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 |
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 |
Census2000 | SGDinference | Census2000 | data.frame | 26120 | 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 | | |
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) | list | | |
signature | cosmicsig | Mutational signatures data from COSMIC, Catalogue Of Somatic Mutations In Cancer (v3.3 - June 2022) | 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 | | |
ECSI | RGCCA | European Customer Satisfaction Index | data.frame | 250 | 24 |
Russett | RGCCA | Russett data | data.frame | 47 | 11 |
tokyo2005 | gwpcormapper | Tokyo 2005 Census Data. | sf | 3134 | 229 |
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 | | |
leeds_buildings | abstr | Datasets from Leeds | sf | 491 | 3 |
leeds_desire_lines | abstr | Datasets from Leeds | sf | 3 | 11 |
leeds_houses | abstr | Datasets from Leeds | sf | 12 | 3 |
leeds_od | abstr | Datasets from Leeds | tbl_df | 3 | 7 |
leeds_site_area | abstr | Datasets from Leeds | sf | 1 | 33 |
leeds_zones | abstr | Datasets from Leeds | sf | 6 | 11 |
montlake_buildings | abstr | Example OSM Buildings Table for Montlake | sf | 2848 | 4 |
montlake_od | abstr | Example OD Table for Montlake | tbl_df | 301 | 6 |
montlake_zones | abstr | Example Zones Table for Montlake | sf | 74 | 2 |
sao_paulo_activity_df_2 | abstr | Example Activity data for São Paulo | tbl_df | 6 | 9 |
sao_paulo_activity_df_20 | abstr | Example Activity data for São Paulo | tbl_df | 88 | 9 |
sao_paulo_activity_sf_2 | abstr | Example Activity data for São Paulo | sf | 6 | 5 |
sao_paulo_activity_sf_20 | abstr | Example Activity data for São Paulo | sf | 88 | 5 |
largerawsample | LKT | Trial sequences for practice participants. | data.frame | 58316 | 29 |
samplelkt | LKT | Trial sequences for practice participants. | data.table | 1941 | 4 |
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 |
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 |
EFM | ATbounds | EFM | data.frame | 14484 | 6 |
RHC | ATbounds | RHC | data.frame | 5735 | 74 |
legumesIds | geneHummus | NCBI taxonomy ids for the legume family | numeric | | |
my_legumes | geneHummus | ARF proteins per legume specie | list | | |
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 |
groups | sRdpData | All self-determination movements | data.frame | 5549 | 5 |
orgs | sRdpData | All self-determination movements' and their factions' use of violent and non-violent tactics | data.frame | 12017 | 19 |
affective | psycho | Data from the Affective Style Questionnaire (ASQ - French Validation) | data.frame | 1251 | 8 |
emotion | psycho | Emotional Ratings of Pictures | data.frame | 912 | 11 |
gp | encryptr | General Practioner (family doctor) practices in Scotland 2018 | spec_tbl_df | 1212 | 12 |
data_test_medmod | betaselectr | Test Dataset with Moderator and Mediator | data.frame | 200 | 6 |
data_test_mod_cat | betaselectr | Test Dataset with Moderator and Categorical Variables | data.frame | 500 | 5 |
data_test_mod_cat2 | betaselectr | Test Dataset with Moderator and Categorical Variables (Version 2) | data.frame | 300 | 5 |
data_test_mod_cat_binary | betaselectr | Test Dataset with a Binary Outcome Variable | data.frame | 300 | 5 |
rem_synthdata | rollmatch | Synthetic dataset to illustrate rolling entry | data.frame | 254400 | 20 |
rem_synthdata_small | rollmatch | Synthetic dataset to illustrate rolling entry (small) | data.frame | 12720 | 20 |
Survey_WHO2007 | anthroplus | Sample Survey Data for the WHO 2007 References | data.frame | 933 | 12 |
holiDaysBOG | quantdates | Bogota holidays dates. | Date | | |
holiDaysLDN | quantdates | London holidays dates. | Date | | |
holiDaysNY | quantdates | New York Stock Exchange holidays dates. | Date | | |
holiDaysNYGB | quantdates | New York Government Bonds holidays dates. | Date | | |
wdBOG | quantdates | Bogota business dates. | Date | | |
wdLDN | quantdates | London business dates. | Date | | |
wdNY | quantdates | New York Stock Exchange business dates. | Date | | |
wdNYGB | quantdates | New York Government Bonds business dates. | Date | | |
HSImetadata | ecorest | Habitat suitability index (HSI) model metadata | data.frame | 351 | 85 |
HSImodels | ecorest | Habitat suitability index (HSI) models | list | | |
cov_refor | binspp | Distance to the reforestration polygon | im | | |
cov_reserv | binspp | Distance to the reservoir | im | | |
cov_slope | binspp | Slope of the area | im | | |
cov_tdensity | binspp | Trees density | im | | |
cov_tmi | binspp | Topographic moisture index | im | | |
trees_N4 | binspp | Spanish oak trees | ppp | | |
x_left_N4 | binspp | Left horizontal corners for trees_N4 dataset | numeric | | |
x_right_N4 | binspp | Right horizontal corners for trees_N4 dataset | numeric | | |
y_bottom_N4 | binspp | Bottom vertical corners for trees_N4 dataset | numeric | | |
y_top_N4 | binspp | Vertical corners for trees_N4 dataset | numeric | | |
B1000 | precrec | Balanced data with 1000 positives and 1000 negatives. | list | | |
B500 | precrec | Balanced data with 500 positives and 500 negatives. | list | | |
IB1000 | precrec | Imbalanced data with 1000 positives and 10000 negatives. | list | | |
IB500 | precrec | Imbalanced data with 500 positives and 5000 negatives. | list | | |
M2N50F5 | precrec | 5-fold cross validation sample. | data.frame | 50 | 4 |
P10N10 | precrec | A small example dataset with several tied scores. | list | | |
votes | sejmRP | Votes from 7th Office of Polish Sejm | data.frame | 2890479 | 9 |
binary | MCPModPack | Example data set with a binary endpoint | data.frame | 100 | 2 |
count | MCPModPack | Example data set with a count endpoint | data.frame | 100 | 2 |
normal | MCPModPack | Example data set with a continuous endpoint | data.frame | 100 | 2 |
rec.n | GMSE | R data for recruitment used in SI4 vignette | data.frame | 1 | 45 |
ssb.n | GMSE | R data for spawning stock biomass used in SI4 vignette | data.frame | 1 | 45 |
test_df | PVplr | DOE RTC Sample PV System Data | data.frame | 85380 | 5 |
example_data | eventstudyr | Sample dataset obtained from the replication archive for Freyaldenhoven et al. (2021) | tbl_df | 2000 | 12 |
community | traitstrap | Community data | tbl_df | 110 | 4 |
trait | traitstrap | Trait data | tbl_df | 705 | 6 |
corp_rep_data | seminr | Measurement Instrument for the Corporate Reputation Model | data.frame | 344 | 50 |
corp_rep_data2 | seminr | A Second Measurement Instrument for the Corporate Reputation Model | data.frame | 347 | 41 |
influencer_data | seminr | Measurement Instrument for the Influencer Model | data.frame | 222 | 28 |
mobi | seminr | Measurement Instrument for the Mobile Phone Industry | data.frame | 250 | 24 |
USpayems | midasr | United States total employment non-farms payroll, monthly, seasonally adjusted. | ts | 903 | 1 |
USqgdp | midasr | United States gross domestic product, quarterly, seasonaly adjusted annual rate. | ts | 268 | 1 |
USrealgdp | midasr | US annual gross domestic product in billions of chained 2005 dollars | ts | | |
USunempr | midasr | US monthly unemployment rate | ts | | |
oos_prec | midasr | Out-of-sample prediction precision data on simulation example | data.frame | 42 | 4 |
rvsp500 | midasr | Realized volatility of S&P500 index | data.frame | 3459 | 2 |
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 |
bgd_msna | tidyrgee | A subset of question responses from the 2019 Host Community MSNA in Bangladesh | tbl_df | 1374 | 15 |
beaked_whale | tagtools | Set of sensor lists for a beaked_whale | animaltag | | |
harbor_seal | tagtools | Set of sensor lists for a harbor seal | animaltag | | |
state_names | zctaCrosswalk | Metadata for Each "State" in zcta_crosswalk | tbl_df | 56 | 4 |
zcta_crosswalk | zctaCrosswalk | 2020 Crosswalk of ZIP Code Tabulation Areas (ZCTAs) | tbl_df | 46960 | 9 |
demonchoice | LaplacesDemon | Demon Choice Data Set | data.frame | 151 | 9 |
demonfx | LaplacesDemon | Demon FX Data Set | data.frame | 1301 | 39 |
demonsessions | LaplacesDemon | Demon Sessions Data Set | data.frame | 26 | 6 |
demonsnacks | LaplacesDemon | Demon Snacks Data Set | data.frame | 39 | 10 |
demontexas | LaplacesDemon | Demon Space-Time Data Set | data.frame | 369 | 43 |
geneLists | diffEnrich | geneLists | list | | |
kegg | diffEnrich | kegg | list | | |
useR2016 | forwards | Data From useR! 2016 Survey | data.frame | 455 | 47 |
CBF | jmotif | A standard UCR Cylinder-Bell-Funnel dataset from http://www.cs.ucr.edu/~eamonn/time_series_data | list | | |
Gun_Point | jmotif | A standard UCR Gun Point dataset from http://www.cs.ucr.edu/~eamonn/time_series_data | list | | |
ecg0606 | jmotif | A PHYSIONET dataset | numeric | | |
lake | elevatr | SpatialPolygonsDataFrame of Lake Sunapee | sf | 1 | 14 |
pt_df | elevatr | Small data frame of xy locations | data.frame | 5 | 2 |
sf_big | elevatr | A sf POINT dataset of random points | sf | 250 | 1 |
campaigns | fec16 | House/Senate Current Campaigns | spec_tbl_df | 1898 | 25 |
candidates | fec16 | Candidates Master metadata | spec_tbl_df | 4699 | 15 |
committees | fec16 | Committees metadata | spec_tbl_df | 17654 | 15 |
contributions | fec16 | Committee contributions metadata | spec_tbl_df | 1000 | 15 |
expenditures | fec16 | Operating Expenditures | spec_tbl_df | 1000 | 20 |
individuals | fec16 | Individual Contributions Master metadata | spec_tbl_df | 1000 | 16 |
pac | fec16 | Political Action Committee (PAC) and Party Summary Financial Information | spec_tbl_df | 12049 | 27 |
results_house | fec16 | House Election Results | tbl_df | 2110 | 13 |
results_president | fec16 | Presidential Election Results | tbl_df | 619 | 6 |
results_senate | fec16 | Senate Election Results | tbl_df | 377 | 10 |
states | fec16 | US States lookup table | tbl_df | 50 | 4 |
transactions | fec16 | Any Transaction From One Committee To Another | spec_tbl_df | 1000 | 16 |
geocod_base | utilsIPEA | Brazilian address | data.table | 5 | 5 |
cran_inventory | CRANsearcher | CRAN inventory snapshot | data.frame | 10902 | 7 |
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 | | |
CRANdf | collidr | CRAN Package and Function Data: 1 May 2019 | data.frame | 294190 | 2 |
expr1 | lineup | Example gene expression data | matrix | 98 | 5000 |
expr2 | lineup | Example gene expression data | matrix | 98 | 5000 |
f2cross | lineup | Example experimental cross data | f2 | | |
genepos | lineup | Genomic positions of genes in simulated expression data | data.frame | 5000 | 2 |
pmap | lineup | Physical map of markers | map | | |
motifs_discords_small | matrixprofiler | Just a synthetic dataset for testing | numeric | | |
sb_cat | LPDynR | Standing Biomass | RasterBrick | | |
sbd_cat | LPDynR | Season Beginning Day | RasterBrick | | |
sl_cat | LPDynR | Season Length | RasterBrick | | |
survey_ghl | ggFishPlots | Greenland halibut measurements from IMR surveys | tbl_df | 618779 | 5 |
artificial2sls | ivgets | Artificial data set for illustration. | data.frame | 100 | 16 |
artificial2sls_contaminated | ivgets | Artificial data set with outliers for illustration. | data.frame | 100 | 16 |
artificial2sls_shiny | ivgets | Artificial data set without outliers prepared for shiny application. | data.frame | 100 | 17 |
example_clinical | spatialTIME | Clinical variables of 229 patients | tbl_df | 229 | 6 |
example_spatial | spatialTIME | Example list of 5 spatial TMA data | list | | |
example_summary | spatialTIME | Marker summaries of 229 samples | tbl_df | 229 | 29 |
GSE96870_intro | rWSBIM1207 | RNA-seq data from Blackmore et al. 2017 | RangedSummarizedExperiment | | |
GSE96870_intro_ranges | rWSBIM1207 | RNA-seq data from Blackmore et al. 2017 | RangedSummarizedExperiment | | |
beers | rWSBIM1207 | Beer consumption data | data.frame | 48 | 8 |
clinical1 | rWSBIM1207 | TCGA data | tbl_df | 516 | 15 |
clinical2 | rWSBIM1207 | TCGA data | tbl_df | 516 | 3 |
clinical_table_ex1 | rWSBIM1207 | TCGA data | tbl_df | 2 | 3 |
expression | rWSBIM1207 | TCGA data | tbl_df | 570 | 8 |
interroC | rWSBIM1207 | Practice datasets | tbl_df | 15 | 4 |
interroL | rWSBIM1207 | Practice datasets | tbl_df | 820 | 7 |
jdf1 | rWSBIM1207 | Data illustrating join operations | tbl_df | 25 | 3 |
jdf2 | rWSBIM1207 | Data illustrating join operations | tbl_df | 25 | 4 |
jdf3 | rWSBIM1207 | Data illustrating join operations | tbl_df | 25 | 4 |
jdf4 | rWSBIM1207 | Data illustrating join operations | tbl_df | 10 | 3 |
jdf5 | rWSBIM1207 | Data illustrating join operations | tbl_df | 5 | 4 |
jdf6 | rWSBIM1207 | Data illustrating join operations | tbl_df | 5 | 4 |
jdf7 | rWSBIM1207 | Data illustrating join operations | tbl_df | 5 | 6 |
mvylng | rWSBIM1207 | Data from Mulvey et al. 2015 | tbl_df | 42066 | 7 |
peptides | rWSBIM1207 | A vector of peptide sequences | character | | |
my_tosr_load | tosr | A list with data to create ToS | list | | |
penobscot | pals | Seismic data horizon offshore of Nova Scotia. | matrix | 463 | 595 |
choicedata | RSGHB | A synthetic discrete choice dataset | data.frame | 10242 | 7 |
NSFG_data | surveyCV | Subset of the 2015-2017 National Survey of Family Growth (NSFG): one birth per respondent. | data.frame | 2801 | 17 |
NSFG_data_everypreg | surveyCV | Subset of the 2015-2017 National Survey of Family Growth (NSFG): all live births per respondent. | data.frame | 5089 | 17 |
site_sp_birds | fundiversity | Site-species matrix of birds along a Tropical Gradient | matrix | 8 | 217 |
site_sp_plants | fundiversity | Site-species matrix of plants along a Tropical Gradient | matrix | 10 | 392 |
traits_birds | fundiversity | Functional Traits of Frugivorous Birds along a Tropical Gradient | matrix | 217 | 4 |
traits_plants | fundiversity | Functional Traits of Fleshy-fruit plants along a Tropical Gradient | matrix | 392 | 4 |
s58 | FreqProf | Occurrence/nonoccurrence data for four behaviors from a single subject | data.frame | 3092 | 4 |
data_category | vegawidget | Example dataset: Categorical data | tbl_df | 10 | 2 |
data_seattle_daily | vegawidget | Example dataset: Seattle daily weather | spec_tbl_df | 1461 | 6 |
data_seattle_hourly | vegawidget | Example dataset: Seattle hourly temperatures | tbl_df | 8759 | 2 |
spec_mtcars | vegawidget | Example vegaspec: mtcars scatterplot | vegaspec_unit | | |
classification_test_data | aai | Fictive data set used to demonstrate some concepts on classification | spec_tbl_df | 10 | 2 |
classification_train_data | aai | Fictive data set used to demonstrate some concepts on classification | spec_tbl_df | 150 | 3 |
perceptron31 | aai | Fictive data set used to demonstrate some concepts in perceptron document | data.frame | 31 | 3 |
test_smet | smetlite | Test SMET data | smet | 47 | 5 |
Books | greed | Books about US politics network dataset | list | | |
Fifa | greed | Fifa data | tbl_df | 6000 | 30 |
Football | greed | American College football network dataset | list | | |
Jazz | greed | Jazz musicians network dataset | dgCMatrix | | |
Ndrangheta | greed | Ndrangheta mafia covert network dataset | list | | |
NewGuinea | greed | NewGuinea data | array | | |
SevenGraders | greed | SevenGraders data | array | | |
Youngpeoplesurvey | greed | Young People survey data | spec_tbl_df | 1010 | 19 |
fashion | greed | Fashion mnist dataset | matrix | 1000 | 784 |
mushroom | greed | Mushroom data | data.frame | 8124 | 23 |
trial | gtsummary | Results from a simulated study of two chemotherapy agents | tbl_df | 200 | 8 |
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 |
OC | spup | Soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar. | RasterLayer | | |
OC_sd | spup | Standard deviation of soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar. | RasterLayer | | |
TN | spup | Soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar. | RasterLayer | | |
TN_sd | spup | Standard deviation of soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar. | RasterLayer | | |
dem30m | spup | Digital Elevation Model of Zlatibor region in Serbia. | SpatialGridDataFrame | | |
dem30m_sd | spup | Standard deviation of Digital Elevation Model of Zlatibor region in Serbia. | SpatialGridDataFrame | | |
woon | spup | Neighbourhood in Rotterdam. | SpatialPolygonsDataFrame | | |
Aeut | poppr | Oomycete root rot pathogen *Aphanomyces euteiches* AFLP data | genind | | |
Pinf | poppr | Phytophthora infestans data from Mexico and South America. | genclone | | |
Pram | poppr | Phytophthora ramorum data from OR Forests and Nurseries (OR and CA) | genclone | | |
monpop | poppr | Peach brown rot pathogen *Monilinia fructicola* | genclone | | |
old_Pinf | poppr | Phytophthora infestans data from Mexico and South America. | genclone | | |
old_partial_clone | poppr | Simulated data illustrating a Minimum Spanning Network based on Bruvo's Distance | genind | | |
partial_clone | poppr | Simulated data illustrating a Minimum Spanning Network based on Bruvo's Distance | genind | | |
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 | | |
lsTamra | serrsBayes | Surface-enhanced Raman spectram of tetramethylrhodamine+DNA (T20) | list | | |
methanol | serrsBayes | Raman spectrum of methanol (CH3OH) | list | | |
result | serrsBayes | SMC particles for TAMRA+DNA (T20) | list | | |
result2 | serrsBayes | SMC particles for methanol (CH3OH) | list | | |
keywords | rjtools | Keywords options for R Journal | character | | |
cancer | BASSLINE | VA Lung Cancer Trial Dataset | matrix | 137 | 11 |
genevzinb | evinf | Simulated data from the EVZBINB distribution | tbl_df | 1000 | 4 |
genevzinb2 | evinf | Simulated data from the EVZBINB distribution | tbl_df | 100 | 4 |
gm_evzinb | evinf | A goodness-of-fit gof tibble for GOF metrics when using modelsummary | tbl_df | 7 | 3 |
hks | evinf | Replication data for Hultman, Kathman, and Shannon (2013) United Nations Peacekeeping and Civilian Protection in Civil War | tbl_df | 3746 | 13 |
BCdata_EventStudy | HonestDiD | Event study estimates from baseline event study specification on profits in Benzarti & Carloni (2019). See discussion in Section 6.1 of Rambachan & Roth (2021). | list | | |
LWdata_EventStudy | HonestDiD | Event study estimates from baseline female specification on employment in Lovenheim & Willen (2019). See discussion in Section 6.2 of Rambachan & Roth (2021). | list | | |
zip.test | tensorBSS | Handwritten Digit Recognition Data | matrix | 2007 | |
zip.train | tensorBSS | Handwritten Digit Recognition Data | matrix | 7291 | |
Cleveland | modgo | Cleveland Dataset ('Cleveland') | data.frame | 303 | 11 |
zcta_crosswalk | zipcodeR | ZCTA to Census Tract (2010) Crosswalk | tbl_df | 148897 | 3 |
zip_code_db | zipcodeR | ZIP Code Database | data.frame | 41877 | 24 |
zip_to_cd | zipcodeR | ZIP Code to Congressional District Relationship File | data.frame | 45914 | 2 |
catalysis | iBART | Single-Atom Catalysis Data | list | | |
iBART_real_data | iBART | iBART Real Data Result | list | | |
iBART_sim | iBART | iBART Simulation Result | list | | |
SCE | scCAN | SCE | list | | |
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 | | |
Melin_et_al_2019 | pollimetry | Bee tongue length measurements for 11 Melittidae bee species. | data.frame | 179 | 7 |
forage_dataset | pollimetry | Bee foraging range measurements for 101 bee species. | data.frame | 425 | 20 |
pollimetry_dataset | pollimetry | Body size and intertegular distance measurements of 4438 pollinating insect specimens. | data.frame | 4434 | 23 |
data_flu_ses | discord | Flu Vaccination and SES Data | data.frame | 12686 | 23 |
data_sample | discord | Sample Data from NLSY | tbl_df | 1200 | 9 |
Altapic | Arothron | example dataset | array | | |
DM_base_sur | Arothron | example dataset | mesh3d | | |
DM_face_sur | Arothron | example dataset | mesh3d | | |
DM_set | Arothron | example dataset | matrix | 32 | |
Lset2D_list | Arothron | example dataset | list | | |
Lset3D_array | Arothron | example dataset | array | | |
MAs_sets | Arothron | example dataset | array | | |
RMs_sets | Arothron | example dataset | array | | |
SCP1.mesh | Arothron | example dataset | mesh3d | | |
SM_set | Arothron | example dataset | array | | |
endo_set | Arothron | example dataset | matrix | 55 | |
femsets | Arothron | example dataset | array | | |
human_skull | Arothron | example dataset | mesh3d | | |
krd1_tooth | Arothron | example dataset | mesh3d | | |
malleus_bone | Arothron | example dataset | mesh3d | | |
primendoR | Arothron | example dataset | list | | |
sinus_set | Arothron | example dataset | matrix | 19 | |
yoda_set | Arothron | example dataset | matrix | 16 | |
yoda_sur | Arothron | example dataset | mesh3d | | |
front41Data | frontier | Data provided with Tim Coelli's Frontier 4.1 | data.frame | 60 | 4 |
riceProdPhil | frontier | Rice Production in the Philippines | data.frame | 344 | 17 |
telecom | frontier | Telecommunications Providers | data.frame | 21 | 4 |
ct1 | pcr | C_T values from qPCR (separate tubes) | tbl_df | 12 | 2 |
ct2 | pcr | C_T values from qPCR (same tubes) | tbl_df | 12 | 2 |
ct3 | pcr | C_T values from qPCR (Serial dilutions) | tbl_df | 21 | 2 |
ct4 | pcr | C_T values from qPCR (Serial dilutions) | tbl_df | 24 | 2 |
cuperdec_database_ex | cuperdec | Example isolation source database input for cuperdec | spec_tbl_df | 569 | 2 |
cuperdec_metadata_ex | cuperdec | Example metadata file input for cuperdec | spec_tbl_df | 229 | 33 |
cuperdec_taxatable_ex | cuperdec | Example taxon table input for cuperdec | spec_tbl_df | 3401 | 230 |
ExampleGDP | voronoiTreemap | ExampleGDP | data.frame | 42 | 6 |
canada | voronoiTreemap | canada | data.table | 247 | 6 |
SampleData | SSplots | Sample time series of commercial fish landings of selected marine resources (2007-2021) | data.frame | 15 | 26 |
isco_occupations_bundle | labourR | ISCO occupations bundle | data.table | 619 | 2 |
occupations_bundle | labourR | ESCO occupations bundle | data.table | 2942 | 5 |
nigeria | rr | Nigeria Randomized Response Survey Experiment on Social Connections to Armed Groups | data.frame | 2457 | 8 |
krack_advice | concorR | Krackhardt High-Tech Managers data | igraph | | |
krack_friend | concorR | Krackhardt High-Tech Managers data | igraph | | |
krack_report | concorR | Krackhardt High-Tech Managers data | igraph | | |
decathlon88 | snha | Men Decathlon data from the 1988 Olympics | data.frame | 33 | 10 |
hkagepop19 | hkdatasets | Land-based non-institutional population by District Council district and age group | tbl_df | 18 | 8 |
hkdc | hkdatasets | Dataset with public domain information on Hong Kong District Councillors (elected 2019). | tbl_df | 452 | 33 |
hkdistrict_summary | hkdatasets | Dataset summarising the labels and regions of Hong Kong's Districts | tbl_df | 18 | 6 |
hkstreetnames20 | hkdatasets | Dataset with Hong Kong Street Names as at 2020. | tbl_df | 4603 | 21 |
latlongShpObject | rLFT | An example sf object outlining boundaries of a group of islands in Alaska in the lat/lon CRS. | sf | 1 | 10 |
pointObject | rLFT | An example sf object with geometry type of POINT, used for testing. | sf | 1 | 2 |
polygonShpObject | rLFT | An example sf object outlining boundaries of a group of islands in Alaska cast as POLYGON. | sf | 37 | 2 |
shpObject | rLFT | An example sf object outlining boundaries of a group of islands in Alaska cast as LINESTRING. | sf | 37 | 2 |
ARN82.eB.apr77 | sads | Biomass of marine animals in colonizing experiments in Baltic Sea | numeric | | |
bci | sads | Tree species abundance in Barro Colorado Island Plot | integer | | |
birds | sads | Abundances of breeding birds in Quacker Run Valley, NY | numeric | | |
grasslands | sads | Coverages of plants species in a plot in Southern Brazilian Grasslands | data.frame | 29 | 4 |
moths | sads | Moths caught with light traps at Rothamsted 1933-1936 | integer | | |
okland | sads | Abundances of land snail species in Norway | numeric | | |
AILped | simcross | Example AIL pedigree | data.frame | 1254 | 5 |
mouseL_cox | simcross | Mouse chromosome lengths | numeric | | |
mouseL_mgi | simcross | Mouse chromosome lengths | numeric | | |
MIsim | rsimsum | Example of a simulation study on missing data | tbl_df | 3000 | 4 |
MIsim2 | rsimsum | Example of a simulation study on missing data | tbl_df | 3000 | 5 |
frailty | rsimsum | Example of a simulation study on frailty survival models | data.frame | 16000 | 6 |
frailty2 | rsimsum | Example of a simulation study on frailty survival models | data.frame | 16000 | 7 |
nlp | rsimsum | Example of a simulation study on survival modelling | data.frame | 30000 | 10 |
relhaz | rsimsum | Example of a simulation study on survival modelling | data.frame | 1200 | 6 |
tt | rsimsum | Example of a simulation study on the t-test | data.frame | 4000 | 8 |
nyc_bor | ptools | NYC Boroughs | SpatialPolygonsDataFrame | | |
nyc_cafe | ptools | NYC Sidewalk Cafes | SpatialPointsDataFrame | | |
nyc_liq | ptools | NYC Alcohol Licenses | SpatialPointsDataFrame | | |
nyc_shoot | ptools | NYPD Open Data on Shootings | SpatialPointsDataFrame | | |
breastcancer | risks | Breast Cancer Data | tbl_df | 192 | 3 |
brcancer | KMunicate | German Breast Cancer Study Data | tbl_df | 686 | 14 |
cancer2 | KMunicate | Patient Survival in Drug Trial | tbl_df | 48 | 8 |
data.doges | dogesr | Load data into the environment | data.frame | 136 | 11 |
doge.families | dogesr | Load the list of families that became doges, and their numbers | data.frame | 65 | 2 |
doges.marriages.sn | dogesr | Data on doges' (and parents) matrimonial links | igraph | | |
doges.years | dogesr | Sub-dataset with the list of doges, their family and when it happened. | data.frame | 123 | 6 |
family.colors | dogesr | Pre-assigned colors for every type of family | list | | |
family.types | dogesr | Load data for Venetian family types into the environment | character | | |
CMM_test_data | CMMs | Test Data | data.frame | 100 | 24 |
juncus | mixchar | Thermogravimetric data for Juncus amabilis | data.frame | 46080 | 2 |
marsilea | mixchar | Thermogravimetric data for Marsilea drumondii | data.frame | 46080 | 2 |
governors | tilemaps | Party Affiliation of US Governors | sf | 48 | 3 |
NIRsoil | prospectr | NIRSoil | data.frame | 825 | 5 |
covid_theme | opitools | keywords relating to COVID-19 pandemics | data.frame | 16 | 1 |
debate_dtd | opitools | Comments on a video of a political debate. | data.frame | 1497 | 1 |
osd_data | opitools | Observed sentiment document (OSD). | data.frame | 1465 | 3 |
policing_dtd | opitools | Twitter posts on police/policing | data.frame | 110 | 1 |
refreshment_theme | opitools | Keywords relating to facilities at train stations | data.frame | 5 | 1 |
reviews_dtd | opitools | Customer reviews from tripadvisor website | data.frame | 457 | 1 |
signage_theme | opitools | Keywords relating to signages at train stations | data.frame | 6 | 1 |
tweets | opitools | Fake Twitter posts on police/policing 2 | data.frame | 1820 | 2 |
concentrationcycles | transmem | Lithium concentration results using a membrane | list | | |
curvelithium | transmem | External standard calibration curve for lithium in water. | data.frame | 8 | 2 |
planelithium | transmem | Bivariated calibration plane for lithium in prescence of sodium. | data.frame | 40 | 3 |
reusecycles | transmem | Membrane reuse capability to transport lithium | list | | |
seawaterLiNaK | transmem | Lithium, sodium and potassium transport profiles across a membrane | list | | |
all_currency_codes | i18n | A vector containing every currency code | character | | |
all_locales | i18n | A vector containing all locale names | character | | |
character_labels | i18n | A table with localized character labels and descriptors | tbl_df | 574 | 3 |
characters | i18n | A table with localized character data | tbl_df | 574 | 12 |
currencies | i18n | A table with localized currency attributes and descriptors | tbl_df | 175070 | 7 |
dates | i18n | A table with localized date attributes and descriptors | tbl_df | 574 | 38 |
dates_generic | i18n | A table with localized generic date attributes and descriptors | tbl_df | 574 | 38 |
day_periods | i18n | A table with rule sets for naming periods of a day | tbl_df | 519 | 5 |
default_locales | i18n | A table containing a mapping of default locale names to base locales | tbl_df | 228 | 2 |
delimiters | i18n | A table with localized delimiter values | tbl_df | 574 | 5 |
layout | i18n | A table with localized layout data | tbl_df | 574 | 3 |
locale_names | i18n | A table with localized language, script, and territory names | tbl_df | 574 | 5 |
num_system_digits | i18n | Vectors of digits from various numbering systems | tbl_df | 53 | 2 |
numbers | i18n | A table with localized numerical attributes and descriptors | tbl_df | 574 | 26 |
script_metadata | i18n | A table with metadata for a wide variety of script types | tbl_df | 170 | 11 |
start_of_week | i18n | A table with the starting day of the week across territories | tbl_df | 151 | 2 |
tz_bcp_id | i18n | A table with BCP47 Olson/IANA-style and canonical time zone IDs | tbl_df | 593 | 3 |
tz_exemplar | i18n | A table with localized names for all time zone exemplar cities | tbl_df | 574 | 443 |
tz_formats | i18n | A table with localized time zone formatting information | tbl_df | 574 | 8 |
tz_map | i18n | A table with names of map-based time zones | tbl_df | 598 | 3 |
tz_metazone_names | i18n | A table with localized time zone names for all metazones | tbl_df | 465 | 160 |
tz_metazone_users | i18n | A table that links canonical tz names with their metazone | tbl_df | 293 | 4 |
units | i18n | A table with localized data on units | tbl_df | 1722 | 1281 |
Fluency | NormData | Verbal fluency data | data.frame | 1241 | 3 |
GCSE | NormData | GCSE exam score | data.frame | 1905 | 3 |
Personality | NormData | Data of the Openness scale of a personality test | data.frame | 2137 | 3 |
STAS | NormData | State-Trait Anger Scale (STAS) | data.frame | 316 | 3 |
Substitution | NormData | Substitution test data | data.frame | 1650 | 5 |
TMAS | NormData | TMAS data | data.frame | 523 | 3 |
VLT | NormData | Verbal Learning Test data | data.frame | 1460 | 5 |
G20 | treemapify | Statistics on the G-20 group of major world economies. | data.frame | 20 | 6 |
datasets | SADISA | Data sets of various tropical forest communities | list | | |
fitresults | SADISA | Maximum likelihood estimates and corresponding likelihood values for various fits to various tropical forest communities | list | | |
BUPA | kerndwd | BUPA's liver disorders data | list | | |
Stickleback | RInSp | Example of data from threespine Stickleback gut contents | data.frame | 265 | 52 |
Trout | RInSp | Example of continuous data from fish prey lengths of brown trout | data.frame | 59 | 7 |
contingencytable.csv | revengc | Contingency table example | data.frame | 11 | 12 |
univariatetable.csv | revengc | Univariate frequency table example | data.frame | 5 | 2 |
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 | | |
airpass.df | s20x | International Airline Passengers | data.frame | 144 | 1 |
apples.df | s20x | Apples Data | data.frame | 104 | 3 |
arousal.df | s20x | Changes in Pupil Size with Emotional Arousal | data.frame | 160 | 3 |
beer.df | s20x | US Beer Production | data.frame | 96 | 1 |
body.df | s20x | Body Image and Ethnicity | data.frame | 246 | 8 |
books.df | s20x | Books Data | data.frame | 400 | 2 |
bursary.df | s20x | Bursary Results for Auckland Secondary Schools | data.frame | 75 | 2 |
butterfat.df | s20x | Butterfat Data | data.frame | 100 | 3 |
camplake.df | s20x | Age and Length of Camp Lake Bluegills | data.frame | 66 | 3 |
chalk.df | s20x | Chalk Data | data.frame | 66 | 3 |
computer.df | s20x | Computer Questionnaire | data.frame | 19 | 2 |
course.df | s20x | Stats 20x Summer School Data | data.frame | 146 | 15 |
course2way.df | s20x | Exam Mark, Gender and Attendance for Stats 20x Summer School Students | data.frame | 40 | 3 |
diamonds.df | s20x | Prices and Weights of Diamonds | data.frame | 48 | 2 |
fire.df | s20x | Fire Damage and Distance from the Fire Station | data.frame | 15 | 2 |
fruitfly.df | s20x | Fruitfly Data | data.frame | 75 | 2 |
house.df | s20x | Sale and Advertised Prices of Houses | data.frame | 100 | 2 |
incomes.df | s20x | Mean Family Incomes | data.frame | 152 | 1 |
lakemary.df | s20x | Ages and Lengths of Lake Mary Bluegills | data.frame | 78 | 2 |
larain.df | s20x | Los Angeles Rainfall | data.frame | 66 | 1 |
mazda.df | s20x | Year and Price of Mazda Cars | data.frame | 123 | 2 |
mening.df | s20x | Monthly Notifications of Meningococcal Disease | data.frame | 144 | 3 |
mergers.df | s20x | Merger Days | data.frame | 38 | 1 |
mozart.df | s20x | Length of Mozart's Movements | data.frame | 88 | 3 |
nail.df | s20x | Nail Polish Data | data.frame | 60 | 2 |
oysters.df | s20x | Oyster Abundances over Different Sites | data.frame | 87 | 2 |
peru.df | s20x | Peruvian Indians | data.frame | 39 | 5 |
rain.df | s20x | Cloud Seeding and Levels of Rainfall | data.frame | 50 | 2 |
seeds.df | s20x | Seeds Data | data.frame | 48 | 3 |
sheep.df | s20x | Sheep Data | data.frame | 100 | 3 |
skulls.df | s20x | Skulls Data | data.frame | 150 | 2 |
snapper.df | s20x | Snapper Weight Data | data.frame | 844 | 2 |
soyabean.df | s20x | Soya Bean Yields | data.frame | 32 | 3 |
teach.df | s20x | Comparison of Three Teaching Methods | data.frame | 30 | 3 |
technitron.df | s20x | Technitron Salary Information | data.frame | 46 | 8 |
thyroid.df | s20x | Effect of a New Drug on Thyroid Weights | data.frame | 16 | 3 |
toothpaste.df | s20x | Crest Toothpaste | data.frame | 20 | 2 |
zoo.df | s20x | Zoo Attendance during an Advertising Campaign | data.frame | 440 | 6 |
aptitude | cocor | Sample dataset: aptitude | list | | |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
COREs | CREAM | | data.frame | 3998 | 3 |
casino | aphid | Dishonest casino. | character | | |
globins | aphid | Globin protein alignment. | matrix | 7 | |
substitution | aphid | Substitution matrices. | list | | |
CE1 | RSEIS | Single Seismogram | list | | |
GH | RSEIS | Earthquake Seismic Data | list | | |
KH | RSEIS | Volcano Seismic Data | list | | |
OH | RSEIS | Delta-O18 isotpe record | list | | |
VELMOD1D | RSEIS | Sample Velocity Model | list | | |
hdCLdata | frailtyMMpen | Simulated High-dimensional Clustered data | data.frame | 200 | 503 |
simdataCL | frailtyMMpen | Simulated Clustered data | data.frame | 500 | 33 |
simdataME | frailtyMMpen | Simulated Multiple Event data | data.frame | 100 | 33 |
simdataRE | frailtyMMpen | Simulated Recurrent Event data | data.frame | 706 | 34 |
oldbooks | LSAfun | A collection of five classic books | list | | |
priming | LSAfun | Simulated data for a Semantic Priming Experiment | data.frame | 514 | 4 |
syntest | LSAfun | A multiple choice test for synonyms and antonyms | data.frame | 20 | 7 |
wonderland | LSAfun | LSA Space: Alice's Adventures in Wonderland | matrix | 1123 | |
ng20 | topicmodels.etm | Bag of words sample of the 20 newsgroups dataset | list | | |
nola_south | twangRDC | An example FSRDC dataset | tbl_df | 18396 | 10 |
TIMSS11G8M.data | subscore | The 2011 TIMSS Grade 8 Mathematics Assessment Dataset | data.frame | 765 | 32 |
scored.data | subscore | Sample scored data | data.frame | 150 | 50 |
test.data | subscore | A list of objects that include both test information and subscores. | list | | |
LeukSurv | AHSurv | The Leukemia Survival Data | data.frame | 1043 | 9 |
bmt | AHSurv | Bone Marrow Transplant (bmt) data set | data.frame | 91 | 3 |
e1684 | AHSurv | Melanoma data set | data.frame | 285 | 5 |
ipass | AHSurv | IRESSA Pan-Asia Study (IPASS) data set | data.frame | 1217 | 3 |
NYTimes | RTextTools | a sample dataset containing labeled headlines from The New York Times. | data.frame | 3104 | 5 |
USCongress | RTextTools | a sample dataset containing labeled bills from the United State Congress. | data.frame | 4449 | 6 |
TPLSdat | TPLSr | Sample participant data from a left-right button press task | list | | |
stackExample | StackImpute | Example data for Louis_Information() | list | | |
example_data | bulletcp | Example of an average of 2D crosscuts from the Hamby 44 data set. | data.frame | 3346 | 2 |
crack_growth | dhglm | Crack-growth Data | data.frame | 241 | 5 |
epilepsy | dhglm | Epilepsy Seizures Data | data.frame | 236 | 7 |
sampleDataForLtmleMSM | ltmle | Sample data, regimes, and summary measures | list | | |
data_gam | oddsratio | data_gam | data.frame | 200 | 11 |
data_glm | oddsratio | data_glm | data.frame | 400 | 4 |
drugs | C443 | Drug consumption data set | data.frame | 1885 | 32 |
run09 | cherryblossom | Cherry Blossom Run data, 2009 | tbl_df | 14974 | 14 |
run12 | cherryblossom | Cherry Blossom Run data, 2012 | tbl_df | 16924 | 9 |
run17 | cherryblossom | Cherry Blossom Run data, 2017 | tbl_df | 19961 | 9 |
Example1_AP | Pareto | Example data: Attachment Points | numeric | | |
Example1_EL | Pareto | Example data: Expected Losses | numeric | | |
anole_lizard | hcop | HCOP ortholog data | tbl_df | 88020 | 9 |
c.elegans | hcop | HCOP ortholog data | tbl_df | 65161 | 9 |
chicken | hcop | HCOP ortholog data | tbl_df | 46422 | 9 |
chimpanzee | hcop | HCOP ortholog data | tbl_df | 42990 | 9 |
cow | hcop | HCOP ortholog data | tbl_df | 40817 | 9 |
dog | hcop | HCOP ortholog data | tbl_df | 38902 | 9 |
fruitfly | hcop | HCOP ortholog data | tbl_df | 58130 | 9 |
horse | hcop | HCOP ortholog data | tbl_df | 39398 | 9 |
macaque | hcop | HCOP ortholog data | tbl_df | 37694 | 9 |
mouse | hcop | HCOP ortholog data | tbl_df | 53386 | 9 |
opossum | hcop | HCOP ortholog data | tbl_df | 55902 | 9 |
pig | hcop | HCOP ortholog data | tbl_df | 40737 | 9 |
platypus | hcop | HCOP ortholog data | tbl_df | 47922 | 9 |
rat | hcop | HCOP ortholog data | tbl_df | 48085 | 9 |
s.cerevisiae | hcop | HCOP ortholog data | tbl_df | 10844 | 9 |
xenopus | hcop | HCOP ortholog data | tbl_df | 219764 | 9 |
zebrafish | hcop | HCOP ortholog data | tbl_df | 115166 | 9 |
FHT | gcdnet | FHT data introduced in Friedman et al. (2010). | list | | |
leukemia.x | supclust | A part of the Golub's famous AML/ALL-leukemia dataset | matrix | 38 | |
leukemia.y | supclust | A part of the Golub's famous AML/ALL-leukemia dataset | numeric | | |
leukemia.z | supclust | A part of the Golub's famous AML/ALL-leukemia dataset | numeric | | |
ArabidopsisMotif | universalmotif | Arabidopsis motif in 'universalmotif' format. | universalmotif | | |
ArabidopsisPromoters | universalmotif | Arabidopsis promoters as a 'DNAStringSet'. | DNAStringSet | | |
JASPAR2018_CORE_DBSCORES | universalmotif | JASPAR2018 CORE database scores | DFrame | | |
examplemotif | universalmotif | Example motif in 'universalmotif' format. | universalmotif | | |
examplemotif2 | universalmotif | Another example motif in 'universalmotif' format. | universalmotif | | |
fontDFroboto | universalmotif | Polygon coordinates for plotting letters. | DFrame | | |
ginhoux | SCORPIUS | scRNA-seq data of dendritic cell progenitors. | list | | |
alveolar | twowaytests | Alveolar Cell Count Data | data.frame | 36 | 3 |
Ksat | geoR | Saturated Hydraulic Conductivity | geodata | | |
ca20 | geoR | Calcium content in soil samples | geodata | | |
camg | geoR | Calcium and magnesium content in soil samples | data.frame | 178 | 10 |
elevation | geoR | Surface Elevations | geodata | | |
gambia | geoR | Gambia Malaria Data | data.frame | 2035 | 8 |
gambia.borders | geoR | Gambia Malaria Data | data.frame | 13878 | 2 |
gambia.map | geoR | Gambia Malaria Data | function | | |
head | geoR | Head observations in a regional confined aquifer | geodata | | |
hoef | geoR | Data for spatial analysis of experiments | data.frame | 25 | 5 |
isaaks | geoR | Data from Isaaks and Srisvastava's book | geodata | | |
kattegat | geoR | Kattegat basin salinity data | geodata | | |
landim1 | geoR | Data from Landim's book | data.frame | 38 | 4 |
parana | geoR | Rainfall Data from Parana State, Brasil | geodata | | |
rongelap | geoR | Radionuclide Concentrations on Rongelap Island | geodata | | |
s100 | geoR | Simulated Data-Sets which Illustrate the Usage of the Package geoR | geodata | | |
s121 | geoR | Simulated Data-Sets which Illustrate the Usage of the Package geoR | grf | | |
s256i | geoR | Simulated Data-Set which Illustrate the Usage of krige.bayes | geodata | | |
sic.100 | geoR | Spatial Interpolation Comparison data | geodata | | |
sic.367 | geoR | Spatial Interpolation Comparison data | geodata | | |
sic.all | geoR | Spatial Interpolation Comparison data | geodata | | |
sic.borders | geoR | Spatial Interpolation Comparison data | matrix | 1286 | 2 |
sic.some | geoR | Spatial Interpolation Comparison data | geodata | | |
soil250 | geoR | Soil chemistry properties data set | data.frame | 250 | 22 |
soja98 | geoR | Soya bean production and other variables in a uniformity trial | data.frame | 256 | 10 |
tce | geoR | TCE concentrations in groundwater in a vertical cross section | geodata | | |
wo | geoR | Kriging example data from Webster and Oliver | geodata | | |
wolfcamp | geoR | Wolfcamp Aquifer Data | geodata | | |
wrc | geoR | Points of a water retention curve data set | data.frame | 250 | 11 |
dict | genCountR | Data from Gendered Language Dictionary Developed by Roberts and Utych (2019) | data.frame | 701 | 15 |
pressure_volume_data | pvcurveanalysis | Pressure volume curve data | data.frame | 160 | 8 |
leaf_drying_data | pvldcurve | Experimentally determined leaf drying data | data.frame | 172 | 9 |
pressure_volume_data | pvldcurve | Pressure volume curve data | data.frame | 160 | 8 |
weather_data | pvldcurve | Weather data | data.frame | 253 | 3 |
gnrs_testfile | GNRS | Names of 21 political divisions | data.frame | 21 | 4 |
Boston | MASSExtra | Boston | data.frame | 506 | 14 |
Cars93 | MASSExtra | Cars93 | data.frame | 93 | 27 |
quine | MASSExtra | quine | data.frame | 146 | 5 |
whiteside | MASSExtra | whiteside | data.frame | 56 | 3 |
dat | gim | Data for example in 'gim' | data.frame | 4000 | 6 |
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 | | |
gam.data | gam | Simulated dataset for gam | data.frame | 100 | 7 |
gam.newdata | gam | Simulated dataset for gam | data.frame | 10 | 2 |
kyphosis | gam | A classic example dataset for GAMs | data.frame | 81 | 4 |
SSRPexact | BayesRep | Data from the Social Sciences Replication Project | data.frame | 21 | 16 |
mdata | norm | Dataset with missing values to illustrate use of package norm | matrix | 25 | 5 |
long | Ymisc | Long Format Sample Data | data.frame | 700 | 5 |
sample_data | Ymisc | Wide Format Sample Data | data.frame | 100 | 16 |
f2008 | mvglmmRank | 2008 FBS College Football Regular Season Data | data.frame | 772 | 9 |
f2009 | mvglmmRank | 2009 FBS College Football Regular Season Data | data.frame | 772 | 9 |
f2010 | mvglmmRank | 2010 FBS College Football Regular Season Data | data.frame | 770 | 9 |
f2011 | mvglmmRank | 2011 FBS College Football Regular Season Data | data.frame | 781 | 9 |
f2012 | mvglmmRank | 2012 FBS College Football Regular Season Data | data.frame | 809 | 9 |
nba2013 | mvglmmRank | 2013 NBA Data | data.frame | 1229 | 11 |
ncaab2012 | mvglmmRank | 2012 NCAA Division I Basketball Results | data.frame | 5253 | 10 |
nfl2012 | mvglmmRank | 2012 NFL Regular Season Data | data.frame | 256 | 9 |
simData_cont | mBvs | A simulated data set containing multivariate normal responses and continuous covariates | list | | |
simData_mzip | mBvs | A simulated data set containing multivariate zero-inflated count responses and a continuous covariate | list | | |
TESTNUMB | TSE | A data set created by merging four smaller data sets. Three of those smaller data sets are data from three surveys (O1, O2, O3); the other is data from a "gold standard" survey (A1). All four smaller data sets consist of the same three variables (Q1, Q2, Q3): responses to the same three questions, asked by each survey from the same 10 respondents (ID), along the same 1-99 response scale. | data.frame | 10 | 13 |
euro_basemap | tricolore | Flat Map of European Continent | gg | | |
euro_example | tricolore | NUTS-2 Level Geodata and Compositional Data for Europe | sf | 319 | 9 |
gtoy1 | RGraphSpace | Toy 'igraph' objects | igraph | | |
gtoy2 | RGraphSpace | Toy 'igraph' objects | igraph | | |
CVT | RSStest | CVT Data | spec_tbl_df | 256 | 9 |
otolith | RSStest | Otolith Data | data.frame | 167 | 7 |
DummyBeta.m | EpiDISH | Dummy beta value matrix | matrix | 2000 | 10 |
LiuDataSub.m | EpiDISH | Whole blood example beta value matrix | matrix | 500 | 50 |
cent12CT.m | EpiDISH | Whole blood reference of 12 blood cell subtypes for EPIC array | matrix | 600 | 12 |
cent12CT450k.m | EpiDISH | Whole blood reference of 12 blood cell subtypes for 450k array | matrix | 600 | 12 |
centBloodSub.m | EpiDISH | Whole blood reference of 188 tsDHS-DMCs and 7 blood cell subtypes | matrix | 188 | 7 |
centDHSbloodDMC.m | EpiDISH | Whole blood reference of 333 tsDHS-DMCs and 7 blood cell subtypes | matrix | 333 | 7 |
centEpiFibFatIC.m | EpiDISH | Reference for breast tissue | matrix | 491 | 4 |
centEpiFibIC.m | EpiDISH | Reference for genenric epithelial tissue | matrix | 716 | 3 |
centUniLIFE.m | EpiDISH | DNAm reference matrix for 19 immune cell-types for blood of any age | matrix | 1906 | 19 |
FH_data | homnormal | Fleming and Harrington Data | data.frame | 21 | 2 |
example_data | metrix | Example data for Metrix package. | data.frame | 30 | 10 |
my_data_googleads | googleadsR | Sample data from the Windsor API. | data.frame | 1676 | 6 |
data.test | FADA | Test dataset simulated with the same distribution as the training dataset data.train. | list | | |
data.train | FADA | Training data | list | | |
ancestryData | Summix | ancestryData | data.frame | 1000 | 10 |
my_mailchimp_data | mailchimpR | Sample of digital marketing data from Mailchimp downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
BSA | dQTG.seq | BSA data | data.frame | 36 | 7 |
i10_wide | socialrisk | Example administrative data. | spec_tbl_df | 29 | 11 |
GO_example | highDmean | An example of GO term data | list | | |
brc_df | QoLMiss | Breast cancer Quality of Life. | data.frame | 100 | 27 |
brc_df_miss | QoLMiss | Breast cancer Quality of Life with missing values. | data.frame | 100 | 27 |
c30_df | QoLMiss | Simulated data for cancer Quality of Life. | data.frame | 100 | 34 |
c30_df_miss | QoLMiss | Data for cancer Quality of Life with missing values. | data.frame | 100 | 34 |
hnc_df | QoLMiss | Head and Neck cancer Quality of Life data. | data.frame | 100 | 39 |
hnc_df_miss | QoLMiss | Head and Neck cancer data for cancer Quality of Life with missing values. | data.frame | 100 | 39 |
lc_df | QoLMiss | Simulated data for Lung cancer Quality of Life. | data.frame | 100 | 16 |
lc_df_miss | QoLMiss | Lung cancer data for cancer Quality of Life with missing values. | data.frame | 100 | 16 |
ovc_df | QoLMiss | Simulated data for Ovarian Cancer Quality of Life. | data.frame | 100 | 32 |
ovc_df_miss | QoLMiss | Ovarian cancer Quality of Life data with missing values. | data.frame | 100 | 32 |
patient_miss | QoLMiss | Cancer Quality of Life data with missing values. | data.frame | 100 | 34 |
thyc_df | QoLMiss | Thyroid cancer Quality of Life. | data.frame | 100 | 38 |
thyc_df_miss | QoLMiss | Thyroid cancer Quality of Life data with missing values. | data.frame | 100 | 38 |
hfaction_cpx12 | WA | A dataset from the HF-ACTION trial | data.frame | 2132 | 4 |
indicatorsDataBGD | washdata | WASH Survey Indicators Data for Dhaka, Bangladesh | data.frame | 1282 | 162 |
popBGD | washdata | Population Data for Dhaka, Bangladesh | tbl_df | 641 | 4 |
ppiMatrixBGD | washdata | PPI Look-up Table for Bangladesh | data.frame | 101 | 10 |
surveyDataBGD | washdata | WASH Survey Raw Data for Dhaka, Bangladesh | data.frame | 1282 | 217 |
Chiapas | rareNMtests | Tree abundance in tropical montane forest plots | data.frame | 224 | 376 |
Leukemia.data | icdGLM | Survival Times of 33 Leukemia Patients | data.frame | 33 | 3 |
TLI.data | icdGLM | TLI Study of 82 Patients | data.frame | 82 | 4 |
HCP | tensorregress | HCP data | list | | |
nations | tensorregress | nations data | list | | |
fac | Rcriticor | factor "year" | factor | | |
sit | Rcriticor | Cereal aphids in Rennes | numeric | | |
sit2 | Rcriticor | Sitobion with replicates | numeric | | |
time | Rcriticor | Fictitious daily temperatures | numeric | | |
time3 | Rcriticor | mean daily temperature data in Rennes (France) | numeric | | |
wy | Rcriticor | Series of fictitious observations | numeric | | |
wy2 | Rcriticor | Series of fictitious observations | numeric | | |
MisLinks | d3Network | A data file of links from Knuth's Les Miserables characters data base. | data.frame | 254 | 3 |
MisNodes | d3Network | A data file of nodes from Knuth's Les Miserables characters data base. | data.frame | 77 | 2 |
Example.Data | EffectTreat | An example dataset | data.frame | 181 | 4 |
CPET_time | SIPETool | SIPETool example files | data.frame | 265 | 14 |
SIFT_filtered | SIPETool | SIPETool example files | data.frame | 1611 | 3 |
SIFT_time | SIPETool | SIPETool example files | data.frame | 3683 | 3 |
raw_SIFT | SIPETool | SIPETool example files | data.frame | 5132 | 1 |
fungi | CNVRG | Fungal endophytes of Astragalus lentiginosus grown near Reno, NV | data.frame | 148 | 27 |
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 |
xue_microbiome_sample | FAVA | Temporal microbiome composition data | tbl_df | 77 | 526 |
xue_species_info | FAVA | Table of species information | data.table | 1346 | 9 |
xue_species_similarity | FAVA | Species similarity matrix for the species included in xue_microbiome_sample | matrix | 524 | 524 |
xue_species_tree | FAVA | Phylogenetic tree for the species included in xue_microbiome_sample | phylo | | |
PrecisionLocus | spacesXYZ | Planckian Loci - stored as Lookup Tables | data.frame | 65 | 7 |
RobertsonLocus | spacesXYZ | Planckian Loci - stored as Lookup Tables | data.frame | 31 | 3 |
BDLtree | SmarterPoland | API to Bank Danych Lokalnych [GUS] | data.frame | 1337 | 4 |
cities_lon_lat | SmarterPoland | Geocoordinates of Largest Cities | data.frame | 8251 | 2 |
countries | SmarterPoland | Birth and death rates, continent and population for selected countries | data.frame | 185 | 5 |
maturaExam | SmarterPoland | Results from Matura Exams in Poland for Math and Language for Years 2010-2015 | data.frame | 3939800 | 4 |
flucyl | memapp | Castilla y Leon influenza crude rates | data.frame | 33 | 8 |
flucylraw | memapp | Castilla y Leon influenza standarised rates | data.frame | 267 | 3 |
Y_RM | RestoreNet | Rhesus Macaque clonal tracking dataset | list | | |
inp_exClnrGrid | macroBiome | Mandatory Data for the Example 'Climate Normal Grid' | list | | |
inp_exPoints | macroBiome | Mandatory Data for the Example 'Points' | list | | |
inp_exSglyGrid | macroBiome | Mandatory Data for the Example 'Single-Year Grid' | list | | |
vegClsNumCodes | macroBiome | Supplemental Data Frame for Decoding Outputs of Vegetation Classifiers | data.frame | 39 | 6 |
bank | gclus | Swiss bank notes data | data.frame | 200 | 7 |
body | gclus | Exploring Relationships in Body Dimensions | data.frame | 507 | 25 |
ozone | gclus | Ozone data from Breiman and Friedman, 1985 | data.frame | 330 | 9 |
wine | gclus | Wine recognition data | data.frame | 178 | 14 |
group_costs | TACforecasting | Synthetic data for attendant hours by age group and injury group | tbl_ts | 14711 | 5 |
PER_2015_magn | vismeteor | | list | | |
PER_2015_rates | vismeteor | | list | | |
TCGA_BRCA | PogromcyDanych | Selected Variables from BReast CAncer Patients of The Cancer Genome Atlas Project | data.frame | 999 | 5 |
WIG | PogromcyDanych | Warszawski Indeks Gieldowy (Warsaw Stock Exchange Index) | data.frame | 248 | 8 |
auta2012 | PogromcyDanych | Offer Prices of Second-hand Cars in 2012 | data.frame | 207602 | 21 |
diagnoza | PogromcyDanych | A Subset of Polish Social Diagnosis Data | data.frame | 38461 | 36 |
diagnozaDict | PogromcyDanych | A Subset of Polish Social Diagnosis Data | data.frame | 36 | 3 |
galton | PogromcyDanych | Galton's and Pearson's Height Data for Parents and Children | data.frame | 928 | 2 |
imiona_warszawa | PogromcyDanych | Names of Infants Born in Warsaw | data.frame | 84816 | 5 |
koty_ptaki | PogromcyDanych | The Fastest Cats and Birds in the World | data.frame | 13 | 7 |
mandatySejmik2014 | PogromcyDanych | Local Government Elections in Poland 2014 | data.frame | 16 | 9 |
pearson | PogromcyDanych | Galton's and Pearson's Height Data for Parents and Children | data.frame | 1078 | 2 |
serialeIMDB | PogromcyDanych | Assessment of Episodes of TV series | data.frame | 20122 | 8 |
skiJumps2013 | PogromcyDanych | Ski Jumps Results season 2013/2014 | data.frame | 2130 | 16 |
skiJumps2013labels | PogromcyDanych | Ski Jumps Results season 2013/2014 | data.frame | 15 | 1 |
CellCycleBeliefs | bgmm | Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases | list | | |
CellCycleCenters | bgmm | Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases | matrix | 5 | 17 |
CellCycleClass | bgmm | Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases | numeric | | |
CellCycleData | bgmm | Data for clustering of 384 cell cycle genes into five clusters corresponding to cell cycle phases | list | | |
Ste12Beliefs | bgmm | Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets. | matrix | 42 | 2 |
Ste12Binding | bgmm | Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets. | numeric | | |
Ste12Data | bgmm | Ste12 knockout data under pheromone treatment versus wild type; Examples of Ste12 targets; Binding p-values of Ste12 to those targets. | numeric | | |
genotypes | bgmm | Fluorescence signals corresponding to a given allele for 333 SNPs | list | | |
miR124Data | bgmm | miRNA transfection data for miR1 and miR124 target genes | numeric | | |
miR1Data | bgmm | miRNA transfection data for miR1 and miR124 target genes | numeric | | |
miRNABeliefs | bgmm | miRNA transfection data for miR1 and miR124 target genes | matrix | 27 | 2 |
miRNAClass | bgmm | miRNA transfection data for miR1 and miR124 target genes | numeric | | |
SMI.12 | copula | SMI Data - 141 Days in Winter 2011/2012 | matrix | 141 | 20 |
gasoil | copula | Daily Crude Oil and Natural Gas Prices from 2003 to 2006 | data.frame | 762 | 3 |
lSMI | copula | SMI Data - 141 Days in Winter 2011/2012 | list | | |
loss | copula | LOSS and ALAE Insurance Data | data.frame | 1500 | 4 |
rdj | copula | Daily Returns of Three Stocks in the Dow Jones | data.frame | 1262 | 4 |
uranium | copula | Uranium Exploration Dataset of Cook & Johnson (1986) | data.frame | 655 | 7 |
gTableOrigin | ed50 | G Table | tbl_df | 49 | 3 |
groupS | ed50 | A Real Experiment Dose Data | data.frame | 36 | 2 |
groupSN | ed50 | A Real Experiment Dose Data | data.frame | 38 | 2 |
ipd | CVThresh | Inductance plethysmography data | rts | | |
LobsterSizeFreqs | LobsterCatch | Lobster size frequency data | data.frame | 31 | 2 |
gr | DTMCPack | Example Data Set: Gambler's ruin on 4 states | matrix | 4 | |
hh | DTMCPack | Harry the SemiPro | matrix | 3 | |
id | DTMCPack | Initial distribution | numeric | | |
maize | genstab | Maize yield trial data | data.frame | 260 | 4 |
SEMSample | SEofM | Sample Data for SEM | data.frame | 120 | 4 |
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 |
NELS88 | copulaData | National Education Longitudinal Study Data | data.frame | 1000 | 11 |
nursingHomes | copulaData | Wisconsin Nursing Homes Utilization Data | data.frame | 2497 | 10 |
shipley | semEff | Simulated Data from Shipley (2009) | data.frame | 1900 | 9 |
shipley.growth | semEff | Candidate Model Set from Shipley 'Growth' Model | list | | |
shipley.sem | semEff | Hypothesised SEM from Shipley (2009) | list | | |
shipley.sem.boot | semEff | Bootstrapped Estimates for Shipley SEM | list | | |
shipley.sem.eff | semEff | Effects for Shipley SEM | semEff | | |
starnames | qs2 | Official list of IAU Star Names | data.frame | 336 | 9 |
albertinedisparue | proustr | Marcel Proust's novel "Albertine disparue" | tbl_df | 259 | 4 |
alombredesjeunesfillesenfleurs | proustr | Marcel Proust's novel "À l’ombre des jeunes filles en fleurs" | tbl_df | 792 | 4 |
ducotedechezswann | proustr | Marcel Proust's novel "Du côté de chez Swann" | tbl_df | 1004 | 4 |
laprisonniere | proustr | Marcel Proust's novel "La Prisonnière" | tbl_df | 365 | 4 |
lecotedeguermantes | proustr | Marcel Proust's novel "Le côté de Guermantes" | tbl_df | 1610 | 4 |
letempretrouve | proustr | Marcel Proust's novel "Le temps retrouvé" | tbl_df | 248 | 4 |
proust_char | proustr | Characters from "À la recherche du temps perdu" | tbl_df | 461 | 1 |
sodomeetgomorrhe | proustr | Marcel Proust's novel "Sodome et Gomorrhe" | tbl_df | 412 | 4 |
stop_words | proustr | Stopwords | tbl_df | 689 | 1 |
Java.sparrow.notes | ZLAvian | Java sparrow note duration and frequency. | data.frame | 22970 | 3 |
data_to_check | SAvalidation | Example data for validation | list | | |
vintages | SAvalidation | | list | | |
WarblerG | pedtricks | Seychelles Warbler Genotypes | data.frame | 307 | 29 |
gryphons | pedtricks | Example dataset for pedtricks examples and tutorial | data.frame | 4918 | 9 |
SwissBirths | munch | Total births per municipality in Switzerland between 1969 and 2012 | data.frame | 105058 | 4 |
SwissPop | munch | Census population by household size per commune in Switzerland between 1970 and 2000 | data.frame | 115840 | 5 |
dt_dates | dataMojo | Anonymized sample data | data.table | 5 | 5 |
dt_groups | dataMojo | Anonymized sample data | data.table | 1000 | 4 |
dt_long | dataMojo | Anonymized sample data | data.table | 10 | 3 |
dt_missing | dataMojo | Anonymized sample data | data.table | 5 | 3 |
dt_values | dataMojo | Anonymized sample data | data.table | 1000 | 3 |
starwars_simple | dataMojo | starwars data | data.table | 2 | 6 |
ath.churchill | ctl | Example metabolite expression data from Arabidopsis Thaliana on 9 metabolites. | list | | |
ath.gary | ctl | Example metabolite expression data from Arabidopsis Thaliana on 9 metabolites. | list | | |
ath.metab | ctl | Example metabolite expression data from Arabidopsis Thaliana on 24 metabolites. | list | | |
ath.metabolites | ctl | Example metabolite expression data from Arabidopsis Thaliana on 24 metabolites. | list | | |
ath.result | ctl | Output of QCLscan after 5000 permutations on the metabolite expression data from Arabidopsis Thaliana. | list | | |
yeast.brem | ctl | Example gene expression data from Saccharomyces cerevisiae on 301 RNA expressions. | list | | |
newa3_stations | scrappy | NEWA v3 Weather Stations dataset | tbl_df | 801 | 10 |
newa_stations | scrappy | NEWA Weather Stations dataset | data.frame | 718 | 3 |
CAex | Matrix | Albers' example Matrix with "Difficult" Eigen Factorization | dgCMatrix | | |
KNex | Matrix | Koenker-Ng Example Sparse Model Matrix and Response Vector | list | | |
USCounties | Matrix | Contiguity Matrix of U.S. Counties | dsCMatrix | | |
wrld_1deg | Matrix | Contiguity Matrix of World One-Degree Grid Cells | dsCMatrix | | |
UNlocations | wpp2019 | United Nations Table of Locations | data.frame | 286 | 32 |
e0F | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 17 |
e0F_supplemental | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Fproj | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Fproj80l | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Fproj80u | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Fproj95l | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Fproj95u | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0M | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 17 |
e0M_supplemental | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 29 | 43 |
e0Mproj | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Mproj80l | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Mproj80u | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Mproj95l | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
e0Mproj95u | wpp2019 | United Nations Time Series of Life Expectancy | data.frame | 249 | 18 |
migration | wpp2019 | Dataset on Migration | data.frame | 250 | 32 |
mxF | wpp2019 | Age-specific Mortality Data | data.frame | 5478 | 33 |
mxM | wpp2019 | Age-specific Mortality Data | data.frame | 5488 | 33 |
percentASFR | wpp2019 | Datasets on Age-specific Distribution of Fertility Rates | data.frame | 1743 | 33 |
pop | wpp2019 | Estimates and Projections of Population Counts | data.frame | 249 | 17 |
popF | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5229 | 18 |
popFT | wpp2019 | Estimates and Projections of Population Counts | data.frame | 249 | 17 |
popFTproj | wpp2019 | Estimates and Projections of Population Counts | data.frame | 250 | 18 |
popFprojHigh | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5208 | 19 |
popFprojLow | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5208 | 19 |
popFprojMed | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5250 | 19 |
popM | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5229 | 18 |
popMT | wpp2019 | Estimates and Projections of Population Counts | data.frame | 249 | 17 |
popMTproj | wpp2019 | Estimates and Projections of Population Counts | data.frame | 250 | 18 |
popMprojHigh | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5208 | 19 |
popMprojLow | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5208 | 19 |
popMprojMed | wpp2019 | Estimates and Projections of Population Counts | data.frame | 5250 | 19 |
popproj | wpp2019 | Estimates and Projections of Population Counts | data.frame | 250 | 18 |
popproj80l | wpp2019 | Estimates and Projections of Population Counts | data.frame | 248 | 18 |
popproj80u | wpp2019 | Estimates and Projections of Population Counts | data.frame | 248 | 18 |
popproj95l | wpp2019 | Estimates and Projections of Population Counts | data.frame | 248 | 18 |
popproj95u | wpp2019 | Estimates and Projections of Population Counts | data.frame | 248 | 18 |
popprojHigh | wpp2019 | Estimates and Projections of Population Counts | data.frame | 248 | 18 |
popprojLow | wpp2019 | Estimates and Projections of Population Counts | data.frame | 248 | 18 |
sexRatio | wpp2019 | Sex Ratio at Birth | data.frame | 249 | 32 |
tfr | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 17 |
tfr_supplemental | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 103 | 45 |
tfrproj80l | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
tfrproj80u | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
tfrproj95l | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
tfrproj95u | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
tfrprojHigh | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
tfrprojLow | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
tfrprojMed | wpp2019 | United Nations Time Series of Total Fertility Rate | data.frame | 249 | 18 |
exampledata | PTERP | Hypothetical data for example | matrix | 2000 | 3 |
data1 | OptSig | Data for the U.S. production function estimation | data.frame | 51 | 3 |
Mississippi | DEEVD | Flood discharge in per second from Mississippi river | numeric | | |
Rhone | DEEVD | Flood discharge in per second from Rhone river | numeric | | |
Suicide | DEEVD | Data of control patients of Suicide study | numeric | | |
ExampleData | PoolTestR | A synthetic dataset for pooled testing | data.frame | 7604 | 6 |
SimpleExampleData | PoolTestR | A synthetic dataset for pooled testing | data.frame | 1152 | 6 |
TruePrev | PoolTestR | A synthetic dataset for pooled testing | data.frame | 900 | 7 |
IllumBeta | RPMM | DNA Methylation Data for Normal Tissue Types | matrix | 217 | 100 |
tissue | RPMM | DNA Methylation Data for Normal Tissue Types | factor | | |
NDVI.Site1 | DBEST | Site 1 | numeric | | |
NDVI.Site2 | DBEST | Site 2 | numeric | | |
TREND.Site1 | DBEST | Site 1 | numeric | | |
TREND.Site2 | DBEST | Site 2 | 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 | |
Regensburg | Fahrmeir | Job Expectation | data.frame | 30 | 4 |
breath | Fahrmeir | Breathing Test | data.frame | 18 | 4 |
caesar | Fahrmeir | Caesarian Birth Study | data.frame | 24 | 7 |
cells | Fahrmeir | Cellular Differentiation | data.frame | 16 | 3 |
credit | Fahrmeir | Credit Score Data From a South German Bank | data.frame | 1000 | 8 |
happy | Fahrmeir | Reported Happiness | data.frame | 24 | 4 |
headneck | Fahrmeir | Head and Neck Cancer data | data.frame | 47 | 4 |
ohio | Fahrmeir | Air Pollution and Health | data.frame | 32 | 6 |
rheuma | Fahrmeir | Data from Patients with Acute Rheumatoid Arthritis | data.frame | 10 | 3 |
tonsil | Fahrmeir | Data Set of Tonsil Size in Children | data.frame | 6 | 3 |
visual | Fahrmeir | Visual Impairment Data | list | | |
wine | Fahrmeir | Bitterness of White Wines | data.frame | 72 | 5 |
female | CIM | OD matrix, female, 2008-2013. | data.frame | 3 | 3 |
male | CIM | OD matrix, male, 2008-2013. | data.frame | 3 | 3 |
pop15_29 | CIM | OD matrix, people aged 15-29, 2010-2011. | data.frame | 33 | 33 |
pop1_14 | CIM | OD matrix, people aged 1-14, 2010-2011. | data.frame | 33 | 33 |
pop30_44 | CIM | OD matrix, people aged 30-34, 2010-2011. | data.frame | 33 | 33 |
pop45_64 | CIM | OD matrix, people aged 45-64, 2010-2011. | data.frame | 33 | 33 |
pop65over | CIM | OD matrix, people aged 65+, 2010-2011. | data.frame | 33 | 33 |
grListExample | crisprDesign | Example of a TxDb object converted to a GRangesList | CompressedGRangesList | | |
grRepeatsExample | crisprDesign | Example of a GRanges object containing repeat elements | GRanges | | |
guideSetExample | crisprDesign | Example of a GuideSet object storing gRNA sequences targeting the CDS of IQSEC3 | GuideSet | | |
guideSetExampleFullAnnotation | crisprDesign | Example of a fully-annotated GuideSet object storing gRNA sequences targeting the CDS of IQSEC3 | GuideSet | | |
guideSetExampleWithAlignments | crisprDesign | Example of a GuideSet object storing gRNA sequences targeting the CDS of IQSEC3 with off-target alignments. | GuideSet | | |
tssObjectExample | crisprDesign | Example of a GRanges object containing TSS coordinates | GRanges | | |
DDARawData | MSstats | Example dataset from a label-free DDA, a controlled spike-in experiment. | data.frame | 2070 | 10 |
DDARawData.Skyline | MSstats | Example dataset from a label-free DDA, a controlled spike-in experiment, processed by Skyline. | data.frame | 11934 | 12 |
DIARawData | MSstats | Example dataset from a label-free DIA, a group comparison study of S.Pyogenes. | data.frame | 980 | 10 |
SRMRawData | MSstats | Example dataset from a SRM experiment with stable isotope labeled reference of a time course yeast study | data.frame | 720 | 10 |
example_SDRF | MSstats | Example SDRF. | data.table | 160 | 28 |
iran.quakes | ETAS | An Iranian Earthquake Catalog | data.frame | 5970 | 5 |
italy.quakes | ETAS | An Italian Earthquake Catalog | data.frame | 2158 | 6 |
japan.quakes | ETAS | A Japanese Earthquake Catalog | data.frame | 13724 | 6 |
exData | rKOMICS | Example dataset | list | | |
matrices | rKOMICS | Example cluster matrices | list | | |
N52 | tidyHeatmap | Example data set N52 | tbl_df | 520 | 15 |
pasilla | tidyHeatmap | Example data set Pasilla | tbl_df | 504 | 8 |
algae.rd | respR | Oxygen production respirometry data | data.table | 1200 | 2 |
background_con.rd | respR | Background respirometry data (constant) | data.table | 20664 | 2 |
background_exp.rd | respR | Background respirometry data (exponential) | data.table | 20664 | 2 |
background_lin.rd | respR | Background respirometry data (linear) | data.table | 20664 | 2 |
flowthrough.rd | respR | Flowthrough respirometry data on the chiton, _Mopalia lignosa_ | data.table | 935 | 4 |
flowthrough_mult.rd | respR | Multi-column flowthrough respirometry data | data.table | 3740 | 15 |
flowthrough_sim.rd | respR | Flowthrough respirometry data with increasing background rate | data.table | 3740 | 4 |
intermittent.rd | respR | Respirometry data of the sea urchin, _Heliocidaris Erythrogramma_ | data.table | 4831 | 2 |
sardine.rd | respR | Respirometry data of the sardine, _Sardinops sagax_ | data.table | 7513 | 3 |
squid.rd | respR | Respirometry data of the squid, _Doryteuthis opalescens_ | data.table | 34120 | 2 |
urchins.rd | respR | Multi-column respirometry data of the sea urchin, _Heliocidaris Erythrogramma_, including background respiration | data.table | 271 | 19 |
zeb_intermittent.rd | respR | Respirometry data of a zebrafish, _Danio rerio_ | data.table | 79251 | 2 |
admix | plmmr | Admix: Semi-simulated SNP data | list | | |
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 | | |
gbif_lite | APCalign | GBIF Australian Plant Data | spec_tbl_df | 129 | 7 |
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 | | |
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 | | |
DrillBitLifetime | lmomco | Lifetime of Drill Bits | data.frame | 128 | 1 |
IRSrefunds.by.state | lmomco | U.S. Internal Revenue Service Refunds by State for Fiscal Year 2006 | data.frame | 51 | 2 |
TX38lgtrmFlow | lmomco | First six L-moments of logarithms of annual mean streamflow and variances for 35 selected long-term U.S. Geological Survey streamflow-gaging stations in Texas | data.frame | 35 | 16 |
USGSsta01515000peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 01515000 | data.frame | 71 | 5 |
USGSsta02366500peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 02366500 | data.frame | 76 | 5 |
USGSsta05405000peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 05405000 | data.frame | 73 | 13 |
USGSsta06766000dvs | lmomco | Daily Mean Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 06766000 | data.frame | 19207 | 5 |
USGSsta08151500peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 08151500 | data.frame | 67 | 4 |
USGSsta08167000peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 08167000 | data.frame | 72 | 13 |
USGSsta08190000peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 08190000 | data.frame | 84 | 13 |
USGSsta09442000peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 09442000 | data.frame | 85 | 4 |
USGSsta14321000peaks | lmomco | Annual Peak Streamflow Data for U.S. Geological Survey Streamflow-Gaging Station 14321000 | data.frame | 100 | 5 |
amarilloprecip | lmomco | Annual Maximum Precipitation Data for Amarillo, Texas | data.frame | 47 | 2 |
canyonprecip | lmomco | Annual Maximum Precipitation Data for Canyon, Texas | data.frame | 72 | 2 |
claudeprecip | lmomco | Annual Maximum Precipitation Data for Claude, Texas | data.frame | 91 | 2 |
clearforkporosity | lmomco | Porosity Data | data.frame | 47 | 1 |
herefordprecip | lmomco | Annual Maximum Precipitation Data for Hereford, Texas | data.frame | 67 | 2 |
tulia6Eprecip | lmomco | Annual Maximum Precipitation Data for Tulia 6E, Texas | data.frame | 50 | 2 |
tuliaprecip | lmomco | Annual Maximum Precipitation Data for Tulia, Texas | data.frame | 48 | 2 |
vegaprecip | lmomco | Annual Maximum Precipitation Data for Vega, Texas | data.frame | 61 | 2 |
chatgpt | volker | ChatGPT Adoption Dataset CG-GE-APR23 | tbl_df | 101 | 19 |
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 | | |
HN3401 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 45 | 45 |
HN3403 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 37 | 37 |
HN3404 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 33 | 33 |
HN3406 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 36 | 36 |
N3401 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 45 | 45 |
N3403 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 37 | 37 |
N3404 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 33 | 33 |
N3406 | RSiena | Network data: excerpt from "Dutch Social Behavior Data Set" of Chris Baerveldt. | matrix | 36 | 36 |
allEffects | RSiena | Internal data frame used to construct effect objects. | data.frame | 736 | 26 |
s501 | RSiena | Network 1 data: excerpt from "Teenage Friends and Lifestyle Study" data. | matrix | 50 | 50 |
s502 | RSiena | Network 2 data: excerpt from "Teenage Friends and Lifestyle Study" data. | matrix | 50 | 50 |
s503 | RSiena | Network 3 data: excerpt from "Teenage Friends and Lifestyle Study" data. | matrix | 50 | 50 |
s50a | RSiena | Alcohol use data: excerpt from "Teenage Friends and Lifestyle Study" data | matrix | 50 | 3 |
s50s | RSiena | Smoking data: excerpt from "Teenage Friends and Lifestyle Study" data | matrix | 50 | 3 |
tmp3 | RSiena | van de Bunt's Freshman dataset, time point 3 | matrix | 32 | 32 |
tmp4 | RSiena | van de Bunt's Freshman dataset, time point 4 | matrix | 32 | 32 |
bs2 | multpois | Between-subjects 2×2 design with dichotomous response data | data.frame | 60 | 4 |
bs3 | multpois | Between-subjects 2×2 design with polytomous response data | data.frame | 60 | 4 |
icecream | multpois | Mixed factorial 2×2 design with polytomous response data | data.frame | 160 | 4 |
ws2 | multpois | Within-subjects 2×2 design with dichotomous response data | data.frame | 60 | 4 |
ws3 | multpois | Within-subjects 2×2 design with polytomous response data | data.frame | 60 | 4 |
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 |
dt_potato | flexFitR | Drone-derived data from a potato breeding trial | tbl_df | 1568 | 8 |
Carcinoma | ggpcp | Data set: Assessment of Carcinoma slides | tbl_df | 118 | 9 |
nasa | ggpcp | Data set: NASA - Data Expo 2006 | data.frame | 41472 | 15 |
covdatasim | coveffectsplot | Correlated Covariates data | spec_tbl_df | 2000 | 5 |
prezista | coveffectsplot | Prezista Drug Label Data | spec_tbl_df | 33 | 6 |
wtage | coveffectsplot | Weight Age CDC growth charts data | spec_tbl_df | 436 | 14 |
fish | parameters | Sample data set | data.frame | 250 | 9 |
qol_cancer | parameters | Sample data set | data.frame | 564 | 7 |
mock_3sp_abd | divent | Mock data | integer | | |
mock_3sp_dist | divent | Mock data | dist | | |
mock_3sp_tree | divent | Mock data | phylo | | |
non_species_columns | divent | Non-Species columns | character | | |
paracou_6_abd | divent | Paracou plot 6 | abundances | 4 | 337 |
paracou_6_fundist | divent | Functional distances between Paracou plot 6 species | dist | | |
paracou_6_taxo | divent | Taxonomy of Paracou plot 6 species | phylo | | |
paracou_6_wmppp | divent | Paracou plot 6 | wmppp | | |
BriggsEx47 | rdecision | Probabilistic results of HIV model | data.frame | 1000 | 7 |
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 |
cuts | coconots | Time Series of Monthly Counts of Claimants Collecting Wage Loss Benefit for Injuries in the Workplace | data.frame | 120 | 1 |
downloads | coconots | Time Series of Daily Downloads of a TeX-Editor | data.frame | 267 | 1 |
goldparticle | coconots | Time Series of Gold particles Counts in a well-efined Colloidal Solution | data.frame | 380 | 1 |
. | 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 | |
ReineckeWell266 | copBasic | Porosity and Permeability Data for Well-266 of the Reinecke Oil Field, Horseshoe Atoll, Texas | data.frame | 235 | 6 |
ReineckeWells | copBasic | Porosity and Permeability Data for the Reinecke Oil Field, Horseshoe Atoll, Texas | data.frame | 1271 | 5 |
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 | | |
AMSP | UKFE | National River Flow Archive (NRFA) annual maximum data for sites suitable for pooling | data.frame | 26539 | 3 |
NRFAData | UKFE | National River Flow Archive descriptors and calculated statistics for sites suitable for pooling | data.frame | 543 | 27 |
QMEDData | UKFE | National River Flow Archive descriptors and calculated statistics for sites suitable for QMED & pooling | data.frame | 897 | 26 |
ThamesPQ | UKFE | Kingston upon Thames daily flow and catchment precipitation 2000-10-01 to 2015-09-30 | data.frame | 5478 | 3 |
UKOutline | UKFE | UK outline | data.frame | 3867 | 2 |
nhanes | campsis | NHANES database (demographics and body measure data combined, from 2017-2018). | data.frame | 8005 | 6 |
library_census | svrep | Public Libraries Survey (PLS): A Census of U.S. Public Libraries in FY2020 | tbl_df | 9245 | 23 |
library_multistage_sample | svrep | Public Libraries Survey (PLS): A Census of U.S. Public Libraries in FY2020 | tbl_df | 193 | 30 |
library_stsys_sample | svrep | Public Libraries Survey (PLS): A Census of U.S. Public Libraries in FY2020 | tbl_df | 219 | 27 |
lou_pums_microdata | svrep | ACS PUMS Data for Louisville | tbl_df | 80 | 86 |
lou_vax_survey | svrep | Louisville Vaccination Survey | tbl_df | 1000 | 6 |
lou_vax_survey_control_totals | svrep | Control totals for the Louisville Vaccination Survey | list | | |
pems | MetricGraph | Traffic speed data from San Jose, California | list | | |
pems_repl | MetricGraph | Traffic speed data with replicates from San Jose, California | list | | |
crul_request | vcr | An HTTP request as prepared by the 'crul' package | list | | |
contours | pliman | Contour outlines from five leaves | list | | |
glass | ordr | Glass composition data | tbl_df | 68 | 16 |
qswur_usa | ordr | U.S. university rankings | tbl_df | 612 | 13 |
db1rl | UAHDataScienceSC | Test Database 1 | data.frame | 20 | 5 |
db2 | UAHDataScienceSC | Test Database 6 | data.frame | 10 | 4 |
db3 | UAHDataScienceSC | Test Database 7 | data.frame | 12 | 4 |
db_flowers | UAHDataScienceSC | Test Database 5 | data.frame | 20 | 5 |
db_per_and | UAHDataScienceSC | Test Database 2 | data.frame | 8 | 4 |
db_per_or | UAHDataScienceSC | Test Database 3 | data.frame | 8 | 4 |
db_per_xor | UAHDataScienceSC | Test Database 4 | data.frame | 8 | 4 |
db_tree_struct | UAHDataScienceSC | Test Database 8 | tree_struct | | |
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 |
uci_heart_failure | rtemis | UCI Heart Failure Data | data.table | 299 | 13 |
fish | insight | Sample data set | data.frame | 250 | 9 |
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 | | |
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 |
mobydick | tall | Lemmatized Text of Moby-Dick (Chapters 1-10) | data.frame | 23548 | 27 |
cabg | qicharts2 | Coronary artery bypass graft operations | data.frame | 2205 | 6 |
cdi | qicharts2 | Clostridium difficile infections | data.frame | 36 | 5 |
complaints | qicharts2 | Complaints | data.frame | 20 | 3 |
gtt | qicharts2 | Patient harm identified using the Global Trigger Tool | data.frame | 340 | 11 |
hospital_infections | qicharts2 | Hospital acquired infections | data.frame | 432 | 5 |
lots | qicharts2 | Lots | data.frame | 20 | 3 |
nhs_accidents | qicharts2 | NHS accidents | data.frame | 20 | 3 |
sample.data.filtered | kollaR | Fixation-filtered sample-by-sample example data | data.frame | 11050 | 5 |
sample.data.fixation1 | kollaR | Fixations from 1 individual | data.frame | 37 | 9 |
sample.data.fixations | kollaR | Fixations from 7 individuals | data.frame | 315 | 9 |
sample.data.processed | kollaR | Pre-processed sample-by-sample example data | data.frame | 11050 | 6 |
sample.data.saccades | kollaR | Saccades from 3 individuals | data.frame | 69 | 11 |
sample.data.unprocessed | kollaR | Unprocessed sample-by-sample example data | data.frame | 33235 | 4 |
stock04 | MRCE | log-returns of 9 stocks from 2004 | matrix | 52 | 9 |
cpt | cogirt | CPT Data | list | | |
ex1 | cogirt | Simulated Data for a Unidimensional Two-Parameter Item Response Model | list | | |
ex2 | cogirt | Simulated Data for a Signal Detection Weighted IRT Model | list | | |
ex3 | cogirt | Simulated Data for a Signal Detection Weighted IRT Model with an Experimental Design | list | | |
ex4 | cogirt | Simulated Data for a Unidimensional Two-Parameter Item Response Model with Two Measurement Occasions | list | | |
ex5 | cogirt | Simulated Single Subject Data for a Signal Detection Weighted IRT Model with an Experimental Design | list | | |
flanker | cogirt | Flanker Data | list | | |
nback | cogirt | N-Back Data | list | | |
plt | cogirt | PLT Data | list | | |
sopt | cogirt | SOPT Data | list | | |
sternberg | cogirt | Sternberg Data | list | | |
mods2018 | SVDMx | SVD-Comp models data set - 'mods2018' | list | | |
mods2022 | SVDMx | SVD-Comp models data set - 'mods2022' | list | | |
mods2024 | SVDMx | SVD-Comp models data set - 'mods2024' | list | | |
datos | saery | Dataset for saery package | data.frame | 2000 | 6 |
SLE_gwas_sub | locuszoomr | SLE GWAS data subset | data.frame | 1990 | 12 |
CEB | popstudy | Children Ever Born Data | tbl_df | 27 | 8 |
CR_births | popstudy | CR_births | data.frame | 8434 | 2 |
CR_deaths | popstudy | CR_deaths | data.frame | 229462 | 3 |
CR_fertility_rates_1950_2011 | popstudy | Costa Rica fertility rates | data.frame | 2170 | 3 |
CR_mortality_rates_1950_2011 | popstudy | Costa Rica mortality rates | data.frame | 7192 | 5 |
CR_mortality_rates_2010_2015 | popstudy | Costa Rica Mortality Rates | data.table | 7656 | 4 |
CR_populations_1950_2011 | popstudy | Costa Rica population | data.frame | 7192 | 5 |
CR_populations_1950_2015 | popstudy | Costa Rica population | data.table | 7656 | 4 |
CR_women_childbearing_age_1950_2011 | popstudy | Costa Rica population | data.frame | 2170 | 3 |
Ecuador1990 | popstudy | Ecuador1990 | tbl_df | 21 | 4 |
Panama1990 | popstudy | Panama1990 | tbl_df | 100 | 2 |
births_deaths | popstudy | Births and deaths data | list | | |
grouped_age_CR_pop | popstudy | grouped_age_CR_pop | tbl_df | 16 | 2 |
karup_king_factors | popstudy | karup_king_factors | tbl_df | 76 | 7 |
fewlevels_en | gendercoder | fewlevels_en | character | | |
manylevels_en | gendercoder | manylevels_en | character | | |
sample | gendercoder | sample | data.frame | 7756 | 1 |
marismas | sta | 10-day Composite NDMI Time Series | numeric | | |
BregFix | flexmix | Artificial Example for Binomial Regression | data.frame | 200 | 5 |
Mehta | flexmix | Mehta Trial | data.frame | 44 | 4 |
NPreg | flexmix | Artificial Example for Normal, Poisson and Binomial Regression | data.frame | 200 | 7 |
Nclus | flexmix | Artificial Example with 4 Gaussians | matrix | 550 | |
NregFix | flexmix | Artificial Example for Normal Regression | data.frame | 200 | 5 |
betablocker | flexmix | Clinical Trial of Beta-Blockers | data.frame | 44 | 4 |
bioChemists | flexmix | Articles by Graduate Students in Biochemistry Ph.D. Programs | data.frame | 915 | 6 |
candy | flexmix | Candy Packs Purchased | data.frame | 21 | 2 |
dmft | flexmix | Dental Data | data.frame | 797 | 5 |
fabricfault | flexmix | Fabric Faults | data.frame | 32 | 2 |
patent | flexmix | Patents and R&D Spending | data.frame | 70 | 4 |
salmonellaTA98 | flexmix | Salmonella Reverse Mutagenicity Assay | data.frame | 18 | 2 |
seizure | flexmix | Epileptic Seizure Data | data.frame | 140 | 4 |
tribolium | flexmix | Tribolium Beetles | data.frame | 27 | 4 |
trypanosome | flexmix | Trypanosome | data.frame | 426 | 2 |
whiskey | flexmix | Survey Data on Brands of Scotch whiskey Consumed | data.frame | 484 | 2 |
whiskey_brands | flexmix | Survey Data on Brands of Scotch whiskey Consumed | data.frame | 21 | 3 |
SampleRWD | CMHSU | Sample RWD Data | data.frame | 1665 | 8 |
AgD_bl | MAICtools | Description of AgD_bl dataset | data.frame | 4 | 13 |
AgD_eff | MAICtools | Description of AgD_eff dataset | data.frame | 5 | 5 |
IPD | MAICtools | Description of IPD dataset | tbl_df | 585 | 18 |
pseudo | MAICtools | Description of pseudo dataset | data.frame | 201 | 6 |
pts | MAICtools | Description of pts dataset | tbl_df | 585 | 20 |
unpts | MAICtools | Description of unpts dataset | tbl_df | 590 | 6 |
AP | mratios | Angina pectoris data | data.frame | 303 | 2 |
ASAT | mratios | ASAT data | data.frame | 34 | 2 |
BW | mratios | Body weights measured in a toxicological study | data.frame | 60 | 2 |
DiabeticMice | mratios | Serum albumin of diabetic mice | data.frame | 57 | 2 |
Mutagenicity | mratios | Mutagenicity assay | data.frame | 31 | 2 |
Penicillin | mratios | Comparing 6 strains with respect to production of antibiotics | data.frame | 48 | 2 |
SRAssay | mratios | Slope ratio assay of panthotenic acid contents in plant tissues | data.frame | 34 | 3 |
angina | mratios | The angina data set | data.frame | 50 | 2 |
bnct | mratios | Boron neutron capture therapy (BNCT) | data.frame | 30 | 3 |
rat.weight | mratios | Body weight of rats in a toxicity study | data.frame | 20 | 2 |
mock_haplotype_dataset | SSHAARP | Mock Haplotype Dataset | data.frame | 24989 | 7 |
solberg_dataset | SSHAARP | Solberg Dataset | data.frame | 20163 | 13 |
Genomicdata | GenomicSig | Nucleotide sequence Data | list | | |
LIRI | ggrisk | ICGC Liver Data from Japan | data.frame | 232 | 6 |
verbal | difR | Verbal Aggression Data Set | data.frame | 316 | 26 |
immigration_perceptions | stmCorrViz | Sample STM Model | list | | |
prostate_cancer | spGARCH | Logarithmic incidence rates of prostate cancer and covariates | list | | |
FACSdata | EDOtrans | Example data of hematologic marker expression. | data.frame | 3000 | 5 |
GMMartificialData | EDOtrans | Example data an artificial Gaussioan mixture. | data.frame | 1000 | 5 |
stlouis | mix | St. Louis Risk Research Project | matrix | 69 | 7 |
concerts | manet | Concerts synthetic network | matrix | 500 | 14 |
deepsouth | manet | Deep South Network | matrix | 18 | 14 |
noordin | manet | Noordin Top terrorist network | data.frame | 79 | 45 |
Data_potato | stlELM | Monthly Average Potato Price of Delhi Market (India) | ts | 127 | 1 |
dataSec | PhysicalActivity | Accelerometer Data Example | data.frame | 238140 | 2 |
deliveryData | PhysicalActivity | Data Example for Mail Delivery Day Classification | data.frame | 20987 | 6 |
HumanMotorResonance | NPCirc | Human motor resonance data | data.frame | 70 | 2 |
cross.beds1 | NPCirc | Cross-beds azimuths (I) | data.frame | 580 | 1 |
cross.beds2 | NPCirc | Cross-beds (II) | data.frame | 104 | 1 |
cycle.changes | NPCirc | Cycle changes | data.frame | 350 | 2 |
dragonfly | NPCirc | Orientations of dragonflies | data.frame | 214 | 1 |
flywheels | NPCirc | Flywheel measurements | data.frame | 60 | 3 |
periwinkles | NPCirc | Orientations of dragonflies | data.frame | 31 | 2 |
pm10 | NPCirc | Pm10 particles in Pontevedra, Spain | data.frame | 1156 | 3 |
sandhoppers | NPCirc | Behavioral plasticity of Talitrus saltator and Talorchestia brito | data.frame | 1828 | 12 |
speed.wind | NPCirc | Wind speed and wind direction data | data.frame | 19488 | 6 |
speed.wind2 | NPCirc | Wind speed and wind direction data | data.frame | 200 | 6 |
spikes | NPCirc | Neuronal spikes in a macaque monkey | data.frame | 68 | 2 |
temp.wind | NPCirc | Temperature and wind direction data | data.frame | 3648 | 4 |
wind | NPCirc | Wind direction data | data.frame | 1752 | 3 |
zebrafish | NPCirc | Zebrafish | data.frame | 502 | 2 |
cognitive | splmm | Kenya School Lunch Intervention Cognitive Dataset | data.frame | 1562 | 26 |
simulated_data | splmm | Dataset simulated for toy example | list | | |
bolus | cold | Bolus data | data.frame | 780 | 4 |
datacold | cold | Data | data.frame | 390 | 4 |
datacoldM | cold | Data with missing values | data.frame | 390 | 4 |
seizure | cold | Epileptic Seizure | data.frame | 236 | 9 |
dots | HDoutliers | One dimensional dots dataset - outlier detection example | data.frame | 50 | 2 |
ex2D | HDoutliers | Two dimensional dataset - outlier detection example | matrix | 510 | |
Asthma | glarma | Daily Presentations of Asthma at Campbelltown Hospital | data.frame | 1461 | 16 |
DriverDeaths | glarma | Single Vehicle Nighttime Driver Deaths in Utah | data.frame | 72 | 6 |
OxBoatRace | glarma | Oxford-Cambridge Boat Race | data.frame | 156 | 6 |
Polio | glarma | Cases of Poliomyelitis in the U.S. | data.frame | 168 | 8 |
RobberyConvict | glarma | Court Convictions for Armed Robbery in New South Wales | data.frame | 150 | 11 |
big5 | RMX | BIG 5 Example Data Set | data.frame | 1076 | 21 |
SESdata | STAND | Samples from Elevated Surfaces of a Smelter | data.frame | 31 | 2 |
aihand | STAND | Industrial Hygiene Air Monitoring Data | data.frame | 15 | 3 |
beTWA | STAND | TWA Beryllium Exposure Data | data.frame | 280 | 2 |
cansdata | STAND | Container Data Used To Evaluate Beryllium Surface Contamination | data.frame | 120 | 4 |
filmbadge | STAND | Quarterly Film Badge Data | data.frame | 28 | 6 |
IBM | NonlinearTSA | IBM | xts | 2013 | 1 |
MarketPrices | NonlinearTSA | MarketPrices | data.frame | 667 | 2 |
Events | hydropeak | Events | data.frame | 296 | 8 |
Q | hydropeak | Flow Fluctuations Q | data.frame | 960 | 3 |
Student | dfphase1 | A simulated dataset | array | | |
colonscopy | dfphase1 | Colonscopy Times | matrix | 5 | 30 |
fe | dfphase1 | Ferric Oxide data | numeric | | |
gravel | dfphase1 | Gravel data | array | | |
ryan | dfphase1 | Ryan data | array | | |
truebeta | srp | A dataset containing true regression coefficients for simulation | data.frame | 356 | 4 |
banks00_07 | npsf | U.S. Commercial Banks Data | data.frame | 3651 | 20 |
banks05 | npsf | U.S. Commercial Banks Data | data.frame | 500 | 7 |
ccr81 | npsf | Program Follow Through at Primary Schools | data.frame | 70 | 9 |
mroz | npsf | Female labor force participation | data.frame | 753 | 20 |
pwt56 | npsf | Penn World Tables 5.6 (compiled in 1995) | data.frame | 104 | 6 |
usmanuf | npsf | US Manufacturing Industry Data | data.frame | 9460 | 6 |
GS | GSelection | Genotypic and phenotypic simulated dataset | matrix | 60 | 111 |
env.single | macc | Simulated single-level dataset | environment | | |
env.three | macc | Simulated three-level dataset | environment | | |
env.two | macc | Simulated two-level dataset | environment | | |
data_USA | EWS | Historical data for the United States | data.frame | 268 | 5 |
data_panel | EWS | Historical data for 13 OECD countries | data.frame | 6903 | 4 |
testdata | DBfit | testdata | data.frame | 40 | 5 |
cbb | hnp | Coffee berry borer trapping data | data.frame | 288 | 4 |
chryso | hnp | _Chrysoperla externa_ mortality data | data.frame | 24 | 4 |
corn | hnp | Corn damage data | data.frame | 40 | 3 |
fungi | hnp | _Diaphorina citri_ mortality data | data.frame | 30 | 5 |
oil | hnp | _Diaphorina citri_ oviposition data | data.frame | 70 | 2 |
orange | hnp | Orange tissue-culture experiment data | data.frame | 150 | 4 |
progeny | hnp | _Sitophilus zeamais_ progeny | data.frame | 40 | 2 |
wolbachia | hnp | _Trichogramma galloi_ parasitism data | data.frame | 106 | 3 |
zaporozec | hydrogeo | Major ions for groundwaters reported by Zaporozec | data.frame | 9 | 15 |
example_data | EstMix | ExampleData | data.frame | 6209 | 5 |
seg_eg | EstMix | Segmentation | PairedPSCNSegments | 4 | 15 |
wgs_eg | EstMix | wgsData | data.frame | 30 | 4 |
E1 | gTests | An edge matrix representing a similarity graph | matrix | 1245 | |
E2 | gTests | An edge matrix representing a similarity graph | matrix | 1245 | |
E3 | gTests | An edge matrix representing a similarity graph | matrix | 1245 | |
counts1 | gTests | A matrix representing counts in the distinct values for the two samples | matrix | 720 | 2 |
counts2 | gTests | A matrix representing counts in the distinct values for the two samples | matrix | 720 | 2 |
counts3 | gTests | A matrix representing counts in the distinct values for the two samples | matrix | 720 | 2 |
ds1 | gTests | A distance matrix on the distinct values | matrix | 720 | |
ds2 | gTests | A distance matrix on the distinct values | matrix | 720 | |
ds3 | gTests | A distance matrix on the distinct values | matrix | 720 | |
example.of.Fisher.exact | ssanv | Object of class 'power.htest' | power.htest | | |
illuNames | NameNeedle | Cell Line Names | character | | |
illuType | NameNeedle | Cell Line Names | factor | | |
rppaNames | NameNeedle | Cell Line Names | character | | |
sf2Names | NameNeedle | Cell Line Names | character | | |
exp_input | CpmERCCutoff | A data frame of expected ERCC1 and ERCC2 ratios | data.frame | 92 | 6 |
mta_dta | CpmERCCutoff | A data frame containing sample-level ERCC meta data | data.frame | 49 | 4 |
obs_input | CpmERCCutoff | A data frame of observed spike in ERCC normalized LCPM data | data.frame | 92 | 50 |
GReg | flexmixNL | Artificial Example for Gamma Regression | data.frame | 200 | 3 |
NReg | flexmixNL | Artificial Example for Normal Regression | data.frame | 200 | 3 |
E | hglm.data | Scottish lip cancer dataset from Clayton and Kaldor (1987) | numeric | | |
O | hglm.data | Scottish lip cancer dataset from Clayton and Kaldor (1987) | numeric | | |
Paff | hglm.data | Scottish lip cancer dataset from Clayton and Kaldor (1987) | numeric | | |
QTLMAS | hglm.data | Simulated Data Set for the QTLMAS 2009 Workshop | data.frame | 1000 | 2116 |
nbr | hglm.data | Scottish lip cancer dataset from Clayton and Kaldor (1987) | matrix | 56 | |
ohioDistrictDistMat | hglm.data | Ohio elementary school dataset | matrix | 616 | 616 |
ohioGrades | hglm.data | Ohio elementary school dataset | data.frame | 9835 | 6 |
ohioMedian | hglm.data | Ohio elementary school dataset | data.frame | 1860 | 2 |
ohioSchools | hglm.data | Ohio elementary school dataset | data.frame | 1967 | 40 |
ohioShape | hglm.data | Ohio elementary school dataset | SpatialPolygonsDataFrame | | |
ohioZipDistMat | hglm.data | Ohio elementary school dataset | matrix | 801 | 801 |
pump | hglm.data | Pump reliability data set from Gaver and O'Muircheartaigh (1987) | data.frame | 10 | 4 |
salamander | hglm.data | Salamander mating data set from McCullagh and Nelder (1989) | data.frame | 360 | 8 |
seeds | hglm.data | Seeds genrmination data set from Crowder (1978) | data.frame | 21 | 5 |
semiconductor | hglm.data | Semiconductor data set from GenStat. | data.frame | 64 | 8 |
Employment.2 | march | Employment status in two categories (march dataset format) | march.Dataset | | |
pewee | march | Song of the Wood Pewee (march dataset format) | march.Dataset | | |
pewee_df | march | Song of the Wood Pewee (data frame format) | data.frame | 1327 | 1 |
pewee_t | march | Song of the Wood Pewee (text format) | data.frame | 1326 | 1 |
sleep | march | Sleep disorders (march dataset format) | march.Dataset | | |
sleep_df | march | Sleep disorders (data frame format) | data.frame | 1000 | 7 |
AASP | dCUR | Academic Achievement Score Projection -AASP- | data.frame | 638 | 205 |
GenoScan.example | GenoScan | Data example for GenoScan (A Genome-Wide Scan Statistic Framework For Whole-Genome Sequence Data Analysis) | list | | |
GenoScan.info | GenoScan | hg19 chromosome sizes | list | | |
mps | mbrdr | Minneapolis School dataset | data.frame | 63 | 15 |
RGB | psyphy | Luminance Calibration Data from Video Projector | data.frame | 84 | 3 |
ecc2 | psyphy | 4-afc Detection and Identification of Letters | data.frame | 48 | 5 |
BBS2001 | Rcapture | Species Richness Data from the North American Breeding Bird Survey (BBS) in 2001 | matrix | 36 | 2 |
HIV | Rcapture | Epidemiological capture-recapture Data on HIV | matrix | 15 | 5 |
bunting | Rcapture | Lazuli Bunting Data | matrix | 255 | 9 |
cbird | Rcapture | Catbird Site Occupancy Data | matrix | 6 | 2 |
duck | Rcapture | Eider Duck Data | matrix | 63 | 7 |
hare | Rcapture | Snowshoe Hare Data | matrix | 68 | 6 |
ill | Rcapture | Illegal immigrants in the Netherlands | matrix | 6 | 2 |
lesbian | Rcapture | Epidemiological capture-recapture Data on the lesbian population | matrix | 15 | 5 |
mvole | Rcapture | Robust Design Data for Adult Male Meadow Voles | matrix | 171 | 30 |
rbvole | Rcapture | Robust Design Data for Red-Back Voles | matrix | 66 | 19 |
gss_dict | gssrdoc | Data Dictionary for the GSS Cumulative Data File 1972-2022 | tbl_df | 6663 | 13 |
gss_panel_doc | gssrdoc | Codebook for the GSS Three Wave Panel | tbl_df | 628 | 9 |
pancancer | bigtcr | Example Pancreatic Cancer Dataset | data.frame | 100 | 3 |
croton | replicatedpp2w | Replicated Point Pattern of Croton | list | | |
my.plants | Fragman | Cranberry biparental population | list | | |
dino_comparisons | MASSTIMATE | Reconstruction body masses and associated limb circumference data from Campione & Evans (2020) | spec_tbl_df | 447 | 8 |
dinos | MASSTIMATE | Dinosaur data from Campione and Evans 2012 | data.frame | 8 | 3 |
dinosbip | MASSTIMATE | Dinosaur data from Campione et al. (2014) | data.frame | 34 | 3 |
extants | MASSTIMATE | Extant limb data from Campione and Evans 2012 | spec_tbl_df | 245 | 13 |
ltns | SmoothTensor | Chicago crime tensor dataset | array | | |
mode_info | SmoothTensor | A list of mode information of the Chicago crime tensor dataset | list | | |
pinkham | mAr | Lydia Pinkham Annual Advertising and Sales data | data.frame | 54 | 2 |
sparrows | mAr | Body measurements of sparrows | data.frame | 49 | 5 |
waves | mAr | Time series of ocean wave height measurements | data.frame | 4096 | 2 |
NWP.rain | WQM | Australia NWP rainfall forecasts at lead 1h over Sydney region | data.table | 818240 | 5 |
sample | WQM | Sample data: Rainfall forecasts data | list | | |
seqfishplus | smfishHmrf | SeqFISHplus dataset | list | | |
operateur_mobile | iperform | operateur_mobile | data.frame | 990 | 6 |
service_mobile | iperform | service_mobile | data.frame | 504 | 5 |
voix_mobile | iperform | voix_mobile | data.frame | 1003 | 4 |
AnomAuth | ctsem | AnomAuth | data.frame | 2722 | 14 |
Oscillating | ctsem | Oscillating | data.frame | 200 | 21 |
ctExample1 | ctsem | ctExample1 | matrix | 100 | 17 |
ctExample1TIpred | ctsem | ctExample1TIpred | matrix | 100 | 18 |
ctExample2 | ctsem | ctExample2 | matrix | 10 | 31 |
ctExample3 | ctsem | ctExample3 | matrix | 1 | 399 |
ctExample4 | ctsem | ctExample4 | matrix | 20 | 79 |
ctstantestdat | ctsem | ctstantestdat | matrix | 300 | 8 |
ctstantestfit | ctsem | ctstantestfit | ctStanFit | | |
datastructure | ctsem | datastructure | matrix | 2 | 16 |
longexample | ctsem | longexample | matrix | 7 | 8 |
tsQuotes | RQuantLib | Vol Cube Example Data Short time series examples | list | | |
vcube | RQuantLib | Vol Cube Example Data | data.table | 1343 | 4 |
Anolis.data | RPANDA | Anolis dataset | list | | |
BGB.examples | RPANDA | BioGeoBEARS stochastic maps | list | | |
Balaenopteridae | RPANDA | Balaenopteridae phylogeny | phylo | | |
Calomys | RPANDA | Calomys phylogeny | phylo | | |
Caprimulgidae | RPANDA | The _Caprimulgidae_ phylogeny. | phylo | | |
Caprimulgidae_ClaDS2 | RPANDA | An example run of ClaDS2. | list | | |
Cetacea | RPANDA | Cetacean phylogeny | phylo | | |
Cetacea_clades | RPANDA | Stochastic map of clade membership in Cetacean phylogeny | simmap | | |
ClaDS0_example | RPANDA | An example run of ClaDS0. | list | | |
InfTemp | RPANDA | Paleotemperature data across the Cenozoic | data.frame | 17632 | 2 |
Phocoenidae | RPANDA | Phocoenidae phylogeny | phylo | | |
Phyllostomidae | RPANDA | Phyllostomidae phylogeny | phylo | | |
Phyllostomidae_genera | RPANDA | Phylogenies of Phyllostomidae genera | list | | |
co2 | RPANDA | co2 data since the Jurassic | data.frame | 53 | 2 |
co2 | RPANDA | co2 data since the Jurassic | data.frame | 17624 | 2 |
coccolithophore | RPANDA | Coccolithophore diversity since the Jurassic | data.frame | 40 | 2 |
d13c | RPANDA | d13c data since the Jurassic | data.frame | 69 | 2 |
foraminifera | RPANDA | Foraminifera diversity since the Jurassic | data.frame | 52 | 2 |
greenalgae | RPANDA | Green algae diversity since the Jurassic | data.frame | 41 | 2 |
landplant | RPANDA | Land plant diversity since the Jurassic | data.frame | 41 | 2 |
mycorrhizal_network | RPANDA | Mycorrhizal network from La Réunion island | list | | |
ostracoda | RPANDA | Ostracod diversity since the Jurassic | data.frame | 52 | 2 |
radiolaria | RPANDA | Radiolaria diversity since the Jurassic | data.frame | 52 | 2 |
redalgae | RPANDA | Red algae diversity since the Jurassic | data.frame | 41 | 2 |
sealevel | RPANDA | Sea level data since the Jurassic | data.frame | 3161 | 2 |
shifts_cetacea | RPANDA | Cetacean shift.estimates results | list | | |
silica | RPANDA | Silica data across the Cenozoic | data.frame | 67 | 2 |
taxo_cetacea | RPANDA | Cetacean taxonomy | data.frame | 89 | 4 |
debt | cccm | Debt Data | data.frame | 106 | 4 |
Amm.dat | phreeqc | The Amm.dat database. | character | | |
ColdChem.dat | phreeqc | The ColdChem.dat database. | character | | |
Kinec.v2.dat | phreeqc | Thermodynamic and rates database from Oelkers and coworkers. | character | | |
Kinec_v3.dat | phreeqc | Thermodynamic and rates database from Oelkers and coworkers. | character | | |
PHREEQC_ThermoddemV1.10_15Dec2020.dat | phreeqc | Thermochemical Database from the BRGM institute (French Geological Survey) | character | | |
Tipping_Hurley.dat | phreeqc | The Tipping_Hurley.dat database | character | | |
core10.dat | phreeqc | The core10.dat database | character | | |
ex1 | phreeqc | Example 1-Speciation Calculation | character | | |
ex10 | phreeqc | Example 10-Aragonite-Strontianite Solid Solution | character | | |
ex11 | phreeqc | Example 11-Transport and Cation Exchange | character | | |
ex12 | phreeqc | Example 12-Advective and Diffusive Flux of Heat and Solutes | character | | |
ex13a | phreeqc | Example 13-Aragonite-Strontianite Solid Solution | character | | |
ex13b | phreeqc | Example 13-Aragonite-Strontianite Solid Solution | character | | |
ex13c | phreeqc | Example 13-Aragonite-Strontianite Solid Solution | character | | |
ex14 | phreeqc | Example 14-Advective Transport, Cation Exchange, Surface Complexation, and Mineral Equilibria | character | | |
ex15 | phreeqc | Example 15-1D Transport: Kinetic Biodegradation, Cell Growth, and Sorption | character | | |
ex15.dat | phreeqc | The ex15.dat database | character | | |
ex16 | phreeqc | Example 16-Inverse Modeling of Sierra Spring Waters | character | | |
ex17 | phreeqc | Example 17-Inverse Modeling With Evaporation | character | | |
ex18 | phreeqc | Example 18-Inverse Modeling of the Madison Aquifer | character | | |
ex19 | phreeqc | Example 19-Modeling Cd+2 Sorption With Linear, Freundlich, and Langmuir Isotherms, and With a Deterministic Distribution of Sorption Sites for Organic Matter, Clay Minerals, and Iron Oxyhydroxides | character | | |
ex2 | phreeqc | Example 2-Equilibration With Pure Phases | character | | |
ex20a | phreeqc | Example 20-Distribution of Isotopes Between Water and Calcite | character | | |
ex20b | phreeqc | Example 20-Distribution of Isotopes Between Water and Calcite | character | | |
ex21 | phreeqc | Example 21-Modeling Diffusion of HTO, 36Cl-, 22Na+, and Cs+ in a Radial Diffusion Cell | character | | |
ex22 | phreeqc | Example 22-Modeling Gas Solubilities: CO2 at High Pressures | character | | |
ex3 | phreeqc | Example 3-Mixing | character | | |
ex4 | phreeqc | Example 4-Evaporation and Homogeneous Redox Reactions | character | | |
ex5 | phreeqc | Example 5-Irreversible Reactions | character | | |
ex6 | phreeqc | Example 6-Reaction-Path Calculations | character | | |
ex7 | phreeqc | Example 7-Gas-Phase Calculations | character | | |
ex8 | phreeqc | Example 8-Surface Complexation | character | | |
ex9 | phreeqc | Example 9-Kinetic Oxidation of Dissolved Ferrous Iron With Oxygen | character | | |
frezchem.dat | phreeqc | The frezchem.dat database | character | | |
iso.dat | phreeqc | The iso.dat database. | character | | |
llnl.dat | phreeqc | The llnl.dat database. | character | | |
minteq.dat | phreeqc | The minteq.dat database. | character | | |
minteq.v4.dat | phreeqc | The minteq.v4.dat database. | character | | |
phreeqc.dat | phreeqc | The phreeqc.dat database | character | | |
phreeqc_rates.dat | phreeqc | Thermodynamic and rates database | character | | |
pitzer.dat | phreeqc | The pitzer.dat database. | character | | |
sit.dat | phreeqc | The sit.dat database. | character | | |
wateq4f.dat | phreeqc | The wateq4f.dat database. | character | | |
gdmDissim | gdm | An example biological dissimilarity matrix | matrix | 94 | 95 |
southwest | gdm | Species and Environmental Data from Southwestern Australia. | data.frame | 29364 | 14 |
address1 | callback | Origin/Gender discrimination and strongly negative mediatic exposure (information technologist) | data.frame | 3684 | 11 |
gender1 | callback | Gender/Maternity discrimination (commercial and administrative jobs in the financial sector) | data.frame | 942 | 14 |
gender2 | callback | Gender/Maternity discrimination (electricians) | data.frame | 564 | 15 |
gender3 | callback | Gender/Maternity discrimination (masons) | data.frame | 532 | 15 |
gender4 | callback | Gender/Maternity discrimination (plumbers) | data.frame | 1152 | 15 |
inter1 | callback | Gender/Origin discrimination (software developer) | data.frame | 2480 | 11 |
labour1 | callback | Labour market history discrimination (accountants) | data.frame | 1475 | 7 |
labour2 | callback | Labour market history discrimination (sales assistant) | data.frame | 1470 | 7 |
mobility1 | callback | Gender discrimination and mobility (management controller) | data.frame | 1200 | 12 |
origin1 | callback | Origin discrimination (accountants) | data.frame | 1097 | 20 |
origin2 | callback | Origin discrimination (waiters) | data.frame | 936 | 20 |
herring | FinePop2 | An example dataset of Atlantic herring. | list | | |
jsmackerel | FinePop2 | An example dataset of Japanese Spanich mackerel in GENEPOP and frequency format. | list | | |
GastricCancer | ELYP | Gastric Cancer Data | matrix | 4 | |
smallcell | ELYP | Smallcell Lung Cancer Data | data.frame | 121 | 4 |
B20 | CVD | Farnsworth B-20 cap colors | data.frame | 20 | 5 |
BowmanTCDS | CVD | Table of color distance scores for quantitative scoring of the Farnsworth panel D-15 test | data.frame | 16 | 16 |
FarnsworthD15 | CVD | Farnsworth D-15 cap colors | data.frame | 16 | 7 |
FarnsworthMunsell100Hue | CVD | Farnsworth D-15 cap colors | data.frame | 85 | 7 |
GellerTCDS | CVD | Table of color distance scores for quantitative scoring of the Lanthony desaturate D-15s test | data.frame | 16 | 16 |
H16 | CVD | Farnsworth H-16 cap colors | data.frame | 17 | 5 |
LanthonyD15 | CVD | Farnsworth H-16 cap colors | data.frame | 16 | 7 |
Roth28 | CVD | Roth-28 cap colors | data.frame | 29 | 6 |
VKStable2 | CVD | Table with results of D-15 tests scored with the Vingrys and King-Smith method | data.frame | 18 | 9 |
dichromaticCopunctalPoint | CVD | Copunctal points derived by Smith and Pokorny (1975) | matrix | 3 | 6 |
example1Lanthony1978 | CVD | Example of cap arrangements for the D-15d test, Simple/Extreme Anomalous Trichromacy | matrix | 15 | 4 |
example2Lanthony1978 | CVD | Example of cap arrangements for the D-15d test, Central Serous Choroidopathy/Optic Neuritis/Autosomal Dominant OpticAtrophy | matrix | 15 | 6 |
exampleBowman1982 | CVD | Example of cap arrangements for the D-15d test | matrix | 15 | 6 |
exampleFM100 | CVD | Example of cap arrangements for the FM-100 test | data.frame | 12 | 22 |
exampleFarnsworth1974 | CVD | Example of cap arrangements for the D-15 test, deuteranope/protanope/tritanope | matrix | 15 | 3 |
exampleNRC1981 | CVD | Example of cap arrangements for the D-15 test, protanope/deuteranope/monochromat | matrix | 15 | 3 |
exampleSimunovic2004 | CVD | Example of cap arrangements for the D-15 test, rodMonochromat/blueConeMonochromat | matrix | 15 | 2 |
neutralPoint | CVD | Neutral points for CIE 1976 uv, CIE 1931 xy and CIE 1960 uv | matrix | 3 | 2 |
typicalD15 | CVD | Typical cap arrangements for the D-15 tests | matrix | 15 | 6 |
HTRU | DEM | HTRU2 | data.frame | 17898 | 9 |
Skin | DEM | Skin segmentation | data.frame | 245057 | 4 |
magic | DEM | Magic | data.frame | 19020 | 11 |
learning_motivation | SDT | Learning Motivation Data | data.frame | 1150 | 6 |
Arigon | pointdensityP | Arigon dataset | data.frame | 80000 | 3 |
clean_crime | pointdensityP | Houston crime dataset | data.frame | 81803 | 17 |
portfolio.pois | GCPM | Example Portfolio Data with Poisson Default Mode | data.frame | 3000 | 11 |
portfolio.pois | GCPM | Example Portfolio Data with Poisson Default Mode | data.frame | 3000 | 11 |
portfolio.pool | GCPM | Pooled Portfolio | data.frame | 1483 | 11 |
portfolio.pool | GCPM | Pooled Portfolio | data.frame | 1483 | 11 |
ColoCan | gss | Colorectal Cancer Mortality Rate in Indiana Counties | data.frame | 184 | 11 |
DiaRet | gss | Diabetic Retinopathy | data.frame | 197 | 13 |
LakeAcidity | gss | Water Acidity in Lakes | data.frame | 112 | 5 |
NO2 | gss | Air Pollution and Road Traffic | data.frame | 500 | 6 |
Sachs | gss | Protein Expression in Human Immune System Cells | data.frame | 7466 | 12 |
aids | gss | AIDS Incubation | data.frame | 295 | 3 |
bacteriuria | gss | Treatment of Bacteriuria | data.frame | 820 | 4 |
buffalo | gss | Buffalo Annual Snowfall | numeric | | |
clim | gss | Average Temperatures During December 1980 Through February 1981 | data.frame | 690 | 2 |
esc | gss | Embryonic Stem Cell from Mouse | data.frame | 1027 | 8 |
eyetrack | gss | Eyesight Fixation in Eyetracking Experiments | data.frame | 13891 | 5 |
gastric | gss | Gastric Cancer Data | data.frame | 90 | 3 |
nox | gss | NOx in Engine Exhaust | data.frame | 88 | 3 |
ozone | gss | Ozone Concentration in Los Angeles Basin | data.frame | 330 | 10 |
penny | gss | Thickness of US Lincoln Pennies | data.frame | 90 | 2 |
stan | gss | Stanford Heart Transplant Data | data.frame | 184 | 4 |
wesdr | gss | Progression of Diabetic Retinopathy | data.frame | 669 | 4 |
wesdr1 | gss | Stages of Diabetic Retinopathy | data.frame | 2049 | 7 |
Example | dynamic | DFI Example Data | data.frame | 500 | 12 |
Holzinger | dynamic | Holzinger & Swineford (1939) Data | data.frame | 301 | 10 |
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 |
landscape | shar | Example landscape (random cluster neutral landscape model). | PackedSpatRaster | | |
species_a | shar | Species a | ppp | | |
species_b | shar | Species b | ppp | | |
ItalianCities | mpt | City-Size Paired-Comparison Task | data.frame | 64 | 6 |
MDHennig2020 | mpt | Moral Dilemma Judgment | data.frame | 16 | 7 |
MDreplication | mpt | Moral Dilemma Judgment | data.frame | 751 | 5 |
PMSmithBayen | mpt | Prospective Memory and Task Importance | data.frame | 24 | 5 |
PMreplication | mpt | Prospective Memory and Task Importance | data.frame | 72 | 5 |
ROCBroeder2009 | mpt | Recognition Receiver Operating Characteristics | data.frame | 20 | 7 |
ROCreplication | mpt | Recognition Receiver Operating Characteristics | data.frame | 48 | 5 |
WSTKlauer2007 | mpt | Wason Selection Task (WST) and Helpful Hints | data.frame | 32 | 4 |
WSTreplication | mpt | Wason Selection Task (WST) and Helpful Hints | data.frame | 1118 | 8 |
WorldCities | mpt | City-Size Paired-Comparison Task | data.frame | 37 | 6 |
agememory | mpt | Age Differences in Episodic Memory | data.frame | 80 | 15 |
proact | mpt | Recall Frequencies for DaPolito's Experiment on Proactive Inhibition | data.frame | 24 | 5 |
pwrsim1 | mpt | Simulation-Based Power Analysis for MPT Models | data.frame | 36 | 4 |
pwrsim2 | mpt | Simulation-Based Power Analysis for MPT Models | data.frame | 15 | 4 |
retroact | mpt | Recall Frequencies in Retroactive Inhibition | data.frame | 30 | 4 |
valence | mpt | World Valence and Source Memory for Vertical Position | data.frame | 128 | 5 |
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 |
concussion | DiscreteGapStatistic | Concussion Data | data.frame | 109 | 21 |
mass | DiscreteGapStatistic | mass data | data.frame | 20 | 15 |
admitted_patients | plot2 | Example Data Set with Admitted Patients | tbl_df | 250 | 7 |
netherlands | plot2 | Example Geography Data Set: the Netherlands | sf | 12 | 3 |
fertilization | RoBTT | Height of 15 plant pairs collected by Darwin | data.frame | 15 | 2 |
alfalfaSG | drcSeedGerm | Field-book for a germination assay with alfalfa | data.frame | 21 | 32 |
barley | drcSeedGerm | A series of germination assays with barley | data.frame | 810 | 7 |
excalibur | drcSeedGerm | Relationship between germination rate and water potential in oilseed rape (var. Excalibur) | data.frame | 27 | 5 |
festuca | drcSeedGerm | Relationship between germination rate and water potential in Festuca arundinacea L. | data.frame | 36 | 3 |
hordeum | drcSeedGerm | Germination of Hordeum spontaneum at different temperatures and water potentials | data.frame | 3024 | 8 |
phalaris | drcSeedGerm | A series of germination assays with Phalaris minor | data.frame | 3024 | 9 |
rape | drcSeedGerm | Germination data from an assay of rapeseed at decreasing water potential levels | data.frame | 294 | 7 |
rape2G | drcSeedGerm | Germination data from an assay of rapeseed at decreasing water potential levels | data.frame | 588 | 8 |
MaskOri | statConfR | Data of 16 participants in a masked orientation discrimination experiment (Hellmann et al., 2023, Exp. 1) | data.frame | 25920 | 6 |
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 |
housing | rineq | Artificial example data on housing conditions | data.table | 10000 | 10 |
air | funcharts | Air quality data | list | | |
condor | mkde | California condor locations | data.frame | 418 | 4 |
dugong | mkde | Dugong locations | data.frame | 426 | 4 |
panda | mkde | Giant panda locations | data.frame | 147 | 4 |
jdg2011_datum | pavement | Japan Geodetic Datum 2011 (JDG2011) Reference Points | data.frame | 19 | 4 |
osm_highway_values | pavement | Values for the 'highway' tag in OpenStreetMap data | character | | |
sample_accidents | pavement | Sample accidents data | sf | 10 | 5 |
sample_roads | pavement | Sample roads data | sf | 6 | 3 |
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 | | |
bp10k | clustra | Simulated blood pressure data | data.table | 167277 | 4 |
Leerkes | WRS2 | Maternal Self-Efficacy | data.frame | 92 | 3 |
Pygmalion | WRS2 | Pygmalion Data | data.frame | 114 | 3 |
WineTasting | WRS2 | Wine Tasting | data.frame | 66 | 3 |
bush | WRS2 | Bushtucker Foods | data.frame | 8 | 5 |
chile | WRS2 | Chile Heat and Length | data.frame | 85 | 3 |
diet | WRS2 | Diet and Weight Loss | data.frame | 76 | 7 |
electric | WRS2 | The Electric Company | data.frame | 192 | 5 |
essays | WRS2 | Academic Writing Data | data.frame | 120 | 4 |
eurosoccer | WRS2 | European Soccer Leagues | data.frame | 96 | 11 |
goggles | WRS2 | Beer Goggles Effect | data.frame | 48 | 3 |
hangover | WRS2 | Hangover Symptoms | data.frame | 120 | 4 |
invisibility | WRS2 | Cloaks of Invisibility | data.frame | 80 | 3 |
movie | WRS2 | Movies and Aggressive Affect | data.frame | 68 | 4 |
picture | WRS2 | Profile Pictures | data.frame | 40 | 4 |
spider | WRS2 | Arachnophobes | data.frame | 24 | 2 |
swimming | WRS2 | Optimistic and Pessimistic Swimmers | data.frame | 58 | 4 |
viagra | WRS2 | Effects of Viagra | data.frame | 15 | 2 |
bitterling | anacor | Bitterling | data.frame | 12 | 12 |
galton | anacor | Galton's RFF data | data.frame | 11 | 14 |
glass | anacor | Glass data | data.frame | 7 | 7 |
maxwell | anacor | Maxwell's data | list | | |
sleeping | anacor | Sleeping Bags | data.frame | 21 | 4 |
spider | anacor | Hunting spider data | list | | |
srole | anacor | Srole Data | data.frame | 4 | 6 |
tocher | anacor | Tocher's eye/hair color data. | data.frame | 4 | 5 |
BPG06dat | multilevelmediation | Simulated dataset from Bauer, Preacher, and Gil (2006) | data.frame | 800 | 4 |
simdat | multilevelmediation | A simulated dataset with moderator | data.frame | 800 | 5 |
fs_lut | freesurfer | Freesurfer look up table (LUT) | data.frame | 1266 | 6 |
testdata_001 | lgpr | A very small artificial test data, used mostly for unit tests | data.frame | 24 | 6 |
testdata_002 | lgpr | Medium-size artificial test data, used mostly for tutorials | data.frame | 96 | 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 |
cell_lines | harmony | List of metadata table and scaled PCs matrix | list | | |
cell_lines_small | harmony | Same as cell_lines but smaller (300 cells). | list | | |
pbmc.ctrl | harmony | Gene expression data of control PBMC from Kang et al. 2017. This contains a sample of 1000 cells from that condition and is used for the Seurat Vignette. | dgCMatrix | | |
pbmc.stim | harmony | Gene expression data of stimulated PBMC from Kang et al. 2017. This contains a sample of 1000 cells from that condition and is used for the Seurat Vignette. | dgCMatrix | | |
HELP | collett | Health evaluation and linkage to primary care | data.frame | 447 | 7 |
IUD | collett | Time to discontinuation of the use of an IUD | data.frame | 18 | 2 |
active_hepatitis | collett | Chronic active hepatitis | data.frame | 44 | 3 |
bcancer | collett | Prognosis for women with breast cancer | data.frame | 45 | 3 |
bladder | collett | Recurrence of bladder cancer | data.frame | 86 | 6 |
bone_marrow | collett | Bone marrow transplantation | data.frame | 37 | 9 |
bone_marrow_tx | collett | Patient outcome following bone marrow transplantation | data.frame | 2204 | 9 |
breast_rfs | collett | Recurrence free survival in breast cancer patients | data.frame | 686 | 11 |
dialysis | collett | Infection in patients on dialysis | data.frame | 13 | 5 |
ducks | collett | Survival of black ducks | data.frame | 50 | 6 |
gcancer | collett | Survival of patients with gastric cancer | data.frame | 90 | 4 |
granulomatous | collett | Chronic granulomatous disease | data.frame | 128 | 12 |
illustration | collett | A numerical illustration | data.frame | 37 | 4 |
kidney | collett | Treatment of hypernephroma | data.frame | 36 | 4 |
kidneytx | collett | Comparisons between kidney transplant centres | data.frame | 1439 | 9 |
lbrdata0 | collett | Data from a cirrhosis study (lbr data) | data.frame | 42 | 3 |
leukaemia | collett | Bone marrow transplantation in the treatment of leukaemia | data.frame | 23 | 8 |
liver | collett | Survival of liver transplant recipients | data.frame | 1761 | 7 |
liver_counting | collett | Data from a cirrhosis study (in counting process format) | data.frame | 54 | 7 |
liverbase | collett | Data from a cirrhosis study (baseline) | data.frame | 12 | 6 |
livertx | collett | Time to death while waiting for a liver transplant | data.frame | 281 | 7 |
lung | collett | Survival of patients registered for a lung transplant | data.frame | 196 | 7 |
mammary | collett | Recurrence of mammary tumours in female rats | data.frame | 254 | 4 |
melanoma | collett | Survival times of patients with melanoma | data.frame | 30 | 4 |
mice | collett | Survival of laboratory mice | data.frame | 181 | 3 |
myeloma | collett | Survival of multiple myeloma patients | data.frame | 48 | 10 |
ovarian | collett | Chemotherapy in ovarian cancer patients | data.frame | 26 | 7 |
prostatic | collett | Comparison of two treatments for prostatic cancer | data.frame | 38 | 8 |
pulmonary | collett | Pulmonary metastasis | data.frame | 11 | 1 |
tamoxifen | collett | Clinical trial of tamoxifen in breast cancer patients | data.frame | 641 | 18 |
tplant | collett | Survival following kidney transplantation | data.frame | 434 | 7 |
ulcer | collett | Recurrence of an ulcer | data.frame | 43 | 6 |
valve | collett | Survival following aortic valve replacement | data.frame | 988 | 11 |
bx | spagmix | Toy Windows | owin | | |
heart | spagmix | Toy Windows | owin | | |
shp1 | spagmix | Toy Windows | owin | | |
shp2 | spagmix | Toy Windows | owin | | |
star | spagmix | Toy Windows | owin | | |
toywin | spagmix | Toy Windows | owin | | |
W_sel | CARME | Adjacency matrix for the South East London set of MSOAs | matrix | 152 | 152 |
GO_IC | ontologySimilarity | Gene Ontology terms information content. | numeric | | |
gene_GO_terms | ontologySimilarity | Gene Ontology annotation of genes | list | | |
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 | | |
bank_marketing | blorr | Bank marketing data set | tbl_df | 4521 | 17 |
hsb2 | blorr | High School and Beyond Data Set | data.frame | 200 | 12 |
stepwise | blorr | Dummy Data Set | data.frame | 20000 | 7 |
E.result | EMAS | An 'Emas' results data. | data.frame | 2000 | 13 |
Mvalue | EMAS | A M-value matrix for 221 participants | matrix | 10 | 221 |
data.m | EMAS | A data for 221 participants | data.frame | 221 | 7 |
gss_all | gssr | General Social Survey Survey Cumulative Data File 1972-2022 R2a | tbl_df | 72390 | 6694 |
gss_panel06_long | gssr | General Social Survey Survey 2006 Three Wave Panel Data | tbl_df | 6000 | 1572 |
gss_panel08_long | gssr | General Social Survey Survey 2008 Three Wave Panel Data | tbl_df | 6069 | 1243 |
gss_panel10_long | gssr | General Social Survey Survey 2010 Three Wave Panel Data | tbl_df | 6132 | 1191 |
gss_panel20 | gssr | General Social Survey Survey 2020 Panel Data | tbl_df | 5215 | 4296 |
gss_sub | gssr | Example subset of the GSS Cumulative Data File 1972-2022 | tbl_df | 72390 | 20 |
invivo_study_samples | designit | A sample list from an in vivo experiment with multiple treatments and 2 strains | data.frame | 59 | 8 |
invivo_study_treatments | designit | A treatment list together with additional constraints on the strain and sex of animals | data.frame | 59 | 3 |
longitudinal_subject_samples | designit | Subject sample list with group and time plus controls | tbl_df | 230 | 9 |
multi_trt_day_samples | designit | Unbalanced treatment and time sample list | tbl_df | 32 | 4 |
plate_effect_example | designit | Example dataset with a plate effect | tbl_df | 54 | 6 |
sampleObj | GeneNMF | Sample dataset to test GeneNMF installation | Seurat | | |
state_fips | wru | Dataset with FIPS codes for US states | tbl_df | 57 | 3 |
surnames2000 | wru | Census Surname List (2000). | data.frame | 157728 | 6 |
surnames2010 | wru | Census Surname List (2010). | data.frame | 167613 | 6 |
voters | wru | Example voter file. | data.frame | 10 | 15 |
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 | | |
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 |
Mnist | rTensor2 | Subset of MNIST training and testing data. | list | | |
raytrace | rTensor2 | Subset of raytrace data | list | | |
nbl_result_matrix_sign_small | CNVScope | Neuroblastoma sample CNV relationship matrix | matrix | 25 | 25 |
PvalSets | qch | Synthetic example to illustrate the main qch functions | data.frame | 10000 | 3 |
PvalSets_cor | qch | Synthetic example to illustrate the main qch functions using gaussian copula | data.table | 10000 | 3 |
exam | inferr | Dummy data set for Cochran's Q test | data.frame | 15 | 3 |
hsb | inferr | High School and Beyond Data Set | data.frame | 200 | 11 |
treatment | inferr | Dummy data set for 2 Sample Proportion test | data.frame | 50 | 2 |
treatment2 | inferr | Dummy data set for 2 Sample Proportion test | data.frame | 200 | 2 |
Aus_athletes | ggridges | Australian athletes | data.frame | 202 | 13 |
Catalan_elections | ggridges | Results from Catalan regional elections (1980-2015) | tbl_df | 20764 | 4 |
lincoln_weather | ggridges | Weather in Lincoln, Nebraska in 2016. | tbl_df | 366 | 24 |
Less_Connected | NetworkExtinction | A sparsely connected foodweb | network | | |
More_Connected | NetworkExtinction | A densely connected foodweb | network | | |
chilean_intertidal | NetworkExtinction | The binaryfoodweb of the intertidal zone in central chile | network | | |
chilean_potential | NetworkExtinction | The potential foodweb of the intertidal zone in central chile | data.frame | 107 | 107 |
chilean_weighted | NetworkExtinction | The weighted foodweb of the intertidal zone in central chile | data.frame | 107 | 107 |
dist | NetworkExtinction | A toymodel distance matrix | matrix | 10 | 10 |
mutual | NetworkExtinction | A mutualistic web | network | | |
net | NetworkExtinction | A toymodel trophic network | network | | |
ABC | Gifi | ABC Customer Satisfaction | data.frame | 208 | 11 |
galo | Gifi | GALO dataset | data.frame | 1290 | 5 |
gubell | Gifi | Guttman-Bell dataset | data.frame | 7 | 5 |
hartigan | Gifi | Hartigan's Hardware | data.frame | 24 | 6 |
house | Gifi | House | data.frame | 439 | 21 |
mammals | Gifi | Mammals dataset | data.frame | 66 | 8 |
neumann | Gifi | Neumann dataset | data.frame | 65 | 3 |
roskam | Gifi | Roskam dataset | data.frame | 9 | 39 |
senate07 | Gifi | Senate votes 2007 | data.frame | 98 | 21 |
sleeping | Gifi | Sleeping Bags | data.frame | 21 | 4 |
small | Gifi | Small dataset | data.frame | 10 | 3 |
duncan | aspect | Duncan dataset | data.frame | 1204 | 12 |
galo | aspect | GALO dataset | data.frame | 1290 | 5 |
wurzer | aspect | Internet terminals | data.frame | 215 | 8 |
mendota | isotone | Number of freezing days at Lake Mendota | data.frame | 12 | 2 |
pituitary | isotone | Size of pituitary fissue | data.frame | 11 | 2 |
posturo | isotone | Repeated posturographic measures | data.frame | 50 | 4 |
fff_shear | cmstatrExt | Example shear stress-shear strain data | tbl_df | 2316 | 3 |
pa12_tension | cmstatrExt | Example stress-strain data | tbl_df | 3105 | 3 |
atc_codes | TreeMineR | Hierarchical tree of the ATC system for classifying drugs | data.frame | 6797 | 1 |
diagnoses | TreeMineR | Test dataset of ICD diagnoses | data.table | 22989 | 3 |
icd_10_se | TreeMineR | Swedish version of the ICD-10 diagnoses code tree | data.frame | 38925 | 1 |
icd_10_se_dict | TreeMineR | Dictionary for the Swedish version of the ICD-10 diagnoses code tree | data.table | 38925 | 2 |
gdp | scoringRules | Data and forecasts for US GDP growth | data.frame | 33616 | 3 |
gdp_mcmc | scoringRules | Data and forecasts for US GDP growth | list | | |
aids | brglm2 | The effects of AZT in slowing the development of AIDS symptoms | data.frame | 4 | 4 |
alligators | brglm2 | Alligator food choice data | data.frame | 80 | 5 |
coalition | brglm2 | Coalition data | data.frame | 314 | 7 |
endometrial | brglm2 | Histology grade and risk factors for 79 cases of endometrial cancer | data.frame | 79 | 4 |
enzymes | brglm2 | Liver Enzyme Data | data.frame | 218 | 6 |
hepatitis | brglm2 | Post-transfusion hepatitis: impact of non-A, non-B hepatitis surrogate tests | data.frame | 28 | 6 |
lizards | brglm2 | Habitat preferences of lizards | data.frame | 23 | 6 |
stemcell | brglm2 | Opinion on Stem Cell Research and Religious Fundamentalism | data.frame | 24 | 4 |
EHB.LLZ | GEOmap | Earthquake Location Data | list | | |
NSWath | GEOmap | Cross sectional Swaths of Earthquakes over Japan | list | | |
coastmap | GEOmap | Global Coast Map | list | | |
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 |
chi11 | HDStIM | Sample data set for CyTOF Stimulation Assay | list | | |
dist_cepii | cepiigeodist | Data on pairs of countries including distance measures and dummy variables indicating common attributes | tbl_df | 50176 | 14 |
geo_cepii | cepiigeodist | Data on countries and their main city or agglomeration | tbl_df | 238 | 34 |
beads | biopixR | Image of microbeads | cimg | | |
beads_large1 | biopixR | Image of microbeads | cimg | | |
beads_large2 | biopixR | Image of microbeads | cimg | | |
droplet_beads | biopixR | Image of microbeads in luminescence channel | cimg | | |
droplets | biopixR | Droplets containing microbeads | cimg | | |
twn_lijst | twn | Taxa Waterbeheer Nederland (TWN) | tbl_df | 29167 | 11 |
twn_literatuur | twn | TWN literatuurlijst | tbl_df | 3877 | 3 |
twn_statuscodes | twn | TWN statuscodes | tbl_df | 6 | 2 |
twn_taxonlevels | twn | TWN taxonlevels | ordered | | |
nkpc | mbreaks | New Keynesian Phillips curve data | data.frame | 151 | 12 |
real | mbreaks | World Health Organization TB data | data.frame | 103 | 1 |
minhanes | miclust | Multiple imputation for nhanes data. | list | | |
Airam_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Aputure_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Convoy_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Fluence_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Generic_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Godox_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Jaxman_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Osram_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Philips_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
QPanel_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Sunwayfoto_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Sylvania_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Toshiba_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
Valoya_lamps | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
amaran_m9.mspct | photobiologyLamps | Spectra for an Amaran AL-M9 LED video light | source_mspct | | |
amber_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
andoer_ir49.mspct | photobiologyLamps | Spectra for an Andoer IR49 LED video lamp | source_mspct | | |
bentham_lamps | photobiologyLamps | Spectra acquired with Bentham spectrometer | character | | |
blue_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
elgato_klm_cct.mspct | photobiologyLamps | Spectra for an Elgato Key Light Mini LED video lamp | source_mspct | | |
elgato_klm_dim.mspct | photobiologyLamps | Spectra for an Elgato Key Light Mini LED video lamp | source_mspct | | |
fluorescent_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
germicidal_lamps | photobiologyLamps | Spectral data for Lamps designed for specific uses | character | | |
green_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
incandescent_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
ir_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
lamp_brands | photobiologyLamps | Spectral data for Lamps from different suppliers | character | | |
lamp_colors | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
lamp_types | photobiologyLamps | Spectral data for Lamps of different types | character | | |
lamps.mspct | photobiologyLamps | Spectral irradiance for diverse lamps | source_mspct | | |
led_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
ledsavers.mspct | photobiologyLamps | Spectra for a multichannel LED bulb | source_mspct | | |
ledsavers_GB_mixes | photobiologyLamps | Spectra for a multichannel LED bulb | character | | |
ledsavers_RB_mixes | photobiologyLamps | Spectra for a multichannel LED bulb | character | | |
ledsavers_RG_mixes | photobiologyLamps | Spectra for a multichannel LED bulb | character | | |
ledsavers_channels | photobiologyLamps | Spectra for a multichannel LED bulb | character | | |
ledsavers_mixes | photobiologyLamps | Spectra for a multichannel LED bulb | character | | |
licor_lamps | photobiologyLamps | Spectra acquired with LI-COR LI-1800 | character | | |
macam_lamps | photobiologyLamps | Spectra acquired with Macam SR-9010-PC | character | | |
mercury_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
multimetal_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
oo_maya_lamps | photobiologyLamps | Spectra acquired with Ocean Optics Maya2000 Pro | character | | |
orange_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
photography_lamps | photobiologyLamps | Spectral data for Lamps designed for specific uses | character | | |
plant_grow_lamps | photobiologyLamps | Spectral data for Lamps designed for specific uses | character | | |
purple_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
qp_uvb313_temp.mspct | photobiologyLamps | Spectral irradiance of UVB lamps at different temperatures. | source_mspct | | |
qp_uvb313_temp.spct | photobiologyLamps | Spectral irradiance of UVB lamps at different temperatures. | source_spct | 777 | 3 |
red_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
sodium_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
sunwayfoto_fl96.mspct | photobiologyLamps | Spectra for a Sunwayfoto FL96 LED video light | source_mspct | | |
uv_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
white_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
xenon_lamps | photobiologyLamps | Spectral data for Lamps of different types | character | | |
yellow_lamps | photobiologyLamps | Spectral data for Lamps of different colours | character | | |
election | samplingbook | German Parliament Election Data | data.frame | 299 | 13 |
influenza | samplingbook | Population and Cases of Influenza for Administrative Districts of Germany | data.frame | 424 | 4 |
money | samplingbook | Money Data Frame | data.frame | 13 | 3 |
pop | samplingbook | Small Suppositious Sampling Example | data.frame | 5 | 3 |
tax | samplingbook | Hypothetical Tax Refund Data Frame | data.frame | 9083 | 5 |
wage | samplingbook | Chinese wage data | data.frame | 231 | 3 |
Metdata | sirad | Weather data | list | | |
fedstat_indicators_names_database | fedstatAPIr | Database of all indicator names presented on the fedstat.ru with hierarchical grouping | data.frame | 9335 | 10 |
default_exif_tags | filenamr | Default EXIF, XMP-dc, and IPTC tags | character | | |
default_exif_xwalk | filenamr | EXIF data column name crosswalk | list | | |
Rversions | rcheology | Previous R versions with dates | data.frame | 150 | 2 |
rcheology | rcheology | Data on objects from current and previous versions of R | tbl_df | 492424 | 10 |
crime | ggbiplot | U. S. Crimes | data.frame | 50 | 10 |
wine | ggbiplot | Wine dataset | data.frame | 178 | 13 |
wine.class | ggbiplot | Wine dataset | factor | | |
cc.genes | Seurat | Cell cycle genes | list | | |
cc.genes.updated.2019 | Seurat | Cell cycle genes: 2019 update | list | | |
afcountries | afrihealthsites | african country names and iso 3 letter country codes | data.frame | 53 | 2 |
df_who_sites | afrihealthsites | africa healthsite points from WHO | tbl_df | 98745 | 12 |
sf_healthsites_af | afrihealthsites | africa healthsite points from healthsites.io | sf | 56854 | 35 |
sfssd | afrihealthsites | south sudan health facility points from moh | sf | 2889 | 18 |
who_type_lookup | afrihealthsites | lookup table to convert 173 WHO facility types to 9 broad categories | tbl_df | 318 | 3 |
vespa | squat | The VESPA dataset | tbl_df | 320 | 7 |
vespa64 | squat | The VESPA64 dataset | tbl_df | 64 | 6 |
HR_data | EIX | Why are our best and most experienced employees leaving prematurely? | data.table | 14999 | 10 |
titanic_data | EIX | Passengers and Crew on the RMS Titanic | data.frame | 2207 | 9 |
ELISA | gtools | Data from an ELISA assay | data.frame | 504 | 5 |
badDend | gtools | Dataset That Crashes Base:::Plot.Dendogram with 'Node Stack Overflow' | matrix | 2047 | 12 |
LongMS | INLAjoint | Simulated univariate longitudinal dataset | data.frame | 317 | 4 |
Longsim | INLAjoint | Simulated multivariate longitudinal dataset | data.frame | 96 | 7 |
SurvMS | INLAjoint | Simulated multi-state survival dataset | list | | |
Survsim | INLAjoint | Simulated competing risks survival dataset | data.frame | 15 | 7 |
rna1 | tidyDenovix | rna1. | tbl_df | 14 | 151 |
rna2 | tidyDenovix | rna2. | data.frame | 15 | 151 |
rna3 | tidyDenovix | rna3. | data.frame | 15 | 152 |
spec | tidyDenovix | spec. | data.frame | 15 | 151 |
X1 | SmCCNet | A synthetic mRNA expression dataset. | matrix | 358 | 500 |
X2 | SmCCNet | A synthetic miRNA expression dataset. | matrix | 358 | 100 |
Y | SmCCNet | A synthetic phenotype dataset. | matrix | 358 | 1 |
Rotations | Tendril | Example of Rotations in package Tendril. | numeric | | |
SubjList | Tendril | Example of SubjList in package Tendril. | data.frame | 500 | 2 |
Tendril.perm.res | Tendril | Example object Tendril as generated by Tendril() and Tendril.perm(). | Tendril | | |
Tendril.res | Tendril | Example object Tendril as generated by Tendril(). | Tendril | | |
TendrilData | Tendril | Example dataframe in package Tendril. | data.frame | 1000 | 4 |
hgdp | geoGraph | Human genome diversity panel - georeferenced data | gData | | |
hgdpPlus | geoGraph | Human genome diversity panel - georeferenced data | gData | | |
rawgraph.10k | geoGraph | Worldwide geographic graphs | gGraph | | |
rawgraph.40k | geoGraph | Worldwide geographic graphs | gGraph | | |
worldgraph.10k | geoGraph | Worldwide geographic graphs | gGraph | | |
worldgraph.40k | geoGraph | Worldwide geographic graphs | gGraph | | |
worldshape | geoGraph | Worldwide geographic graphs | SpatialPolygonsDataFrame | | |
flu1918data | DSAIDE | 1918 Influenza mortality data | tbl_df | 15 | 2 |
norodata | DSAIDE | Cases of norovirus during an outbreak | data.frame | 10 | 2 |
sample_data1 | validata | Sample Data | tbl_df | 125 | 6 |
fev | rigr | FEV dataset | data.frame | 654 | 7 |
mri | rigr | MRI dataset | spec_tbl_df | 735 | 30 |
psa | rigr | PSA dataset | data.frame | 50 | 9 |
salary | rigr | Salary dataset | data.frame | 19792 | 11 |
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 |
VWB23_pilot | POMADE | Co-Teaching Dataset | tbl_df | 76 | 9 |
artificialJointLongData | longitudinalData | ~ Data: artificialJointLongData ~ | data.frame | 150 | 34 |
artificialLongData | longitudinalData | ~ Data: artificialLongData ~ | data.frame | 200 | 12 |
ev_pib | TractorTsbox | Évolution du PIB français jusqu'au T1 2022 | ts | | |
index_return | bayesestdft | Stock Market Index Return Data | data.frame | 419 | 4 |
mc_sim_Q | variationalDCM | Artificial Q-matrix for MC-DINA model | matrix | 90 | 7 |
sim_Q_J30K3 | variationalDCM | Artificial Q-matrix for 30 items 3 attributes | matrix | 30 | 3 |
sim_Q_J80K5 | variationalDCM | Artificial Q-matrix for 80 items 5 attributes | matrix | 80 | 5 |
maize_pop | tuckerR.mmgg | 31 Native Populations of Maize from Province of Buenos Aires | data.frame | 31 | 20 |
onlineretail | onlineretail | Online Retail Data Set | data.frame | 541909 | 8 |
biozones | geoscale | Ammonite biozone ages for the Upper Cretaceous | data.frame | 50 | 4 |
size | geoscale | Body-size data from trilobite specimens | matrix | 1747 | 12 |
timescales | geoscale | Geological time scale from Harland et al., (2012) | list | | |
traits | geoscale | A time-series for one trait. | data.frame | 30 | 2 |
AbUnits | MAINT.Data | Abalone Data Set | factor | | |
AbaDF | MAINT.Data | Abalone Data Set | data.frame | 4177 | 7 |
AbaloneIdt | MAINT.Data | Abalone Data Set | IData | | |
Cars | MAINT.Data | Cars Data Set | data.frame | 27 | 9 |
ChinaTemp | MAINT.Data | China Temperatures Data Set | data.frame | 899 | 9 |
FlightsDF | MAINT.Data | New York City flights Data Set | data.frame | 327346 | 4 |
FlightsIdt | MAINT.Data | New York City flights Data Set | IData | | |
FlightsUnits | MAINT.Data | New York City flights Data Set | factor | | |
LoansbyPurpose_minmaxDt | MAINT.Data | Loans by purpose: minimum and maximum Data Set | data.frame | 14 | 8 |
LoansbyRiskLvs_minmaxDt | MAINT.Data | Loans by risk levels: minimum and maximum Data Set | data.frame | 35 | 8 |
LoansbyRiskLvs_qntlDt | MAINT.Data | Loans by risk levels: ten and ninety per cent quantiles Data Set | data.frame | 35 | 8 |
rr98 | rtdists | Ratcliff and Rouder (1998, Exp. 1) Luminance Discrimination Data | data.frame | 24358 | 12 |
speed_acc | rtdists | Speed-Accuracy Data from Wagenmakers, Ratcliff, Gomez, & McKoon (2008, Experiment 1) | data.frame | 31522 | 9 |
builtin_itembank | dscore | Collection of items fitting the Rasch model | data.frame | 4835 | 8 |
builtin_itemtable | dscore | Collection of items from instruments measuring early child development | data.frame | 3843 | 3 |
builtin_keys | dscore | Available keys for calculating the D-score | data.frame | 6 | 10 |
builtin_references | dscore | Collection of age-conditional reference distributions | data.frame | 1499 | 20 |
gsample | dscore | Sample of 10 children from the GSED Phase 1 study | data.frame | 10 | 295 |
milestones | dscore | Outcomes on developmental milestones for preterm-born children | data.frame | 100 | 62 |
sample_hf | dscore | Sample of 10 children from GSED HF | data.frame | 10 | 57 |
sample_lf | dscore | Sample of 10 children from gto (LF) | data.frame | 10 | 157 |
sample_sf | dscore | Sample of 10 children from gpa (SF) | data.frame | 10 | 141 |
NPannual | ldsr | Annual streamflow at Nakhon Phanom | data.table | 46 | 2 |
NPpc | ldsr | Selected principal components | data.table | 813 | 3 |
theta | ldsr | LDS parameters | list | | |
bonilla_tillery | rdss | Replication data for Bonilla and Tillery (2020), American Political Science Review (obtained from Dataverse 10.7910/DVN/IUZDQI) | tbl_df | 849 | 10 |
clingingsmith_etal | rdss | Replication data for David Clingingsmith, Asim Ijaz Khwaja, Michael Kremer (2020): Estimating the Impact of The Hajj: Religion and Tolerance in Islam's Global Gathering. The Quarterly Journal of Economics, Volume 124, Issue 3, August 2009, Pages 1133-1170 | spec_tbl_df | 958 | 8 |
fairfax | rdss | Shapefile of Fairfax County, Virginia, voting precincts | sf | 238 | 10 |
foos_etal | rdss | Replication data for Foos, John, Muller, and Cunningham (2021), Journal of Politics (derived from from Dataverse 10.7910/DVN/NDPXND) | tbl_df | 8375 | 5 |
la_voter_file | rdss | Voter file sample for Los Angeles County | data.frame | 1000 | 4 |
lapop_brazil | rdss | Data used in student exercises for RDSS based on LAPOP survey of Brazil in 2018 | tbl_df | 10000 | 10 |
data_att1 | dcm2 | Simulated Data for a Single Attribute Assessment | list | | |
sample_data | dcm2 | Simulated Data for Testing Functions | list | | |
fadul1.1_processed | cmcR | Processed versions of the fadul1.1_raw and fadul1.2_raw datasets using preProcess_* functions from the cmcR package | x3p | | |
fadul1.2_processed | cmcR | Processed versions of the fadul1.1_raw and fadul1.2_raw datasets using preProcess_* functions from the cmcR package | x3p | | |
UsAndThem | loon | Data to re-create Hans Rosling's famous "Us and Them" animation | data.frame | 9855 | 8 |
minority | loon | Canadian Visible Minority Data 2006 | data.frame | 33 | 18 |
olive | loon | Fatty Acid Composition of Italian Olive Oils | data.frame | 572 | 10 |
oliveAcids | loon | Just the Fatty Acid Composition of Italian Olive Oils | data.frame | 572 | 8 |
oliveLocations | loon | Geographic location of each Italian olive growing area named in the 'olive' data. | data.frame | 9 | 3 |
cvt | invivoPKfit | CvTdb data | tbl_df | 7918 | 62 |
cvt_date | invivoPKfit | CvTdb download date | Date | | |
model_1comp | invivoPKfit | 1-compartment model | list | | |
model_1comp_cl_nonrest | invivoPKfit | 1-compartment model that assumes non-restrictive clearance | list | | |
model_1comp_cl_rest | invivoPKfit | 1-compartment model that assumes restrictive clearance | list | | |
model_1comp_fup | invivoPKfit | 1-compartment model that assumes restrictive clearance optimizes Fup | list | | |
model_2comp | invivoPKfit | 2-compartment model | list | | |
model_flat | invivoPKfit | Flat model | list | | |
pkdataset_nheerlcleaned | invivoPKfit | Toxicokinetic data from the "Concentration vs. Time Database" | data.table | 2454 | 19 |
status_data_info | invivoPKfit | Status ID for data summary info | integer | | |
status_fit | invivoPKfit | Status ID for fitting | integer | | |
status_init | invivoPKfit | Status ID for initialization | integer | | |
status_prefit | invivoPKfit | Status ID for pre-fitting | integer | | |
status_preprocess | invivoPKfit | Status ID for preprocessing | integer | | |
time_conversions | invivoPKfit | Time conversion table | data.frame | 121 | 3 |
time_units | invivoPKfit | Allowable time units | character | | |
Simulated_data | ATE.ERROR | Simulated Data | data.frame | 5000 | 6 |
aerolineas | datos | Nombres de aerolíneas | tbl_df | 16 | 2 |
aeropuertos | datos | Datos de aeropuertos | tbl_df | 1458 | 8 |
atmosfera | datos | Datos atmosféricos | tbl_df | 41472 | 11 |
aviones | datos | Datos de aviones | tbl_df | 3322 | 9 |
bateadores | datos | Tabla de estadísticas de bateo | data.frame | 113799 | 22 |
clima | datos | Datos de clima | tbl_df | 26115 | 15 |
comunes | datos | Modelos comunes de vehículos | tbl_df | 347 | 4 |
datos_credito | datos | Datos de crédito | data.frame | 4454 | 14 |
diamantes | datos | Precio de 50.000 diamantes | tbl_df | 53940 | 10 |
dirigentes | datos | Tabla de dirigentes | data.frame | 3749 | 10 |
encuesta | datos | Muestra de variables categóricas de una encuesta social | tbl_df | 21483 | 9 |
fiel | datos | Datos del géiser Viejo Fiel (Old Faithful) | data.frame | 272 | 2 |
flores | datos | Datos sobre la flor Iris de Edgar Anderson | data.frame | 150 | 5 |
frutas | datos | Vectores de caracteres dentro del paquete stringr | character | | |
jardineros | datos | Tabla de estadísticas de jardineros | data.frame | 151507 | 18 |
lanzadores | datos | Tabla de estadísticas de lanzadores | data.frame | 51368 | 30 |
millas | datos | Datos de economía de combustible de 1999 y 2008 para 38 modelos populares de automóviles | tbl_df | 234 | 11 |
mtautos | datos | Pruebas de ruta de automóviles de Motor Trend | data.frame | 32 | 11 |
nombres | datos | Nombres de bebés | tbl_df | 1924665 | 5 |
oms | datos | Datos de tuberculosis de la Organización Mundial de la Salud | tbl_df | 7240 | 60 |
oraciones | datos | Vectores de caracteres dentro del paquete stringr | character | | |
paises | datos | Datos de Gapminder | tbl_df | 1704 | 6 |
palabras | datos | Vectores de caracteres dentro del paquete stringr | character | | |
personas | datos | Tabla de personas | data.frame | 21010 | 26 |
pinguinos | datos | Medidas de tamaño de pingüinos adultos en busca de comida cerca de la estación Palmer en la Antártica. | tbl_df | 344 | 8 |
premios_dirigentes | datos | Tabla de premios de los dirigentes | data.frame | 193 | 6 |
presidencial | datos | Periodos de 12 presidentes, desde Eisenhower a Trump | tbl_df | 12 | 4 |
salarios | datos | Tabla de salarios | data.frame | 26428 | 5 |
tabla1 | datos | Registros de tuberculosis de la Organización Mundial de la Salud (1era variante) | tbl_df | 6 | 4 |
tabla2 | datos | Registros de tuberculosis de la Organización Mundial de la Salud (2da variante) | tbl_df | 12 | 4 |
tabla3 | datos | Registros de tuberculosis de la Organización Mundial de la Salud (3ra variante) | tbl_df | 6 | 3 |
tabla4a | datos | Registros de tuberculosis de la Organización Mundial de la Salud (variante 4a) | tbl_df | 3 | 3 |
tabla4b | datos | Registros de tuberculosis de la Organización Mundial de la Salud (variante 4b) | tbl_df | 3 | 3 |
tabla5 | datos | Registros de tuberculosis de la Organización Mundial de la Salud (5ta variante) | tbl_df | 6 | 4 |
vehiculos | datos | Datos de economía de combustible | tbl_df | 33442 | 12 |
vuelos | datos | Datos de vuelos | tbl_df | 336776 | 19 |
aus_temp | sugarglider | Australian Weather Data for 2022 | tbl_df | 348 | 7 |
flights | sugarglider | Flight Summary from Airports with the Most Cancellations | grouped_df | 120 | 6 |
historical_temp | sugarglider | Historical Australian Weather Data from 2021-2022 | tbl_df | 696 | 8 |
train | sugarglider | Hourly Train Station Patronage 2023-2024 | tbl_df | 4503 | 12 |
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 |
teams_links | hoopR | *Men's College Basketball KenPom Teams Dictionary* Team link KenPom reference lookup for the package | data.frame | 7950 | 7 |
CP_500tr | Rtapas | Nuclear and chloroplast dataset of orchids | multiPhylo | | |
CPtr | Rtapas | Nuclear and chloroplast dataset of orchids | phylo | | |
NUC_500tr | Rtapas | Nuclear and chloroplast dataset of orchids | multiPhylo | | |
NUCtr | Rtapas | Nuclear and chloroplast dataset of orchids | phylo | | |
am_matrix | Rtapas | amph_trem dataset | matrix | 59 | 17 |
amphipod | Rtapas | amph_trem dataset | phylo | | |
amphipod_1000tr | Rtapas | amph_trem dataset | multiPhylo | | |
np_matrix | Rtapas | Nuclear and chloroplast dataset of orchids | matrix | 52 | 52 |
pp_treesPACo_cong | Rtapas | Nuclear and chloroplast dataset of orchids | matrix | 10 | 52 |
pp_treesPACo_incong | Rtapas | Nuclear and chloroplast dataset of orchids | matrix | 10 | 52 |
trematode | Rtapas | amph_trem dataset | phylo | | |
trematode_1000tr | Rtapas | amph_trem dataset | multiPhylo | | |
sp500 | VARcpDetectOnline | S&P 500 Daily Log Returns and Corresponding Dates | list | | |
AlonDS | HiDimDA | Alon Colon Cancer Data Set | data.frame | 62 | 2001 |
asep | IHSEP | An IHSEP data set | list | | |
Bogota_airstations_df | ColombiAPI | Bogota Air Stations Coordinates | data.frame | 10 | 3 |
Bogota_business_Date | ColombiAPI | Bogota Business Dates | Date | | |
Bogota_holidays_Date | ColombiAPI | Bogota Holidays Dates | Date | | |
Bogota_malls_tbl_df | ColombiAPI | Bogota Malls Information | spec_tbl_df | 42 | 6 |
Cannabis_Licenses_tbl_df | ColombiAPI | Cannabis Licenses Information | spec_tbl_df | 92 | 10 |
Colombia_coffee_tbl_df | ColombiAPI | Colombian Coffee 2016 Export/Import | spec_tbl_df | 106 | 35 |
Medellin_rain_tbl_df | ColombiAPI | Medellin Rainfall Information | spec_tbl_df | 185705 | 8 |
Tulua_Public_Schools_tbl_df | ColombiAPI | Tulua Public Schools Information | spec_tbl_df | 105 | 8 |
bale3g.v1 | gimms | Bale Mountains NDVI3g.v1 | RasterStack | | |
kili3g.v0 | gimms | Kilimanjaro NDVI3g.v0 | RasterStack | | |
activity_mapuche | classicnets | Daily activities in Coipuco by 22 Mapuches 1969. | list | | |
bank_room | classicnets | Western Electric Company | list | | |
children_companionships | classicnets | The Companionships of Preschool Children | list | | |
corner_boys | classicnets | Corner Boys | list | | |
eies | classicnets | The Electronic Information Exchange System (EIES) | list | | |
gossip_net | classicnets | Gossip network (1969) | list | | |
howe_court | classicnets | Howe Court | list | | |
informalhelp_mapuche | classicnets | Mapuches in Coipuco | list | | |
informalorg_corneville | classicnets | Informal Organization of the Corneville S&A Club 1939 and 1940 | list | | |
karate_club | classicnets | Karate Club | list | | |
kin_mapuche | classicnets | Mapuches in Coipuco | list | | |
labor_mapuche | classicnets | Medieria in Coipuco by Mapuches 1968-69. | list | | |
lozi_kings | classicnets | The genealogy of the Lozi Kings | list | | |
miner_dispute | classicnets | Dispute of Worker of a Miner | list | | |
participation_mapuche | classicnets | Mapuches in Coipuco | list | | |
pupils_classroom | classicnets | Friendship choices among pupils | list | | |
southern_women | classicnets | Southern Women | list | | |
tolman_court | classicnets | Tolman Court | list | | |
quake | RHawkes | An RHawkes earthquake data set | data.frame | 483 | 2 |
tms | RHawkes | mid-price change times of the AUD/USD exchange rate | list | | |
australia_PISA2012 | GGally | Programme for International Student Assessment (PISA) 2012 Data for Australia | data.frame | 8247 | 32 |
baseball | GGally | Yearly batting records for all major league baseball players | data.frame | 21699 | 22 |
flea | GGally | Historical data used for classification examples. | data.frame | 74 | 7 |
happy | GGally | Data related to happiness from the General Social Survey, 1972-2006. | data.frame | 51020 | 10 |
nasa | GGally | Data from the Data Expo JSM 2006. | data.frame | 41472 | 17 |
pigs | GGally | United Kingdom Pig Production | data.frame | 48 | 8 |
psychademic | GGally | UCLA canonical correlation analysis data | data.frame | 600 | 8 |
tips | GGally | Tipping data | data.frame | 244 | 7 |
twitter_spambots | GGally | Twitter spambots | network | | |
Rmonize_DEMO | Rmonize | Demo objects to provide illustrative examples | list | | |
samplePatients | accept | Sample Patient Characteristics Inputs | tbl_df | 2 | 14 |
consider | radiant.basics | Car brand consideration | tbl_df | 1000 | 2 |
demand_uk | radiant.basics | Demand in the UK | tbl_df | 572 | 2 |
newspaper | radiant.basics | Newspaper readership | tbl_df | 580 | 3 |
salary | radiant.basics | Salaries for Professors | tbl_df | 397 | 6 |
speech_list | komment | speech list data | tbl_df | 7173 | 6 |
collins08 | codyn | Konza data from Collins et al. 2008 | data.frame | 2057 | 4 |
knz_001d | codyn | Data from Konza Prairie, watershed 001d | data.frame | 8767 | 4 |
pplots | codyn | Phosphorus plots data from Avolio et al. 2014 | data.frame | 1232 | 6 |
acs_age_sex_race_ethnicity_vars | sociome | ACS variables for age, sex, race, and ethnicity | tbl_df | 65 | 2 |
acs_vars | sociome | ACS variable names for ADI and ADI-3 calculation | tbl_df | 139 | 10 |
decennial_age_sex_race_ethnicity_vars | sociome | Decennial Census variables for age, sex, race, and ethnicity | tbl_df | 130 | 3 |
decennial_vars | sociome | Decennial census variable names for ADI calculation | tbl_df | 137 | 4 |
fcs01 | dietry | Sample Food Consumption Score (FCS) data from World Food Programme (WFP) VAM Resource Centre | data.frame | 26 | 18 |
CanadianWeather | RMixtComp | Canadian average annual weather cycle | list | | |
prostate | RMixtComp | Prostate Cancer Data | list | | |
simData | RMixtComp | Simulated Heterogeneous data | list | | |
titanic | RMixtComp | Titanic data set | data.frame | 1309 | 8 |
test_incurred_dataset_inflated | SPLICE | Incurred Case Estimates Dataset | data.frame | 31250 | 9 |
test_incurred_dataset_noInf | SPLICE | Incurred Case Estimates Dataset | data.frame | 31250 | 9 |
arroyo_wetland | twriTemplates | Water quality data from two sites on the Arroyo Colorado | spec_tbl_df | 7005 | 13 |
dissolved_oxygen | twriTemplates | Dissolved oxygen measurements from the Tres Palacios river | tbl_df | 236 | 6 |
easterwood_weather | twriTemplates | Temperature and precipitation data at Easterwood Airport | spec_tbl_df | 4045 | 5 |
mission_aransas_nerr | twriTemplates | Water quality data for Mission and Aransas National Estuarine Research Reserve (NERR) | spec_tbl_df | 70080 | 10 |
neon_stage_discharge | twriTemplates | Discharge measurements from field-based surveys | data.frame | 134 | 36 |
uranium_tds | twriTemplates | Uranium and total dissolved solids | tbl_df | 44 | 4 |
study | codebookr | Simulated study data. | tbl_df | 20 | 10 |
age | haplo.ccs | Example Dataset for 'haplo.ccs' | numeric | | |
case | haplo.ccs | Example Dataset for 'haplo.ccs' | numeric | | |
gender | haplo.ccs | Example Dataset for 'haplo.ccs' | numeric | | |
geno | haplo.ccs | Example Dataset for 'haplo.ccs' | matrix | 1320 | |
race | haplo.ccs | Example Dataset for 'haplo.ccs' | numeric | | |
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 |
dat | coxsei | A simulated data set from a CoxSEI model | data.frame | 307 | 6 |
studies | subgxe | Synthetic data for subgxe | list | | |
cora | cora | CORA data set | data.frame | 1879 | 16 |
cora_gold | cora | Cora Gold | data.frame | 64578 | 2 |
cora_gold_update | cora | Cora Gold Update | data.frame | 1879 | 2 |
sample_data | nebula | An example data set for testing nebula | list | | |
sample_seurat | nebula | An example data set for testing scToNeb | Seurat | | |
P_example | scistreer | Example genotype probability matrix | matrix | 1000 | 25 |
P_small | scistreer | Smaller example genotype probability matrix | matrix | 100 | 25 |
gtree_small | scistreer | Smaller example annotated tree built from P_small | tbl_graph | | |
mut_nodes_small | scistreer | Mutation placements calculated from tree_small and P_small | data.frame | 9 | 2 |
tree_small | scistreer | Smaller example tree built from P_small | phylo | | |
tree_upgma | scistreer | Example tree built using UPGMA from P_small | phylo | | |
glike | ldsep | Genotype log-likelihoods from 'uit' | array | | |
gp | ldsep | Posterior probabilities from 'uit' | array | | |
uit | ldsep | Updog fits on the data from Uitdewilligen et. al. (2013) | multidog | | |
ystr_kits | malan | Kit information about Y-STR markers | tbl_df | 88 | 2 |
ystr_markers | malan | Mutational information about Y-STR markers | tbl_df | 29 | 5 |
Env | SSDM | A stack of three environmental variables | RasterStack | | |
Occurrences | SSDM | Plant occurrences data frame | data.frame | 57 | 3 |
Attendance | mixpoissonreg | Attendance Records data set | data.frame | 314 | 4 |
tacks | mixsqp | Beckett & Diaconis tack rolling example. | list | | |
fakeexperiment | aphylo | Fake Experimental Data | matrix | 4 | 3 |
faketree | aphylo | Fake Phylogenetic Tree | matrix | 6 | 2 |
aicha | ggsegAicha | aicha atlas | brain_atlas | | |
aicha_3d | ggsegAicha | aicha atlas | ggseg3d_atlas | 4 | 4 |
noas_example | noasr | Example NOAS data | tbl_df | 10 | 8 |
commonbean | climatrends | Example of input data using local data | list | | |
innlandet | climatrends | Example of input data using local data | clima_df | 182 | 4 |
lonlatsf | climatrends | Example of input data using local data | sf | 5 | 1 |
rain_dat | climatrends | Example of input data using local data | matrix | 10 | 15 |
temp_dat | climatrends | Example of input data using local data | array | | |
construction | disaggR | Total GFCF in construction at current prices | ts | | |
consumption_catering | disaggR | Total consumption in accommodation and food services at current prices | ts | | |
turnover | disaggR | Turnover indicator in construction | ts | | |
turnover_catering | disaggR | Turnover indicator in accommodation and food services | ts | | |
kb_experience | katilingban | Katilingban consultants' experience list | tbl_df | 141 | 12 |
DMP | MBmca | Bimodal melting curve experiment on the surface of microbeads. | data.frame | 76 | 7 |
DualHyb | MBmca | Surface melting curve data from direct hybridization experiment of short oligonucleotide probes with bimodal melting curve pattern. | data.frame | 69 | 5 |
MultiMelt | MBmca | Surface melting curve data from direct hybridization experiment of short oligonucleotides. | data.frame | 55 | 25 |
Wuppertal_df | DescriptiveStats.OBeu | Wuppertal Fiscal Data extracted from Open Spending API | data.frame | 6225 | 10 |
Wuppertal_openspending | DescriptiveStats.OBeu | Wuppertal Fiscal Data extracted from Open Spending API | character | | |
sample_json_link_openspending | DescriptiveStats.OBeu | Sample data from Open Spending | character | | |
dres | dcifer | Dcifer results | array | | |
dsmp | dcifer | Sample data | list | | |
revals | dcifer | Parameter grid | list | | |
airlines | nycflights23 | Airline names. | tbl_df | 14 | 2 |
airports | nycflights23 | Airport metadata | tbl_df | 1251 | 8 |
flights | nycflights23 | Flights data | tbl_df | 435352 | 19 |
planes | nycflights23 | Plane metadata. | tbl_df | 4840 | 9 |
weather | nycflights23 | Hourly weather data | tbl_df | 26204 | 15 |
costs | nlsur | PRICE AND QUANTITY INDEXES OF CAPITAL, LABOR, ENERGY, AND OTHER INTERMEDIATE INPUTS and TOTAL COST AND COST SHARES OF CAPITAL, LABOR, ENERGY, AND OTHER INTERMEDIATE MATERIALS - U.S. MANUFACTURING 1947-1971 | data.frame | 25 | 14 |
colors_group | rcolors | 270 ncl colors | list | | |
rcolors | rcolors | rcolors: 270 'NCL' Color Tables in R Language | list | | |
logindata.opal.demo | DSOpal | DataSHIELD login data file | data.frame | 3 | 6 |
PCs_1000G | PCAmatchR | First 20 principal components of 2504 individuals from the 1000 Genome Project | data.frame | 2504 | 24 |
eigenvalues_1000G | PCAmatchR | First 20 eigenvalues of 2504 individuals from the 1000 Genome Project | data.frame | 20 | 1 |
eigenvalues_all_1000G | PCAmatchR | All eigenvalues of 2504 individuals from the 1000 Genome Project | data.frame | 2504 | 1 |
Goolam | scDHA | Goolam | list | | |
Goolam_result | scDHA | Goolam_result | list | | |
Glendalough | Bchron | Glendalough data | data.frame | 6 | 6 |
Sluggan | Bchron | Sluggan Moss data | data.frame | 31 | 6 |
TestChronData | Bchron | Example chronology file for use with the BchronRSL function. | data.frame | 27 | 6 |
TestRSLData | Bchron | Relative sea level data | data.frame | 24 | 3 |
intcal13 | Bchron | Northern hemisphere 2013 calibration curve | data.frame | 5141 | 5 |
intcal20 | Bchron | Northern hemisphere 2020 calibration curve | data.frame | 9501 | 5 |
marine13 | Bchron | Marine 2013 calibration curve | data.frame | 4801 | 5 |
marine20 | Bchron | Marine 2020 calibration curve | data.frame | 5501 | 5 |
normal | Bchron | Data for dummy calibration of normally distributed ages | data.frame | 2 | 3 |
shcal13 | Bchron | Southern hemisphere 2013 calibration curve | data.frame | 5141 | 5 |
shcal20 | Bchron | Southern hemisphere 2020 calibration curve | data.frame | 9501 | 5 |
dataXY | PPtreeregViz | Simulated data | data.frame | 100 | 5 |
insurance | PPtreeregViz | Insurance Data | data.frame | 1338 | 7 |
tiwqs_data | gWQS | Measurement of 38 nutrients (NHANES dataset) | data.frame | 5960 | 50 |
wqs_data | gWQS | Exposure concentrations of 34 PCB (simulated dataset) | data.frame | 500 | 68 |
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 |
elk_data | moveHMM | Elk data set from Morales et al. (2004, Ecology) | data.frame | 735 | 4 |
example | moveHMM | Example dataset | list | | |
haggis_data | moveHMM | Wild haggis data set from Michelot et al. (2016, Methods Eco Evol) | data.frame | 1200 | 5 |
FungusTreeNetwork | shinySbm | FungusTreeNetwork | list | | |
jpaddresses | jisx0402 | Japanese addresses and their coordinates | tbl_df | 277656 | 6 |
jpprefs | jisx0402 | Prefecture codes of Japan | tbl_df | 47 | 2 |
municipality | jisx0402 | Municipality codes of Japan | tbl_df | 4542 | 6 |
p_leaf | DYNATE | p_leaf | data.table | 4214 | 5 |
snp_dat | DYNATE | snp_dat | data.frame | 54282 | 6 |
carpet | radiant.multivariate | Carpet cleaners | tbl_df | 18 | 6 |
city | radiant.multivariate | City distances | tbl_df | 45 | 3 |
city2 | radiant.multivariate | City distances 2 | tbl_df | 78 | 3 |
computer | radiant.multivariate | Perceptions of computer (re)sellers | tbl_df | 5 | 8 |
movie | radiant.multivariate | Conjoint data for Movie theaters | tbl_df | 18 | 6 |
mp3 | radiant.multivariate | Conjoint data for MP3 players | tbl_df | 18 | 6 |
retailers | radiant.multivariate | Perceptions of retailers | tbl_df | 6 | 10 |
shopping | radiant.multivariate | Shopping attitudes | tbl_df | 20 | 7 |
toothpaste | radiant.multivariate | Toothpaste attitudes | tbl_df | 60 | 10 |
tpbrands | radiant.multivariate | Toothpaste brands | tbl_df | 45 | 4 |
aqi | tidyindex | Air Quality Index (AQI) | tbl_df | 272 | 9 |
aqi_ref_tbl | tidyindex | Air Quality Index (AQI) | tbl_df | 5 | 3 |
aus_climate | tidyindex | Weather data for in-situ stations in Queensland from 1990 to 2020 | tbl_df | 52373 | 9 |
gggi | tidyindex | Global Gender Gap Index (2023) | tbl_df | 146 | 22 |
gggi_weights | tidyindex | Global Gender Gap Index (2023) | tbl_df | 14 | 7 |
hdi | tidyindex | Human Development Index (2022) | tbl_df | 191 | 8 |
hdi_scales | tidyindex | Human Development Index (2022) | tbl_df | 4 | 5 |
pollutant_ref_tbl | tidyindex | Air Quality Index (AQI) | tbl_df | 30 | 5 |
queensland | tidyindex | Weather data for in-situ stations in Queensland from 1990 to 2020 | tbl_df | 11252 | 9 |
tenterfield | tidyindex | Weather data for in-situ stations in Queensland from 1990 to 2020 | tbl_df | 369 | 9 |
covid19 | SEIRfansy | COVID-19 Cases Time Series in India | data.frame | 236 | 7 |
trips0 | traipse | Simulated track data | tbl_df | 1500 | 4 |
ipi_c_eu | ggdemetra | Industrial Production Indices in manufacturing in the European Union | mts | 360 | 34 |
ipi_c_eu_df | ggdemetra | Industrial Production Indices in manufacturing in the European Union | data.frame | 360 | 35 |
monterey | affinity | Monterey Bay elevation | matrix | 270 | |
Alcohol | mosaicData | Alcohol Consumption per Capita | data.frame | 411 | 3 |
Birthdays | mosaicData | US Births in 1969 - 1988 | data.frame | 372864 | 7 |
Births | mosaicData | US Births | data.frame | 7305 | 8 |
Births2015 | mosaicData | US Births | data.frame | 365 | 8 |
Births78 | mosaicData | US Births | data.frame | 365 | 8 |
BirthsCDC | mosaicData | US Births | data.frame | 3652 | 8 |
BirthsSSA | mosaicData | US Births | data.frame | 5479 | 8 |
CPS85 | mosaicData | Data from the 1985 Current Population Survey (CPS85) | data.frame | 534 | 11 |
Cards | mosaicData | Standard Deck of Cards | character | | |
CoolingWater | mosaicData | CoolingWater | data.frame | 222 | 2 |
Countries | mosaicData | Countries | data.frame | 288 | 3 |
Dimes | mosaicData | Weight of dimes | data.frame | 30 | 2 |
Galton | mosaicData | Galton's dataset of parent and child heights | data.frame | 898 | 6 |
Gestation | mosaicData | Data from the Child Health and Development Studies | tbl_df | 1236 | 23 |
GoosePermits | mosaicData | Goose Permit Study | data.frame | 11 | 3 |
HELPfull | mosaicData | Health Evaluation and Linkage to Primary Care | data.frame | 1472 | 788 |
HELPmiss | mosaicData | Health Evaluation and Linkage to Primary Care | data.frame | 470 | 28 |
HELPrct | mosaicData | Health Evaluation and Linkage to Primary Care | data.frame | 453 | 30 |
HeatX | mosaicData | Data from a heat exchanger laboratory | data.frame | 6 | 7 |
KidsFeet | mosaicData | Foot measurements in children | data.frame | 39 | 8 |
Marriage | mosaicData | Marriage records | data.frame | 98 | 15 |
Mites | mosaicData | Mites and Wilt Disease | data.frame | 47 | 2 |
RailTrail | mosaicData | Volume of Users of a Rail Trail | data.frame | 90 | 11 |
Riders | mosaicData | Volume of Users of a Massachusetts Rail Trail | data.frame | 90 | 12 |
SAT | mosaicData | State by State SAT data | data.frame | 50 | 8 |
SaratogaHouses | mosaicData | Houses in Saratoga County (2006) | data.frame | 1728 | 16 |
SnowGR | mosaicData | Snowfall data for Grand Rapids, MI | data.frame | 119 | 15 |
SwimRecords | mosaicData | 100 m Swimming World Records | data.frame | 62 | 3 |
TenMileRace | mosaicData | Cherry Blossom Race | data.frame | 8636 | 5 |
Utilities | mosaicData | Utility bills | data.frame | 117 | 12 |
Utilities2 | mosaicData | Utility bills | data.frame | 117 | 19 |
Weather | mosaicData | Weather | tbl_df | 3655 | 25 |
Whickham | mosaicData | Data from the Whickham survey | data.frame | 1314 | 3 |
anxiety | AnxietySleep | Data from the anxiety and confinement study. | data.table | 617 | 7 |
SampleInput | InterVA4 | 10 records of Sample Input | data.frame | 20 | 246 |
causetext | InterVA4 | Translation list of COD codes | matrix | 68 | 3 |
probbase | InterVA4 | Conditional probability of InterVA4.02 | matrix | 246 | 81 |
probbase3 | InterVA4 | Conditional probability of InterVA4.03 | matrix | 246 | 81 |
census_variables_2012 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2013 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2014 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2015 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2016 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2017 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2018 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2019 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2020 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2021 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_2022 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2012 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2013 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2014 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2015 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2016 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2017 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2018 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2019 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2020 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2021 | findSVI | List of census variables for SVI calculation | list | | |
census_variables_exp_2022 | findSVI | List of census variables for SVI calculation | list | | |
cty_cz_2020_xwalk | findSVI | Relationship file (crosswalk) between US counties and commuting zones | data.frame | 3222 | 2 |
state_valid | findSVI | Table of valid full names/abbreviations/FIPS codes of 52 states | tbl_df | 52 | 3 |
variable_cal_exp_2012 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 33 | 3 |
variable_cal_exp_2013 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_cal_exp_2014 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_cal_exp_2015 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_cal_exp_2016 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_cal_exp_2017 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_cal_exp_2018 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_cal_exp_2019 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_cal_exp_2020 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_cal_exp_2021 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_cal_exp_2022 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_e_ep_calculation_2012 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 33 | 3 |
variable_e_ep_calculation_2013 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_e_ep_calculation_2014 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_e_ep_calculation_2015 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_e_ep_calculation_2016 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_e_ep_calculation_2017 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_e_ep_calculation_2018 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 35 | 3 |
variable_e_ep_calculation_2019 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_e_ep_calculation_2020 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_e_ep_calculation_2021 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
variable_e_ep_calculation_2022 | findSVI | Table of census variables and formula for SVI calculation | tbl_df | 51 | 3 |
zcta_state_xwalk2019 | findSVI | Relationship file (crosswalk) for ZCTAs by state | data.frame | 519726 | 5 |
zcta_state_xwalk2020 | findSVI | Relationship file (crosswalk) for ZCTAs by state | tbl_df | 538426 | 5 |
zcta_state_xwalk2021 | findSVI | Relationship file (crosswalk) for ZCTAs by state | tbl_df | 538328 | 5 |
zcta_state_xwalk2022 | findSVI | Relationship file (crosswalk) for ZCTAs by state | tbl_df | 538152 | 5 |
neisseria | sglasso | Neisseria Data Set | matrix | 4 | 100 |
auto | GSE | Automobile data | data.frame | 205 | 26 |
boston | GSE | Boston Housing Data | data.frame | 506 | 12 |
calcium | GSE | Calcium data | data.frame | 178 | 8 |
geochem | GSE | Geochemical Data | data.frame | 53 | 20 |
horse | GSE | Horse-colic data | data.frame | 368 | 9 |
wages | GSE | Wages and Hours | data.frame | 39 | 10 |
plrv | geneticae | Clones from the PLRV population | data.frame | 504 | 6 |
example._plot.df_agg | rBiasCorrection | example._plot.df_agg | data.table | 9 | 3 |
example._plot_coef_c | rBiasCorrection | example._plot_coef_c | list | | |
example._plot_coef_h | rBiasCorrection | example._plot_coef_h | list | | |
example.data_calibration | rBiasCorrection | example.data_calibration | list | | |
example.data_experimental | rBiasCorrection | example.data_experimental | list | | |
beetle | AICcmodavg | Flour Beetle Data | data.frame | 8 | 4 |
bullfrog | AICcmodavg | Bullfrog Occupancy and Common Reed Invasion | data.frame | 50 | 23 |
calcium | AICcmodavg | Blood Calcium Concentration in Birds | data.frame | 20 | 3 |
cement | AICcmodavg | Heat Expended Following Hardening of Portland Cement | data.frame | 13 | 5 |
dry.frog | AICcmodavg | Frog Dehydration Experiment on Three Substrate Types | data.frame | 121 | 16 |
fat | AICcmodavg | Fat Data and Body Measurements | data.frame | 252 | 26 |
gpa | AICcmodavg | GPA Data and Standardized Test Scores | data.frame | 20 | 5 |
iron | AICcmodavg | Iron Content in Food | data.frame | 36 | 3 |
lizards | AICcmodavg | Habitat Preference of Lizards | data.frame | 48 | 6 |
min.trap | AICcmodavg | Anuran Larvae Counts in Minnow Traps Across Pond Type | data.frame | 24 | 6 |
newt | AICcmodavg | Newt Capture-mark-recapture Data | data.frame | 78 | 11 |
pine | AICcmodavg | Strength of Pine Wood Based on the Density Adjusted for Resin Content | data.frame | 42 | 3 |
salamander | AICcmodavg | Salamander Capture-mark-recapture Data | data.frame | 36 | 7 |
tortoise | AICcmodavg | Gopher Tortoise Distance Sampling Data | data.frame | 410 | 5 |
turkey | AICcmodavg | Turkey Weight Gain | data.frame | 30 | 2 |
tempanomalies | meteorits | Global Annual Temperature Anomalies (Land Meteorological Stations) (1880-2015) | data.frame | 136 | 3 |
Blending | limSolve | A linear inverse blending problem | list | | |
Chemtax | limSolve | An overdetermined linear inverse problem: estimating algal composition based on pigment biomarkers. | list | | |
E_coli | limSolve | An underdetermined linear inverse problem: the Escherichia Coli Core Metabolism Model. | list | | |
Minkdiet | limSolve | An underdetermined linear inverse problem: estimating diet composition of Southeast Alaskan Mink. | list | | |
RigaWeb | limSolve | An underdetermined linear inverse problem: the Gulf of Riga *spring* planktonic food web | list | | |
Data_Maize | vmdTDNN | Monthly International Maize Price Data | ts | 126 | 1 |
quote_source | tidystats | A Many Labs replication of Lorge & Curtiss (1936) | spec_tbl_df | 6343 | 15 |
CancerGram_predictions | CancerGram | Prediction of anticancer peptides | list | | |
knee | glmmLasso | Clinical pain study on knee data | data.frame | 381 | 7 |
soccer | glmmLasso | German Bundesliga data for the seasons 2008-2010 | data.frame | 54 | 16 |
customer_churn_tbl | correlationfunnel | Customer Churn Data Set for a Telecommunications Company | spec_tbl_df | 7043 | 21 |
marketing_campaign_tbl | correlationfunnel | Marketing Data for a Bank | tbl_df | 45211 | 18 |
adae | tidyCDISC | ADAE | tbl_df | 1191 | 59 |
adlbc | tidyCDISC | ADLBC | tbl_df | 37132 | 46 |
adsl | tidyCDISC | ADSL | tbl_df | 254 | 54 |
adtte | tidyCDISC | ADTTE | tbl_df | 254 | 26 |
advs | tidyCDISC | ADVS | tbl_df | 32139 | 35 |
example_dat1 | tidyCDISC | Example Data Set 1 | list | | |
example_dat2 | tidyCDISC | Example Data Set 2 | list | | |
penguins | PUGMM | Penguins | data.frame | 342 | 5 |
epl_20_21 | MultRegCMP | Scores English Premier League Season 2020-2021 | spec_tbl_df | 380 | 4 |
ms_locale_names | mscstts | Names of Microsoft Locales | tbl_df | 49 | 2 |
ms_locales_df | mscstts | Detailed Names of Microsoft Locales and Voices | data.frame | 214 | 10 |
adherence | cvAUC | Data set: Simulated Pooled Repeated Measures Data | data.frame | 2002 | 7 |
admissions | cvAUC | Data set: Simulated Admissions Data with Binary Outcome | data.frame | 500 | 6 |
AA_sequence | TDbook | Data set containing multiple sequence alignment information | AAbin | | |
df_Candidaauris_data | TDbook | Population genetics data No.1 | data.frame | 305 | 20 |
df_NJIDqgsS | TDbook | Population genetics data No.2 | data.frame | 1832 | 12 |
df_alleles | TDbook | Allele table | data.frame | 386 | 385 |
df_alltax_info | TDbook | Data frame containing hierarchical relationship | data.frame | 1351 | 7 |
df_bar_data | TDbook | Trait data | data.frame | 386 | 2 |
df_barplot_attr | TDbook | Data set of the abundance of microbes at the body sites of greatest prevalence | data.frame | 332 | 3 |
df_difftax | TDbook | Data frame containing taxa and factor information and pvalue | data.frame | 36 | 3 |
df_info | TDbook | Sampling information data set | data.frame | 386 | 6 |
df_inode_data | TDbook | Nodedata to be mapped to tree | data.frame | 6 | 6 |
df_ring_heatmap | TDbook | Data set of the abundance and types of microbes | data.frame | 2324 | 3 |
df_svl | TDbook | Data frame containing slv information | data.frame | 100 | 1 |
df_tip_data | TDbook | Tipdata to be mapped to tree | data.frame | 7 | 10 |
df_tippoint | TDbook | Data set of the abundance and types of microbes | data.frame | 332 | 4 |
dna_HPV58_aln | TDbook | A DNAbin class to store the aligned sequnces of species of HPV58.tree | DNAbin | | |
text_RMI_tree | TDbook | Tree to display with symbolic points indicating partitioned bootstrap values | character | | |
tree_Candidaauris | TDbook | Tree No.1 to display with visualized population genetics data | phylo | | |
tree_HPV58 | TDbook | Tree to display with dot and line plots of pairwise nucleotide sequence distances | phylo | | |
tree_NJIDqgsS | TDbook | Tree No.2 to display with visualized population genetics data | phylo | | |
tree_anole | TDbook | Tree to color with continuous state transition in edges | phylo | | |
tree_boots | TDbook | Tree to be mapped to data | phylo | | |
tree_hmptree | TDbook | Tree to display with multiple graphs for multi-dimensional data | phylo | | |
tree_long_branch_example | TDbook | Tree to display with shrunk outlier long branch | phylo | | |
tree_nex | TDbook | Tree to display with silhouettes from Phylopic | phylo | | |
tree_nwk | TDbook | Tree to display with sampling information, SNP and Trait data | phylo | | |
tree_seq_nwk | TDbook | Tree to display with multiple sequence alignment | phylo | | |
tree_treenwk_30.4.19 | TDbook | Tree to group and highlight | phylo | | |
asia | jti | Asia | tbl_df | 5000 | 8 |
asia2 | jti | Asia2 | list | | |
ger_macro | bootCT | Investment, Income and Consumption dataset. | tbl_df | 92 | 4 |
ita_macro | bootCT | Export, Foreign Investment and GDP dataset. | data.frame | 51 | 4 |
smk_crit | bootCT | Critical values of the F-test on the independent variables in the conditional ARDL model. | data.frame | 144 | 17 |
gh_repos | tibblify | GitHub Repositories | list | | |
gh_users | tibblify | GitHub Users | list | | |
got_chars | tibblify | Game of Thrones POV characters | list | | |
politicians | tibblify | Politicians | list | | |
homologadas_publindex | margaret | "Publindex homologación de revistas" 2003 to 2022 | spec_tbl_df | 836701 | 5 |
national_publindex | margaret | Publindex Nacional de revistas 2022 | spec_tbl_df | 7669 | 5 |
scimago_categories | margaret | Scimago categories | spec_tbl_df | 968670 | 5 |
opioidData | ihclust | Opioid Dispensing Rates | data.frame | 3085 | 18 |
acs5_2013 | stcos | Shapes and ACS estimates for Boone County, MO. | sf | 87 | 9 |
acs5_2014 | stcos | Shapes and ACS estimates for Boone County, MO. | sf | 87 | 9 |
acs5_2015 | stcos | Shapes and ACS estimates for Boone County, MO. | sf | 85 | 9 |
acs5_2016 | stcos | Shapes and ACS estimates for Boone County, MO. | sf | 87 | 9 |
acs5_2017 | stcos | Shapes and ACS estimates for Boone County, MO. | sf | 87 | 9 |
columbia_neighbs | stcos | City of Columbia neighborhoods. | sf | 4 | 2 |
apodos | presentes | apodos | tbl_df | 5362 | 4 |
centros_clandestinos_detencion | presentes | centros_clandestinos_detencion | spec_tbl_df | 762 | 7 |
parque_de_la_memoria | presentes | parque_de_la_memoria | tbl_df | 8751 | 30 |
victimas_accionar_represivo_ilegal | presentes | victimas_accionar_represivo_ilegal | data.table | 8753 | 21 |
victimas_accionar_represivo_ilegal_sin_denuncia_formal | presentes | victimas_accionar_represivo_ilegal_sin_denuncia_formal | data.table | 784 | 22 |
Data | SQI | This is data to be included in my package | tbl_df | 60 | 12 |
dcars | lsbclust | Dutch Cars Data | array | | |
lov | lsbclust | List-of-values Data Set | data.frame | 4514 | 12 |
supermarkets | lsbclust | Dutch Supermarkets Data Set | array | | |
JAR | SensoMineR | JAR | data.frame | 141 | 13 |
cards | SensoMineR | Cards | data.frame | 16 | 81 |
compo.cocktail | SensoMineR | Composition of the cocktails data | data.frame | 16 | 4 |
cream_id | SensoMineR | Cream Ideal Data | data.frame | 774 | 29 |
cream_signa | SensoMineR | Data description of the consumers who made the Ideal for the cream | data.frame | 86 | 24 |
hedo.cocktail | SensoMineR | Cocktails hedonic scores | data.frame | 16 | 100 |
hedochoc | SensoMineR | Chocolates hedonic scores | data.frame | 6 | 222 |
hedochoc | SensoMineR | Chocolates hedonic scores | data.frame | 6 | 222 |
napping.don | SensoMineR | An example of Napping data | data.frame | 10 | 22 |
napping.words | SensoMineR | An example of "illustrative" variables to enhance results from Napping data | data.frame | 10 | 14 |
perfume | SensoMineR | Perfume | data.frame | 12 | 30 |
perfume_fcp | SensoMineR | Perfume data obtained by free choice profiling | data.frame | 12 | 47 |
perfume_ideal | SensoMineR | Perfume Ideal Data | data.frame | 1442 | 45 |
senso.cocktail | SensoMineR | Sensory data for 16 cocktails | data.frame | 16 | 13 |
sensochoc | SensoMineR | Sensory data for 6 chocolates | data.frame | 348 | 18 |
sensopanels | SensoMineR | Sensory profiles given by 7 panels | data.frame | 6 | 98 |
smoothies | SensoMineR | Smoothies | data.frame | 8 | 72 |
videos | SensoMineR | Videos data obtained with Holos | list | | |
retail | caretForecast | Grouped sales data from an Australian Retailer | tbl_df | 13986 | 3 |
retail_wide | caretForecast | Sales data from an Australian Retailer in time series format | mts | 333 | 42 |
GSE50081 | signatureSurvival | Survival data from cohort GSE50081 | data.frame | 129 | 3596 |
TCGA_forestplt | signatureSurvival | Data for forestplot | data.frame | 28 | 5 |
TCGA_survivalData | signatureSurvival | TCGA data for survival analysis | data.frame | 572 | 162 |
TS_signature | signatureSurvival | A signature constructed with a set of tumor suppressor genes | data.frame | 28 | 2 |
results | signatureSurvival | results of univariate Cox proportional hazard analysis of patients with ADC in three cohorts. | list | | |
signature_weight | signatureSurvival | Weights of genes in a signature | data.frame | 84 | 2 |
BcellLymphomaCD79 | ImpactEffectsize | Example data of bimodal CD79 expression. | data.frame | 258429 | 2 |
FeatureselectionData | ImpactEffectsize | Example data with two groups and the Impact effet size measure. | data.frame | 2000 | 21 |
FlowcytometricData | ImpactEffectsize | Example data of hematologic marker expression. | data.frame | 2796 | 9 |
SameMeansData | ImpactEffectsize | Example artificial data with two groups of same means but different data distribution shapes. | data.frame | 2000 | 7 |
StocksFluctuation | ImpactEffectsize | Example data of stock fluctuation. | data.frame | 5522 | 2 |
p1Seasonal | mbr | Seasonal streamflow at P.1 station | data.table | 246 | 3 |
pc3seasons | mbr | Principal components of tree rings | list | | |
brd_countdata | neonOS | Count data table from Breeding landbird point counts (DP1.10003.001) | spec_tbl_df | 472 | 26 |
brd_perpoint | neonOS | Per-point data table from Breeding landbird point counts (DP1.10003.001) | spec_tbl_df | 54 | 31 |
cfc_lignin_test_dups | neonOS | Lignin data table from Plant foliar traits (DP1.10026.001) | spec_tbl_df | 26 | 25 |
cfc_lignin_variables | neonOS | Variables file, subset to lignin table, from Plant foliar traits (DP1.10026.001) | data.frame | 26 | 8 |
teaching_r_questions | shinysurveys | A sample CSV file for demographic questions | tbl_df | 54 | 7 |
PBS | tsibbledata | Monthly Medicare Australia prescription data | tbl_ts | 67596 | 9 |
ansett | tsibbledata | Passenger numbers on Ansett airline flights | tbl_ts | 7407 | 4 |
aus_livestock | tsibbledata | Australian livestock slaughter | tbl_ts | 29364 | 4 |
aus_production | tsibbledata | Quarterly production of selected commodities in Australia. | tbl_ts | 218 | 7 |
aus_retail | tsibbledata | Australian retail trade turnover | tbl_ts | 64532 | 5 |
gafa_stock | tsibbledata | GAFA stock prices | tbl_ts | 5032 | 8 |
global_economy | tsibbledata | Global economic indicators | tbl_ts | 15150 | 9 |
hh_budget | tsibbledata | Household budget characteristics | tbl_ts | 88 | 8 |
nyc_bikes | tsibbledata | NYC Citi Bike trips | tbl_ts | 4268 | 12 |
olympic_running | tsibbledata | Fastest running times for Olympic races | tbl_ts | 312 | 4 |
pelt | tsibbledata | Pelt trading records | tbl_ts | 91 | 3 |
vic_elec | tsibbledata | Half-hourly electricity demand for Victoria, Australia | tbl_ts | 52608 | 5 |
expert.estimates | emulator | Expert estimates for Goldstein input parameters | data.frame | 16 | 3 |
results.table | emulator | Results from 100 Goldstein runs | data.frame | 100 | 27 |
toy | emulator | A toy dataset | matrix | 10 | 6 |
game_info | data.iquizoo | Game information | tbl_df | 405 | 6 |
Golf | bamlss | Prices of Used Cars Data | data.frame | 172 | 6 |
LondonBoroughs | bamlss | London Fire Data | SpatialPolygons | | |
LondonBoundaries | bamlss | London Fire Data | SpatialPolygons | | |
LondonFStations | bamlss | London Fire Data | SpatialPointsDataFrame | | |
LondonFire | bamlss | London Fire Data | SpatialPointsDataFrame | | |
TempIbk | bamlss | Temperature data. | data.frame | 1798 | 17 |
fatalities | bamlss | Weekly Number of Fatalities in Austria | data.frame | 1087 | 3 |
simdata | bamlss | Reference data. | list | | |
AustralianElectionPolling | pscl | Political opinion polls in Australia, 2004-07 | data.frame | 239 | 14 |
AustralianElections | pscl | elections to Australian House of Representatives, 1949-2016 | data.frame | 27 | 19 |
EfronMorris | pscl | Batting Averages for 18 major league baseball players, 1970 | data.frame | 18 | 7 |
RockTheVote | pscl | Voter turnout experiment, using Rock The Vote ads | data.frame | 85 | 6 |
UKHouseOfCommons | pscl | 1992 United Kingdom electoral returns | data.frame | 521 | 12 |
absentee | pscl | Absentee and Machine Ballots in Pennsylvania State Senate Races | data.frame | 22 | 8 |
admit | pscl | Applications to a Political Science PhD Program | data.frame | 106 | 6 |
bioChemists | pscl | article production by graduate students in biochemistry Ph.D. programs | data.frame | 915 | 6 |
ca2006 | pscl | California Congressional Districts in 2006 | data.frame | 53 | 13 |
iraqVote | pscl | U.S. Senate vote on the use of force against Iraq, 2002. | data.frame | 100 | 6 |
nj07 | pscl | rollcall object, National Journal key votes of 2007 | rollcall | | |
partycodes | pscl | political parties appearing in the U.S. Congress | list | | |
politicalInformation | pscl | Interviewer ratings of respondent levels of political information | data.frame | 1807 | 8 |
presidentialElections | pscl | elections for U.S. President, 1932-2016, by state | tbl_df | 1097 | 4 |
prussian | pscl | Prussian army horse kick data | data.frame | 280 | 3 |
s109 | pscl | rollcall object, 109th U.S. Senate (2005-06). | rollcall | | |
sc9497 | pscl | votes from the United States Supreme Court, from 1994-1997 | list | | |
state.info | pscl | information about the American states needed for U.S. Congress | list | | |
unionDensity | pscl | cross national rates of trade union density | data.frame | 20 | 4 |
vote92 | pscl | Reports of voting in the 1992 U.S. Presidential election. | data.frame | 909 | 9 |
sce_zero_test | TREG | Test SummarizedExperiment data | SummarizedExperiment | | |
datrand | binaryMM | Simulated data set | data.frame | 24999 | 4 |
madras | binaryMM | Madras Longitudinal Schizophrenia Study: Thought Disorder Subset | data.frame | 922 | 5 |
barleyh20 | pbANOVA | barleyh20 data | data.frame | 112 | 6 |
fedata | pbANOVA | fedata data | data.frame | 20 | 2 |
potato | pbANOVA | potato data | data.frame | 75 | 5 |
Hofmann | iccbeta | A multilevel dataset from Hofmann, Griffin, and Gavin (2000). | data.frame | 1000 | 7 |
simICCdata | iccbeta | Simulated data example from Aguinis and Culpepper (2015). | data.frame | 900 | 6 |
QMwind | plotdap | QMwind Data | griddap_nc | | 2 |
murSST | plotdap | murSST Data | griddap_nc | | 2 |
sardines | plotdap | sardine Data | tabledap | 56 | 5 |
Penn46_ascii | hettx | Sample data set | data.frame | 6384 | 12 |
ToyData | hettx | Toy data set | data.frame | 500 | 7 |
BSsucc | primer | Secondary succession data | data.frame | 147 | 3 |
CandG | primer | Data drawn approximately from Collins and Glenn (1991) | numeric | | |
ClostExp | primer | Closterium Population Data | nfnGroupedData | 144 | 5 |
coneflower | primer | Smooth coneflower data | data.frame | 136 | 4 |
coneflowerrecruits | primer | Smooth coneflower new recruits | data.frame | 36 | 3 |
coneflowerseeds | primer | Smooth coneflower seed data | data.frame | 19 | 2 |
moths | primer | Moth Species Richness | data.frame | 21 | 6 |
ross | primer | Weekly deaths from bubonic plague in Bombay in 1905-06 | data.frame | 32 | 2 |
sparrows | primer | Song Sparrow Data Set | data.frame | 36 | 3 |
weeds | primer | Percent cover of six perennial herbaceous plants | data.frame | 15140 | 8 |
ecoli_core | fbar | A small E. coli model, created from a number of sources. | tbl_df | 95 | 8 |
iJO1366 | fbar | A full size E. coli model. | tbl_df | 2583 | 8 |
nutrient_types | fbar | A subset of exchange reactions annotated to indicate typical availability | tbl_df | 25 | 2 |
category_unicode_crosswalk | tidyEmoji | Emoji category, Unicode crosswalk | tbl_df | 10 | 2 |
emoji_unicode_crosswalk | tidyEmoji | Emoji name, Unicode, and Emoji category crosswalk | tbl_df | 4536 | 3 |
FIalfalfa | simET | A example dataset of alfalfa under flood irrigation | data.frame | 161 | 22 |
SDIalfalfa | simET | A example dataset of alfalfa under subsurface drip irrigation | data.frame | 161 | 22 |
ppl | mozambique | Mozambique populated places locations | sf | 2530 | 7 |
settlements | mozambique | Mozambique settlements locations | sf | 24602 | 24 |
df_household | mpindex | Sample dataset of households | tbl_df | 198 | 21 |
df_household_roster | mpindex | Sample dataset of household members | tbl_df | 905 | 8 |
FISH | shapeR | An example data file | data.frame | 240 | 18 |
shape | shapeR | An example shapeR instance including 160 images. | shapeR | | |
shape | shapeR | An example shapeR instance including 160 images. | shapeR | | |
flows_1030500 | gumboot | Observed and simulated flows for a single location | data.frame | 6940 | 3 |
hcdn_conus_sites | gumboot | Locations of HCDN sites in CONUS | data.frame | 670 | 3 |
ht01.multipleclass | HandTill2001 | Example Data for Multiple Classes | data.frame | 214 | 7 |
ht01.twoclass | HandTill2001 | Example Data for Binary Classes | data.frame | 189 | 2 |
powcal | etrm | Historical daily closing prices for 11 calendar year power futures contracts | data.frame | 3253 | 12 |
powfutures130513 | etrm | Closing prices for power futures contracts at trading date 2013-05-13 | data.frame | 32 | 5 |
powpriors130513 | etrm | Example priors at trading date 2015-05-13 | data.frame | 3885 | 3 |
singlecell | countprop | Single cell sequencing data from mouse embryonic stem cells in G1 phase | matrix | 96 | 10 |
example_df | ggmuller | Example dataframe | data.frame | 1266 | 5 |
example_edges | ggmuller | Example adjacency matrix | data.frame | 6 | 2 |
example_pop_df | ggmuller | Example population dataframe | data.frame | 49 | 3 |
dis_data | BayesDissolution | A dissolution data set taken from Ocana et al. (2009). | data.frame | 24 | 9 |
primes | primes | Pre-computed Prime Numbers | integer | | |
anoteropsis | spider | Cytochrome oxidase I (COI) sequences of New Zealand _Anoteropsis_ species | DNAbin | 33 | |
dolomedes | spider | Cytochrome oxidase I (COI) sequences of New Zealand _Dolomedes_ species | DNAbin | 37 | |
salticidae | spider | Cytochrome oxidase I (COI) sequences of world-wide species of Salticidae | DNAbin | | |
sarkar | spider | Dummy sequences illustrating the categories of diagnostic nucleotides | DNAbin | 8 | |
woodmouse | spider | Cytochrome b Gene Sequences of Woodmice | DNAbin | 15 | |
berne | geoGAM | Berne - soil mapping case study | data.frame | 1052 | 238 |
berne.grid | geoGAM | Berne - very small extract of prediction grid | data.frame | 4594 | 228 |
garamba | popbayes | African mammals survey in the Garamba National Park | data.frame | 141 | 11 |
species_info | popbayes | Species information dataset | data.frame | 15 | 9 |
.CF.AC | cubfits | Default Controlling Options | list | | |
.CF.CONF | cubfits | Default Controlling Options | list | | |
.CF.CT | cubfits | Default Controlling Options | list | | |
.CF.DP | cubfits | Default Controlling Options | list | | |
.CF.GV | cubfits | Default Controlling Options | list | | |
.CF.OP | cubfits | Default Controlling Options | list | | |
.CF.PARAM | cubfits | Default Controlling Options | list | | |
.CF.PT | cubfits | Default Controlling Options | list | | |
.CO.CT | cubfits | Default Controlling Options | list | | |
.cubfitsEnv | cubfits | Default Controlling Options | environment | | |
b.Init | cubfits | Datasets for Demonstrations | list | | |
ex.test | cubfits | Datasets for Demonstrations | list | | |
ex.train | cubfits | Datasets for Demonstrations | list | | |
yassour | cubfits | Yassour 2009 Yeast Experiment Dataset | data.frame | 6303 | 5 |
yassour.PM.appr | cubfits | Posterior Results of Yassour 2009 Yeast Experiment Dataset | list | | |
yassour.PM.fits | cubfits | Posterior Results of Yassour 2009 Yeast Experiment Dataset | list | | |
yassour.info | cubfits | Posterior Results of Yassour 2009 Yeast Experiment Dataset | list | | |
myGOs | ViSEAGO | myGOs dataset | GO_SS | | |
dfhera | africovid | subnational covid data from HERA | tbl_df | 64292 | 17 |
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 | |
dgrp2.3R.5k.data | GenomeAdmixR | A subset of sequencing data from the Drosophila Genetics Reference Panel | genomeadmixr_data | | |
baseball | prefmod | Data (paired comparisons): Baseball Games | numeric | | |
carconf | prefmod | Data (partial rankings): Car Configurator | data.frame | 435 | 9 |
cemspc | prefmod | Data (paired comparisons with undecided): CEMS (Community of European management schools) | data.frame | 303 | 17 |
dat4 | prefmod | Data (paired comparisons): dat4 | data.frame | 100 | 6 |
euro55.2.des | prefmod | Design data frame for a paired comparison pattern model for rankings (Eurobarometer 55.2) | data.frame | 5760 | 9 |
immig | prefmod | Data (paired comparisons with undecided and forced 'NA's): Negative Attitudes towards Immigrants | data.frame | 98 | 9 |
issp2000 | prefmod | Data (Likert items): ISSP 2000 Survey on Environmental Issues | data.frame | 1595 | 11 |
music | prefmod | Data (ratings): Music (US General social survey 1993) | data.frame | 1597 | 21 |
salad | prefmod | Data (ranks): Salad Dressings (Critchlow and Fligner) | data.frame | 32 | 4 |
tennis | prefmod | Data (paired comparisons): Preferred Interview Partner | data.frame | 16 | 5 |
trdel | prefmod | Data (paired comparisons): Training delivery modes | data.frame | 198 | 13 |
xmpl | prefmod | Data (Likert items): Example Data Set | data.frame | 100 | 5 |
ausiot | clptheory | AUS IO Table | data.frame | 840 | 67 |
aussea | clptheory | Socio Economic Accounts | data.frame | 840 | 21 |
usaiot | clptheory | USA IO Table | data.frame | 840 | 67 |
usarwb | clptheory | Real Wage Bundle, USA | data.frame | 840 | 3 |
usasea | clptheory | Socio Economic Accounts | data.frame | 840 | 21 |
nor_covid19_cases_by_time_location_csfmt_rts_v1 | cstidy | Covid-19 data for PCR-confirmed cases in Norway (nation and county) | csfmt_rts_data_v1 | 11028 | 18 |
nor_covid19_icu_and_hospitalization_csfmt_rts_v1 | cstidy | Norwegian Covid-19 data for ICU and hospitalization | csfmt_rts_data_v1 | 919 | 18 |
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 |
reference | zoolog | References | list | | |
referenceSets | zoolog | References | data.frame | 4 | 15 |
referencesDatabase | zoolog | References | list | | |
zoologTaxonomy | zoolog | Taxonomy hierarchy for 'zoolog' | data.frame | 16 | 5 |
zoologThesaurus | zoolog | Thesaurus Set for 'zoolog' | list | | |
Sachs | GGMnonreg | Data: Sachs Network | data.frame | 7466 | 11 |
asd_ocd | GGMnonreg | Data: Autism and Obssesive Compulsive Disorder | data.frame | 17 | 17 |
bfi | GGMnonreg | Data: 25 Personality items representing 5 factors | data.frame | 2800 | 27 |
csws | GGMnonreg | Data: Contingencies of Self-Worth Scale (CSWS) | data.frame | 680 | 36 |
depression_anxiety_t1 | GGMnonreg | Data: Depression and Anxiety (Time 1) | data.frame | 403 | 16 |
depression_anxiety_t2 | GGMnonreg | Data: Depression and Anxiety (Time 2) | data.frame | 403 | 16 |
gss | GGMnonreg | Data: 1994 General Social Survey | data.frame | 1002 | 7 |
ifit | GGMnonreg | Data: ifit Intensive Longitudinal Data | data.frame | 197 | 8 |
iri | GGMnonreg | Data: Interpersonal Reactivity Index (IRI) | data.frame | 1973 | 29 |
ptsd | GGMnonreg | Data: Post-Traumatic Stress Disorder | data.frame | 221 | 20 |
ptsd_cor1 | GGMnonreg | Cor: Post-Traumatic Stress Disorder (Sample # 1) | data.frame | 16 | 16 |
ptsd_cor2 | GGMnonreg | Cor: Post-Traumatic Stress Disorder (Sample # 2) | data.frame | 16 | 16 |
ptsd_cor3 | GGMnonreg | Cor: Post-Traumatic Stress Disorder (Sample # 3) | data.frame | 16 | 16 |
ptsd_cor4 | GGMnonreg | Cor: Post-Traumatic Stress Disorder (Sample # 4) | data.frame | 16 | 16 |
rsa | GGMnonreg | Data: Resilience Scale of Adults (RSA) | data.frame | 675 | 34 |
tas | GGMnonreg | Data: Toronto Alexithymia Scale (TAS) | data.frame | 1925 | 21 |
women_math | GGMnonreg | Data: Women and Mathematics | matrix | 1190 | 6 |
mddSpList_v1_2 | mddmaps | Species list from Mammal Diversity Database | tbl_df | 3268 | 5 |
mddSpList_v1_3 | mddmaps | Species list from Mammal Diversity Database | tbl_df | 6513 | 5 |
mddSpList_v1_4 | mddmaps | Species list from Mammal Diversity Database | tbl_df | 6533 | 5 |
enrichr_output_macrophage | GeneTonic | A sample output from Enrichr | list | | |
fgseaRes | GeneTonic | A sample output from fgsea | data.frame | 7341 | 8 |
gostres_macrophage | GeneTonic | A sample output from g:Profiler | list | | |
res_macrophage_IFNg_vs_naive | GeneTonic | A sample 'DESeqResults' object | DESeqResults | | |
topgoDE_macrophage_IFNg_vs_naive | GeneTonic | A sample 'res_enrich' object | data.frame | 500 | 9 |
candy | MatrixCorrelation | Candy data | list | | |
FCGE_Data_GMP | rhcoclust | A real glutathione metabolism pathway (GMP) dataset for 'rhcoclust' package | tbl_df | 62 | 120 |
FCGE_Data_PPARs | rhcoclust | A real PPAR signaling pathways (PPAR-SP) dataset for 'rhcoclust' package | tbl_df | 88 | 120 |
simu_data | rhcoclust | A predefined simulated data for 'rhcoclust' package | matrix | 50 | 36 |
dressing | ffmanova | Dressing data | data.frame | 29 | 7 |
airport | grand | US Air Traffic Network | igraph | | |
cosponsor | grand | US Senate Co-Sponsorship Network | igraph | | |
senate | grand | US Senate Network | igraph | | |
test_data | OVL.CI | Simulated data with normal distributions to showcase the CI'S Overlap Coefficient (OVL) calculation | tbl_df | 100 | 2 |
exPftList | shinyRadioMatrix | PFT List | data.frame | 14 | 2 |
exTaxonList | shinyRadioMatrix | Taxon List | data.frame | 31 | 3 |
x | MetabolicSyndrome | An example data frame | data.frame | 23 | 7 |
dataHuman | FBFsearch | Cell signalling pathway data | list | | |
dataPub | FBFsearch | Publishing productivity data | list | | |
dataSim100 | FBFsearch | DAG model with 100 nodes and 100 edges | list | | |
dataSim200 | FBFsearch | DAG model with 200 nodes and 100 edges | list | | |
dataSim50 | FBFsearch | DAG model with 50 nodes and 100 edges | list | | |
dataSim6 | FBFsearch | DAG model with 6 nodes and 5 edges | list | | |
dataSimHuman | FBFsearch | Simulated cell signalling pathway data | list | | |
blood | sinaplot | Expression data from 2095 AML/ALL and healthy bone marrow cells. | data.frame | 2095 | 2 |
USAmap | geomapdata | Global Maps | list | | |
cosogeol | geomapdata | Coso Geothermal Region Faults and Geology | list | | |
cosomap | geomapdata | Coso Geothermal Region Faults and Geology | list | | |
faults | geomapdata | Coso Geothermal Region Faults and Geology | list | | |
fujitopo | geomapdata | Topographic DEM of Japan | list | | |
hiways | geomapdata | Coso Geothermal Region Faults and Geology | list | | |
japmap | geomapdata | Maps in GEOmap | list | | |
kamaleutmap | geomapdata | Maps in GEOmap | list | | |
kammap | geomapdata | Maps in GEOmap | list | | |
meijimap | geomapdata | Maps in GEOmap | list | | |
owens | geomapdata | Coso Geothermal Region Faults and Geology | list | | |
usacity | geomapdata | City Locations and Populations(USA) | list | | |
worldcity | geomapdata | City Locations and Populations(USA) | list | | |
worldmap | geomapdata | Global Maps | list | | |
Data | yuima | Five minutes Log SPX prices | list | | |
MWK151 | yuima | Graybill - Methuselah Walk - PILO - ITRDB CA535 | zoo | | |
diseasome | BioNAR | Barabasi's Diseasome Network | igraph | | |
Bcell_peaks | seqsetvis | 4 random peaks for paired-end data | GRanges | | |
CTCF_in_10a_bigWig_urls | seqsetvis | FTP URL path for vignette data. | character | | |
CTCF_in_10a_narrowPeak_grs | seqsetvis | list of GRanges that results in 100 random subset when overlapped | list | | |
CTCF_in_10a_narrowPeak_urls | seqsetvis | FTP URL path for vignette data. from | character | | |
CTCF_in_10a_overlaps_gr | seqsetvis | 100 randomly selected regions from overlapping CTCF peaks in 10a cell ChIP-seq | GRanges | | |
CTCF_in_10a_profiles_dt | seqsetvis | Profiles for 100 randomly selected regions from overlapping CTCF peaks in 10a cell ChIP-seq Results from fetching bigwigs with CTCF_in_10a_overlaps_gr. | data.table | 4200 | 9 |
CTCF_in_10a_profiles_gr | seqsetvis | Profiles for 100 randomly selected regions from overlapping CTCF peaks in 10a cell ChIP-seq Results from CTCF_in_10a_overlaps_gr | GRanges | | |
chromHMM_demo_bw_states_gr | seqsetvis | MCF10A CTCF profiles at 20 windows per chromHMM state, hg38. | GRanges | | |
chromHMM_demo_chain_url | seqsetvis | URL to download hg19ToHg38 liftover chain from UCSC | character | | |
chromHMM_demo_overlaps_gr | seqsetvis | overlap of MCF10A CTCF with MCF7 chromHMM states, hg38. | GRanges | | |
chromHMM_demo_segmentation_url | seqsetvis | URL to download hg19 MCF7 chromHMM segmentation | character | | |
chromHMM_demo_state_colors | seqsetvis | original state name to color mappings stored in segmentation bed | character | | |
chromHMM_demo_state_total_widths | seqsetvis | state name to total width mappings, hg38 | numeric | | |
test_peaks | seqsetvis | 4 random peaks for single-end data and 4 control regions 30kb downstream from each peak. | GRanges | | |
degradation_tstats | qsvaR | Degradation time t-statistics | data.frame | 45082 | 1 |
rse_tx | qsvaR | Example of RSE object with RNA-seq transcript quantification data | RangedSummarizedExperiment | | |
transcripts | qsvaR | Transcripts for Degradation Models | list | | |
gaussplot_sample_data | gaussplotR | Sample data set | data.frame | 36 | 11 |
sunspots2019 | tsibbletalk | Yearly mean total sunspot number (1700 - 2019) | tbl_ts | 320 | 2 |
tourism_monthly | tsibbletalk | Monthly Australian domestic overnight trips | tbl_ts | 80696 | 5 |
x_bounds | clickableImageMap | clickableImageMap data sets | list | | |
x_cal.m | clickableImageMap | clickableImageMap data sets | list | | |
x_cal.pullDown | clickableImageMap | clickableImageMap data sets | list | | |
x_cal2 | clickableImageMap | clickableImageMap data sets | list | | |
x_clickCoord | clickableImageMap | clickableImageMap data sets | matrix | 2 | 2 |
x_gtab | clickableImageMap | clickableImageMap data sets | gtable | | |
x_l | clickableImageMap | clickableImageMap data sets | list | | |
x_m | clickableImageMap | clickableImageMap data sets | matrix | 2 | 10 |
x_mtab | clickableImageMap | clickableImageMap data sets | gtable | | |
x_mtab2 | clickableImageMap | clickableImageMap data sets | gtable | | |
x_rcnames | clickableImageMap | clickableImageMap data sets | logical | | |
x_rows | clickableImageMap | clickableImageMap data sets | numeric | | |
x_tab | clickableImageMap | clickableImageMap data sets | gtable | | |
x_y | clickableImageMap | clickableImageMap data sets | numeric | | |
moodyContactSim | tsna | Jim Moody's example dynamic contact simulation network | networkDynamic | | |
datasaem | sae.prop | Data generated based on Multivariate Fay Herriot Model with Additive Logistic Transformation | data.frame | 30 | 12 |
datasaem.ns | sae.prop | Data generated based on Multivariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Cases | data.frame | 30 | 15 |
datasaeu | sae.prop | Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation | data.frame | 30 | 4 |
datasaeu.ns | sae.prop | Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Cases | data.frame | 30 | 5 |
holzinger | faoutlier | Description of holzinger data | data.frame | 101 | 9 |
holzinger.outlier | faoutlier | Description of holzinger data with 1 outlier | data.frame | 101 | 9 |
colombia | sensemakr | Data from the 2016 referendum for peace with the FARC in Colombia. | data.frame | 1123 | 16 |
darfur | sensemakr | Data from survey of Darfurian refugees in eastern Chad. | data.frame | 1276 | 14 |
ascvd_pooled_coef | CVrisk | Model coefficients for ASCVD 10y ACC/AHA model | tbl_df | 4 | 17 |
frs_coef | CVrisk | Model coefficients for ASCVD 10y FRS model | tbl_df | 2 | 10 |
frs_simple_coef | CVrisk | Model coefficients for ASCVD 10y FRS simple model | tbl_df | 2 | 9 |
mesa_cac_coef | CVrisk | mesa_cac_coef | tbl_df | 1 | 15 |
mesa_coef | CVrisk | mesa_coef | tbl_df | 1 | 14 |
sample_data | CVrisk | Sample patient data | data.frame | 7 | 13 |
faith_tree | abdiv | Example data for Faith's phylogenetic diversity | phylo | | |
leprieur_tree | abdiv | Example data for phylogenetic nestedness and turnover components | phylo | | |
lozupone_panel_a | abdiv | Example data for UniFrac distance | data.frame | 14 | 3 |
lozupone_panel_b | abdiv | Example data for UniFrac distance | data.frame | 14 | 3 |
lozupone_tree | abdiv | Example data for UniFrac distance | phylo | | |
clans | liberia | clans | sf | 305 | 19 |
counties | liberia | counties | sf | 15 | 12 |
districts | liberia | districts | sf | 136 | 13 |
enumerationArea | liberia | enumerationArea | tbl_df | 751 | 8 |
grandBassaEA | liberia | grandBassaEA | sf | 468 | 19 |
greaterMonroviaEA | liberia | greaterMonroviaEA | sf | 1967 | 19 |
locality | liberia | locality | tbl_df | 3412 | 9 |
monrovia | liberia | monrovia | tbl_df | 166 | 6 |
settlements | liberia | settlements | sf | 14013 | 16 |
lpjclassdata | lpjclass | lpjclassdata | list | | |
jones2020.tracks | pawscore | Jones et al. (2020) paw trajectory data | list | | |
dnase | tpr | rhDNase Data | data.frame | 767 | 6 |
GuelphP | Kendall | Phosphorous Concentrations in Speed River, Monthly | ts | | |
PrecipGL | Kendall | Annual precipitation, inches, Great Lakes, 1900-1986 | ts | | |
cookie | groc | Near-Infrared (NIR) Spectroscopy of Biscuit Doughs | data.frame | 72 | 704 |
prim7 | groc | prim7 Dataset | data.frame | 500 | 7 |
MvBinaryExample | MvBinary | Simulated binary data: MvBinaryExample | matrix | 400 | |
plants | MvBinary | Real binary data: Plants | matrix | 35583 | 69 |
RNADuplexSampleClustReads | DuplexDiscovereR | RNA duplex reads of SPLASH, clustered and assigned to duplex groups | StrictGInteractions | | |
RNADuplexSampleDGs | DuplexDiscovereR | RNA duplex reads of SPLASH, clustered and collapsed to duplex groups | StrictGInteractions | | |
RNADuplexSampleGI | DuplexDiscovereR | RNA duplex reads of SPLASH derived from chimeric alignments | StrictGInteractions | | |
RNADuplexesGeneCounts | DuplexDiscovereR | Gene counts on human chromosome 22, embryonic stem cells | spec_tbl_df | 1445 | 2 |
RNADuplexesRawBed | DuplexDiscovereR | Chimeric reads of SPLASH converted to .bedpe fromat | spec_tbl_df | 2040 | 10 |
RNADuplexesRawChimSTAR | DuplexDiscovereR | Chimeric reads of SPLASH | tbl_df | 5000 | 21 |
SampleGeneAnnoGR | DuplexDiscovereR | Gene coordinates on human chromosome 22 | GRanges | | |
SampleSmallGI | DuplexDiscovereR | RNA duplex reads of SPLASH derived from chimeric alignments | StrictGInteractions | | |
SampleSpliceJncGR | DuplexDiscovereR | Gene coordinates on human chromosome 22 | GRanges | | |
CN.features | sigminer | Classification Table of Copy Number Features Devised by Wang et al. for Method 'W' | data.table | 80 | 5 |
centromeres.T2T | sigminer | Location of Centromeres at Genome Build T2T | data.frame | 23 | 3 |
centromeres.hg19 | sigminer | Location of Centromeres at Genome Build hg19 | data.frame | 24 | 3 |
centromeres.hg38 | sigminer | Location of Centromeres at Genome Build hg38 | data.frame | 24 | 3 |
centromeres.mm10 | sigminer | Location of Centromeres at Genome Build mm10 | data.frame | 21 | 3 |
centromeres.mm9 | sigminer | Location of Centromeres at Genome Build mm9 | data.frame | 21 | 3 |
chromsize.T2T | sigminer | Chromosome Size of Genome Build T2T | data.frame | 25 | 2 |
chromsize.hg19 | sigminer | Chromosome Size of Genome Build hg19 | data.frame | 93 | 2 |
chromsize.hg38 | sigminer | Chromosome Size of Genome Build hg38 | data.frame | 455 | 2 |
chromsize.mm10 | sigminer | Chromosome Size of Genome Build mm10 | data.frame | 66 | 2 |
chromsize.mm9 | sigminer | Chromosome Size of Genome Build mm9 | data.frame | 35 | 2 |
cytobands.T2T | sigminer | Location of Chromosome Cytobands at Genome Build T2T | data.frame | 862 | 5 |
cytobands.hg19 | sigminer | Location of Chromosome Cytobands at Genome Build hg19 | data.frame | 862 | 5 |
cytobands.hg38 | sigminer | Location of Chromosome Cytobands at Genome Build hg38 | data.frame | 862 | 5 |
cytobands.mm10 | sigminer | Location of Chromosome Cytobands at Genome Build mm10 | data.frame | 403 | 5 |
cytobands.mm9 | sigminer | Location of Chromosome Cytobands at Genome Build mm9 | data.frame | 403 | 5 |
simulated_catalogs | sigminer | A List of Simulated SBS-96 Catalog Matrix | list | | |
transcript.T2T | sigminer | Merged Transcript Location at Genome Build T2T | data.table | 18250 | 4 |
transcript.hg19 | sigminer | Merged Transcript Location at Genome Build hg19 | data.table | 18357 | 4 |
transcript.hg38 | sigminer | Merged Transcript Location at Genome Build hg38 | data.table | 18288 | 4 |
transcript.mm10 | sigminer | Merged Transcript Location at Genome Build mm10 | data.table | 20964 | 4 |
transcript.mm9 | sigminer | Merged Transcript Location at Genome Build mm9 | data.table | 21719 | 4 |
cultivo2008 | baystability | Data for Genotypes by Environment Interaction (GEI) | tbl_df | 900 | 4 |
cultivo2009 | baystability | Data for Genotypes by Environment Interaction (GEI) | tbl_df | 900 | 4 |
admissions | MAPA | Total Non-elective G&A Admissions (FFCEs) | ts | 62 | 1 |
anorexia.sub | granova | Family Treatment Weight change data for young female anorexia patients. | data.frame | 17 | 2 |
arousal | granova | Arousal in Rats | data.frame | 10 | 4 |
blood_lead | granova | Blood lead levels of lead workers' children matched with similar control children. | data.frame | 33 | 2 |
poison | granova | Poison data from Biological Experiment | data.frame | 48 | 6 |
rat | granova | Weight gains of rats fed different diets | data.frame | 60 | 3 |
british_isles | rgeopat2 | British Isles | sf | 1 | 1 |
dict | acronames | Dictionary of words | tbl_df | 175393 | 2 |
lifeexpect | fmcmc | Life expectancy in the US (2020) | data.frame | 1000 | 3 |
Ss96.rat | SSrat | school, year 1996 | data.frame | 193 | 32 |
Ss97.rat | SSrat | school, year 1997 | data.frame | 189 | 32 |
Ss98.rat | SSrat | school, year 1998 | data.frame | 189 | 33 |
bg | SSrat | Background data of 8 groups from a longitudinal study on Dutch primary schools | data.frame | 205 | 4 |
example1.rat | SSrat | Example 1 of rating data that can be processed further to obtain social status determinations | data.frame | 10 | 13 |
example1a.rat | SSrat | Example 1a of rating data with names of the raters (and assessed) | data.frame | 10 | 14 |
example2.rat | SSrat | Example 2 of rating data with more raters than assessed | data.frame | 7 | 13 |
example3.rat | SSrat | Example 3 of rating data with more assessors than assessed | data.frame | 10 | 10 |
example4.rat | SSrat | Example 4 of rating data with some missing data | data.frame | 10 | 13 |
example5.rat | SSrat | Example 5 of rating data Newcomb and Bukowski '3-point ratings' | data.frame | 10 | 13 |
example6.rat | SSrat | Example 6 of rating data of two groups of unequal size | data.frame | 20 | 13 |
example7.rat | SSrat | Example 7 of rating data of three groups and two different rating scales | data.frame | 30 | 13 |
klas2.rat | SSrat | Example klas2 of rating data with names of the raters (assessed) and some missing values | data.frame | 11 | 15 |
datafiles | RepoGenerator | List of a few datasets commonly used in our workshops. | data.frame | 14 | 7 |
ImpactData | endoSwitch | A dataset on adoption of conservation agriculture in Zambia | data.table | 408 | 31 |
crown_rad | likelihood | Dataset of Tree DBH and Crown Radius | data.frame | 99 | 3 |
from_sortie | likelihood | Generated Tree Allometry Dataset | data.frame | 112 | 5 |
countriesHigh | rworldxtra | a high resolution world map, a vector map of 253 country boundaries | SpatialPolygonsDataFrame | | |
example | RsqMed | Example dataset | list | | |
asset | CHNCapitalStock | Assets | data.frame | 2157 | 6 |
data.example | OSsurvival | Simulated data for the example. | list | | |
ca1d.erlotinib | fracprolif | CA1d cells in 16 micromolar erlotinib | data.frame | 163 | 8 |
ca1d.erlotinib.totals | fracprolif | CA1d cell counts in various concentrations erlotinib | data.frame | 73 | 11 |
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 | | |
ASTI | MPsychoR | Adult Self-Transcendence Inventory | data.frame | 1129 | 27 |
AvalanchePrep | MPsychoR | Preparanedness Backcountry Skiing | data.frame | 1355 | 4 |
BSSS | MPsychoR | Brief Sensation Seeking Scale Questions (BSSS-8) | data.frame | 1626 | 8 |
Bergh | MPsychoR | Generalized Prejudice Dataset | data.frame | 861 | 11 |
BrainIQ | MPsychoR | Brain Size and Intelligence | data.frame | 40 | 7 |
CEAQ | MPsychoR | Children's Empathic Attitudes Questionnaire (CEAQ) | data.frame | 208 | 19 |
FamilyIQ | MPsychoR | Family Intelligence | data.frame | 399 | 8 |
HRB | MPsychoR | Health Risk Behavior | data.frame | 538 | 20 |
HarvardPsych | MPsychoR | Research Topics Harvard Psychology Faculty | matrix | 29 | 43 |
KoreanSpeech | MPsychoR | Korean Speech Data | data.frame | 84 | 5 |
Lakes | MPsychoR | Response to challenge scale | data.frame | 15520 | 5 |
NeuralActivity | MPsychoR | Neural Activity | list | | |
NeuralHM | MPsychoR | Neural Activity | list | | |
NeuralScales | MPsychoR | Neural Activity | data.frame | 60 | 16 |
NeuralScanner | MPsychoR | Neural Activity | data.frame | 960 | 16 |
Pashkam | MPsychoR | Goal-Directed Visual Processing | list | | |
Paskvan | MPsychoR | Cognitive appraisal of work intensification | data.frame | 803 | 4 |
Privacy | MPsychoR | Internet Privacy | data.frame | 405 | 10 |
RWDQ | MPsychoR | Work design questionnaire R package authors | data.frame | 1055 | 18 |
Rmotivation | MPsychoR | Motivational structure of R package authors | data.frame | 852 | 45 |
Rmotivation2 | MPsychoR | Psychometric structure of R package authors | data.frame | 764 | 18 |
Rogers | MPsychoR | Co-Morbid Obsessive-Compulsive Disorder and Depression | data.frame | 408 | 26 |
Rogers_Adolescent | MPsychoR | Co-Morbid Obsessive-Compulsive Disorder and Depression - Adolescents | data.frame | 87 | 26 |
SDOwave | MPsychoR | Longitudinal Social Dominance Orientation (SDO) | data.frame | 612 | 20 |
Wenchuan | MPsychoR | Wenchuan PTSD Dataset | data.frame | 362 | 17 |
WilPat | MPsychoR | Wilson-Patterson Conservatism Scale | data.frame | 804 | 51 |
Wilmer | MPsychoR | Verbal Paired-Associates Memory Test (VPMT) | data.frame | 1471 | 27 |
YouthDep | MPsychoR | Youth Depression Indicators | data.frame | 2290 | 27 |
ageiat | MPsychoR | Time Series Implicit Association Test (Age) | numeric | | |
bandpref | MPsychoR | Band Preferences | data.frame | 10 | 4 |
chile | MPsychoR | Chile dataset. | data.frame | 85 | 3 |
condom | MPsychoR | Attitude towards condoms | data.frame | 500 | 7 |
granularity | MPsychoR | Granularity | data.frame | 143 | 3 |
iatfaces | MPsychoR | Implicit Association Test (Faces) | data.frame | 320 | 4 |
learnemo | MPsychoR | Learning related emotions in mathematics | data.frame | 111 | 11 |
storcap | MPsychoR | EEG Visual Working Memory Storage Capacity | data.frame | 468000 | 5 |
tension | MPsychoR | Perceived Tension in Music Over Time | data.frame | 29 | 801 |
yaass | MPsychoR | YAASS dataset | data.frame | 30 | 6 |
zareki | MPsychoR | Neuropsychological Test Battery for Number Processing and Calculation in Children | data.frame | 341 | 18 |
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 | | |
landmark.squares | cholera | Centers of city squares. | data.frame | 2 | 4 |
landmark.squaresB | cholera | Centers of city squares. | data.frame | 2 | 6 |
landmarks | cholera | Orthogonal projection of landmarks onto road network. | data.frame | 19 | 12 |
landmarksB | 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 |
oxford.weather | cholera | Oxford monthly weather data, January 1853 - February 2024. | data.frame | 2054 | 9 |
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 | 7 |
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 | | |
dataCompartment4 | rPBK | An example data set with 4 compartment | tbl_df | 21 | 7 |
dataMaleGammarusSingle | rPBK | An example data set with 1 compartment | tbl_df | 22 | 4 |
fitPBK_C4 | rPBK | An example of fitPBK object | fitPBK | | |
components | streetmixr | Streetmix components | list | | |
illustrations | streetmixr | Streetmix CC-BY-SA illustrations | data.frame | 273 | 8 |
people | streetmixr | Streetmix CC-BY-SA illustrations of people | data.frame | 36 | 5 |
example_data1 | BayesianLaterality | Example dataset with a single measurement of three individuals. | tbl_df | 3 | 2 |
example_data2 | BayesianLaterality | Example dataset with three measurements each on 100 individuals. | tbl_df | 300 | 4 |
inpat_bg | ROlogit | Inpatient blood glucose data for 2487 patients | data.frame | 2487 | 10 |
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 |
barto | genoPlotR | Comparison of 4 Bartonella genomes | list | | |
bbone | genoPlotR | Mauve backbone of 4 Bartonella genomes | list | | |
chrY_subseg | genoPlotR | Comparisons of subsegments of the Y chromosome in human and chimp | list | | |
comparisons | genoPlotR | Three genes data set | list | | |
dna_segs | genoPlotR | Three genes data set | list | | |
AirPassengersX12 | x12 | x12Single object | x12Single | | |
AirPassengersX12Batch | x12 | x12Batch object | x12Batch | | |
regex_cheat | qdapRegex | A dataset containing the regex chunk name, the regex string, and a description of what the chunk does. | data.frame | 25 | 3 |
regex_supplement | qdapRegex | Supplemental Canned Regular Expressions | list | | |
regex_usa | qdapRegex | Canned Regular Expressions (United States of America) | list | | |
aps | aplore3 | APS data | data.frame | 508 | 14 |
burn1000 | aplore3 | BURN1000 data | data.frame | 1000 | 9 |
burn13m | aplore3 | BURN13M data | data.frame | 388 | 11 |
burn_eval_1 | aplore3 | BURN_EVAL_1 data | data.frame | 500 | 9 |
burn_eval_2 | aplore3 | BURN_EVAL_2 data | data.frame | 500 | 9 |
chdage | aplore3 | CHDAGE data | data.frame | 100 | 4 |
glow11m | aplore3 | GLOW11M data | data.frame | 238 | 16 |
glow500 | aplore3 | GLOW500 data | data.frame | 500 | 15 |
glow_bonemed | aplore3 | GLOW_BONEMED data | data.frame | 500 | 18 |
glow_mis_comp | aplore3 | GLOW_MIS_COMP data | data.frame | 500 | 10 |
glow_mis_wmissing | aplore3 | GLOW_MIS_WMISSING data | data.frame | 500 | 10 |
glow_rand | aplore3 | GLOW_RAND data | data.frame | 500 | 15 |
icu | aplore3 | ICU data | data.frame | 200 | 21 |
lowbwt | aplore3 | LOWBWT data | data.frame | 189 | 11 |
myopia | aplore3 | MYOPIA data | data.frame | 618 | 18 |
nhanes | aplore3 | NHANES data | data.frame | 6482 | 21 |
polypharm | aplore3 | POLYPHARM data | data.frame | 3500 | 14 |
scale_example | aplore3 | SCALE_EXAMPLE data | data.frame | 500 | 2 |
Pathways.DAVID | ClueR | DAVID pathway annotations | list | | |
Pathways.KEGG | ClueR | KEGG pathway annotations | list | | |
Pathways.biocarta | ClueR | Biocarta pathway annotations | list | | |
Pathways.reactome | ClueR | Reactome pathway annotations | list | | |
PhosphoELM.human | ClueR | Phospho.ELM annotations for human | list | | |
PhosphoELM.mouse | ClueR | Phospho.ELM annotations for mouse | list | | |
PhosphoSite.human | ClueR | PhosphoSitePlus annotations for human | list | | |
PhosphoSite.mouse | ClueR | PhosphoSitePlus annotations for mouse | list | | |
adipocyte | ClueR | Mouse adipocyte differentiation gene expression (microarray) data | matrix | 24213 | 8 |
hES | ClueR | Human embryonic stem cell phosphoproteomics data | matrix | 3416 | 5 |
movies | ggplot2movies | Movie information and user ratings from IMDB.com. | tbl_df | 58788 | 24 |
annot.file | dinamic | Cytoband annotation data frame | data.frame | 811 | 4 |
wilms.data | dinamic | DNA copy number data from Wilms' tumor | matrix | 97 | 3288 |
wilms.markers | dinamic | Array comparative genomic hybridization marker data | data.frame | 3288 | 3 |
alpe_d_huez | learningr | Alpe d'Huez | data.frame | 36 | 7 |
alpe_d_huez2 | learningr | Alpe d'Huez | data.frame | 36 | 7 |
crab_tag | learningr | Crab tag | list | | |
deer_endocranial_volume | learningr | Deer Endocranial Volume | data.frame | 33 | 8 |
english_monarchs | learningr | English Monarchs | data.frame | 259 | 5 |
gonorrhoea | learningr | Gonorrhoea | data.frame | 600 | 5 |
hafu | learningr | Hafu | data.frame | 296 | 9 |
hafu2 | learningr | Hafu | data.frame | 296 | 11 |
obama_vs_mccain | learningr | Obama vs. McCain | data.frame | 51 | 15 |
Sample_disease_gene_set | RVA | This is data to be included in package | data.frame | 1800 | 6 |
Sample_summary_statistics_table | RVA | This is data to be included in package | data.frame | 12045 | 6 |
Sample_summary_statistics_table1 | RVA | This is data to be included in package | data.frame | 12045 | 6 |
c2BroadSets | RVA | This is data to be included in package | GeneSetCollection | | |
count_table | RVA | This is data to be included in package | data.frame | 3934 | 164 |
sample_annotation | RVA | This is data to be included in package | data.frame | 164 | 6 |
sample_count_cpm | RVA | This is data to be included in package | data.frame | 4 | 164 |
wpA2020 | RVA | This is data to be included in package | data.frame | 26693 | 5 |
ic25kHz_12_sgmnt1 | ionChannelData | Ion channel data | data.frame | 200001 | 1 |
ic25kHz_13_sgmnt2 | ionChannelData | Ion channel data | data.frame | 200000 | 1 |
ic25kHz_14_sgmnt2 | ionChannelData | Ion channel data | data.frame | 200000 | 1 |
ic25kHz_15_sgmnt2 | ionChannelData | Ion channel data | data.frame | 200000 | 1 |
ic50kHz_06_sgmnt2 | ionChannelData | Ion channel data | data.frame | 199926 | 1 |
ic50kHz_08_sgmnt2 | ionChannelData | Ion channel data | data.frame | 200000 | 1 |
ic50kHz_09_sgmnt1 | ionChannelData | Ion channel data | data.frame | 200000 | 1 |
ic50kHz_10_sgmnt1 | ionChannelData | Ion channel data | data.frame | 200000 | 1 |
beans | beans | Dry beans | tbl_df | 13611 | 17 |
myGene | bossR | clinical dataset | data.frame | 500 | 3 |
countMatrix | variancePartition | A simulated dataset of gene counts | matrix | 19364 | 24 |
geneCounts | variancePartition | Simulation dataset for examples | matrix | 200 | 100 |
geneExpr | variancePartition | Simulation dataset for examples | matrix | 200 | 100 |
info | variancePartition | Simulation dataset for examples | data.frame | 100 | 6 |
metadata | variancePartition | A simulated dataset of gene counts | data.frame | 24 | 5 |
SMI | MSGARCH | Swiss market index dataset | zoo | | |
dem2gbp | MSGARCH | DEM/GBP exchange rate log-returns | numeric | | |
ex_sales | hpiR | Subset of Seattle Home Sales | data.frame | 5348 | 16 |
seattle_sales | hpiR | Seattle Home Sales | data.frame | 43313 | 16 |
oregon.bird.dist | maptree | Presence/Absence of Bird Species in Oregon, USA | data.frame | 389 | 248 |
oregon.bird.names | maptree | Names of Bird Species in Oregon, USA | data.frame | 248 | 2 |
oregon.border | maptree | Boundary of Oregon, USA | data.frame | 485 | 2 |
oregon.env.vars | maptree | Environmental Variables for Oregon, USA | data.frame | 389 | 10 |
oregon.grid | maptree | Hexagonal Grid Cell Polygons covering Oregon, USA | data.frame | 3112 | 2 |
Sim_data | probe | Simulated high-dimensional data set for sparse linear regression | list | | |
Sim_data_cov | probe | Simulated high-dimensional data set for sparse linear regression with non-sparse covariates. | list | | |
Sim_data_test | probe | Simulated high-dimensional test data set for sparse linear regression | list | | |
australia | simts | Quarterly Increase in Stocks Non-Farm Total, Australia | tbl_df | 127 | 2 |
hydro | simts | Mean Monthly Precipitation, from 1907 to 1972 | ts | | |
savingrt | simts | Personal Saving Rate | gts | 691 | 1 |
gadarian | stm | Gadarian and Albertson data | data.frame | 341 | 4 |
gadarianFit | stm | Gadarian and Albertson data | STM | | |
poliblog5k.docs | stm | CMU 2008 Political Blog Corpus | list | | |
poliblog5k.meta | stm | CMU 2008 Political Blog Corpus | data.frame | 5000 | 4 |
poliblog5k.voc | stm | CMU 2008 Political Blog Corpus | character | | |
titanic | vip | Survival of Titanic passengers | data.frame | 1309 | 6 |
titanic_mice | vip | Survival of Titanic passengers | mild | | |
ais | lqr | Australian institute of sport data | data.frame | 202 | 14 |
resistance | lqr | Tumor-cell resistance to death | data.frame | 425 | 3 |
Missouri | CensSpatial | TCDD concentrations in Missouri (1971). | data.frame | 127 | 5 |
depth | CensSpatial | Depths of a geological horizon. | data.frame | 100 | 6 |
MDDConnectivity | Isinglandr | Estimation data for the Ising network of major depressive disorder | matrix | 9 | 9 |
MDDThresholds | Isinglandr | Estimation data for the Ising network of major depressive disorder | numeric | | |
crimes | rcrimeanalysis | Example data from the Chicago Data Portal | spec_tbl_df | 25000 | 22 |
palate | rticulate | Palate profile dataset. | tbl_df | 42 | 14 |
stimuli | rticulate | Stimuli dataset. | tbl_df | 12 | 11 |
tongue | rticulate | Tongue contours dataset. | tbl_df | 3612 | 28 |
Cholesterol | qrLMM | Framingham cholesterol study | data.frame | 1044 | 6 |
Orthodont | qrLMM | Growth curve data on an orthdontic measurement | data.frame | 108 | 4 |
novelforest_data | novelforestSG | Novel Forest Raw Dataset | list | | |
X.varx | bigtime | VARX Time Series Example ('varx.example') | matrix | 200 | 3 |
Y.var | bigtime | VAR Time Series Example ('var.example') | matrix | 200 | 5 |
Y.varma | bigtime | VARMA Time Series Example ('varma.example') | matrix | 200 | 3 |
Y.varx | bigtime | VARX Time Series Example ('varx.example') | matrix | 200 | 3 |
basins_tehri | damAOI | Polygon for river basins around tehri dam | sf | 317 | 2 |
system | damAOI | Polygons for the 'Areas of Interest' around two dams which form a system together. | sf | 8 | 4 |
tehri | damAOI | Polygon for Tehri dam in India | sf | 1 | 2 |
ibmspko | MTS | Monthly simple returns of the stocks of International Business Machines (IBM) and Coca Cola (KO) and the S&P Composite index (SP) | data.frame | 612 | 4 |
qgdp | MTS | Quarterly real gross domestic products of United Kingdom, Canada, and the United States | data.frame | 126 | 5 |
tenstocks | MTS | Monthly simple returns of ten U.S. stocks | data.frame | 132 | 11 |
euro_dates | dispeRse | Coordinates and earliest dates (med cal BP) for European Neolithic sites. Dataset adapted from the supplementary information in Pinhasi et al. 2005 (https://doi.org/10.1371/journal.pbio.0030410). | SpatialPointsDataFrame | | |
euro_npp | dispeRse | Transformed Net Primary Production (NPP) from 11 ka to 4 ka at 1000 yr steps. Calculated with the Miami formula using paleoclimatic data from Beyer et al. 2020. (https://doi.org/10.1038/s41597-020-0552-1). Clipped to max=1350, squared and scaled to [0,1]. | RasterStack | | |
euro_terr | dispeRse | Reclassified terrain layer with elevation > 1750 m as barriers and rivers and coastline as corridors. Terrain reclassified from SRTM. Rivers rasterized from GSHHG (https://www.soest.hawaii.edu/pwessel/gshhg/) and coastline rasterized from rnaturalearth. | RasterLayer | | |
ppnb | dispeRse | Coordinates and earliest dates (med cal BP) for Late Pre-Pottery Neolithic B sites in the Near East. Dataset adapted from the supplementary information in Pinhasi et al. 2005 (https://doi.org/10.1371/journal.pbio.0030410). | data.frame | 9 | 3 |
nh0506 | natstrat | Homocysteine and smoking example data | data.frame | 2928 | 11 |
nh0506_3groups | natstrat | Homocysteine and smoking example data with multiple control groups | data.frame | 4457 | 11 |
pbmc1 | scAnnotate | pbmc1 | data.frame | 598 | 2001 |
pbmc2 | scAnnotate | pbmc2 | data.frame | 644 | 2001 |
predict_label | scAnnotate | predict_label | character | | |
landed | funneljoin | Example dataset of landing events | tbl_df | 11 | 2 |
registered | funneljoin | Example dataset of registration events | tbl_df | 10 | 2 |
books | litRiddle | Measurements of 401 novels | data.frame | 401 | 25 |
frequencies | litRiddle | Word frequencies (5000 most frequent words) of 401 novels. | matrix | 401 | 5000 |
motivations | litRiddle | Reviewers' motivations for their scores (if given) | data.frame | 211031 | 8 |
respondents | litRiddle | Respondents' Answers | data.frame | 13541 | 29 |
reviews | litRiddle | Reviewers' scores | data.frame | 448055 | 7 |
data | DPTM | A simulation data used for examples | list | | |
starvz_sample_lu | starvz | Small StarVZ data of LU Factorization | starvz_data | | |
nonlineardata | controlfunctionIV | nonlineardata | data.frame | 3733 | 9 |
data.check | SoyNAM | Datasets | data.frame | 9043 | 18 |
data.check.in | SoyNAM | Datasets | data.frame | 1650 | 21 |
data.check.qa | SoyNAM | Datasets | data.frame | 10721 | 11 |
data.line | SoyNAM | Datasets | data.frame | 60744 | 18 |
data.line.in | SoyNAM | Datasets | data.frame | 12790 | 21 |
data.line.qa | SoyNAM | Datasets | data.frame | 62572 | 11 |
gen.in | SoyNAM | Datasets | matrix | 5555 | 4240 |
gen.qa | SoyNAM | Datasets | matrix | 5180 | 4312 |
gen.raw | SoyNAM | Datasets | matrix | 5590 | 4611 |
allowed_inputs | dynwrap | All allowed inputs for a TI method | tbl_df | 16 | 2 |
allowed_outputs | dynwrap | All allowed outputs for a TI method | tbl_df | 14 | 5 |
example_dataset | dynwrap | Example dataset | dynwrap::with_dimred | | |
example_trajectory | dynwrap | Example trajectory | dynwrap::with_dimred | | |
prior_usages | dynwrap | Metadata on prior usages | tbl_df | 3 | 2 |
priors | dynwrap | Metadata on priors | tbl_df | 13 | 6 |
trajectory_type_dag | dynwrap | A DAG connecting different trajectory types | tbl_graph | | |
trajectory_types | dynwrap | Metadata on the trajectory types | tbl_df | 9 | 6 |
wrapper_types | dynwrap | Metadata on wrapper types | tbl_df | 7 | 4 |
canva_palettes | ggthemes | 150 Color Palettes from Canva | list | | |
ggthemes_data | ggthemes | Palette and theme data | list | | |
Dforimpute | DDPNA | a data used for Dataimpute function. | list | | |
ProteomicData | DDPNA | Small example proteomic quantification data extract from txt or csv file used to demonstrate data extract function. | list | | |
Sample_ID_data | DDPNA | A small uniprot ID information used to do ID convert in example. | list | | |
imputedData | DDPNA | an imputed data used for downstream analysis | proteomic_data | | |
net | DDPNA | A network which is obtained by WGCNA-package blockwiseModule function. it can short the time of Module analysis example time. | list | | |
cmrdata_sim | cmR | Simulated data for CMR package. | array | | |
input_sim | cmR | Simulated data for CMR package. | numeric | | |
maxresp_sim | cmR | Simulated data for CMR package. | array | | |
lizards | brglm | Habitat Preferences of Lizards | data.frame | 23 | 6 |
LongData | HDJM | Simulated Longtidunal Data | data.frame | 48700 | 4 |
SurvData | HDJM | Simulated Survival Data | data.frame | 100 | 4 |
extdata_fnames | arctools | Names of exemplary accelerometry data file. | character | | |
belgium_parliament | tokenizers.bpe | Dataset from 2017 with Questions asked in the Belgium Federal Parliament | data.frame | 2000 | 3 |
cyrpa_ripr | loewesadditivity | CyRPA and RIPR | data.frame | 38 | 15 |
rh5_ama1ron2 | loewesadditivity | RH5 and AMA1RON2 | data.frame | 38 | 15 |
rh5_rh4 | loewesadditivity | RH5 and RH4 | data.frame | 48 | 3 |
sim.data | promotionImpact | Daily Total Sales | data.frame | 958 | 2 |
sim.promotion | promotionImpact | Promotion Schedule | data.frame | 50 | 4 |
sim.promotion.sales | promotionImpact | Daily Promotion Sales with Promotion information | data.frame | 1486 | 6 |
hepatitisA | curstatCI | Hepatitis A data | data.frame | 83 | 3 |
rubella | curstatCI | Rubella data | data.frame | 225 | 3 |
nca.example | NCA | NCA example data with 2 independent and 1 dependent variables | data.frame | 28 | 3 |
nca.example2 | NCA | NCA example data with 3 independent and 1 dependent variables | data.frame | 48 | 4 |
natlongsurv | kutils | Smoking, Happiness, and other survey responses | data.frame | 2867 | 29 |
dd | MiSPU | The estimate of Dirichlet-multinomial distribution | list | | |
throat.meta | MiSPU | Meta data of the throat microbiome samples. | data.frame | 60 | 16 |
throat.otu.tab | MiSPU | OTU count table from 16S sequencing of the throat microbiome samples. | data.frame | 60 | 856 |
throat.tree | MiSPU | UPGMA tree of the OTUs from 16S sequencing of the throat microbiome samples. | phylo | | |
ChemicalManufacturingProcess | AppliedPredictiveModeling | Chemical Manufacturing Process Data | data.frame | 176 | 58 |
abalone | AppliedPredictiveModeling | Abalone Data | data.frame | 4177 | 9 |
bio | AppliedPredictiveModeling | Hepatic Injury Data | data.frame | 281 | 184 |
cars2010 | AppliedPredictiveModeling | Fuel Economy Data | data.frame | 1107 | 14 |
cars2011 | AppliedPredictiveModeling | Fuel Economy Data | data.frame | 245 | 14 |
cars2012 | AppliedPredictiveModeling | Fuel Economy Data | data.frame | 95 | 14 |
chem | AppliedPredictiveModeling | Hepatic Injury Data | data.frame | 281 | 192 |
classes | AppliedPredictiveModeling | Two Class Example Data | factor | | |
concrete | AppliedPredictiveModeling | Compressive Strength of Concrete from Yeh (1998) | data.frame | 1030 | 9 |
diagnosis | AppliedPredictiveModeling | Alzheimer's Disease CSF Data | factor | | |
fingerprints | AppliedPredictiveModeling | Permeability Data | matrix | 165 | 1107 |
injury | AppliedPredictiveModeling | Hepatic Injury Data | factor | | |
logisticCreditPredictions | AppliedPredictiveModeling | Logistic Regression Predictions for the Credit Data | data.frame | 200 | 4 |
mixtures | AppliedPredictiveModeling | Compressive Strength of Concrete from Yeh (1998) | data.frame | 1030 | 9 |
permeability | AppliedPredictiveModeling | Permeability Data | matrix | 165 | 1 |
predictors | AppliedPredictiveModeling | Alzheimer's Disease CSF Data | data.frame | 333 | 130 |
predictors | AppliedPredictiveModeling | Alzheimer's Disease CSF Data | data.frame | 208 | 2 |
schedulingData | AppliedPredictiveModeling | HPC Job Scheduling Data | data.frame | 4331 | 8 |
segmentationOriginal | AppliedPredictiveModeling | Cell Body Segmentation | data.frame | 2019 | 119 |
solTestX | AppliedPredictiveModeling | Solubility Data | data.frame | 316 | 228 |
solTestXtrans | AppliedPredictiveModeling | Solubility Data | data.frame | 316 | 228 |
solTestY | AppliedPredictiveModeling | Solubility Data | numeric | | |
solTrainX | AppliedPredictiveModeling | Solubility Data | data.frame | 951 | 228 |
solTrainXtrans | AppliedPredictiveModeling | Solubility Data | data.frame | 951 | 228 |
solTrainY | AppliedPredictiveModeling | Solubility Data | numeric | | |
Firm | PLreg | Firm Cost | data.frame | 73 | 7 |
PeruVotes | PLreg | Peru Blank Votes | data.frame | 194 | 2 |
bodyfat_Aeolus | PLreg | Body Fat of Little Brown Bat | data.frame | 159 | 4 |
ex_binary | triptych | Example data set of binary observations and probability forecasts | tbl_df | 1000 | 11 |
acupuncture | RefBasedMI | Sample data: acupuncture trial | data.frame | 802 | 11 |
antidepressant | RefBasedMI | Sample data: antidepressant trial | data.frame | 688 | 14 |
asthma | RefBasedMI | Sample data: asthma trial | data.frame | 732 | 5 |
stroke | multiCA | Stroke types over time | data.frame | 45 | 3 |
Simdata | TGST | Simulated data for package illustration | data.frame | 8000 | 2 |
Europe | BioStatR | Durées de travail en Europe | data.frame | 25 | 2 |
Extrait_Taille | BioStatR | Mesures de fruits d'arbustes | data.frame | 80 | 3 |
Mesures | BioStatR | Mesures de fruits d'arbustes | data.frame | 252 | 3 |
Mesures5 | BioStatR | Mesures de fruits d'arbustes | data.frame | 252 | 5 |
Quetelet | BioStatR | Indices de Quetelet | data.frame | 66 | 3 |
dataAr1 | saeHB.panel | Sample Data for Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model | data.frame | 100 | 6 |
dataAr1Ns | saeHB.panel | Sample Data for Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model with Non Sampled Area | data.frame | 100 | 6 |
dataPanel | saeHB.panel | Sample Data for Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model when 'rho = 0' | data.frame | 100 | 6 |
dataPanelNs | saeHB.panel | Sample Data for Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model when 'rho = 0' with Non Sampled Area | data.frame | 100 | 6 |
depress | lboxcox | Depression dataset | data.frame | 8893 | 5 |
sentence_example | RcppJagger | An example sentence | data.frame | 1 | 1 |
finalized_stroopdata | prepdat | Finalized Table 'prepdat::prep()' returns for 'stroopdata' According to the Example in 'prepdat::prep()'. | data.frame | 15 | 98 |
stroopdata | prepdat | Reaction-times and accuracy for color naming in a Stroop task (e.g., Stroop, 1935). | data.frame | 5400 | 10 |
Dfexample | regspec | Synthetic Data for Testing Functions in the regspec Package. | ts | | |
Dpexample2 | regspec | Synthetic Data for Testing Functions in the regspec Package. | numeric | | |
Dpexample3 | regspec | Synthetic Data for Testing Functions in the regspec Package. | numeric | | |
retail | regspec | Retail Sales Index (RSI) data | data.frame | 315 | 2 |
spec.true | regspec | Synthetic Data for Testing Functions in the regspec Package. | matrix | 200 | 2 |
trav.mly | regspec | Visits abroad by UK residents | matrix | 36 | 3 |
trav.qly | regspec | Visits abroad by UK residents | matrix | 28 | 3 |
my_appsflyer_data | appsflyeR | Sample of digital marketing data from AppsFlyer downloaded by means of the Windsor.ai API. | data.frame | 14 | 5 |
amf_base | amerifluxr | BASE data example | data.frame | 336 | 36 |
amf_bif | amerifluxr | BADM data example | tbl_df | 443 | 5 |
simuN5 | ABSSeq | Simulated study with random outliers | list | | |
AetLTR | TE | LTR retrotransposons in _Aegilops tauschii_ | tbl_df | 18024 | 14 |
AlyLTR | TE | LTR retrotransposons in _Arabidopsis lyrata_ | tbl_df | 397 | 7 |
cmip6 | quadmesh | CMIP6 sample | RasterBrick | | |
etopo | quadmesh | World topography map | RasterLayer | | |
worldll | quadmesh | World raster map | RasterLayer | | |
xymap | quadmesh | World map | matrix | 82403 | 2 |
faketrial | wrappedtools | Results from a simulated clinical trial with interaction effects. | tbl_df | 300 | 24 |
growth | pampe | Example Data for pampe function from the pampe package | data.frame | 61 | 25 |
simulation1 | hosm | Simulation 1 for High Order Spatial Matrix | data.frame | 4 | 5 |
simulation2 | hosm | Simulation 2 for High Order Spatial Matrix | data.frame | 5 | 6 |
simulation3 | hosm | Simulation 3 for High Order Spatial Matrix | data.frame | 5 | 6 |
simulation4 | hosm | Simulation 4 for High Order Spatial Matrix | data.frame | 4 | 5 |
simulation5 | hosm | Simulation 5 for High Order Spatial Matrix | data.frame | 4 | 5 |
dataset.all.species | sperich | Angiosperm Dataset | data.frame | 45677 | 3 |
dataset.height | sperich | Height-Information Dataset | data.frame | 7626 | 3 |
dataset.landwater | sperich | Land-Water-Information Dataset | data.frame | 8455 | 3 |
flood | jointPm | Example data of flood levels and dependence strength between extreme rainfall and extreme storm tides from a coastal catchment | list | | |
data | ICSS | Sample data for Inclan/Tiao (1994) | numeric | | |
binarizationExample | BiTrinA | An artificial data set consisting of ten artificial feature vectors. | matrix | 10 | |
trinarizationExample | BiTrinA | An artificial data set consisting of ten artificial feature vectors. | matrix | 100 | |
example1 | Rwclust | Example Graph 1 | data.frame | 35 | 3 |
example2 | Rwclust | Example Graph 2 | data.frame | 15 | 3 |
anoctua | blmeco | Presence-absence data of Little owls in nest boxes | data.frame | 361 | 3 |
blackstork | blmeco | Breeding success of Black storks in Latvia | data.frame | 1130 | 3 |
cortbowl | blmeco | stress hormone data of nestling barn owls which were either treated with a corticosterone-implant or with a placebo-implant as control | data.frame | 287 | 6 |
ellenberg | blmeco | Hohenheim groundwater table experiment of Heinz Ellenberg | data.frame | 264 | 29 |
frogs | blmeco | Counts of the number of frogs in a water body | data.frame | 481 | 10 |
mdat | blmeco | Simulated set of correlated variables | data.frame | 100 | 6 |
nightingales | blmeco | Nightingale territory occupancy data | array | | |
parusmajor | blmeco | Number of migrating Great tits | data.frame | 434 | 3 |
periparusater | blmeco | The data contain morphological measurements taken from museum skins of Coal tits (Periparus ater) | data.frame | 19 | 6 |
pondfrog | blmeco | Fake Data of the Numbers of Frogs in Ponds | data.frame | 130 | 9 |
pondfrog1 | blmeco | Fake Data: Number of Frogs in Ponds | data.frame | 130 | 4 |
redstart | blmeco | Common Redstart (Phoenicurus phoenicurus) counts | data.frame | 342 | 5 |
resprouts | blmeco | Survival data of tree sprouts | data.frame | 41 | 4 |
roostingsiteuse | blmeco | Roosting site use by little owls | data.frame | 42 | 5 |
spermdepletion | blmeco | Sperm depletion data in a hermaphrodite sea slug | data.frame | 264 | 6 |
survival_swallows | blmeco | Telemetry data of Barn swallow fledglings | list | | |
swallowfarms | blmeco | Number of fledged Barn Swallows per nest | data.frame | 63 | 6 |
swallows | blmeco | Data set with number of nesting swallows per barn | data.frame | 27 | 6 |
wildflowerfields | blmeco | Territory numbers of Whitethroat in wildflowerfields | data.frame | 136 | 8 |
wingbowl | blmeco | Growth rate data of Barn owl nestlings and corticosterone | data.frame | 209 | 7 |
yellow_bellied_toad | blmeco | Site-occupancy data for Yellow-bellied toads | list | | |
wdbc | kdevine | Wisconsin Diagnostic Breast Cancer (WDBC) | data.frame | 569 | 31 |
SBM_net | CommKern | Simulated functional and structural connectivity with nested hierarchical community structure | spinglass_net | | |
simasd_array | CommKern | Simulated Array | array | | |
simasd_comm_df | CommKern | Simulated partitions of nodes to communities from HMS algorithm | data.frame | 80 | 49 |
simasd_covars | CommKern | Simulated demographics dataset modeled of a subset of the preprocessed ABIDE database | data.frame | 49 | 8 |
simasd_hamil_df | CommKern | Simulated Hamiltonian values from HMS algorithm | data.frame | 49 | 2 |
azteca | Rsampling | An experiment on ant recruitment | data.frame | 21 | 3 |
embauba | Rsampling | Vine infestation on Cecropia trees | data.frame | 152 | 2 |
peucetia | Rsampling | Preference of hunting spiders by hairy leaves | data.frame | 27 | 6 |
pielou | Rsampling | Aphids recorded on goldenrods | data.frame | 10 | 12 |
rhyzophora | Rsampling | Allometry in mangrove trees | data.frame | 24 | 4 |
weight_behavior | hdbma | Weight_Behavior Data Set | data.frame | 691 | 15 |
karate | hydra | Zachary's karate club network data | list | | |
sqnData0 | SQN | example data | matrix | 6000 | 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 |
ColesData | FAmle | Annual Maximum Sea Levels at Port Pirie, South Australia | data.frame | 65 | 2 |
floodsNB | FAmle | New Brunswick (Canada) Flood Dataset | list | | |
station01AJ010 | FAmle | Annual Maximum Daily Mean Flow Data (NB, Canada) | numeric | | |
yarns | FAmle | Yarns Failure Data | data.frame | 100 | 1 |
s_curve_noise | quollr | S-curve dataset with noise dimensions | tbl_df | 100 | 8 |
s_curve_noise_test | quollr | S-curve dataset with noise dimensions for test | tbl_df | 25 | 8 |
s_curve_noise_training | quollr | S-curve dataset with noise dimensions for training | tbl_df | 75 | 8 |
s_curve_noise_umap | quollr | UMAP embedding for S-curve dataset which with noise dimensions | tbl_df | 75 | 3 |
s_curve_noise_umap_predict | quollr | Predicted UMAP embedding for S-curve dataset which with noise dimensions | tbl_df | 25 | 3 |
s_curve_noise_umap_scaled | quollr | Scaled UMAP embedding for S-curve dataset which with noise dimensions | tbl_df | 75 | 3 |
liver | goweragreement | Ordinal data from a radiological study of congenital diaphragmatic hernia. | data.frame | 47 | 4 |
sampleMiceDefs | miceRanger | Sample miceDefs object built off of iris dataset. Included so examples don't run for too long. | miceDefs | | |
example_cli | imsig | Example clinical data file for survival analysis with ImSig | data.frame | 59 | 2 |
example_data | imsig | Example transcriptomics data | data.frame | 568 | 60 |
sig | imsig | ImSig genes | data.frame | 569 | 2 |
BigCity | BayesSampling | Full Person-level Population Database | data.frame | 150266 | 12 |
SMSA | OkNNE | Standard Metropolitan Statistical Areas | data.frame | 59 | 15 |
Maize_wqs | Ghat | The Wisconsin Quality Synthetic (WQS) maize population datasets. | list | | |
Teeth500 | pseudoCure | Dental data for illustration | data.frame | 500 | 20 |
diabetes | nestfs | Diabetes data with interaction terms | data.frame | 442 | 65 |
prostate | depthTools | Gene Expression Data from Tumoral and Normal Prostate Samples and Labels | matrix | 50 | 101 |
Freq | fst4pg | Frequencies of the American and European HGDP populations | matrix | 49636 | 13 |
Info | fst4pg | Marker information | tbl_df | 49636 | 5 |
NbGamete | fst4pg | Number of gametes of the American and European HGDP populations | matrix | 49636 | 13 |
crps | extremeIndex | Observations of 6-h rainfall amount with CRPS values of 3 calibrated ensemble forecasts for one lead time across France. | matrix | 112221 | 4 |
toydata | CHMM | Toy example - observations for 5 correlated samples. | matrix | 1000 | 5 |
toystatus | CHMM | Toy example - status for 5 correlated samples. | matrix | 1000 | 5 |
counts | DoubleExpSeq | Exon Inclusion Counts | matrix | 4000 | 15 |
groups | DoubleExpSeq | Group Structure of the Toy Data Set | character | | |
offsets | DoubleExpSeq | Exon Total Counts | matrix | 4000 | 15 |
Data_saekernel | saekernel | Sample Data for Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel | data.frame | 100 | 3 |
Election1982 | SLPresElection | Presidential Election 1982 | spec_tbl_df | 2018 | 6 |
Election1988 | SLPresElection | Presidential Election 1988 | spec_tbl_df | 1406 | 6 |
Election1994 | SLPresElection | Presidential Election 1994 | spec_tbl_df | 2040 | 6 |
Election1999 | SLPresElection | Presidential Election 1999 | spec_tbl_df | 3446 | 6 |
Election2005 | SLPresElection | Presidential Election 2005 | spec_tbl_df | 3494 | 6 |
Election2010 | SLPresElection | Presidential Election 2010 | spec_tbl_df | 5434 | 6 |
Election2015 | SLPresElection | Presidential Election 2015 | spec_tbl_df | 4692 | 6 |
trekfonts | trekfont | Available Star Trek fonts. | character | | |
npis | npi | Sample results from the NPI Registry | npi_results | 10 | 11 |
abstract.table | labeleR | abstract.table | data.frame | 8 | 4 |
attendance.table | labeleR | attendance.table | data.frame | 4 | 1 |
badges.table | labeleR | badges.table | data.frame | 8 | 2 |
collection.table | labeleR | collection.table | data.frame | 40 | 8 |
herbarium.table | labeleR | herbarium.table | data.frame | 6 | 19 |
multichoice.table | labeleR | multichoice.table | data.frame | 7 | 5 |
participation.table | labeleR | participation.table | data.frame | 4 | 5 |
tiny.table | labeleR | tiny.table | data.frame | 40 | 8 |
pbc_subset | MJMbamlss | PBC Subset | data.frame | 336 | 10 |
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 20240201 | tbl_df | 27961 | 26 |
fibdata | tbeptools | All Fecal Indicator Bacteria (FIB) data as of 20240814 | tbl_df | 77526 | 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 |
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 20241011 | tbl_df | 12765 | 13 |
phytodata | tbeptools | Phytoplankton data current as of 10312024 | tbl_df | 40521 | 8 |
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 |
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 |
basicRelationships | ribd | Basic relationships | data.frame | 12 | 7 |
jicaque | ribd | Jicaque pedigree | data.frame | 22 | 4 |
horses | tidysdm | Coordinates of radiocarbon dates for horses | tbl_df | 788 | 3 |
lacerta | tidysdm | Coordinates of presences for Iberian emerald lizard | tbl_df | 1268 | 3 |
lacerta_ensemble | tidysdm | A simple ensemble for the lacerta data | simple_ensemble | 4 | 3 |
lacerta_rep_ens | tidysdm | A repeat ensemble for the lacerta data | repeat_ensemble | 6 | 4 |
lacertidae_background | tidysdm | Coordinates of presences for lacertidae in the Iberian peninsula | sf | 34677 | 2 |
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 |
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 |
BSG2014 | robmed | Business simulation game data | data.frame | 89 | 13 |
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 |
cancerVarNames | FRESA.CAD | Data frame used in several examples of this package | data.frame | 7 | 2 |
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 |
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 | data.frame | 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 | matrix | 7 | 5 |
powerboat | hyper2 | Powerboat dataset | hyper2 | | |
powerboat_maxp | hyper2 | Powerboat dataset | numeric | | |
powerboat_table | hyper2 | Powerboat dataset | data.frame | 21 | 8 |
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 | data.frame | 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 | data.frame | 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 |
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 | data.frame | 7 | 9 |
zacslist | hyper2 | Counterstrike | list | | |
CDEC.snow.courses | sharpshootR | CDEC Snow Course List | data.frame | 259 | 9 |
HHM | sharpshootR | Highland Meadows | data.frame | 3469 | 12 |
OSDexamples | sharpshootR | Example output from soilDB::fetchOSD() | list | | |
amador | sharpshootR | SSURGO Data Associated with the Amador Soil Series | data.frame | 42 | 3 |
plssMeridians | sharpshootR | LL2PLSS | data.frame | 87 | 3 |
table5.2 | sharpshootR | Table 5.2 from Hole and Campbell, 1985. | matrix | 18 | 18 |
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 |
depress | Keng | Depression and Coping | data.frame | 94 | 234 |
sumstats | sffdr | Subset of p-values from the UK Biobank analysis | data.frame | 10000 | 4 |
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 |
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 |
mydata | openair | Example air quality monitoring data for openair | tbl_df | 65533 | 10 |
telework_data | OPSR | Telework Data | data.frame | 1584 | 35 |
timeuse_data | OPSR | TimeUse+ Data | data.frame | 824 | 40 |
feat1 | QFeatures | Feature example data | QFeatures | | |
feat2 | QFeatures | Feature example data | QFeatures | | |
feat3 | QFeatures | Example 'QFeatures' object after processing | QFeatures | | |
ft_na | QFeatures | Feature example data | QFeatures | | |
hlpsms | QFeatures | hyperLOPIT PSM-level expression data | data.frame | 3010 | 28 |
se_na2 | QFeatures | Feature example data | SummarizedExperiment | | |
filteredProfile | PhyloProfile | An example of a filtered phylogenetic profile | data.frame | 168 | 21 |
finalProcessedProfile | PhyloProfile | An example of a final processed & filtered phylogenetic profile | data.frame | 88 | 12 |
fullProcessedProfile | PhyloProfile | An example of a fully processed phylogenetic profile | data.frame | 168 | 15 |
idList | PhyloProfile | NCBI ID list for experimental data sets | data.frame | 95 | 41 |
mainLongRaw | PhyloProfile | An example of a raw long input file | data.frame | 168 | 5 |
ppTaxonomyMatrix | PhyloProfile | An example of a taxonomy matrix | data.frame | 10 | 162 |
ppTree | PhyloProfile | An example of a taxonomy tree in newick format | data.frame | 1 | 1 |
profileWithTaxonomy | PhyloProfile | An example of a raw long input file together with the taxonomy info | data.frame | 20 | 13 |
rankList | PhyloProfile | NCBI rank list for experimental data sets | data.frame | 95 | 41 |
taxonNamesReduced | PhyloProfile | NCBI Taxonomy reduced data set | data.frame | 757 | 4 |
taxonomyMatrix | PhyloProfile | Taxonomy matrix for experimental data sets | data.frame | 95 | 149 |
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 |
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 |
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.connection | archeofrag | Dataset: Refitting relationships between pottery fragments from the Tai site | matrix | 279 | 2 |
tai.fragments | archeofrag | Dataset: Refitting relationships between pottery fragments from the Tai site | data.frame | 815 | 8 |
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 | 27 |
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 |
hfaction_cpx12 | 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 |
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 |
typhoid_curves_nostrat_100 | serocalculator | Small example of antibody response curve parameters for typhoid | curve_params | 500 | 7 |
eez_rg | oceanic | Eez Coefficients | sf | 243 | 16 |
port_sf | oceanic | port position | sf | 421 | 2 |
ex_data | stpm | This is the longitudinal genetic dataset. | list | | |
samp.A | StatMatch | Artificial data set resembling EU-SILC survey | data.frame | 3009 | 13 |
samp.B | StatMatch | Artificial data set resembling EU-SILC survey | data.frame | 6686 | 12 |
samp.C | StatMatch | Artificial data set resembling EU-SILC survey | data.frame | 980 | 14 |
EFV | CICI | Pharmacoepidemiological HIV treatment data | data.frame | 5000 | 23 |
EFVfull | CICI | Pharmacoepidemiological HIV treatment data | data.frame | 5000 | 33 |
traffic.model.n1 | QWDAP | The estabulished model by Stepwise Regression of the 'N1' station | QWMODEL | | |
traffic.n1 | QWDAP | Data of the 'N1' station | QWMS | | |
traffic.qw | QWDAP | A set of modes generated by quantum walk | CTQW | | |
trafficflow | QWDAP | Highway traffic flow data | data.frame | 720 | 7 |
heart | AssocBin | Heart Disease Diagnosis Data | data.frame | 920 | 15 |
sp500pseudo | AssocBin | De-Garched S&P 500 returns | matrix | 755 | 461 |
test_data | multiROC | Example dataset | data.frame | 85 | 9 |
realdata_alpha | SPPcomb | A list of matrices containing value of alpha at each location. | list | | |
realdata_covariates | SPPcomb | A data list of matrices containing covariates of cases and controls. | list | | |
accident | hmmm | factory accident data | data.frame | 72 | 5 |
depression | hmmm | longitudinal study of mental depression | data.frame | 32 | 6 |
drinks | hmmm | soft-drinks data | data.frame | 269 | 5 |
kentucky | hmmm | Kentucky traffic accident data | data.frame | 50 | 4 |
madsen | hmmm | Madsen data | data.frame | 72 | 5 |
polbirth | hmmm | political orientation and teenage birth control data | data.frame | 28 | 3 |
relpol | hmmm | religion and political orientation data | data.frame | 21 | 3 |
relpolbirth | hmmm | religion, political orientation and teenage birth control data | data.frame | 84 | 4 |
Data1 | SNFtool | Data1 | data.frame | 200 | 2 |
Data2 | SNFtool | Data2 | data.frame | 200 | 2 |
dataL | SNFtool | dataL | list | | |
label | SNFtool | Labels for dataL dataset | integer | | |
aHDLt | tightenBlock | Alcohol and HDL Cholesterol | data.frame | 1624 | 14 |
centenarian | depend.truncation | Japanese Centenarians Data | data.frame | 21 | 19 |
Body | OTE | Exploring Relationships in Body Dimensions | data.frame | 507 | 25 |
Galaxy | OTE | Radial Velocity of Galaxy NGC7531 | data.frame | 323 | 5 |
BHS | simexaft | Busselton Health Study | data.frame | 100 | 18 |
rhDNase | simexaft | rhDNase Data Set | data.frame | 641 | 11 |
insem | Sunclarco | Insemination Data | data.frame | 10513 | 5 |
lalonde | randChecks | Lalonde (1986) Data | data.frame | 614 | 9 |
lalonde.matched.card | randChecks | A Cardinality Matched Dataset for the Lalonde (1986) Data | data.frame | 240 | 10 |
lalonde.matched.ps | randChecks | A 1:1 Propensity Score Matched Dataset for the Lalonde (1986) Data | data.frame | 370 | 10 |
data_land | PrInDT | Landscape analysis | data.frame | 149 | 24 |
data_speaker | PrInDT | Subject pronouns and a predictor with one very frequent level | data.frame | 3370 | 6 |
data_vowel | PrInDT | Vowel length | data.frame | 82 | 22 |
data_zero | PrInDT | Subject pronouns | data.frame | 1024 | 7 |
SVCdata | varycoef | Sampled SVC Data | list | | |
house | varycoef | Lucas County House Price Data | data.frame | 25357 | 25 |
ABAB | SCRT | Hypothetical ABAB data | data.frame | 24 | 2 |
sample_data | rciplot | Sample Data from Jacobson & Truax (1991) | data.frame | 30 | 3 |
CD4 | QRegVCM | The CD4 dataset | data.frame | 1817 | 6 |
wages | QRegVCM | The wages dataset | data.frame | 6402 | 7 |
GSS | FactorCopula | The 1994 General Social Survey | data.frame | 464 | 7 |
PE | FactorCopula | Political-economic risk of 62 countries for the year 1987 | matrix | 62 | 5 |
PTSD | FactorCopula | The Post-traumatic stress disorder (PTSD) | matrix | 221 | 20 |
TAS | FactorCopula | Toronto Alexithymia Scale (TAS) | matrix | 1925 | 20 |
Airpollution | timsac | Airpollution Data | numeric | | |
Amerikamaru | timsac | Amerikamaru Data | matrix | 896 | |
Blsallfood | timsac | Blsallfood Data | numeric | | |
Canadianlynx | timsac | Time series of Canadian lynx data | numeric | | |
LaborData | timsac | Labor force Data | numeric | | |
Powerplant | timsac | Power Plant Data | matrix | 500 | |
bispecData | timsac | Univariate Test Data | numeric | | |
locarData | timsac | Non-stationary Test Data | numeric | | |
nonstData | timsac | Non-stationary Test Data | numeric | | |
Dodgeram | approxmatch | Dodge ram pk 2500 data on side airbag (SAB) usage from 1995 to 2015 | data.frame | 6953 | 33 |
chr20 | binhf | DNA datasets | numeric | | |
mhc | binhf | DNA datasets | numeric | | |
pintens | binhf | pintens | numeric | | |
Depression | QDComparison | Jackson's CESD Depression Scores | data.frame | 372 | 2 |
Earnings1978 | QDComparison | LaLonde's 1978 Earnings Data | data.frame | 445 | 2 |
Fundraising | QDComparison | Gneezy's Fundraising Data with a Gift Wage | data.frame | 23 | 2 |
Microfinance | QDComparison | Informal Borrowing in Neighborhoods of Hyderabad, India | data.frame | 6811 | 2 |
NMES | QDComparison | National Medicare Expenditure Survey (NMES) Data on Cost of Hospitalizations | data.frame | 9416 | 2 |
AEdata | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 66 | 5 |
AudiencePercent | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | matrix | 6 | 4 |
Design_2.8_2 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 64 | 2 |
Discrete4 | HH | Discrete with four levels color dataset. | character | | |
NZScienceTeaching | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 10 | 7 |
PoorChildren | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 6 | 5 |
ProfChal | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 7 |
ProfDiv | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | matrix | 7 | 4 |
R282 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 64 | 11 |
R282.y | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | numeric | | |
SFF8121 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | array | | |
abc | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 12 | 5 |
abrasion | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 30 | 3 |
acacia | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 2 | 2 |
aeanonym | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 66 | 5 |
animal | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 3 |
anneal | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 4 |
apple | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 4 |
ara | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 277 | 6 |
balance | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 17 | 2 |
barleyp | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 3 |
batch | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 25 | 2 |
bean | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 4 |
birthweight | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 56 | 4 |
blood | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 2 |
blyth | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | matrix | 2 | 4 |
breast | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 2 |
budworm | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 12 | 3 |
byss | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 72 | 7 |
c3c4 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 194 | 2 |
catalystm | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 2 |
cc135 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 18 | 8 |
cc176 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 96 | 6 |
cement | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 13 | 5 |
census4 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 400 | 8 |
cereals | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 77 | 17 |
chimp | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 40 | 3 |
circuit | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 6 |
co2 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
col3x2 | HH | col3x2 color dataset | character | | |
concord | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 496 | 6 |
crash | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 12 | 3 |
crime | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 6 | 2 |
darwin | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 15 | 2 |
diamond | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 2 |
display | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 4 |
distress | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 50 | 2 |
draft | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 372 | 3 |
draft70mn | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 31 | 12 |
drunk | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 2 | 5 |
eggs | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 4 |
elnino | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
employM16 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
energy | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 88 | 6 |
esr | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 32 | 3 |
fabricwear | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 2 |
fat | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 47 | 5 |
fat.dat | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 252 | 19 |
feed | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 30 | 3 |
filmcoat | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 27 | 3 |
filter | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 4 |
fruitflies | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 7 | 3 |
furnace | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 3 |
girlht | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 70 | 3 |
glasses | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 2 | 2 |
golf | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 105 | 14 |
gum | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 4 |
gunload | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 4 |
har1 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 2 |
har2 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 78 | 2 |
har3 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 30 | 2 |
hardness | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 2 |
heartvalve | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 4 |
hooppine | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 50 | 4 |
hospital | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 12 | 4 |
hotdog | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 54 | 3 |
houseprice | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 107 | 5 |
hpErie | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 28 | 12 |
htwt | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 39 | 7 |
iceskate | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 23 | 19 |
icu | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 200 | 21 |
income | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 51 | 3 |
inconsistent | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 210 | 2 |
intubate | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 20 | 4 |
ironpot | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 3 |
jury | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | array | | |
kangaroo | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 25 | 3 |
kidney | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 33 | 4 |
kyphosis | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 81 | 4 |
lake | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 20 | 2 |
leukemia | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 51 | 9 |
lft.asat | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 3938 | 4 |
lifeins | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 18 | 3 |
longley | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 7 |
lymph | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 53 | 7 |
maiz | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 40 | 4 |
manhours | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 25 | 8 |
market | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 48 | 4 |
mice | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 74 | 2 |
mileage | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 82 | 6 |
mortality | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 2 | 2 |
mpg | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 50 | 3 |
muscle | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 2 |
njgolf | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 105 | 115 |
normtemp | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 130 | 3 |
notch | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 99 | 2 |
oats | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 72 | 7 |
odoffna | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 12 | 3 |
operator | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 20 | 2 |
oral | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | matrix | 2 | 2 |
ozone | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
paper | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 160 | 2 |
patient | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 64 | 2 |
plasma | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 50 | 3 |
political | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 2 | 2 |
potency | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 15 | 2 |
pox | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 7 | 2 |
product | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
psycho | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 20 | 7 |
pulmonary | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 6 | 4 |
pulse | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 68 | 2 |
radioact | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 32 | 6 |
rent | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 67 | 6 |
retard | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 4 |
rhiz.alfalfa | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 60 | 5 |
rhiz.clover | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 60 | 5 |
rhizobium1 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 60 | 3 |
rhizobium3 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 60 | 3 |
salary | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 60 | 2 |
salinity | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 30 | 2 |
salk | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 4 |
seeding | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 26 | 2 |
selfexam | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | table | 3 | 3 |
shipment | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 20 | 3 |
sickle | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 41 | 2 |
skateslc | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 45 | 4 |
smokers | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 28 | 4 |
spacshu | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 138 | 2 |
spindle | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 3 |
sprint | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 23 | 2 |
stopdist | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 63 | 2 |
surface | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 3 |
tablet1 | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 2 |
teachers | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 32 | 2 |
testing | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 3 |
testscore | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 54 | 7 |
tires | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 16 | 4 |
tongue | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 24 | 2 |
tser.mystery.X | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
tser.mystery.Y | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
tser.mystery.Z | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
tsq | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | ts | | |
turkey | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 30 | 2 |
tv | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 40 | 5 |
usair | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 41 | 7 |
uscrime | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 47 | 15 |
vocab | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 54 | 1 |
vulcan | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 60 | 4 |
washday | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 21 | 2 |
water | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 61 | 3 |
weightloss | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 50 | 2 |
weld | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 36 | 5 |
wheat | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 20 | 6 |
wool | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 27 | 4 |
workstation | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 30 | 3 |
yates | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 72 | 7 |
yatesppl | HH | Datasets for Statistical Analysis and Data Display, Heiberger and Holland | data.frame | 72 | 9 |
DCA | confidence | Annual Average 1,2-dichloroethane Concentration | data.frame | 3 | 10 |
EQR | confidence | Annual Average Environmental Quality Ratio for Macrofauna. | data.frame | 3 | 11 |
metal | confidence | Simulated Metal Contents | data.frame | 8 | 8 |
spdata | SpNMF | spdata | matrix | 80 | |
Pinus | QuESTr | Transcriptomes of Pinus roots under a Temperature Gradient | list | | |
College | mvdalab | Data for College Level Examination Program and the College Qualification Test | data.frame | 87 | 3 |
Penta | mvdalab | Penta data set | data.frame | 30 | 17 |
Wang_Chen | mvdalab | Bivariate process data. | data.frame | 25 | 2 |
Wang_Chen_Sim | mvdalab | Simulated process data from a plastics manufacturer. | data.frame | 50 | 3 |
plusMinusDat | mvdalab | plusMinusDat data set | data.frame | 201 | 201 |
Climat | multisensi | Climate data | data.frame | 3126 | 4 |
biomasseX | multisensi | A factorial input design for the main example | data.frame | 2187 | 7 |
biomasseY | multisensi | Output of the biomasse model for the plan provided in the package | data.frame | 2187 | 22 |
diseno1 | estadistica | Datos simulados de dos muestras tomadas en periodos de tiempo distintos. La muestra 1 es tomada en enero y la muestra 2 en junio. | data.frame | 610 | 2 |
diseno2 | estadistica | Datos simulados de dos muestras tomadas en periodos de tiempo distintos. La muestra 1 es tomada en enero y la muestra 2 en junio. | data.frame | 1085 | 2 |
ejem_bidi | estadistica | Data: Ejemplo de dos variables (ejem_bidi) | data.frame | 100 | 2 |
hogares | estadistica | Data: Hogares | data.frame | 10 | 3 |
salarios2018 | estadistica | Data: Encuesta cuatrienal de estructura salarial (2018) | data.frame | 216726 | 7 |
startup | estadistica | Data: Datos de empresas emergentes (startups) | data.frame | 21 | 4 |
turistas | estadistica | Data: Turistas por paises (WTO) | data.frame | 130 | 3 |
turistas2 | estadistica | Data: Turistas internacionales Comunidad Valenciana | data.frame | 80 | 2 |
viajes_vendidos | estadistica | Data: Viajes vendidos | data.frame | 5 | 3 |
altman | irrCAC | Dataset describing the Altman's Benchmarking Scale | data.frame | 5 | 3 |
cac.ben.gerry | irrCAC | Ratings of 12 units from 2 raters named Ben and Gerry | data.frame | 12 | 4 |
cac.dist.g1g2 | irrCAC | Distribution of 4 raters by subject and by category, for 14 Subjects that belong to 2 groups "G1" and "G2" | data.frame | 14 | 7 |
cac.dist4cat | irrCAC | Distribution of 4 raters by Category and Subject - Subjects allocated in 2 groups A and B. | data.frame | 15 | 4 |
cac.raw.g1g2 | irrCAC | Dataset of raw ratings from 4 Raters on 14 Subjects that belong to 2 groups named "G1" and "G2" | data.frame | 14 | 6 |
cac.raw.gender | irrCAC | Rating Data from 4 Raters and 15 human Subjects, 9 of whom are female and 6 males. | data.frame | 15 | 5 |
cac.raw4raters | irrCAC | Rating Data from 4 Raters and 12 Subjects. | data.frame | 12 | 4 |
cac.raw5obser | irrCAC | Scores assigned by 5 observers to 20 experimental units. | data.frame | 15 | 5 |
cont3x3abstractors | irrCAC | Distribution of 100 pregnant women by pregnancy type and by abstractor. | data.frame | 3 | 3 |
cont4x4diagnosis | irrCAC | Distribution of 223 Psychiatric Patients by Type of of Psychiatric Disorder and Diagnosis Method. | data.frame | 4 | 4 |
distrib.6raters | irrCAC | Distribution of 6 psychiatrists by Subject/patient and diagnosis Category. | data.frame | 15 | 5 |
fleiss | irrCAC | Dataset describing Fleiss' Benchmarking Scale | data.frame | 3 | 3 |
landis.koch | irrCAC | Dataset describing the Landis & Koch Benchmarking Scale | data.frame | 6 | 3 |
cpain | sanon | Chronic Pain Data | data.frame | 193 | 4 |
heartburn | sanon | Relief of heartburn Data | data.frame | 60 | 11 |
resp | sanon | Respiratory Disorder Data | data.frame | 111 | 9 |
sebor | sanon | Seborrheic Dermatitis Data | data.frame | 167 | 8 |
skin | sanon | Skin Condition Data | data.frame | 172 | 6 |
gEx | gatom | Example metabolic graph with atom topology. | igraph | | |
gene.de.rawEx | gatom | Example gene differential expression data. | tbl_df | 617 | 4 |
gsEx | gatom | Example scored metabolic graph with atom topology. | igraph | | |
mEx | gatom | Example metabolic module. | igraph | | |
met.de.rawEx | gatom | Example metabolite differential abundance data. | tbl_df | 54 | 4 |
met.kegg.dbEx | gatom | Example KEGG-based metabolite database object | list | | |
networkEx | gatom | Example KEGG-based network object | list | | |
org.Mm.eg.gatom.annoEx | gatom | Example organism annotation object | list | | |
data | ShapePattern | This is a generic data environment that provides some demo data and control functionality. | environment | | |
EW_popIMD_14 | IBMPopSim | England and Wales (EW) 2014 population and death rates by Index of Multiple Deprivation (IMD). | list | | |
EW_pop_14 | IBMPopSim | England and Wales (EW) 2014 population, death and birth rates. | list | | |
EW_pop_out | IBMPopSim | Example of "human population" after 100 years of simulation. | data.frame | 35555 | 3 |
EWdata_hmd | IBMPopSim | England and Wale mortality data (source: Human Mortality Database) | demogdata | | |
toy_params | IBMPopSim | Toy parameters for IBMPopSim-human_popIMD vignette example. | list | | |
anticoagulation | metainc | Sampled odds ratios from meta-analysis on the association between parenteral anticoagulation and mortality in patients with cancer | matrix | 5000 | 18 |
anticoagulation_df | metainc | Meta-analysis on the association between parenteral anticoagulation and mortality in patients with cancer | escalc | 18 | 7 |
montelukast | metainc | Sampled effect sizes (mean differences) from Krishnamoorthy et al. (2020) | matrix | 5000 | 9 |
GSS2014 | vannstats | General Social Survey, 2014 | data.frame | 2538 | 676 |
UCR2015 | vannstats | Uniform Crime Reports, 2015 (County-Level) | data.frame | 3108 | 102 |
WBBN2019 | vannstats | Well-Being and Basic Needs Survey, 2019 (Individual-Level) | data.frame | 7694 | 23 |
howell_aids_long | vannstats | Howell Student AIDS Knowledge Data (Long Form) | data.frame | 12 | 3 |
howell_aids_wide | vannstats | Howell Student AIDS Knowledge Data (Wide Form) | data.frame | 4 | 4 |
Bus | ThreeWay | Bus data | matrix | 7 | 185 |
Kinship | ThreeWay | Kinship terms data | array | | |
TV | ThreeWay | TV data | list | | |
meaudret | ThreeWay | Meaudret data | array | | |
BLSALLFOOD | TSSS | BLSALLFOOD Data | ts | | |
HAKUSAN | TSSS | Ship's Navigation Data | mts | 1000 | 4 |
Haibara | TSSS | Haibara Data | mts | 400 | 2 |
MYE1F | TSSS | Seismic Data | ts | | |
NLmodel | TSSS | The Nonlinear State-Space Model Data | mts | 100 | 2 |
Nikkei225 | TSSS | Nikkei225 | ts | | |
PfilterSample | TSSS | Sample Data for Particle Filter and Smoother | ts | | |
Rainfall | TSSS | Rainfall Data | ts | | |
Sunspot | TSSS | Sunspot Number Data | ts | | |
Temperature | TSSS | Temperatures Data | ts | | |
WHARD | TSSS | Wholesale Hardware Data | ts | | |
D65 | colourvision | CIE Standard Illuminant D65 in quantum flux (umol/m2/s) | data.frame | 107 | 2 |
Rb | colourvision | Brazilian savannah background reflectance spectrum. | data.frame | 401 | 2 |
bee | colourvision | Honeybee photoreceptors | data.frame | 401 | 4 |
cantius_L | paleoTS | Time-series of the length of lower first molar for the Cantius lineage | paleoTS | | |
dorsal.spines | paleoTS | Time-series of dorsal spine data from a fossil stickleback lineage | paleoTS | | |
pos | falcon | Position (bp) | integer | | |
readMatrix | falcon | An example reads count data | data.frame | 6309 | 4 |
tauhat | falcon | Estimated Break Points | numeric | | |
br_2020 | fio | Brazil input-output matrix, year 2020, 51 sectors | iom | | |
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 | | |
Bcells | flowAI | flowSet of B cells. | flowSet | | |
radiata | RcppSMC | Radiata pine dataset (linear regression example) | data.frame | 42 | 3 |
HMDB_mapper_vec | cosmosR | Toy Input Transcription Data Set | character | | |
meta_network | cosmosR | Meta Prior Knowledge Network | data.frame | 100488 | 3 |
toy_RNA | cosmosR | Toy Input Transcription Data Set | numeric | | |
toy_metabolic_input | cosmosR | Toy Metabolic Input Data | numeric | | |
toy_network | cosmosR | Toy Input Network | data.frame | 101 | 3 |
toy_signaling_input | cosmosR | Toy Signaling Input | numeric | | |
dental | actuar | Individual Dental Claims Data Set | numeric | | |
gdental | actuar | Grouped Dental Claims Data Set | grouped.data | 10 | 2 |
hachemeister | actuar | Hachemeister Data Set | matrix | 5 | 25 |
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 | | |
pj_officer_level_balanced | staggered | Procedural Justice Training Program in the Chicago Police Department | tbl_df | 560520 | 12 |
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 |
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 |
Tiramisu | EpiStats | A foodborne disease outbreak dataset | data.frame | 291 | 21 |
presidential_debates_2012 | textstem | 2012 U.S. Presidential Debates | tbl_df | 2912 | 5 |
sam_i_am | textstem | Sam I Am Text | character | | |
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 |
selection_files | Rraven | A list of 'Raven' selection tables. | list | | |
bnMCMCResults | genMCMCDiag | Results from a Bayesian Network MCMC algorithm on simulated data | list | | |
uniMCMCResults | genMCMCDiag | Results from a univariate MCMC algorithm on a simulated posterior | list | | |
benthos | dimensio | Benthos | data.frame | 92 | 13 |
colours | dimensio | Colours | data.frame | 4 | 4 |
countries | dimensio | Countries | data.frame | 13 | 6 |
ternary_forecast_example | CalSim | Ternary probability forecast and observations. | data.frame | 10000 | 6 |
dd_ip | beezdiscounting | Delay Discounting Data | data.frame | 600 | 3 |
five.fivetrial_dd | beezdiscounting | Example Qualtrics output from the 5.5 trial delay discounting template. | tbl_df | 4 | 175 |
five.fivetrial_pd | beezdiscounting | Example Qualtrics output from the 5.5 trial probability discounting template. | tbl_df | 4 | 166 |
mcq27 | beezdiscounting | Example 27-item MCQ data | data.frame | 54 | 3 |
USgdp | corbouli | USgdp | ts | | |
mtcars_messy | clickR | Messy Motor Trend Car Road Tests Dataset | data.frame | 32 | 13 |
DBH | ForestFit | Trees height and diameter at breast height | data.frame | 5731 | 14 |
HW | ForestFit | Mixed norther hardwood | data.frame | 25 | 2 |
SW | ForestFit | Southern loblolly pine plantation | data.frame | 18 | 2 |
simulist_data | diseasystore | simulist_data | tbl_df | 11330 | 9 |
GermanCredit | caret | German Credit Data | data.frame | 1000 | 62 |
Sacramento | caret | Sacramento CA Home Prices | data.frame | 932 | 9 |
absorp | caret | Fat, Water and Protein Content of Meat Samples | matrix | 215 | |
bbbDescr | caret | Blood Brain Barrier Data | data.frame | 208 | 134 |
cars | caret | Kelly Blue Book resale data for 2005 model year GM cars | data.frame | 804 | 18 |
cox2Class | caret | COX-2 Activity Data | factor | | |
cox2Descr | caret | COX-2 Activity Data | data.frame | 462 | 255 |
cox2IC50 | caret | COX-2 Activity Data | numeric | | |
dhfr | caret | Dihydrofolate Reductase Inhibitors Data | data.frame | 325 | 229 |
endpoints | caret | Fat, Water and Protein Content of Meat Samples | matrix | 215 | |
fattyAcids | caret | Fatty acid composition of commercial oils | data.frame | 96 | 7 |
logBBB | caret | Blood Brain Barrier Data | numeric | | |
mdrrClass | caret | Multidrug Resistance Reversal (MDRR) Agent Data | factor | | |
mdrrDescr | caret | Multidrug Resistance Reversal (MDRR) Agent Data | data.frame | 528 | 342 |
oilType | caret | Fatty acid composition of commercial oils | factor | | |
potteryClass | caret | Pottery from Pre-Classical Sites in Italy | factor | | |
scat | caret | Morphometric Data on Scat | data.frame | 110 | 19 |
scat_orig | caret | Morphometric Data on Scat | data.frame | 122 | 20 |
segmentationData | caret | Cell Body Segmentation | data.frame | 2019 | 61 |
prostate_nodal | cutpointr | Nodal involvement and acid phosphatase levels in 53 prostate cancer patients | data.frame | 53 | 2 |
suicide | cutpointr | Suicide attempts and DSI sum scores of 532 subjects | data.frame | 532 | 4 |
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 |
annual_mortality_rates_2015 | nhppp | Human mortality database age and sex specific rates for all cause deaths | data.table | 3 | 113 |
meve | FedData | The boundary of Mesa Verde National Park | sfc_POLYGON | | |
NWPrimates_data | DAMOCLES | Dated phylogenetic tree of the New World Primates in nexus format and presence-absence matrix for species in Manu | list | | |
exp_data | ofpetrial | Experiment data | tbl_df | 1 | 14 |
plot_info | ofpetrial | Plot information | tbl_df | 1 | 11 |
rate_info | ofpetrial | Rate information | tbl_df | 1 | 11 |
td_curved | ofpetrial | Trial design (single-input) for a curved field | tbl_df | 1 | 27 |
td_single_input | ofpetrial | Trial design (single-input) | tbl_df | 1 | 27 |
td_two_input | ofpetrial | Trial design (two-input) | tbl_df | 2 | 27 |
danube | graphicalExtremes | Upper Danube basin dataset | list | | |
flights | graphicalExtremes | Flights delay data | list | | |
antidepressant_data | rbmi | Antidepressant trial data | tbl_df | 608 | 11 |
BLI_2017 | Compind | Better Life Index 2017 indicators | data.frame | 35 | 12 |
EU_2020 | Compind | Europe 2020 indicators | data.frame | 30 | 191 |
EU_NUTS1 | Compind | EU NUTS1 Transportation data | data.frame | 34 | 6 |
data_HPI | Compind | Happy Planet Index 2017-2019 indicators | data.frame | 432 | 12 |
ImbC | UBL | Synthetic Imbalanced Data Set for a Multi-class Task | data.frame | 1000 | 3 |
ImbR | UBL | Synthetic Regression Data Set | data.frame | 1000 | 3 |
england | footBayes | English league results 1888-2022 | data.frame | 203956 | 12 |
italy | footBayes | Italy league results 1934-2022 | data.frame | 27684 | 8 |
elk_2010_permutations | aniSNA | A list of 100 igraph objects obtained by permuting the raw elk_data_2010 and obtaining network from those | list_permuted_networks | | |
elk_all_interactions_2010 | aniSNA | Dataset of all possible interactions from elk_data_2010 | data.frame | 7615 | 5 |
elk_data_2010 | aniSNA | Data to showcase functions in our package | data.frame | 123568 | 4 |
elk_interactions_2010 | aniSNA | Dataset of interactions from elk_data_2010 using first mode as the spatial threshold | data.frame | 2393 | 5 |
elk_network_2010 | aniSNA | An igraph object depicting the network obtained from elk_interactions_2010 | igraph | | |
BreastCancer_na.rm | spinifex | Wisconsin Breast Cancer Database | data.frame | 675 | 9 |
PimaIndiansDiabetes_long | spinifex | Pima Indians Diabetes Dataset, long | data.frame | 724 | 7 |
PimaIndiansDiabetes_wide | spinifex | Pima Indians Diabetes Dataset, wide | data.frame | 392 | 9 |
penguins_na.rm | spinifex | Size measurements for adult foraging penguins near Palmer Station, Antarctica | data.frame | 333 | 7 |
weather_na.rm | spinifex | Sample dataset of daily weather observations from Canberra airport in Australia. | data.frame | 354 | 20 |
wine | spinifex | The wine dataset from the UCI Machine Learning Repository. | data.frame | 178 | 14 |
MCMC_data | MCMCvis | Simulated MCMC output data | mcmc.list | | |
MCMC_data2 | MCMCvis | Simulated MCMC output data - #2 | mcmc.list | | |
bridge | emuR | Three-columned matrix | matrix | 13 | 3 |
coutts | emuR | Segment list of words, read speech, female speaker of Australian English from database epgcoutts | emusegs | 4 | 4 |
coutts.epg | emuR | EPG-compressed trackdata from the segment list coutts | trackdata | | |
coutts.l | emuR | Vector of word label from the segment list coutts | AsIs | | |
coutts.rms | emuR | rms Data to coutts segment list | trackdata | | |
coutts.sam | emuR | Trackdata of acoustic waveforms from the segment list coutts | trackdata | | |
coutts2 | emuR | Segment list, same as coutts but at a slower speech rate | emusegs | 4 | 4 |
demo.all | emuR | Emu segment list | emusegs | 28 | 4 |
demo.all.rms | emuR | Emu track data for a rms track for segment list demo.all | trackdata | | |
demo.vowels | emuR | Emu segment List | emusegs | 6 | 4 |
demo.vowels.f0 | emuR | F0 track data for segment list demo.vowels | trackdata | | |
demo.vowels.fm | emuR | Formant track data for segment list demo.vowels | trackdata | | |
dip | emuR | Segment list of diphthongs, two speakers one male, one female , Standard North German, read speech from database kielread | emusegs | 186 | 4 |
dip.fdat | emuR | Trackdata of formants from the segment list dip | trackdata | | |
dip.l | emuR | Vector of phoneme labels from the segment list dip | AsIs | | |
dip.spkr | emuR | Vector of speaker labels from the segment list dip | AsIs | | |
e.dft | emuR | Spectral vector of a single E vowel produced by a male speaker of Standard North German. | numeric | | |
engassim | emuR | Segment list of a sequence of syllable final n or N preceding k or g , isolated words single speaker, Australian English female from database epgassim. | emusegs | 32 | 4 |
engassim.epg | emuR | EPG-compressed trackdata from the segment list engassim | trackdata | | |
engassim.l | emuR | Vector of phonetic labels from the segment list engassim: nK = nk,ng , sK = sk,sg | character | | |
engassim.w | emuR | Vector of word labels from the segment list engassim. | character | | |
fric | emuR | Segment list of word-medial s or z one male speaker of Standard North German, read speech from database kielread. | emusegs | 34 | 4 |
fric.dft | emuR | Spectral trackdata object from the segment list fric. | trackdata | | |
fric.l | emuR | Vector of labels from the segment list fric | AsIs | | |
fric.w | emuR | Vector of word labels from the segment list fric. | character | | |
isol | emuR | Segment list of vowels in a d d context isolated word speech, one male speaker of Australian English from database isolated. | emusegs | 13 | 4 |
isol.fdat | emuR | Trackdata of formants from the segment list isol | trackdata | | |
isol.l | emuR | Vector of vowel phoneme labels from the segment list isol | AsIs | | |
polhom | emuR | Segment list of four Polish homorganic fricatives from database epgpolish. | emusegs | 40 | 4 |
polhom.epg | emuR | EPG-compressed trackdata from the segment list polhom | trackdata | | |
polhom.l | emuR | Vector of phonetic labels from the segment list polhom | AsIs | | |
vowlax | emuR | Segment list of four lax vowels, read speech, one male and one female speaker of Standard North German from database kielread. | emusegs | 410 | 4 |
vowlax.df | emuR | Data frame of various parameters and labels from the segment list vowlax | data.frame | 410 | 9 |
vowlax.dft.5 | emuR | Spectral matrix centred at the temporal midpoint of the vowels from the segment list vowlax. | matrix | 410 | 129 |
vowlax.fdat | emuR | Trackdata of formants from the segment list vowlax | trackdata | | |
vowlax.fdat.5 | emuR | Matrix of formant data extracted at the temporal midpoint from the segment list vowlax. | matrix | 410 | 4 |
vowlax.fund | emuR | Trackdata of fundamental frequency from the segment list vowlax | trackdata | | |
vowlax.fund.5 | emuR | Vector of fundamental frequency extracted at the temporal midpoint from the segment list vowlax. | numeric | | |
vowlax.l | emuR | Vector of phoneme labels from the segment list vowlax | AsIs | | |
vowlax.left | emuR | Vector of labels preceding the vowels from the segment list vowlax | character | | |
vowlax.right | emuR | Vector of labels following the vowels from the segment list vowlax | character | | |
vowlax.rms | emuR | Trackdata of RMS energy from the segment list vowlax | trackdata | | |
vowlax.rms.5 | emuR | Vector of RMS energy values at the temporal midpoint extracted at the temporal midpoint from the segment list vowlax | numeric | | |
vowlax.spkr | emuR | Vector of speaker labels from the segment list vowlax. | character | | |
vowlax.word | emuR | Vector of word labels from th |