptoms in Pregnancy | data.frame | 60 | 5 |
Vietnam | vcdExtra | Student Opinion about the Vietnam War | data.frame | 40 | 4 |
Vote1980 | vcdExtra | Race and Politics in the 1980 Presidential Vote | data.frame | 28 | 4 |
WorkerSat | vcdExtra | Worker Satisfaction Data | data.frame | 8 | 4 |
Yamaguchi87 | vcdExtra | Occupational Mobility in Three Countries | data.frame | 75 | 4 |
Tipton_Pusto | simhelpers | Results for Figure 2 of Tipton & Pustejovsky (2015) | spec_tbl_df | 15300 | 8 |
alpha_res | simhelpers | Cronbach's alpha simulation results | data.frame | 1000 | 3 |
t_res | simhelpers | t-test simulation results | tbl_df | 1000 | 5 |
welch_res | simhelpers | Welch t-test simulation results | tbl_df | 16000 | 11 |
GPvam.benchmark | GPvam | Benchmarks of the program using simulated data. | data.frame | 160 | 9 |
vam_data | GPvam | Simulated Data | data.frame | 3750 | 5 |
tnbc | ranktreeEnsemble | Gene expression profiles in triple-negative breast cancer cell | data.frame | 215 | 337 |
ge_macro_trial01 | beeca | Output from the Ge et al (2011) SAS macro applied to the trial01 dataset | tbl_df | 1 | 6 |
margins_trial01 | beeca | Output from the Margins SAS macro applied to the trial01 dataset | tbl_df | 1 | 11 |
trial01 | beeca | Example trial dataset 01 | tbl_df | 268 | 9 |
trial02_cdisc | beeca | Example CDISC Clinical Trial Dataset in ADaM Format | tbl_df | 254 | 13 |
china_city | leafletZH | city data for China | sf | 476 | 10 |
china_province | leafletZH | province data for China | sf | 34 | 10 |
elements | rfordummies | Periodic table of elements. | data.frame | 118 | 9 |
AA_knowledge_test | mycaas | Example of a test to showcase the Adaptive Assessment tools | assessment | | |
colon | dcsvm | Simplified Gene Expression Data from Alon et al. (1999) | list | | |
bladder | frailtyHL | Bladder Cancer Data | data.frame | 396 | 13 |
bladder0 | frailtyHL | Bladder cancer data | data.frame | 410 | 5 |
cgd | frailtyHL | Chronic Granulomatous Disease (CGD) Infection Data | data.frame | 203 | 16 |
kidney | frailtyHL | Kidney Infection Data | data.frame | 76 | 10 |
rats | frailtyHL | Rats data | data.frame | 150 | 4 |
ren | frailtyHL | Mammary tumor data | data.frame | 254 | 6 |
renal | frailtyHL | Renal transplant data | data.frame | 1395 | 9 |
test | frailtyHL | Simulated data with clustered competing risks | data.frame | 250 | 6 |
testdataLong | RHRT | Long term data | numeric | | |
testdataLong_Ann | RHRT | Long term data annotations | character | | |
distributions | exams.forge.data | Distributions | data.frame | 13 | 6 |
skalenniveau | exams.forge.data | Skalenniveau | data.frame | 45 | 2 |
sos100 | exams.forge.data | Precomputed Sum of Squared Data | matrix | 381 | |
sos1000 | exams.forge.data | Precomputed Sum of Squared Data | matrix | 229830 | |
sos200 | exams.forge.data | Precomputed Sum of Squared Data | matrix | 2433 | |
sos400 | exams.forge.data | Precomputed Sum of Squared Data | matrix | 15533 | |
sos800 | exams.forge.data | Precomputed Sum of Squared Data | matrix | 118696 | |
WQ_Q | hydroEvents | Example water quality and streamflow data | list | | |
dataBassRiver | hydroEvents | Streamflow data | numeric | | |
dataCatchment | hydroEvents | Catchment data | list | | |
dataLoch | hydroEvents | Rainfall data | numeric | | |
data_P_WL | hydroEvents | Example sub-daily rainfall and tidal water level data | list | | |
specimens | CompareTests | Fictitious data on specimens tested by two methods | data.frame | 402 | 3 |
fivenets | ergmito | Example of a group of small networks | list | | |
dsl | IOHanalyzer | Example DataSetList used in tests / examples | DataSetList | | |
dsl_large | IOHanalyzer | Larger example DataSetList used in tests / examples | DataSetList | | |
cobre32 | fcaR | Data for Differential Diagnosis for Schizophrenia | matrix | 105 | 32 |
cobre61 | fcaR | Data for Differential Diagnosis for Schizophrenia | data.frame | 105 | 61 |
planets | fcaR | Planets data | matrix | 9 | 7 |
vegas | fcaR | Data for Tourist Destination in Las Vegas | matrix | 504 | 25 |
data1 | DSWE | Wind Energy data set containing 47,542 data points | data.frame | 47542 | 7 |
data2 | DSWE | Wind Energy data set containing 48,068 data points | data.frame | 48068 | 7 |
Example | dSVA | Example data for dSVA | list | | |
disch | CoSMoS | Daily streamflow data data | data.table | 23315 | 2 |
precip | CoSMoS | Hourly station precipitation data | data.table | 79633 | 2 |
version | tgver | Version of the tgvejs npm package bundled in 'tgver' | character | | |
cbm_spatial | SWMPrExtension | Spatial Data from Chesapeake Bay - Maryland | sf | 1 | 1 |
counties_4269 | SWMPrExtension | US County Map | sf | 3220 | 4 |
elk_spatial | SWMPrExtension | Spatial Data from Elkhorn Slough | sf | 1 | 1 |
elknmnut | SWMPrExtension | Nutrient Data from Elkhorn Slough - North Marsh Station | swmpr | 214 | 13 |
elksmwq | SWMPrExtension | Water Quality Data from Elkhorn Slough - South Marsh Station | swmpr | 140256 | 25 |
sampling_stations | SWMPrExtension | Detailed NERRS site data | data.frame | 356 | 17 |
sampling_stations_backup | SWMPrExtension | A Backup of Detailed NERRS site data | data.frame | 355 | 17 |
us_4269 | SWMPrExtension | US State Map | sf | 52 | 3 |
RateTable_Means_1p_Clades | EvoPhylo | Mean clock rates by node and clade (single clock) | data.frame | 79 | 3 |
RateTable_Means_3p_Clades | EvoPhylo | Mean clock rates by node and clade (3 clock partitions) | data.frame | 79 | 5 |
characters | EvoPhylo | A morphological phylogenetic data matrix | matrix | 178 | 43 |
post_trees | EvoPhylo | Multiple phylogenetic clock trees | treedataList | | |
posterior1p | EvoPhylo | Posterior parameter samples (single clock) | data.frame | 10000 | 21 |
posterior3p | EvoPhylo | Posterior parameter samples (3 clock partions) | data.frame | 10000 | 28 |
tree1p | EvoPhylo | Phylogenetic tree with a single clock partition | treedata | | |
tree3p | EvoPhylo | Phylogenetic tree with 3 clock partitions | treedata | | |
tree_clock1 | EvoPhylo | BEAST2 phylogenetic tree with clock rates from partition 1 | treedata | | |
tree_clock2 | EvoPhylo | BEAST2 phylogenetic tree with clock rates from partition 2 | treedata | | |
fit_200 | brokenstick | Broken stick model with nine lines for 200 children | brokenstick | | |
fit_200_light | brokenstick | Broken stick model with nine lines for 200 children (light) | brokenstick | | |
smocc_200 | brokenstick | Infant growth of 0-2 years, SMOCC data extract | tbl_df | 1942 | 7 |
weightloss | brokenstick | Weight loss self-measurement data | data.frame | 756 | 6 |
fake_questionnaire_data | longmixr | Fake questionnaire data | data.frame | 400 | 20 |
sample_inputs | strand | Sample security inputs for examples and testing | data.frame | 31980 | 7 |
sample_pricing | strand | Sample security pricing data for examples and testing | data.frame | 31980 | 8 |
sample_secref | strand | Sample security reference data for examples and testing | data.frame | 492 | 4 |
df1 | ptmixed | Example dataset with longitudinal counts | data.frame | 18 | 5 |
grp_cor | visualizationQualityControl | 10 sample to sample correlations | matrix | 10 | 10 |
grp_cor_data | visualizationQualityControl | grp_cor_data | list | | |
grp_data | visualizationQualityControl | | matrix | 100 | |
grp_exp_data | visualizationQualityControl | grp_exp_data | list | | |
grp_info | visualizationQualityControl | 10 sample meta-data | data.frame | 10 | 2 |
DataExam2.1 | eda4treeR | Data for Example 2.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 16 | 2 |
DataExam2.2 | eda4treeR | Data for Example 2.2 from Experimental Design and Analysis for Tree Improvement | data.frame | 16 | 4 |
DataExam3.1 | eda4treeR | Data for Example 3.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 80 | 6 |
DataExam3.1.1 | eda4treeR | Data for Example 3.1.1 from Experimental Design and Analysis for Tree Improvement | tbl_df | 10 | 6 |
DataExam4.3 | eda4treeR | Data for Example 4.3 from Experimental Design and Analysis for Tree Improvement | data.frame | 72 | 8 |
DataExam4.3.1 | eda4treeR | Data for Example 4.3.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 72 | 8 |
DataExam4.4 | eda4treeR | Data for Example 4.4 from Experimental Design and Analysis for Tree Improvement | data.frame | 32 | 5 |
DataExam5.1 | eda4treeR | Data for Example 5.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 108 | 4 |
DataExam5.2 | eda4treeR | Data for Example 5.2 from Experimental Design and Analysis for Tree Improvement | tbl_df | 222 | 4 |
DataExam6.2 | eda4treeR | Data for Example 6.2 from Experimental Design and Analysis for Tree Improvement | data.frame | 192 | 9 |
DataExam8.1 | eda4treeR | Data for Example 8.1 from Experimental Design and Analysis for Tree Improvement | tidytable | 236 | 7 |
DataExam8.2 | eda4treeR | Data for Example 8.2 from Experimental Design and Analysis for Tree Improvement | tbl_df | 300 | 13 |
example_data | ccml | The input data for example | data.frame | 10 | 5 |
locations | helminthR | Table of geographic location names, and associated coordinates | data.frame | 498 | 3 |
my_data | windsoraiR | Sample data from the Windsor API. | data.frame | 1677 | 6 |
pfms_dna | ggseqlogo | List of position frequency matrices for transcription factors | list | | |
seqs_aa | ggseqlogo | List of aligned kinase-substrate binding sequences | list | | |
seqs_dna | ggseqlogo | List of aligned transcription factor binding sequences | list | | |
housing | fasterElasticNet | Housing data from kaggle | data.frame | 10153 | 140 |
fred_md | BVAR | FRED-MD and FRED-QD: Databases for Macroeconomic Research | data.frame | 788 | 118 |
fred_qd | BVAR | FRED-MD and FRED-QD: Databases for Macroeconomic Research | data.frame | 262 | 233 |
Survey_WHO2007 | anthroplus | Sample Survey Data for the WHO 2007 References | data.frame | 933 | 12 |
HCAHPS2022 | rmcorr | Nested and multivariate survey measures of hospital patient experience and other measures | tbl_df | 53 | 14 |
bland1995 | rmcorr | Repeated measurements of intramural pH and PaCO2 | data.frame | 47 | 3 |
gilden2010 | rmcorr | Repeated measurements of reaction time and accuracy | data.frame | 44 | 4 |
marusich2016_exp2 | rmcorr | Repeated measurements of dyads performance and subjective situation awareness | data.frame | 84 | 4 |
raz2005 | rmcorr | Repeated measurements of age and cerebellar volume | data.frame | 144 | 4 |
twedt_dist_measures | rmcorr | Repeated measures and multivariate measures of perceived distance | data.frame | 230 | 7 |
endosulfan | ssd4mosaic | Summary of 48 to 96-hour acute toxicity values for endosulfan | data.frame | 88 | 3 |
fluazinam | ssd4mosaic | 48-hour acute toxicity values for fluazinam | data.frame | 14 | 4 |
salinity_family | ssd4mosaic | 72-hour acute salinity tolerance of macro-invertebrates grouped by family. | data.frame | 108 | 3 |
salinity_order | ssd4mosaic | 72-hour acute salinity tolerance of macro-invertebrates grouped by order. | data.frame | 108 | 3 |
new_long2 | JMbdirect | longitudinal- survival dataset | tbl_df | 5639 | 15 |
new_surv2 | JMbdirect | survival data | tbl_df | 1000 | 15 |
ARG_MAZ | sapfluxnetr | ARG_MAZ sapfluxnet site | sfn_data | | |
ARG_TRE | sapfluxnetr | ARG_TRE sapfluxnet site | sfn_data | | |
AUS_CAN_ST2_MIX | sapfluxnetr | AUS_CAN_ST2_MIX sapfluxnet site | sfn_data | | |
sfn_metadata_ex | sapfluxnetr | sfn_metadata cache file for example data (ARG_MAZ, ARG_TRE and AUS_CAN_ST2_MIX) | list | | |
deltadelta_data | dabestr | Data to produce a delta2 Dabest plot | tbl_df | 40 | 5 |
minimeta_data | dabestr | Data to produce a mini-meta Dabest plot | tbl_df | 120 | 4 |
non_proportional_data | dabestr | Non-proportional data for Estimation plots. | tbl_df | 180 | 4 |
proportional_data | dabestr | Numerical Binary data for Proportion Plots | tbl_df | 400 | 4 |
ecuador | sperrorest | J. Muenchow's Ecuador landslide data set | data.frame | 751 | 13 |
maipo | sperrorest | Fruit-tree crop classification: the Maipo dataset | data.frame | 7713 | 68 |
Johnston_Flight_heights_SOSS | stochLAB | Summarized flight height profiles from Johnston et al (2014) | data.frame | 30000 | 3 |
band_spreadsheet_dt | stochLAB | Parameter values and outputs from Band's Collision Risk spreadsheet ("Final_Report_SOSS02_BandSpreadSheetWorkedExampl1.xlsm") | list | | |
band_spreadsheet_dt_2 | stochLAB | Parameter values and outputs from Band's Collision Risk spreadsheet, example nr. 2 | list | | |
bird_pars_wide_example | stochLAB | Example of bird parameters stored in wide format | data.frame | 3 | 16 |
chord_prof_5MW | stochLAB | Rotor blade chord profile | data.frame | 21 | 2 |
dens_tnorm_wide_example | stochLAB | Example of Truncated Normal parameters for monthly estimates of bird density | data.frame | 3 | 25 |
generic_fhd_bootstraps | stochLAB | Bootstrap samples of generic FHDs of 25 seabird species | list | | |
rotor_grids_test | stochLAB | Sample rotor grids for generated_rotor_grids unit test | list | | |
turb_pars_wide_example | stochLAB | Example of turbine and windfarm parameters stored in wide format | data.frame | 3 | 51 |
wndspd_rtn_ptch_example | stochLAB | Example of data with relationship between wind speed, rotation speed and blade pitch | data.frame | 30 | 3 |
weight_behavior | BayesianMediationA | Weight_Behavior Data Set | data.frame | 691 | 15 |
simuGene | geeVerse | A Simulated Genetic Data from HapGen2 | matrix | 1000 | 500 |
yeastG1 | geeVerse | A Subset of Yeast Cell Cycle Gene Expression Data (G1 Phase) | data.frame | 1132 | 99 |
cascades | NetworkInference | Example cascades | cascade | | |
policies | NetworkInference | US State Policy Adoption (SPID) | data.frame | 17835 | 3 |
policies_metadata | NetworkInference | US State Policy Adoption (SPID) | data.frame | 728 | 7 |
sim_validation | NetworkInference | Larger simulated validation network. | list | | |
validation | NetworkInference | Validation output from netinf source. | data.frame | 5 | 6 |
LAB_DETAILS | labNorm | Available lab names | data.frame | 93 | 8 |
creatinine_data | labNorm | Example values of Hemoglobin and Creatinine | data.frame | 1000 | 3 |
hemoglobin_data | labNorm | Example values of Hemoglobin and Creatinine | data.frame | 1000 | 3 |
STEHE | VIC5 | Sample datasets of the Stehekin for the running of the VIC model provided by UW Hydro | list | | |
veglib_IGBP | VIC5 | IGBP vegetation library for VIC model | data.table | 17 | 58 |
myotis | bioacoustics | Audio recording of myotis species from United-Kingdom | Wave | | |
zc | bioacoustics | Audio recording of myotis species from United-Kingdom | zc | | |
EDH | sdam | Epigraphic Database Heidelberg dataset | dataset | | |
retn | sdam | Roman Empire transport network and Mediterranean region | list | | |
rp | sdam | Roman province names and acronyms as in EDH dataset | list | | |
rpd | sdam | Roman provinces dates from EDH dataset | list | | |
rpmcd | sdam | Caption maps and affiliation dates of Roman provinces | list | | |
rpmp | sdam | Maps of ancient Roman provinces and Italian regions | list | | |
covid19swiss | covid19swiss | The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Switzerland Outbreak Dataset | data.frame | 62200 | 7 |
GriffingData1 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 1 | data.frame | 256 | 4 |
GriffingData2 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 2 | data.frame | 144 | 4 |
GriffingData3 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 3 | data.frame | 224 | 4 |
GriffingData4 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 4 | data.frame | 112 | 4 |
HaymanData | DiallelAnalysisR | Data for Diallel Analysis using Hayman's Approach | data.frame | 256 | 4 |
PartialDiallelData | DiallelAnalysisR | Data for Partial Diallel Analysis | data.frame | 160 | 4 |
SAAD | ADMUR | Radiocarbon dataset for South American Arid Diagonal (SAAD) | data.frame | 1527 | 10 |
bluhm2421 | ADMUR | Radiocarbon dataset from Bluhm and Surovell 2018 | data.frame | 2421 | 4 |
bryson1848 | ADMUR | Radiocarbon dataset from Bryson et al. 2006 | data.frame | 1848 | 6 |
data1 | ADMUR | Toy radiocarbon dataset | data.frame | 100 | 4 |
data2 | ADMUR | Toy radiocarbon dataset | data.frame | 100 | 4 |
data3 | ADMUR | Toy radiocarbon dataset | data.frame | 100 | 4 |
data4 | ADMUR | Toy radiocarbon dataset | data.frame | 300 | 4 |
intcal13 | ADMUR | Northern hemisphere 2013 calibration curve | data.frame | 5141 | 3 |
intcal20 | ADMUR | Northern hemisphere 2020 calibration curve | data.frame | 9501 | 3 |
shcal13 | ADMUR | Southern hemisphere 2013 calibration curve | data.frame | 5141 | 3 |
shcal20 | ADMUR | Southern hemisphere 2020 calibration curve | data.frame | 9501 | 3 |
toy | ADMUR | Toy population model | data.frame | 4 | 2 |
IGT | hmmr | Iowa Gambling Task data | data.frame | 3000 | 8 |
MAR_simulation_results | hmmr | Missing at random (MAR) simulation results. | data.frame | 21 | 9 |
MNAR_simulation_results | hmmr | Missing not at random (MNAR) simulation results. | data.frame | 21 | 9 |
SEsamples | hmmr | Bootstrap Samples for Simple 2-State Model | matrix | 1000 | |
WPT | hmmr | Weather Prediction Task Data | data.frame | 9600 | 11 |
balance8 | hmmr | Repeated measures on the balance scale | data.frame | 8032 | 23 |
balance8pars | hmmr | Parameter estimates of models for the balance8 data set | list | | |
confint | hmmr | Confidence intervals Visser et al (2000) | data.frame | 10 | 9 |
conservation | hmmr | Conservation of liquid | data.frame | 101 | 6 |
dccs | hmmr | Dimensional Change Card Sort Task Data | data.frame | 93 | 15 |
dccs_boot_LR | hmmr | dccs boot LR | boot | | |
dccslong | hmmr | Dimensional Change Card Sort Task Data | data.frame | 558 | 5 |
disc42 | hmmr | Discrimination Learning Data | data.frame | 274 | 1 |
discrimination | hmmr | Discrimination Learning Data | data.frame | 3139 | 1 |
perth | hmmr | Perth dams water levels. | data.frame | 107 | 3 |
simplehmm | hmmr | Hmm toy data set from Visser et al (2000) | data.frame | 2000 | 1 |
speed1 | hmmr | Speed Accuracy Switching Data | data.frame | 168 | 3 |
speed_boot_LR | hmmr | speed boot LR | boot | | |
speed_boot_LR_extra | hmmr | speed boot LR | boot | | |
speed_boot_par | hmmr | speed boot par | boot | | |
DTcv | mixtox | critical value for Dunnett's test | matrix | 1050 | 8 |
antibiotox | mixtox | Toxicity of Seven Antibiotics on Photobacteria | list | | |
cytotox | mixtox | Cytotoxicity of Heavy Metal Ions and Ionic Liquids on MCF-7 | list | | |
hormesis | mixtox | Non-monotonic Concentration-response Data | list | | |
staval | mixtox | Starting Values for 13 Sigmoidal and 4 Hormetic Models | list | | |
Battig | WordPools | Battig - Montague Categorized Word Norms | data.frame | 5231 | 9 |
CatProp | WordPools | Joelson-Hermann Category Properties | data.frame | 56 | 24 |
Paivio | WordPools | Paivio, Yuille & Madigan Word Pool | data.frame | 925 | 9 |
TWP | WordPools | The Toronto Word Pool | data.frame | 1093 | 12 |
keyATM_data_bills | keyATM | Bills data | list | | |
epu | sentometrics | Monthly U.S. Economic Policy Uncertainty index | data.frame | 403 | 4 |
list_lexicons | sentometrics | Built-in lexicons | list | | |
list_valence_shifters | sentometrics | Built-in valence word lists | list | | |
usnews | sentometrics | Texts (not) relevant to the U.S. economy | data.frame | 4145 | 7 |
stocks | RTransferEntropy | Daily stock data for 10 stocks from 2000-2017 | data.table | 46940 | 4 |
easy_adtte | easysurv | Formatted Copy of ggsurvfit::adtte | tbl_df | 2199 | 19 |
easy_bc | easysurv | Formatted Copy of flexsurv::bc | data.frame | 686 | 4 |
easy_lung | easysurv | Formatted Copy of survival::lung | data.frame | 228 | 10 |
berlin | segmentr | Daily temperatures from weather stations in Berlin | matrix | 7 | 730 |
Weights | vibass | Weights of children | data.frame | 100 | 6 |
covidregionaldataUK | incidence2 | Regional data for COVID-19 cases in the UK | data.frame | 6370 | 13 |
all_mpas | dafishr | Marine Protected Areas (MPAs) of Mexico | sf | 24 | 6 |
mpas_buffers | dafishr | Buffer around remote Marine Protected Areas, MPAs, of Mexico | sf | 5 | 3 |
mx_coastline | dafishr | Mexican coastline | sf | 177 | 4 |
mx_coastline_buffer | dafishr | Buffer around the Mexican coastline | sf | 1 | 4 |
mx_eez | dafishr | Mexico shape | sf | 1 | 3 |
mx_eez_pacific | dafishr | Economic Exclusive Zone (EEZ) of the Pacific side of Mexico | sf | 1 | 2 |
mx_inland | dafishr | Area inland of Mexico | sf | 1 | 3 |
mx_ports | dafishr | Ports and Marinas of Mexico | sf | 237 | 3 |
mx_shape | dafishr | Mexico mainland | sf | 1 | 3 |
pacific_landings | dafishr | Catch data from the vessels in Mexico | grouped_df | 23231 | 6 |
pelagic_vessels_permits | dafishr | List of vessels with pelagic fishing permits | tbl_df | 719 | 2 |
remote_mpas | dafishr | Remote Marine Protected Areas (MPAs) of Mexico | sf | 5 | 3 |
sample_dataset | dafishr | Vessel Monitoring System, VMS, sample dataset from Mexican fishery commission | spec_tbl_df | 10000 | 9 |
drug_regimen_list | genieBPC | List of Drug Regimen Names by Cohort | tbl_df | 1490 | 4 |
genie_panels | genieBPC | Genomic Panels Included in GENIE BPC Data | data.frame | 12 | 3 |
nsclc_test_data | genieBPC | Simulated fake GENIE BPC data for function examples and tests | list | | |
regimen_abbreviations | genieBPC | List of Drug Regimen Abbreviations | data.frame | 12 | 2 |
synapse_tables | genieBPC | 'Synapse' table IDs | tbl_df | 186 | 5 |
ikeogu.2017 | waves | Example vis-NIRS and reference dataset | tbl_df | 175 | 2155 |
normforest | geodiv | NDVI errors for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
orelevation | geodiv | SRTM elevation for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
orforest | geodiv | NDVI for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
excalibdata | inctools | The dataset 'excalibdata.Rdata' contains example data from an evaluation of an assay measuring recency of infection. At an assay result of <10, the specimen is considered to be recently infected. It further contains viral load data, which is commonly used to reduce false recency. For example, when recency is defined as assay result <10 and viral load > 1000, the FRR is substantially lower (but the MDRI is also reduced). | data.frame | 1460 | 7 |
example_filtered_SNV_df | vivaldi | Example Dataframe The DF_filt_SNVs dataframe created in the vignette | tbl_df | 735 | 57 |
winequality | sgd | Wine quality data of white wine samples from Portugal | data.frame | 4898 | 12 |
AlgaeCO2 | abd | Carbon Dioxide and Growth Rate in Algae | data.frame | 14 | 2 |
Antilles | abd | Antilles Bird Immigration Dates | data.frame | 37 | 1 |
Aspirin | abd | Effects of Aspirin on Cancer Rates | data.frame | 39876 | 2 |
BeeGenes | abd | Foraging Gene Expression | data.frame | 6 | 3 |
BeeLifespans | abd | Bee Lifespans | data.frame | 33 | 1 |
Beetles | abd | Beetle Wings and Horns | data.frame | 19 | 2 |
BirdSexRatio | abd | Sex Ratios in Birds | data.frame | 15 | 1 |
Blackbirds | abd | Testosterone Levels in Blackbirds | data.frame | 13 | 6 |
BodyFatHeatLoss | abd | Heat Loss and Body Fat | data.frame | 12 | 2 |
BrainExpression | abd | Proteolipid Protein 1 Gene Expression | data.frame | 45 | 2 |
BrookTrout | abd | Salmon Survival in the Presence of Brook Trout | data.frame | 12 | 4 |
BrookTrout2 | abd | Salmon Survival in the Presence of Brook Trout | data.frame | 12 | 9 |
Cavalry | abd | Deaths from Horse Kicks | data.frame | 5 | 2 |
Chickadees | abd | Alarm Calls in Chickadees | data.frame | 13 | 3 |
ChimpBrains | abd | Brodmann's Area 44 in Chimps | data.frame | 20 | 3 |
Cichlids | abd | Cichlid Mating Preference | data.frame | 53 | 2 |
CichlidsGnRH | abd | GnRH Levels in Cichlids | data.frame | 11 | 2 |
Clearcuts | abd | Biomass Change in Rainforests near Clearcuts | data.frame | 36 | 1 |
CocaineDopamine | abd | Effects of Cocaine on Dopamine Receptors | data.frame | 34 | 2 |
Convictions | abd | Frequency of Convictions for a Cohort of English Boys | data.frame | 15 | 2 |
ConvictionsAndIncome | abd | Convictions and Income Level in a Cohort of English Boys | data.frame | 395 | 2 |
Crickets | abd | Immunity and Sperm Viability in Crickets | data.frame | 41 | 2 |
DEET | abd | DEET and Mosquito Bites | data.frame | 52 | 2 |
DaphniaLongevity | abd | Daphnia Longevity | data.frame | 32 | 2 |
DaphniaResistance | abd | Daphnia Resistance to Cyanobacteria | data.frame | 32 | 2 |
DayOfBirth | abd | Day of Birth | data.frame | 7 | 2 |
DesertBirds | abd | Desert Bird Census Data | data.frame | 43 | 2 |
Dioecy | abd | Dioecy vs. Monomorphism in Plants | data.frame | 28 | 3 |
Dolphins | abd | Dolphin Swimming Behavior | data.frame | 8 | 1 |
DungBeetles | abd | Heritability of Body Condition in Dung Beetles | data.frame | 36 | 2 |
Earthworms | abd | Earthworm Diversity and Soil Nitrogen Levels | data.frame | 39 | 2 |
Earwigs | abd | Earwig Density and Forceps | data.frame | 7 | 2 |
Eelgrass | abd | Eelgrass Genotypes | data.frame | 32 | 2 |
ElVerde | abd | Diet Breadth in a Rainforest Community | data.frame | 38 | 2 |
ElectricFish | abd | Electric Fish | data.frame | 12 | 3 |
EndangeredSpecies | abd | Endangered and Threatened Species | data.frame | 11 | 2 |
FingerRatio | abd | 2D:4D Finger Ratio | data.frame | 46 | 2 |
Fireflies | abd | Spermatophore Mass in Fireflies | data.frame | 35 | 1 |
FireflyFlash | abd | Firefly Flash Duration | data.frame | 35 | 1 |
FlyTestes | abd | Testes Size in Flies | data.frame | 8 | 2 |
FlycatcherPatch | abd | Forehead Patch Size in Collared Flycatachers | data.frame | 30 | 2 |
GeneRegulation | abd | Gene Regulation in Saccharomyces | data.frame | 26 | 2 |
GlidingSnakes | abd | GlidingSnakes | data.frame | 8 | 1 |
GodwitArrival | abd | Godwit Arrival Dates | data.frame | 10 | 2 |
Grassland | abd | Grassland Diversity | data.frame | 10 | 2 |
GreatTitMalaria | abd | Malaria in Populations of Great Tit | data.frame | 65 | 2 |
Greenspace | abd | Diversity in Urban Green Space | data.frame | 15 | 6 |
Guppies | abd | Ornamentation and Attractiveness in Guppies | data.frame | 36 | 2 |
Hemoglobin | abd | Hemoglobin Levels in High Altitude Populations | data.frame | 40 | 3 |
HippocampusLesions | abd | Memory and the Hippocampus | data.frame | 57 | 2 |
HornedLizards | abd | Horn Length and Predation Status of Horned Lizards | data.frame | 185 | 2 |
HumanBodyTemp | abd | Human Body Temperature | data.frame | 25 | 1 |
HumanGeneLengths | abd | Human Gene Lengths | data.frame | 20290 | 1 |
Hurricanes | abd | Intense Hurricanes | data.frame | 4 | 2 |
Iguanas | abd | Iguana Body Length Changes | data.frame | 64 | 1 |
IntertidalAlgae | abd | Intertidal Algae | data.frame | 64 | 3 |
JetLagKnees | abd | Circadian Rhythm Phase Shift | data.frame | 22 | 2 |
KenyaFinches | abd | Body Mass and Beak Length in Three Species of Finches in Kenya | data.frame | 45 | 3 |
LanguageBrains | abd | Brain Structure in Bilingual Humans | data.frame | 22 | 2 |
LarvalFish | abd | Exploited Larval Fish | data.frame | 28 | 3 |
Lefthanded | abd | Left-handedness and Rates of Violence | data.frame | 8 | 2 |
LionCubs | abd | Time to Reproduction in Female Lions | data.frame | 14 | 2 |
LionNoses | abd | Lion Age and Nose Coloration | data.frame | 32 | 2 |
LiverPreparation | abd | Liver Preparation | data.frame | 5 | 2 |
LizardBite | abd | Bite Force in Collard Lizards | data.frame | 11 | 2 |
LizardSprint | abd | Sprint Speeds in Canyon Lizards | data.frame | 68 | 2 |
Lobsters | abd | Lobster Orientation | data.frame | 15 | 1 |
LodgepolePines | abd | Lodgepole Pine Cone Masses | data.frame | 16 | 4 |
LupusMice | abd | Autoimmune Reactivity in Lupus-prone Mice | data.frame | 20 | 2 |
Lynx | abd | Population Cycles of Lynx in Canada 1752-1819 | data.frame | 68 | 2 |
MarineReserve | abd | Marine Reserve Biomass | data.frame | 32 | 1 |
MassExtinctions | abd | Mass Extinction Frequency | data.frame | 21 | 2 |
MoleRats | abd | Energy Expenditure in Mole Rats | data.frame | 35 | 3 |
Mosquitoes | abd | Body Size in Anopheles Mosquitoes | data.frame | 20 | 2 |
MouseEmpathy | abd | Mouse Empathy | data.frame | 42 | 3 |
NeanderthalBrains | abd | Cranial Capacity in Neanderthals and Modern Humans | data.frame | 39 | 3 |
NematodeLifespan | abd | Effects of Trimethadione on Lifespan in Nematodes | data.frame | 200 | 2 |
NeotropicalTrees | abd | Photosynthesis in Neotropical Trees | data.frame | 9 | 2 |
Newts | abd | Tetrodotoxin Resistance in Garter Snakes | data.frame | 12 | 2 |
NoSmokingDay | abd | No Smoking Day | data.frame | 10 | 3 |
NorthSeaCod | abd | Atlantic Cod Recruits | data.frame | 39 | 1 |
OstrichTemp | abd | Ostrich Body and Brain Temperatures | data.frame | 6 | 3 |
Penguins | abd | Penguin Heart Rate | data.frame | 24 | 2 |
PlantPersistence | abd | Population Persistence Times | data.frame | 16 | 2 |
Pollen | abd | Sterility in Hybrid Pollens | data.frame | 23 | 2 |
Powerball | abd | Powerball Tickets Sold | data.frame | 7 | 2 |
PrimateMetabolism | abd | Primate Metabolic Rates | data.frame | 17 | 2 |
PrimateWBC | abd | Primate White Blood Cell Counts and Promiscuity | data.frame | 9 | 2 |
ProgesteroneExercise | abd | Progesterone and Exercise | data.frame | 30 | 2 |
Pseudoscorpions | abd | Multiple Mating in Pseudoscorpions | data.frame | 36 | 2 |
Pufferfish | abd | Pufferfish Mimicry | data.frame | 20 | 2 |
Rattlesnakes | abd | Temperature Change and Meal Size in Rattlesnakes | data.frame | 17 | 2 |
Rigormortis | abd | Rigormortis and Time of Death | data.frame | 12 | 2 |
RopeTrick | abd | Indian Rope Trick | data.frame | 21 | 2 |
SagebrushCrickets | abd | Sagebrush Cricket Mating Times | data.frame | 24 | 2 |
SalmonColor | abd | Pacific Salmon Color | data.frame | 35 | 2 |
Seedlings | abd | Number of Seedlings Per Quadrat | data.frame | 8 | 2 |
Selection | abd | Data for Meta-analysis | data.frame | 814 | 8 |
SexualSelection | abd | Sexual Conflict | data.frame | 25 | 4 |
ShadParasites | abd | Shad Parasites | data.frame | 7 | 2 |
ShrinkingSeals | abd | Seal Body Lengths and Age | data.frame | 9665 | 2 |
ShuttleDisaster | abd | Ambient Temperature and O-Ring Failures | data.frame | 23 | 2 |
Silversword | abd | Rate of Speciation in Silverswords | data.frame | 21 | 1 |
SleepAndPerformance | abd | Sleep and Learning | data.frame | 10 | 2 |
SockeyeFemales | abd | Body Masses of Female Sockeye Salmon | data.frame | 228 | 1 |
Sparrows | abd | Lifetime Reproductive Success in House Sparrows | data.frame | 9 | 3 |
SpiderColonies | abd | Social Spiders | data.frame | 17 | 3 |
SpiderSpeed | abd | Spider Running Speeds after Amputation | data.frame | 16 | 2 |
Stalkies1 | abd | Eye Widths in Stalk-Eyed Flies | data.frame | 9 | 1 |
Stalkies2 | abd | Stalk-eyed Fly Eyespan | data.frame | 45 | 2 |
SticklebackPlates | abd | Number of Lateral Plates in Sticklebacks | data.frame | 344 | 2 |
SticklebackPreference | abd | Mating Preferences in Sticklebacks | data.frame | 9 | 1 |
Sumo | abd | Sumo Wrestling Wins | data.frame | 16 | 2 |
SyrupSwimming | abd | Syrup Swimming | data.frame | 18 | 1 |
TeenDeaths | abd | Causes of Teenage Deaths | data.frame | 11 | 2 |
Telomeres | abd | Telomere Shortening | data.frame | 39 | 2 |
TimeOfDeath | abd | Hypoxanthine and Time Since Death | data.frame | 48 | 2 |
Toads | abd | Right-handed Toads | data.frame | 19 | 2 |
Tobacco | abd | Flower Length in Tobacco Plants | data.frame | 13 | 3 |
Tobacco2 | abd | Flower Length in Tobacco Plants | data.frame | 617 | 2 |
ToothAge | abd | Radioactive Teeth | data.frame | 20 | 2 |
TreeSeedlings | abd | Tree Seedlings and Sunflecks | data.frame | 21 | 2 |
Trematodes | abd | Frequencies of Fish Eaten by Trematode Infection Level | data.frame | 141 | 2 |
Trillium | abd | Trillium Recruitment near Clearcuts | data.frame | 8 | 3 |
Truffles | abd | Truffle Distribution | data.frame | 5 | 2 |
TsetseLearning | abd | Dietary Learning in Tsetse Flies | data.frame | 13 | 2 |
TwoKids | abd | Number of Boys in Two-Child Families | data.frame | 3 | 2 |
VampireBites | abd | Vampire Bat Bites | data.frame | 4 | 3 |
VasopressinVoles | abd | Vasopressin Manipulation in the Meadow Vole | data.frame | 31 | 2 |
Vines | abd | Climbing Vines | data.frame | 48 | 2 |
VoleDispersal | abd | Home Range Size in Field Voles | data.frame | 5 | 3 |
WalkingStickFemurs | abd | Walking Stick Femur Length | data.frame | 50 | 2 |
WalkingStickHeads | abd | Walking Stick Head Width | data.frame | 50 | 2 |
WeddellSeals | abd | Energetic Cost of Diving | data.frame | 10 | 3 |
WillsDebates | abd | Presidential "Wills" | data.frame | 8 | 6 |
WillsPresidents | abd | Presidential "Wills" | data.frame | 16 | 6 |
WolfTeeth | abd | Wolf Tooth Measurements | data.frame | 35 | 1 |
Wolves | abd | Inbreeding in Wolves | data.frame | 24 | 2 |
WorldCup | abd | World Cup Goals | data.frame | 7 | 2 |
WrasseSexes | abd | Distribution of Wrasses | data.frame | 3 | 3 |
YeastGenes | abd | Yeast Regulatory Genes | data.frame | 6 | 2 |
ZebraFinchBeaks | abd | Mate Preference in Zebra Finches | data.frame | 10 | 1 |
ZebraFinches | abd | Zebra Finch Carotenoids | data.frame | 20 | 3 |
ZooMortality | abd | Home Range Size and Mortality | data.frame | 20 | 2 |
Zooplankton | abd | Zooplankton Depredation | data.frame | 15 | 3 |
dataInfo | abd | 'abd' Data Sets | data.frame | 143 | 5 |
aud | nullabor | Conversion rate of 1 Australian Doller (AUD) to 1 US Dollar | data.frame | 44 | 2 |
electoral | nullabor | Polls and election results from the 2012 US Election | list | | |
lal | nullabor | Los Angeles Lakers play-by-play data. | data.frame | 17235 | 33 |
tips | nullabor | Tipping data | data.frame | 244 | 7 |
turk_results | nullabor | Sample turk results | data.frame | 95 | 4 |
wasps | nullabor | Wasp gene expression data. | data.frame | 50 | 43 |
directors | soc.ca | Directors dataset | data.frame | 100 | 204 |
moschidis | soc.ca | Moschidis example | data.frame | 68 | 4 |
pe13 | soc.ca | The Field of the Danish Power Elite | tbl_df | 423 | 46 |
political_space97 | soc.ca | French Political Space example | tbl_df | 2980 | 22 |
taste | soc.ca | Taste dataset | data.frame | 1253 | 9 |
simdata | LPWC | Example datasets for LPC | data.frame | 200 | 10 |
conceptual_diagram_data | MIMSunit | The input accelerometer data used to generate the conceptual diagram (Figure 1) in the manuscript. | data.frame | 1704 | 5 |
cv_different_algorithms | MIMSunit | Coefficient of variation values for different acceleration data summary algorithms | data.frame | 30 | 3 |
edge_case | MIMSunit | A short snippet of raw accelerometer signal from a device that has ending data maxed out. | data.frame | 20001 | 4 |
measurements_different_devices | MIMSunit | The mean and standard deviation of accelerometer summary measure for different acceleration data summary algorithms and for different devices. | data.frame | 235 | 8 |
rest_on_table | MIMSunit | A short snippet of raw accelerometer signal from a device resting on a table. | data.frame | 4999 | 4 |
sample_raw_accel_data | MIMSunit | Sample raw accelerometer data | data.frame | 480 | 4 |
discharge_df | streamDepletr | Streamflow for Sixmile Creek and Dorn Creek. | data.frame | 1460 | 3 |
stream_lines | streamDepletr | Stream network for Sixmile Creek Watershed, Wisconsin, USA. Extracted from US NHDPlus v2.1 national seamless dataset. | sf | 49 | 3 |
oisst | palr | Sea surface temperature (SST). | matrix | 160 | |
Npop | forestPSD | Data for forest population number of within different age class. | data.frame | 11 | 3 |
db_function_all_patients_table_template | ReviewR | Database Table Function: All Patients Table Template | character | | |
db_function_subject_table_template | ReviewR | Database Table Function: Subject Table Template | character | | |
db_module_template | ReviewR | Database Module Template | character | | |
redcap_survey_complete | ReviewR | REDCap Survey Complete | tbl_df | 3 | 2 |
redcap_widget_map | ReviewR | REDCap Widget Map | tbl_df | 9 | 3 |
supported_data_models | ReviewR | Supported Data Model Schemas | grouped_df | 12 | 4 |
synPUF | ReviewR | synPUF | tbl_df | 23 | 2 |
lawyers.adjacency.advice | MEclustnet | Adjacency matrix detailing the presence or absence of advice links between the 'Lazega Lawyers'. | matrix | 71 | 71 |
lawyers.adjacency.coworkers | MEclustnet | Adjacency matrix detailing the presence or absence of coworker links between the 'Lazega Lawyers'. | matrix | 71 | 71 |
lawyers.adjacency.friends | MEclustnet | Adjacency matrix detailing the presence or absence of friendship links between the 'Lazega Lawyers'. | matrix | 71 | 71 |
lawyers.covariates | MEclustnet | A matrix of covariates of the 'Lazega Lawyers'. | data.frame | 71 | 8 |
us.twitter.adjacency | MEclustnet | Directed adjacency matrix detailing the presence or absence of Twitter friend/follower links between US politicians. | matrix | 69 | |
us.twitter.covariates | MEclustnet | A matrix of covariates of the US politicians. | data.frame | 69 | 8 |
carrot | LinkageMapView | a carrot comparative linkage map data frame kindly provided by Massimo Iorizzo: Cavagnaro et al. BMC Genomics 2014, 15:1118 | data.frame | 126 | 3 |
oat | LinkageMapView | oat consensus map data frame | data.frame | 16668 | 3 |
package_rss | newscatcheR | RSS table from python package newscatcher | spec_tbl_df | 4505 | 7 |
tokyo2005 | gwpcormapper | Tokyo 2005 Census Data. | sf | 3134 | 229 |
Census2000 | SGDinference | Census2000 | data.frame | 26120 | 3 |
dentus | evolqg | Example multivariate data set | data.frame | 300 | 5 |
dentus.tree | evolqg | Tree for dentus example species | phylo | | |
ratones | evolqg | Linear distances for five mouse lines | data.frame | 329 | 47 |
ECSI | RGCCA | European Customer Satisfaction Index | data.frame | 250 | 24 |
Russett | RGCCA | Russett data | data.frame | 47 | 11 |
CAN01AD002 | CSHShydRology | Streamflow data | data.frame | 32234 | 2 |
CAN05AA008 | CSHShydRology | CAN05AA008 | data.frame | 25252 | 5 |
HYDAT_list | CSHShydRology | List of Water Survey of Canada hydrometic stations. | data.frame | 7791 | 20 |
flowAtlantic | CSHShydRology | Annual maxima from sites in the Atlantic region of Canada | list | | |
example_data | eHDPrep | Example data for eHDPrep | tbl_df | 1000 | 12 |
example_edge_tbl | eHDPrep | Example ontology as an edge table for semantic enrichment | tbl_df | 25 | 2 |
example_mapping_file | eHDPrep | Example mapping file for semantic enrichment | tbl_df | 12 | 2 |
example_ontology | eHDPrep | Example ontology as a network graph for semantic enrichment | tbl_graph | | |
background | inldata | Background Concentrations | data.frame | 73 | 5 |
benchmarks | inldata | Benchmark Concentrations | data.frame | 89 | 10 |
cities | inldata | Cities and Towns | sf | 11 | 3 |
counties | inldata | County Boundaries | sf | 8 | 3 |
crs | inldata | Coordinate Reference System | crs | | |
dem | inldata | Digital Elevation Model | PackedSpatRaster | | |
dl | inldata | Laboratory Detection Limits | data.frame | 10 | 5 |
esrp | inldata | Eastern Snake River Plain Boundary | sf | 1 | 1 |
facilities | inldata | Idaho National Laboratory Facilities | sf | 7 | 3 |
gwl | inldata | Groundwater Levels | data.frame | 85870 | 10 |
idaho | inldata | State of Idaho Boundary | sf | 1 | 1 |
inl | inldata | Idaho National Laboratory Boundary | sf | 1 | 1 |
iwd | inldata | Industrial Waste Ditch | sf | 1 | 1 |
lakes | inldata | Lakes and Ponds | sf | 53 | 6 |
mountains | inldata | Mountain Ranges and Buttes | sf | 15 | 2 |
parameters | inldata | Parameter Information | data.frame | 171 | 10 |
percponds | inldata | Percolation Ponds | sf | 47 | 5 |
roads | inldata | Road Network | sf | 7094 | 4 |
samples | inldata | Discrete Sample Data | data.frame | 427523 | 24 |
sites | inldata | Site Information | sf | 533 | 26 |
streams | inldata | Rivers and Streams | sf | 912 | 6 |
swm | inldata | Surface-Water Measurements | data.frame | 1545 | 7 |
units | inldata | Units of Measurement | data.frame | 19 | 3 |
apodemus | Momocs | Data: Outline coordinates of Apodemus (wood mouse) mandibles | Out | | |
bot | Momocs | Data: Outline coordinates of beer and whisky bottles. | Out | | |
chaff | Momocs | Data: Landmark and semilandmark coordinates on cereal glumes | Ldk | | |
charring | Momocs | Data: Outline coordinates from an experimental charring on cereal grains | Out | | |
flower | Momocs | Data: Measurement of iris flowers | TraCoe | | |
hearts | Momocs | Data: Outline coordinates of hand-drawn hearts | Out | | |
molars | Momocs | Data: Outline coordinates of 360 molars | Out | | |
mosquito | Momocs | Data: Outline coordinates of mosquito wings. | Out | | |
mouse | Momocs | Data: Outline coordinates of mouse molars | Out | | |
nsfishes | Momocs | Data: Outline coordinates of North Sea fishes | Out | | |
oak | Momocs | Data: Configuration of landmarks of oak leaves | Ldk | | |
olea | Momocs | Data: Outline coordinates of olive seeds open outlines. | Opn | | |
shapes | Momocs | Data: Outline coordinates of various shapes | Out | | |
trilo | Momocs | Data: Outline coordinates of cephalic outlines of trilobite | Out | | |
wings | Momocs | Data: Landmarks coordinates of mosquito wings | Ldk | | |
lgr2 | burnr | Los Griegos Peak plot2 fire-history data | fhx | 2681 | 3 |
lgr2_meta | burnr | Metadata for the Los Griegos Peak fire-history dataset | data.frame | 26 | 2 |
pgm | burnr | Peggy Mesa fire-history data | fhx | 2395 | 3 |
pgm_meta | burnr | Metadata for the Peggy Mesa fire-history dataset | data.frame | 41 | 5 |
pgm_pdsi | burnr | Reconstructed PDSI time series for the Peggy Mesa fire-history dataset | data.frame | 2004 | 1 |
pme | burnr | Pajarito Mountain East fire-history data | fhx | 1960 | 3 |
pmr | burnr | Pajarito Mountain Ridge fire-history data | fhx | 4119 | 3 |
pmw | burnr | Pajarito Mountain West fire-history data | fhx | 1311 | 3 |
COSMIC_v3.0 | cosmicsig | Mutational signatures data from COSMIC, Catalogue Of Somatic Mutations In Cancer (v3.0 - May 2019) | list | | |
COSMIC_v3.1 | cosmicsig | Mutational signatures data from COSMIC, Catalogue Of Somatic Mutations In Cancer (v3.1 - June 2020) | list | | |
COSMIC_v3.2 | cosmicsig | Mutational signatures data from COSMIC, Catalogue Of Somatic Mutations In Cancer (v3.2 - March 2021) | list | | |
COSMIC_v3.3 | cosmicsig | Mutational signatures data from COSMIC, Catalogue Of Somatic Mutations In Cancer (v3.3 - June 2022) | list | | |
etiology | cosmicsig | List of mutational signatures's proposed etiology summarized from COSMIC, Catalogue Of Somatic Mutations In Cancer (v3.3 - June 2022) | li |