madelon | MDFS | An artificial dataset called MADELON | list | | |
rasch1000 | ConjointChecks | 1000 sampled 3-matrices from simulated Rasch data. | checks | | |
mm_data | moRphomenses | Sample hormone dataset | data.frame | 2015 | 4 |
mort | jstable | DATASET_TITLE | data.frame | 17562 | 24 |
facts | roundhouse | Chuck Norris facts | data.frame | 574 | 1 |
data_dictionary_ideology | LSX | Seed words for analysis of left-right political ideology | dictionary2 | | |
data_dictionary_sentiment | LSX | Seed words for analysis of positive-negative sentiment | dictionary2 | | |
data_textmodel_lss_russianprotests | LSX | A fitted LSS model on street protest in Russia | textmodel_lss | | |
pbc2 | HQM | Mayo Clinic Primary Biliary Cirrhosis Data | data.frame | 1945 | 20 |
deciduous_polygon | sephora | 128 NDVI pixels from a MOD13Q1 time series | matrix | 128 | 506 |
PEN_death | BayesGP | The monthly all-cause mortality for male with age less than 40 in Pennsylvania. | data.frame | 298 | 8 |
ccData | BayesGP | A simulated dataset from the case-crossover model. | data.frame | 3596 | 6 |
covid_canada | BayesGP | The COVID-19 daily death data in Canada. | data.frame | 787 | 10 |
calls | stR | Number of phone calls dataset | msts | | |
electricity | stR | Electricity consumption dataset | msts | 5520 | 6 |
grocery | stR | Grocery and supermarkets turnover | ts | | |
long_data | CIMPLE | long_data | data.table | 7214 | 6 |
surv_data | CIMPLE | long_data | data.table | 1828 | 6 |
train_data | CIMPLE | long_data | data.frame | 6727 | 5 |
sampleDepthData | s2dv | Sample of Experimental Data for Forecast Verification In Function Of Latitudes And Depths | list | | |
sampleMap | s2dv | Sample Of Observational And Experimental Data For Forecast Verification In Function Of Longitudes And Latitudes | list | | |
sampleTimeSeries | s2dv | Sample Of Observational And Experimental Data For Forecast Verification As Area Averages | list | | |
AMR | FishResp | Active Metabolic Rate: Final Data | data.frame | 12 | 16 |
AMR.clean | FishResp | Active Metabolic Rate: Corrected Raw Data | data.frame | 7200 | 17 |
AMR.raw | FishResp | Active Metabolic Rate: Raw Data | data.frame | 1800 | 16 |
AMR.slope | FishResp | Active Metabolic Rate: Extracted Slope(s) | data.frame | 12 | 12 |
SMR | FishResp | Standard Metabolic Rate: Final Data | data.frame | 12 | 16 |
SMR.clean | FishResp | Standard Metabolic Rate: Corrected Raw Data | data.frame | 76800 | 17 |
SMR.raw | FishResp | Standard Metabolic Rate: Raw Data | data.frame | 19200 | 16 |
SMR.slope | FishResp | Standard Metabolic Rate: Extracted Slope(s) | data.frame | 12 | 12 |
info | FishResp | Info about Individuals and Chambers | data.frame | 4 | 4 |
post | FishResp | Post Raw Data | data.frame | 2400 | 7 |
pre | FishResp | Pre Raw Data | data.frame | 4800 | 7 |
results | FishResp | Results of Analysis: SMR, AMR and MS | data.frame | 36 | 18 |
Aar | compositions | Composition of glaciar sediments from the Aar massif (Switzerland) | tbl_df | 87 | 31 |
Activity10 | compositions | Activity patterns of a statistician for 20 days | matrix | 20 | 6 |
Activity31 | compositions | Activity patterns of a statistician for 20 days | matrix | 20 | 6 |
AnimalVegetation | compositions | Animal and vegetation measurement | matrix | 100 | 6 |
ArcticLake | compositions | Artic lake sediment samples of different water depth | matrix | 39 | 4 |
Bayesite | compositions | Permeabilities of bayesite | matrix | 21 | 6 |
Blood23 | compositions | Blood samples | matrix | 40 | 3 |
Boxite | compositions | Compositions and depth of 25 specimens of boxite | matrix | 25 | 6 |
ClamEast | compositions | Color-size compositions of 20 clam colonies from East Bay | matrix | 20 | 6 |
ClamWest | compositions | Color-size compositions of 20 clam colonies from West Bay | matrix | 20 | 6 |
Coxite | compositions | Compositions, depths and porosities of 25 specimens of coxite | matrix | 25 | 7 |
DiagnosticProb | compositions | Diagnostic probabilities | matrix | 30 | 4 |
Firework | compositions | Firework mixtures | matrix | 81 | 7 |
Glacial | compositions | Compositions and total pebble counts of 92 glacial tills | matrix | 92 | 5 |
Hongite | compositions | Compositions of 25 specimens of hongite | matrix | 25 | 5 |
HouseholdExp | compositions | Household Expenditures | matrix | 40 | 5 |
Hydrochem | compositions | Hydrochemical composition data set of Llobregat river basin water (NE Spain) | data.frame | 485 | 19 |
Kongite | compositions | Compositions of 25 specimens of kongite | matrix | 25 | 5 |
Metabolites | compositions | Steroid metabolite patterns in adults and children | matrix | 67 | 4 |
PogoJump | compositions | Honk Kong Pogo-Jumps Championship | matrix | 28 | 4 |
Sediments | compositions | Proportions of sand, silt and clay in sediments specimens | matrix | 21 | 4 |
SerumProtein | compositions | Serum Protein compositions of blood samples | matrix | 36 | 5 |
ShiftOperators | compositions | Shifts of machine operators | matrix | 27 | 4 |
Skulls | compositions | Measurement of skulls | matrix | 102 | 7 |
SkyeAFM | compositions | AFM compositions of 23 aphyric Skye lavas | matrix | 23 | 3 |
Supervisor | compositions | Proportions of supervisor's statements assigned to different categories | matrix | 18 | 13 |
WhiteCells | compositions | White-cell composition of 30 blood samples by two different methods | matrix | 30 | 6 |
Yatquat | compositions | Yatquat fruit evaluation | matrix | 40 | 7 |
jura259 | compositions | The jura dataset | data.frame | 359 | 11 |
juraset | compositions | The jura dataset | data.frame | 359 | 11 |
sa.dirichlet | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.dirichlet.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.dirichlet.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.dirichlet5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.dirichlet5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.dirichlet5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.groups | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.groups.area | compositions | Simulated amount datasets | factor | | |
sa.groups.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.groups.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.groups5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.groups5.area | compositions | Simulated amount datasets | factor | | |
sa.groups5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.groups5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.lognormals | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.lognormals.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.lognormals.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.lognormals5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.lognormals5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.lognormals5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.missings | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.missings5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.outliers1 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers2 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers3 | compositions | Simulated amount datasets | acomp | 101 | 3 |
sa.outliers4 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers5 | compositions | Simulated amount datasets | acomp | 100 | 3 |
sa.outliers6 | compositions | Simulated amount datasets | acomp | 120 | 3 |
sa.tnormals | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.tnormals.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.tnormals.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.tnormals5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.tnormals5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.tnormals5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.uniform | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.uniform.dil | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.uniform.mix | compositions | Simulated amount datasets | matrix | 60 | 3 |
sa.uniform5 | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.uniform5.dil | compositions | Simulated amount datasets | matrix | 60 | 5 |
sa.uniform5.mix | compositions | Simulated amount datasets | matrix | 60 | 5 |
NDVI | GPoM | A time series of vegetation index measured from satellite | data.frame | 1000 | 4 |
P1FxCh | GPoM | A data set for testing periodicity | matrix | 1000 | 6 |
P1FxChP2 | GPoM | A data set for testing periodicity | matrix | 1000 | 8 |
RosYco | GPoM | Twelve Rossler-1976 time series (exclusively variable y) | matrix | 3000 | 12 |
Ross76 | GPoM | Time series of the Rossler-1976 system | deSolve | 4000 | 4 |
TS | GPoM | Time series resulting from the integration of a non stationary system | matrix | 6001 | |
TSallMod_nVar3_dMax2 | GPoM | Time series of three-dimensional chaotic sytems (for vignette 'VII_Retro-Modelling') | list | | |
allMod_nVar3_dMax2 | GPoM | Numerical description of a list of eighteen three-dimensional chaotic sytems (see vignette '7_Retro-Modelling') | list | | |
allToTest | GPoM | A list providing the description of six models tested by the function 'autoGPoMoTest'. | list | | |
data_vignetteIII | GPoM | Output of the vignette 'III_Modelling' | list | | |
data_vignetteVI | GPoM | Output of the vignette 'VI_Sensitivity' | list | | |
data_vignetteVII | GPoM | Output of the vignette 'VII_Retro-Modelling' | list | | |
svrlTS | GPoM | A data set for the global modeling of time series in association | list | | |
maize | heteromixgm | Maize data | list | | |
gcdata | aghq | Globular Clusters data for Milky Way mass estimation | tbl_df | 70 | 25 |
gcdatalist | aghq | Transformed Globular Clusters data | list | | |
ann.data | disprose | Chlamydia pneumoniae genome annotation. | spec_tbl_df | 2218 | 9 |
blast.fill | disprose | Local BLAST results with added content. | data.frame | 72 | 19 |
blast.raw | disprose | Local BLAST results. | data.frame | 72 | 19 |
meta.all | disprose | Metadata of all available Chlamydia pneumoniae's sequences. | data.frame | 9062 | 21 |
meta.target | disprose | Metadata of target Chlamydia pneumoniae's sequences. | data.frame | 183 | 21 |
yeast | som | yeast cell cycle | data.frame | 6601 | 18 |
maas | sgeostat | maas- zinc measurements | data.frame | 155 | 3 |
maas.bank | sgeostat | maas.bank - coordinates | data.frame | 37 | 2 |
dental | dstat | Dental Problems Caused by Smoking | data.frame | 441 | 5 |
lalive | dstat | Unemployment Duration Following an Increase in Unemployment Benefits | data.frame | 2782 | 17 |
x_obs | absorber | Observation matrix x of five variables | matrix | 700 | |
y_obs | absorber | Values of the response variable of the noisy observation set of five input variables | numeric | | |
invData | qape | Population data - investments in Poland at NUTS 4 level | data.frame | 2280 | 6 |
realestData | qape | Population data - real estate in Poland at NUTS 4 level | data.frame | 1504 | 7 |
sim | PanelCount | Simulated dataset with self-selection at both individual and individual-time level | data.frame | 2000 | 6 |
plasma | rlmDataDriven | plasma | data.frame | 273 | 3 |
counties | vipor | Census ata on US counties | data.frame | 3143 | 8 |
integrations | vipor | Data on HIV integration sites from several studies | data.frame | 12436 | 4 |
ch1.GM03563 | breakpoint | Fibroblast cell line (GM03563) data | data.frame | 135 | 1 |
X | adapt4pv | Simulated data for the adapt4pv package | dgCMatrix | | |
Y | adapt4pv | Simulated data for the adapt4pv package | numeric | | |
Balt | TrendSLR | Ocean water level data for Baltimore, USA | data.frame | 115 | 2 |
s | TrendSLR | sample 'msl.trend' object | msl.trend | | |
t | TrendSLR | sample 'custom.trend' object | custom.trend | | |
brca | iDINGO | Modified TCGA Breast Cancer data | list | | |
gbm | iDINGO | Modified TCGA Glioblastoma data | data.frame | 156 | 19 |
data_n | sdpdth | A simulated data set for testing | list | | |
data_nw | sdpdth | A simulated data set for testing | matrix | 12 | |
data_th | sdpdth | A simulated data set for testing | list | | |
data_w | sdpdth | A simulated data set for testing | matrix | 16 | |
WGScan.example | WGScan | Data example for WGScan (A Genome-Wide Scan Statistic Framework For Whole-Genome Sequence Data Analysis) | list | | |
WGScan.info | WGScan | hg19 chromosome sizes | list | | |
ptg_stud_data | LogRegEquiv | Student Performance Data Set | data.frame | 649 | 31 |
ptg_stud_f_test | LogRegEquiv | Student Performance Data Set - female testing data | data.frame | 77 | 30 |
ptg_stud_f_train | LogRegEquiv | Student Performance Data Set - female training data | data.frame | 306 | 30 |
ptg_stud_m_test | LogRegEquiv | Student Performance Data Set - male testing data | data.frame | 53 | 30 |
ptg_stud_m_train | LogRegEquiv | Student Performance Data Set - male training data | data.frame | 213 | 30 |
agpop | SDAResources | agpop data | tbl_df | 3078 | 15 |
agpps | SDAResources | agpps data | tbl_df | 15 | 34 |
agsrs | SDAResources | agsrs data | tbl_df | 300 | 15 |
agstrat | SDAResources | agstrat data | tbl_df | 300 | 17 |
algebra | SDAResources | algebra data | tbl_df | 299 | 3 |
anthrop | SDAResources | anthrop data | tbl_df | 3000 | 2 |
anthsrs | SDAResources | anthsrs data | tbl_df | 200 | 3 |
anthuneq | SDAResources | anthuneq data | tbl_df | 200 | 3 |
artifratio | SDAResources | artifratio data | tbl_df | 70 | 10 |
asafellow | SDAResources | asafellow data | tbl_df | 106 | 7 |
auditresult | SDAResources | auditresult data | tbl_df | 25 | 4 |
auditselect | SDAResources | auditselect data | tbl_df | 44 | 6 |
azcounties | SDAResources | azcounties data | tbl_df | 13 | 5 |
baseball | SDAResources | baseball data | tbl_df | 797 | 30 |
books | SDAResources | books data | tbl_df | 60 | 5 |
census1920 | SDAResources | census1920 data | tbl_df | 48 | 2 |
census2010 | SDAResources | census2010 data | tbl_df | 50 | 2 |
cherry | SDAResources | cherry data | tbl_df | 31 | 3 |
classes | SDAResources | classes data | tbl_df | 15 | 2 |
classpps | SDAResources | classpps data | tbl_df | 20 | 4 |
classppsjp | SDAResources | classppsjp data | tbl_df | 5 | 9 |
college | SDAResources | college data | tbl_df | 1372 | 29 |
collegerg | SDAResources | collegerg data | tbl_df | 50 | 32 |
collshr | SDAResources | collshr data | tbl_df | 10 | 34 |
coots | SDAResources | coots data | tbl_df | 368 | 6 |
counties | SDAResources | counties data | tbl_df | 100 | 18 |
crimes | SDAResources | crimes data | tbl_df | 5000 | 7 |
deadtrees | SDAResources | deadtrees data | tbl_df | 25 | 2 |
divorce | SDAResources | divorce data | tbl_df | 32 | 20 |
gini | SDAResources | gini data | tbl_df | 213 | 14 |
golfsrs | SDAResources | golfsrs data | tbl_df | 120 | 16 |
gpa | SDAResources | gpa data | tbl_df | 20 | 3 |
healthjournals | SDAResources | healthjournals data | tbl_df | 198 | 7 |
htcdf | SDAResources | htcdf data | tbl_df | 65 | 4 |
htpop | SDAResources | htpop data | tbl_df | 2000 | 2 |
htsrs | SDAResources | htsrs data | tbl_df | 200 | 3 |
htstrat | SDAResources | htstrat data | tbl_df | 200 | 3 |
hunting | SDAResources | hunting data | tbl_df | 36 | 5 |
impute | SDAResources | impute data | tbl_df | 20 | 6 |
integerwt | SDAResources | integerwt data | tbl_df | 2000 | 2 |
intellonline | SDAResources | intellonline data | tbl_df | 983 | 8 |
intelltel | SDAResources | intelltel data | tbl_df | 1838 | 8 |
intellwts | SDAResources | intellwts data | tbl_df | 8 | 7 |
ipums | SDAResources | ipums data | tbl_df | 53461 | 15 |
journal | SDAResources | journal data | tbl_df | 26 | 3 |
measles | SDAResources | measles data | tbl_df | 307 | 13 |
mysteries | SDAResources | mysteries data | tbl_df | 60 | 17 |
nhanes | SDAResources | nhanes data | tbl_df | 9971 | 25 |
nybight | SDAResources | nybight data | tbl_df | 107 | 7 |
otters | SDAResources | otters data | tbl_df | 82 | 3 |
ozone | SDAResources | ozone data | tbl_df | 730 | 27 |
pitcount | SDAResources | pitcount data | tbl_df | 100 | 7 |
profresp | SDAResources | profresp data | tbl_df | 2404 | 15 |
profrespacs | SDAResources | profrespacs data | tbl_df | 18 | 4 |
radon | SDAResources | radon data | tbl_df | 1003 | 5 |
rectlength | SDAResources | rectlength data | tbl_df | 100 | 2 |
rnt | SDAResources | rnt data | data.frame | 50 | 6 |
sample70 | SDAResources | sample70 data | tbl_df | 70 | 10 |
santacruz | SDAResources | santacruz data | tbl_df | 10 | 3 |
schools | SDAResources | schools data | tbl_df | 200 | 8 |
seals | SDAResources | seals data | tbl_df | 40 | 2 |
shapespop | SDAResources | shapespop data | tbl_df | 20000 | 5 |
shorebirds | SDAResources | shorebirds data | tbl_df | 201 | 3 |
sp500 | SDAResources | sp500 data | tbl_df | 500 | 6 |
spanish | SDAResources | spanish data | tbl_df | 196 | 3 |
srs30 | SDAResources | srs30 data | tbl_df | 30 | 1 |
ssc | SDAResources | ssc data | tbl_df | 150 | 3 |
statepop | SDAResources | statepop data | tbl_df | 100 | 12 |
statepps | SDAResources | statepps data | tbl_df | 51 | 5 |
swedishlcs | SDAResources | swedishlcs data | tbl_df | 13 | 6 |
syc | SDAResources | syc data | tbl_df | 2621 | 28 |
teachers | SDAResources | teachers data | tbl_df | 310 | 6 |
teachmi | SDAResources | teachmi data | tbl_df | 31 | 4 |
teachnr | SDAResources | teachnr data | data.frame | 26 | 4 |
uneqvar | SDAResources | uneqvar data | tbl_df | 100 | 2 |
vietnam | SDAResources | vietnam data | tbl_df | 2064 | 8 |
vius | SDAResources | vius data | tbl_df | 98682 | 26 |
viusca | SDAResources | viusca data | tbl_df | 2192 | 26 |
winter | SDAResources | winter data | tbl_df | 985 | 18 |
wtshare | SDAResources | wtshare data | tbl_df | 114 | 4 |
D243 | RobustAFT | Sample of 100 hospital stays for medical back problems | data.frame | 100 | 11 |
MCI | RobustAFT | Sample of 75 Hospital Stays | data.frame | 75 | 6 |
Z243 | RobustAFT | Sample of 100 hospital stays for medical back problems | data.frame | 100 | 14 |
yeastG1 | PGEE | Yeast cell-cycle gene expression data | data.frame | 1132 | 99 |
data | lddmm | Example dataset | tbl_df | 24254 | 6 |
choe2.L | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | numeric | | |
choe2.degenes | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | logical | | |
choe2.mapping | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | numeric | | |
choe2.mat | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | matrix | 6 | 11475 |
choe2.probe.name | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | character | | |
choe2.symbol.name | st | A Subset of the Choe et al. (2005) "Golden Spike" Experiment | character | | |
gobyData | eDNAjoint | gobyData | list | | |
greencrabData | eDNAjoint | greencrabData | list | | |
pmf_list | binomialRF | A prebuilt distribution for correlated binary data | list | | |
AST | CoDiNA | AST | data.frame | 3384 | 3 |
CTR | CoDiNA | CTR | data.frame | 17471 | 3 |
GLI | CoDiNA | GLI | data.frame | 172245 | 3 |
OLI | CoDiNA | OLI | data.frame | 64791 | 3 |
motorcycledata | adlift | Motorcycle data. | data.frame | 133 | 2 |
primlist | schoolmath | A vector containing primes from 0 to 9999999 | numeric | | |
CarMileageData | gvlma | Car Mileage Data Recorded at Each Gasoline Fill-Up | data.frame | 205 | 7 |
genotypes | hsphase | Example of Genotype Data Set | matrix | 90 | 9601 |
map | hsphase | Example Map File for Genetic Data | data.frame | 11333 | 3 |
pedigree | hsphase | Example Pedigree Data Set | data.frame | 90 | 2 |
Cardiological.CR | iRegression | Cardiological Interval Data Set (Centre and Range) | data.frame | 11 | 6 |
Cardiological.MinMax | iRegression | Cardiological Interval Data Set | data.frame | 11 | 6 |
soccer.bivar | iRegression | Soccer Interval Data Set | data.frame | 20 | 6 |
taxus_bin | diffval | _Taxus baccata_ forests | matrix | 209 | 33 |
SuessR.reference.data | SuessR | Reference dataset for the SuessR Package | data.frame | 692 | 8 |
microscopy | fiberLD | Data of uncut fiber lengths in the increment core | numeric | | |
ofa | fiberLD | Example of increment core data | numeric | | |
rnaedit_df | rnaEditr | Example breast cancer RNA editing dataset. | data.frame | 272 | 221 |
t_rnaedit_df | rnaEditr | Transposed breast cancer example dataset. | data.frame | 50 | 20 |
democracy_data | CausalQueries | Development and Democratization: Data for replication of analysis in *Integrated Inferences* | data.frame | 84 | 5 |
institutions_data | CausalQueries | Institutions and growth: Data for replication of analysis in *Integrated Inferences* | tbl_df | 79 | 5 |
lipids_data | CausalQueries | Lipids: Data for Chickering and Pearl replication | data.frame | 8 | 3 |
dataHBME | saeHB.ME | Sample Data for Small Area Estimation with Measurement Error using Hierarchical Bayesian Method under Normal Distribution | data.frame | 30 | 8 |
dataTMEHB | saeHB.ME | Sample Data for Small Area Estimation with Measurement Error using Hierarchical Bayesian Method under Student-t Distribution | data.frame | 30 | 5 |
dataME | saeME | dataME | data.frame | 100 | 5 |
datamix | saeME | datamix | data.frame | 100 | 9 |
ei_NZ_2002 | ei.Datasets | Ecological inference data sets of the 2002 New Zealand General Election. | tbl_df | 69 | 6 |
ei_NZ_2005 | ei.Datasets | Ecological inference data sets of the 2005 New Zealand General Election. | tbl_df | 69 | 6 |
ei_NZ_2008 | ei.Datasets | Ecological inference data sets of the 2008 New Zealand General Election. | tbl_df | 70 | 6 |
ei_NZ_2011 | ei.Datasets | Ecological inference data sets of the 2011 New Zealand General Election. | tbl_df | 70 | 6 |
ei_NZ_2014 | ei.Datasets | Ecological inference data sets of the 2014 New Zealand General Election. | tbl_df | 71 | 6 |
ei_NZ_2017 | ei.Datasets | Ecological inference data sets of the 2017 New Zealand General Election. | tbl_df | 71 | 6 |
ei_NZ_2020 | ei.Datasets | Ecological inference data sets of the 2020 New Zealand General Election. | tbl_df | 72 | 6 |
ei_SCO_2007 | ei.Datasets | Ecological inference data sets of the 2007 Scottish National Assembly. | tbl_df | 73 | 6 |
elas | OptimalCutpoints | Leukocyte Elastase Data | data.frame | 141 | 3 |
BeckLee_ages | dispRity | Beck and Lee 2014 datasets | data.frame | 14 | 2 |
BeckLee_disparity | dispRity | BeckLee_disparity | dispRity | | |
BeckLee_mat50 | dispRity | Beck and Lee 2014 datasets | matrix | 50 | |
BeckLee_mat99 | dispRity | Beck and Lee 2014 datasets | matrix | 99 | |
BeckLee_tree | dispRity | Beck and Lee 2014 datasets | phylo | | |
charadriiformes | dispRity | Charadriiformes | list | | |
demo_data | dispRity | Demo datasets | list | | |
disparity | dispRity | disparity | dispRity | | |
HDL | informedSen | Light Daily Alcohol and HDL Cholesterol Levels | data.frame | 800 | 9 |
SDcorn | saws | Mammary tumors in Sprague-Dawley rats fed Corn Oil | data.frame | 104 | 10 |
dietfat | saws | Mammary Tumors and Different Types of Dietary Fat in Rodents | data.frame | 442 | 9 |
micefat | saws | Dietary fat and Mammary tumors in Mice | data.frame | 57 | 5 |
usmacro_growth | bayesianVARs | Data from the US-economy | matrix | 247 | 21 |
X1 | vottrans | Example data X1 | matrix | 119 | 7 |
Y1 | vottrans | Example Data Y1 | matrix | 119 | 7 |
ECBYieldCurve | YieldCurve | Yield curve data spot rate, AAA-rated bonds, maturities from 3 months to 30 years | xts | 655 | 32 |
FedYieldCurve | YieldCurve | Federal Reserve interest rates | xts | 372 | 8 |
metalonda_test_data | MetaLonDA | Simulated data of OTU abundance for 2 phenotypes each has 5 subjects at 10 time-points | matrix | 8 | 100 |
ascarr | cgwtools | Banner Versions of Characters | array | | |
Aids2 | MASS | Australian AIDS Survival Data | data.frame | 2843 | 7 |
Animals | MASS | Brain and Body Weights for 28 Species | data.frame | 28 | 2 |
Boston | MASS | Housing Values in Suburbs of Boston | data.frame | 506 | 14 |
Cars93 | MASS | Data from 93 Cars on Sale in the USA in 1993 | data.frame | 93 | 27 |
Cushings | MASS | Diagnostic Tests on Patients with Cushing's Syndrome | data.frame | 27 | 3 |
DDT | MASS | DDT in Kale | numeric | | |
GAGurine | MASS | Level of GAG in Urine of Children | data.frame | 314 | 2 |
Insurance | MASS | Numbers of Car Insurance claims | data.frame | 64 | 5 |
Melanoma | MASS | Survival from Malignant Melanoma | data.frame | 205 | 7 |
OME | MASS | Tests of Auditory Perception in Children with OME | data.frame | 1097 | 7 |
Pima.te | MASS | Diabetes in Pima Indian Women | data.frame | 332 | 8 |
Pima.tr | MASS | Diabetes in Pima Indian Women | data.frame | 200 | 8 |
Pima.tr2 | MASS | Diabetes in Pima Indian Women | data.frame | 300 | 8 |
Rabbit | MASS | Blood Pressure in Rabbits | data.frame | 60 | 5 |
Rubber | MASS | Accelerated Testing of Tyre Rubber | data.frame | 30 | 3 |
SP500 | MASS | Returns of the Standard and Poors 500 | numeric | | |
Sitka | MASS | Growth Curves for Sitka Spruce Trees in 1988 | data.frame | 395 | 4 |
Sitka89 | MASS | Growth Curves for Sitka Spruce Trees in 1989 | data.frame | 632 | 4 |
Skye | MASS | AFM Compositions of Aphyric Skye Lavas | data.frame | 23 | 3 |
Traffic | MASS | Effect of Swedish Speed Limits on Accidents | data.frame | 184 | 4 |
UScereal | MASS | Nutritional and Marketing Information on US Cereals | data.frame | 65 | 11 |
UScrime | MASS | The Effect of Punishment Regimes on Crime Rates | data.frame | 47 | 16 |
VA | MASS | Veteran's Administration Lung Cancer Trial | data.frame | 137 | 8 |
abbey | MASS | Determinations of Nickel Content | numeric | | |
accdeaths | MASS | Accidental Deaths in the US 1973-1978 | ts | | |
anorexia | MASS | Anorexia Data on Weight Change | data.frame | 72 | 3 |
bacteria | MASS | Presence of Bacteria after Drug Treatments | data.frame | 220 | 6 |
beav1 | MASS | Body Temperature Series of Beaver 1 | data.frame | 114 | 4 |
beav2 | MASS | Body Temperature Series of Beaver 2 | data.frame | 100 | 4 |
biopsy | MASS | Biopsy Data on Breast Cancer Patients | data.frame | 699 | 11 |
birthwt | MASS | Risk Factors Associated with Low Infant Birth Weight | data.frame | 189 | 10 |
cabbages | MASS | Data from a cabbage field trial | data.frame | 60 | 4 |
caith | MASS | Colours of Eyes and Hair of People in Caithness | data.frame | 4 | 5 |
cats | MASS | Anatomical Data from Domestic Cats | data.frame | 144 | 3 |
cement | MASS | Heat Evolved by Setting Cements | data.frame | 13 | 5 |
chem | MASS | Copper in Wholemeal Flour | numeric | | |
coop | MASS | Co-operative Trial in Analytical Chemistry | data.frame | 252 | 4 |
cpus | MASS | Performance of Computer CPUs | data.frame | 209 | 9 |
crabs | MASS | Morphological Measurements on Leptograpsus Crabs | data.frame | 200 | 8 |
deaths | MASS | Monthly Deaths from Lung Diseases in the UK | ts | | |
drivers | MASS | Deaths of Car Drivers in Great Britain 1969-84 | ts | | |
eagles | MASS | Foraging Ecology of Bald Eagles | data.frame | 8 | 5 |
epil | MASS | Seizure Counts for Epileptics | data.frame | 236 | 9 |
farms | MASS | Ecological Factors in Farm Management | data.frame | 20 | 4 |
fgl | MASS | Measurements of Forensic Glass Fragments | data.frame | 214 | 10 |
forbes | MASS | Forbes' Data on Boiling Points in the Alps | data.frame | 17 | 2 |
galaxies | MASS | Velocities for 82 Galaxies | numeric | | |
gehan | MASS | Remission Times of Leukaemia Patients | data.frame | 42 | 4 |
genotype | MASS | Rat Genotype Data | data.frame | 61 | 3 |
geyser | MASS | Old Faithful Geyser Data | data.frame | 299 | 2 |
gilgais | MASS | Line Transect of Soil in Gilgai Territory | data.frame | 365 | 9 |
hills | MASS | Record Times in Scottish Hill Races | data.frame | 35 | 3 |
housing | MASS | Frequency Table from a Copenhagen Housing Conditions Survey | data.frame | 72 | 5 |
immer | MASS | Yields from a Barley Field Trial | data.frame | 30 | 4 |
leuk | MASS | Survival Times and White Blood Counts for Leukaemia Patients | data.frame | 33 | 3 |
mammals | MASS | Brain and Body Weights for 62 Species of Land Mammals | data.frame | 62 | 2 |
mcycle | MASS | Data from a Simulated Motorcycle Accident | data.frame | 133 | 2 |
menarche | MASS | Age of Menarche in Warsaw | data.frame | 25 | 3 |
michelson | MASS | Michelson's Speed of Light Data | data.frame | 100 | 3 |
minn38 | MASS | Minnesota High School Graduates of 1938 | data.frame | 168 | 5 |
motors | MASS | Accelerated Life Testing of Motorettes | data.frame | 40 | 3 |
muscle | MASS | Effect of Calcium Chloride on Muscle Contraction in Rat Hearts | data.frame | 60 | 3 |
newcomb | MASS | Newcomb's Measurements of the Passage Time of Light | numeric | | |
nlschools | MASS | Eighth-Grade Pupils in the Netherlands | data.frame | 2287 | 6 |
npk | MASS | Classical N, P, K Factorial Experiment | data.frame | 24 | 5 |
npr1 | MASS | US Naval Petroleum Reserve No. 1 data | data.frame | 104 | 4 |
oats | MASS | Data from an Oats Field Trial | data.frame | 72 | 4 |
painters | MASS | The Painter's Data of de Piles | data.frame | 54 | 5 |
petrol | MASS | N. L. Prater's Petrol Refinery Data | data.frame | 32 | 6 |
phones | MASS | Belgium Phone Calls 1950-1973 | list | | |
quine | MASS | Absenteeism from School in Rural New South Wales | data.frame | 146 | 5 |
road | MASS | Road Accident Deaths in US States | data.frame | 26 | 6 |
rotifer | MASS | Numbers of Rotifers by Fluid Density | data.frame | 20 | 5 |
ships | MASS | Ships Damage Data | data.frame | 40 | 5 |
shoes | MASS | Shoe wear data of Box, Hunter and Hunter | list | | |
shrimp | MASS | Percentage of Shrimp in Shrimp Cocktail | numeric | | |
shuttle | MASS | Space Shuttle Autolander Problem | data.frame | 256 | 7 |
snails | MASS | Snail Mortality Data | data.frame | 96 | 6 |
steam | MASS | The Saturated Steam Pressure Data | data.frame | 14 | 2 |
stormer | MASS | The Stormer Viscometer Data | data.frame | 23 | 3 |
survey | MASS | Student Survey Data | data.frame | 237 | 12 |
synth.te | MASS | Synthetic Classification Problem | data.frame | 1000 | 3 |
synth.tr | MASS | Synthetic Classification Problem | data.frame | 250 | 3 |
topo | MASS | Spatial Topographic Data | data.frame | 52 | 3 |
waders | MASS | Counts of Waders at 15 Sites in South Africa | data.frame | 15 | 19 |
whiteside | MASS | House Insulation: Whiteside's Data | data.frame | 56 | 3 |
wtloss | MASS | Weight Loss Data from an Obese Patient | data.frame | 52 | 2 |
diabetes | elasticnet | Blood and other measurements in diabetics | data.frame | 442 | 3 |
pitprops | elasticnet | Pitprops correlation data | matrix | 13 | 13 |
dutch | longevity | Dutch survival data | tbl_df | 305143 | 11 |
ewsim | longevity | England and Wales simulated supercentenarian data | data.frame | 179 | 3 |
idlmetadata | longevity | IDL metadata | tbl_df | 21 | 4 |
japanese | longevity | Japanese survival data | tbl_df | 1038 | 4 |
japanese2 | longevity | Japanese survival data (2) | tbl_df | 216 | 4 |
elec92 | optiscale | Public Opinion During the 1992 U.S. Presidential Election | data.frame | 1653 | 7 |
mpdta | did | County Teen Employment Dataset | data.frame | 2500 | 6 |
SP500_idioerr | bspcov | SP500 dataset | list | | |
colon | bspcov | colon dataset | data.frame | 2000 | 62 |
tissues | bspcov | tissues dataset | numeric | | |
conflictData | clarkeTest | Conflict Data | data.frame | 4381 | 9 |
ex1 | SplitSplitPlot | Dados de exemplo de um experimento em DQL. | data.frame | 288 | 5 |
birch.df | ggpp | Birch seedlings' size | data.frame | 350 | 8 |
birch_dw.df | ggpp | Birch seedlings' size | data.frame | 700 | 5 |
ivy.df | ggpp | Ivy photosynthesis light response | data.frame | 36 | 6 |
quadrant_example.df | ggpp | Gene expression data | data.frame | 1015 | 7 |
volcano_example.df | ggpp | Gene expression data | data.frame | 1218 | 6 |
weather_18_june_2019.df | ggpp | Weather data | data.frame | 1440 | 18 |
coloc_test_data | coloc | Simulated data to use in testing and vignettes in the coloc package | list | | |
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 |
participation.table | labeleR | participation.table | data.frame | 4 | 5 |
tiny.table | labeleR | tiny.table | data.frame | 40 | 8 |
seniors | poissonreg | Alcohol, Cigarette, and Marijuana Use for High School Seniors | tbl_df | 8 | 4 |
dailydata | nser | Daily data of a stock | spec_tbl_df | 499 | 7 |
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 |
NHANES | hexbin | NHANES Data : National Health and Nutrition Examination Survey | data.frame | 9575 | 15 |
amt_fisher | amt | GPS tracks from four fishers | track_xyt | 14230 | 6 |
amt_fisher_covar | amt | Environmental data for fishers | list | | |
deer | amt | Relocations of 1 red deer | track_xyt | 826 | 4 |
sh | amt | Relocations of one Red Deer | data.frame | 1500 | 4 |
sh_forest | amt | Forest cover | PackedSpatRaster | | |
uhc_hab | amt | Simulated habitat rasters for demonstrating UHC plots | data.frame | 6400 | 9 |
uhc_hsf_locs | amt | Simulated HSF location data for demonstrating UHC plots | data.frame | 1894 | 2 |
uhc_issf_locs | amt | Simulated iSSF location data for demonstrating UHC plots | data.frame | 2135 | 3 |
hfdata | PeerPerformance | Hedge fund data | matrix | 60 | 100 |
fewlevels_en | gendercoder | fewlevels_en | character | | |
manylevels_en | gendercoder | manylevels_en | character | | |
sample | gendercoder | sample | data.frame | 7756 | 1 |
codes | weathercan | Meaning of climate normal 'codes' | tbl_df | 4 | 2 |
finches | weathercan | RFID Data on finch visits to feeders | tbl_df | 16886 | 10 |
flags | weathercan | Meaning of coded 'flags' | tbl_df | 15 | 2 |
glossary | weathercan | Glossary of units and terms | tbl_df | 79 | 5 |
glossary_normals | weathercan | Glossary of terms for Climate Normals | tbl_df | 169 | 3 |
kamloops | weathercan | Hourly weather data for Kamloops | tbl_df | 4368 | 37 |
kamloops_day | weathercan | Daily weather data for Kamloops | tbl_df | 182 | 37 |
normals_measurements | weathercan | List of climate normals measurements for each station | tbl_df | 307891 | 5 |
pg | weathercan | Hourly weather data for Prince George | tbl_df | 4368 | 37 |
SFM_metric | medfate | Standard fuel models (Albini 1976, Scott & Burgan 2005) | data.frame | 53 | 18 |
SpParamsDefinition | medfate | Data tables with species parameter definitions and values | data.frame | 156 | 6 |
SpParamsMED | medfate | Data tables with species parameter definitions and values | data.frame | 217 | 157 |
exampleforest | medfate | Description of a forest stand. | forest | | |
exampleforest2 | medfate | Description of a forest stand. | forest | | |
examplemeteo | medfate | Example daily meteorology data | data.frame | 365 | 8 |
exampleobs | medfate | Example observed data | data.frame | 365 | 11 |
poblet_trees | medfate | Example forest inventory data | data.frame | 717 | 4 |
HFD | bage | Components from Human Fertility Database | bage_ssvd | | |
HMD | bage | Components from Human Mortality Database | bage_ssvd | | |
LFP | bage | Components from OECD Labor Force Participation Data | bage_ssvd | | |
isl_deaths | bage | Deaths in Iceland | tbl_df | 5300 | 5 |
kor_births | bage | Births in South Korea | tbl_df | 1872 | 5 |
nld_expenditure | bage | Per Capita Health Expenditure in the Netherlands, 2003-2011 | tbl_df | 1296 | 4 |
nzl_divorces | bage | Divorces in New Zealand | tbl_df | 242 | 5 |
nzl_households | bage | People in One-Person Households in New Zealand | tbl_df | 528 | 5 |
nzl_injuries | bage | Fatal Injuries in New Zealand | tbl_df | 912 | 6 |
usa_deaths | bage | Accidental Deaths in the USA | data.frame | 72 | 2 |
x | flowMeans | xSample | data.frame | 17640 | 6 |
simdata | fect | Simulated data | data.frame | 7000 | 18 |
simdata1 | fect | Simulated data | data.frame | 7000 | 17 |
turnout | fect | EDR and voter turnout in the US | data.frame | 1128 | 6 |
lalonde | MatchIt | Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999). | data.frame | 614 | 9 |
data_hlme | lcmm | Simulated dataset for hlme function | data.frame | 326 | 6 |
data_lcmm | lcmm | Simulated dataset for lcmm and Jointlcmm functions | data.frame | 1678 | 12 |
paquid | lcmm | Longitudinal data on cognitive and physical aging in the elderly | data.frame | 2250 | 12 |
simdataHADS | lcmm | Simulated dataset simdataHADS | data.frame | 1140 | 13 |
config | scROSHI | Test config file | data.frame | 7 | 2 |
marker_list | scROSHI | Marker gene list for the test SCE data Set | list | | |
test_sce_data | scROSHI | Test SCE Data Set | SingleCellExperiment | | |
gameadd | seqimpute | Example data set: Game addiction | data.frame | 500 | 11 |
af_colour_palettes | afcharts | Analysis Function colour palettes | list | | |
af_colour_values | afcharts | Analysis Function colour names and hex codes | character | | |
ansur | psyntur | Anthropometric data from US Army Personnel | spec_tbl_df | 6068 | 9 |
faithfulfaces | psyntur | Faithfulness from a Photo? | tbl_df | 170 | 7 |
jobsatisfaction | psyntur | Job Satisfaction Data for Two-Way ANOVA | tbl_df | 58 | 4 |
pairedsleep | psyntur | Paired sleep data | tbl_df | 10 | 3 |
schizophrenia | psyntur | Age of Onset of Schizophrenia Data | tbl_df | 251 | 2 |
selfesteem | psyntur | Self-Esteem Score Data for One-way Repeated Measures ANOVA | tbl_df | 10 | 4 |
selfesteem2 | psyntur | Self Esteem Score Data for Two-way Repeated Measures ANOVA | tbl_df | 24 | 5 |
selfesteem2_long | psyntur | Self Esteem Score Data for Two-way Repeated Measures ANOVA: Long format | tbl_df | 72 | 4 |
test_psychometrics | psyntur | Psychometrics raw data from testing or demo purposes | tbl_df | 44 | 30 |
vizverb | psyntur | Visual versus Verbal Perception and Responses | tbl_df | 80 | 4 |
Female_parity_fert_list_UK | DemoKin | | list | | |
Female_parity_mortality_list_UK | DemoKin | | list | | |
Male_parity_fert_list_UK | DemoKin | | list | | |
Male_parity_mortality_list_UK | DemoKin | | list | | |
Parity_transfers_by_age_list_UK | DemoKin | | list | | |
Redistribution_by_parity_list_UK | DemoKin | | list | | |
U_caswell_2021 | DemoKin | Historic and projected survival ratios from Sweden used in Caswell (2021) | matrix | 111 | 230 |
demokin_codes | DemoKin | DemoKin codes, Caswell (2020) codes, and useful labels. | data.frame | 18 | 5 |
f_caswell_2021 | DemoKin | Historic and projected fertility ratios from Sweden used in Caswell (2021) | matrix | 111 | 230 |
fra_asfr_sex | DemoKin | Fertility for France (2012) by sex in Caswell (2022). | matrix | 101 | 2 |
fra_surv_sex | DemoKin | Survival probability for France (2012) by sex in Caswell (2022). | matrix | 101 | 2 |
kin_svk1990_caswell2020 | DemoKin | Output for Slovakia 1990 in Caswell (2020). | list | | |
pi_caswell_2021 | DemoKin | Historic and projected motherยดs age distribution of childbearing from Sweden used in Caswell (2021) | matrix | 111 | 230 |
svk_Hxs | DemoKin | Age where assign offspring of individuals in each partity stage (Caswell, 2021). All to zero age in this case. | matrix | 110 | 6 |
svk_Uxs | DemoKin | Probability of transition among parity stage for Slovakia in 1990, for each age, conditional on survival (Caswell, 2021). | list | | |
svk_fxs | DemoKin | Female Slovakian fertility rates by parity stage in 1990 (Caswell, 2021) | matrix | 110 | 6 |
svk_pxs | DemoKin | Female Slovakian survival probabilities by parity stage in 1990 (Caswell, 2021) | matrix | 110 | 6 |
swe_Sx | DemoKin | Female swedish survival ratios from 1900 to 2015 | matrix | 101 | 119 |
swe_asfr | DemoKin | Swedish age-specific fertility rates from 1900 to 2015 | matrix | 101 | 119 |
swe_pop | DemoKin | Female swedish population from 1900 to 2015 | matrix | 101 | 119 |
swe_px | DemoKin | Female swedish survival probabilities from 1900 to 2015 | matrix | 101 | 119 |
abcd_demo | r2dii.data | An asset-based company dataset for demonstration | tbl_df | 4972 | 12 |
co2_intensity_scenario_demo | r2dii.data | A prepared co2 intensity climate scenario dataset for demonstration | tbl_df | 22 | 7 |
data_dictionary | r2dii.data | Column definitions of all datasets | tbl_df | 96 | 4 |
gics_classification | r2dii.data | Dataset to bridge (translate) common sector-classification codes | tbl_df | 282 | 5 |
increasing_or_decreasing | r2dii.data | Determine if a technology is increasing or decreasing | tbl_df | 20 | 3 |
isic_classification | r2dii.data | Dataset to bridge (translate) common sector-classification codes | tbl_df | 830 | 6 |
iso_codes | r2dii.data | Countries and codes | tbl_df | 286 | 2 |
loanbook_demo | r2dii.data | A loanbook dataset for demonstration | tbl_df | 283 | 13 |
nace_classification | r2dii.data | Dataset to bridge (translate) common sector-classification codes | tbl_df | 1047 | 6 |
naics_classification | r2dii.data | Dataset to bridge (translate) common sector-classification codes | tbl_df | 2125 | 5 |
overwrite_demo | r2dii.data | A demonstration dataset used to overwrite specific entity names or sectors | tbl_df | 2 | 5 |
psic_classification | r2dii.data | Dataset to bridge (translate) common sector-classification codes | tbl_df | 1271 | 5 |
region_isos | r2dii.data | A dataset outlining various region definitions | tbl_df | 9262 | 3 |
region_isos_demo | r2dii.data | A dataset outlining various region definitions | tbl_df | 358 | 3 |
scenario_demo_2020 | r2dii.data | A prepared climate scenario dataset for demonstration | tbl_df | 1512 | 8 |
sector_classifications | r2dii.data | A view of available sector classification datasets | tbl_df | 6559 | 4 |
sic_classification | r2dii.data | Dataset to bridge (translate) common sector-classification codes | tbl_df | 1005 | 5 |
census.at.school.5000 | iNZightMR | Census at School 5000 | data.frame | 5000 | 72 |
gallupGPS6 | GeomArchetypal | Gallup Global Preferences Study processed data set of six variables | data.frame | 76132 | 6 |
med | regmed | Simulated dataset for regmed package | matrix | 100 | 200 |
x | regmed | Simulated dataset for regmed package | matrix | 100 | 10 |
y | regmed | Simulated dataset for regmed package | matrix | 100 | 2 |
syngrowth | growthcleanr | syngrowth | data.frame | 77721 | 6 |
ups | baldur | Spiked-in data set of peptides | spec_tbl_df | 10599 | 13 |
yeast | baldur | Spiked-in data set of reversibly oxidized cysteines | spec_tbl_df | 2235 | 7 |
cranJuly2014 | miniCRAN | Stored version of available.packages() | matrix | 5588 | 17 |
lung_FDA | mxfda | Multiplex imaging data from a non-small cell lung cancer study | mxFDA | | |
lung_df | mxfda | Multiplex imaging data from a non-small cell lung cancer study. | tbl_df | 285412 | 18 |
ovarian_FDA | mxfda | Multiplex imaging data from an ovarian cancer tumor microarray | mxFDA | | |
train_choice | RprobitB | Stated Preferences for Train Traveling | data.frame | 2929 | 11 |
ExampleData | mnet | Internal mnet functions | data.frame | 20000 | 5 |
dataKoval13 | mnet | Internal mnet functions | data.frame | 5716 | 14 |
PhenoarchDat1 | statgenHTP | Greenhouse data for a maize experiment in the PhenoArch platform. | data.frame | 40573 | 12 |
PhenovatorDat1 | statgenHTP | Growth chamber data for an Arabidopsis experiment in the Phenovator platform. | data.frame | 103839 | 10 |
RootDat1 | statgenHTP | Greenhouse data for an experiment in the RootPhAir platform. | data.frame | 16275 | 10 |
noCorrectedRoot | statgenHTP | Root data corrected for outliers for single observations. | data.frame | 15934 | 10 |
spatCorrectedArch | statgenHTP | Maize data corrected for spatial trends. | data.frame | 40573 | 10 |
spatCorrectedVator | statgenHTP | Arabidopsis data corrected for spatial trends. | data.frame | 103801 | 13 |
spatPredArch | statgenHTP | Maize data, genotypic predictions. | data.frame | 5940 | 6 |
Stamp | mixR | 1872 Hidalgo Stamp Data | numeric | | |
Stamp2 | mixR | 1872 Hidalgo Stamp Data (Binned) | matrix | 62 | 3 |
alpinaCompData | chemodiv | Arabis alpina floral scent compounds | data.frame | 15 | 3 |
alpinaCompDis | chemodiv | Arabis alpina floral scent compound dissimilarity matrix | matrix | 15 | 15 |
alpinaMolNet | chemodiv | Arabis alpina floral scent molecular network | tbl_graph | | |
alpinaNPCTable | chemodiv | Arabis alpina floral scent NPClassifier table | data.frame | 15 | 6 |
alpinaPopData | chemodiv | Arabis alpina populations | data.frame | 87 | 1 |
alpinaSampData | chemodiv | Arabis alpina floral scent data | data.frame | 87 | 15 |
alpinaSampDis | chemodiv | Arabis alpina floral scent sample dissimilarity matrix | matrix | 87 | 87 |
minimalCompData | chemodiv | Minimal compound dataset | data.frame | 3 | 3 |
minimalCompDis | chemodiv | Minimal compound dissimilarity matrix | matrix | 3 | 3 |
minimalMolNet | chemodiv | Minimal molecular network | tbl_graph | | |
minimalNPCTable | chemodiv | Minimal NPClassifier table | data.frame | 3 | 7 |
minimalSampData | chemodiv | Minimal sample dataset | data.frame | 4 | 3 |
minimalSampDis | chemodiv | Minimal sample dissimilarity matrix | matrix | 4 | 4 |
BriggsEx47 | rdecision | Probabilistic results of HIV model | data.frame | 1000 | 7 |
income | kernlab | Income Data | data.frame | 8993 | 14 |
musk | kernlab | Musk data set | data.frame | 476 | 167 |
promotergene | kernlab | E. coli promoter gene sequences (DNA) | data.frame | 106 | 58 |
reuters | kernlab | Reuters Text Data | list | | |
rlabels | kernlab | Reuters Text Data | factor | | |
spam | kernlab | Spam E-mail Database | data.frame | 4601 | 58 |
spirals | kernlab | Spirals Dataset | matrix | 300 | |
ticdata | kernlab | The Insurance Company Data | data.frame | 9822 | 86 |
db | relMix | Allele database | data.frame | 324 | 3 |
db2 | relMix | Allele database for 22 markers | data.frame | 206 | 3 |
TDHeat05 | statgenSTA | Field data for a wheat experiment in Mexico | TD | | |
TDMaize | statgenSTA | Field data for a maize experiment in Tlaltizapan, Mexico | TD | | |
dropsRaw | statgenSTA | DROPS data set | data.frame | 6486 | 26 |
sample_ohlc_data | ichimoku | Sample OHLC Price Data | data.frame | 256 | 6 |
ea_wbids | cde | Details of name and index of all sites/catchments. | data.frame | 5237 | 9 |
heasman_reid_1961_chains | chainbinomial | Common Cold Data | data.frame | 24 | 2 |
heasman_reid_1961_crowding | chainbinomial | Common Cold Data | data.frame | 5 | 4 |
heasman_reid_1961_intro_case_status | chainbinomial | Common Cold Data | data.frame | 5 | 5 |
seldata | selection.index | Selection Index DataSet | data.frame | 75 | 9 |
weight | selection.index | Weight dataset | data.frame | 7 | 3 |
GerberGreenImai | Matching | Gerber and Green Dataset used by Imai | data.frame | 10829 | 26 |
lalonde | Matching | Lalonde Dataset | data.frame | 445 | 12 |
gtfs_reference | gtfsio | GTFS reference | list | | |
C10E14 | AMCP | The data used in Chapter 10, Exercise 14 | data.frame | 63 | 4 |
C10E7 | AMCP | The data used in Chapter 10, Exercise 7 | data.frame | 45 | 3 |
C10E9 | AMCP | The data used in Chapter 10, Exercise 9 | data.frame | 72 | 4 |
C10T5 | AMCP | The data used in Chapter 10, Table 5 | data.frame | 40 | 3 |
C10T9 | AMCP | The data used in Chapter 10, Table 9 | data.frame | 24 | 3 |
C11E17 | AMCP | The data used in Chapter 11, Exercise 17 | data.frame | 14 | 4 |
C11E18 | AMCP | The data used in Chapter 11, Exercise 18 | data.frame | 12 | 3 |
C11E19 | AMCP | The data used in Chapter 11, Exercise 19 | data.frame | 14 | 4 |
C11E21 | AMCP | The data used in Chapter 11, Exercise 21 | data.frame | 42 | 3 |
C11E22 | AMCP | The data used in Chapter 11, Exercise 22 | data.frame | 19 | 7 |
C11E23 | AMCP | The data used in Chapter 11, Exercise 23 | data.frame | 183 | 3 |
C11E24 | AMCP | The data used in Chapter 11, Exercise 24 | data.frame | 90 | 3 |
C11E3 | AMCP | The data used in Chapter 11, Exercise 3 | data.frame | 5 | 4 |
C11E5 | AMCP | The data used in Chapter 11, Exercise 5 | data.frame | 5 | 3 |
C11T1 | AMCP | The data used in Chapter 11, Table 1 | data.frame | 6 | 2 |
C11T19 | AMCP | The data used in Chapter 11, Table 19 | data.frame | 24 | 3 |
C11T20 | AMCP | The data used in Chapter 11, Table 20 | data.frame | 15 | 3 |
C11T4 | AMCP | The data used in Chapter 11, Table 4 | data.frame | 10 | 4 |
C11T5 | AMCP | The data used in Chapter 11, Table 5 | data.frame | 12 | 4 |
C12E17 | AMCP | The data used in Chapter 12, Exercise 17 | data.frame | 14 | 5 |
C12E18 | AMCP | The data used in Chapter 12, Exercise 18 | data.frame | 10 | 3 |
C12E19 | AMCP | The data used in Chapter 12, Exercise 19 | data.frame | 47 | 6 |
C12E21 | AMCP | The data used in Chapter 12, Exercise 21 | data.frame | 36 | 4 |
C12E9 | AMCP | The data used in Chapter 12, Exercise 9 | data.frame | 10 | 4 |
C12T1 | AMCP | The data used in Chapter 12, Table 1 | data.frame | 10 | 6 |
C12T11 | AMCP | The data used in Chapter 12, Table 11 | data.frame | 10 | 3 |
C12T15 | AMCP | The data used in Chapter 12, Table 15 | data.frame | 10 | 3 |
C12T21 | AMCP | The data used in Chapter 12, Table 21 | data.frame | 18 | 5 |
C12T7 | AMCP | The data used in Chapter 12, Table 7 | data.frame | 10 | 3 |
C12T9 | AMCP | The data used in Chapter 12, Table 9 | data.frame | 10 | 2 |
C13E10 | AMCP | The data used in Chapter 13, Exercise 10 | data.frame | 14 | 4 |
C13E13 | AMCP | The data used in Chapter 13, Exercise 13 | data.frame | 14 | 4 |
C13E14 | AMCP | The data used in Chapter 13, Exercise 14 | data.frame | 13 | 3 |
C13E22 | AMCP | The data used in Chapter 13, Exercise 22 | data.frame | 5 | 3 |
C13E23 | AMCP | The data used in Chapter 13, Exercise 23 | data.frame | 19 | 7 |
C13E24 | AMCP | The data used in Chapter 13, Exercise 24 | data.frame | 183 | 3 |
C13E25 | AMCP | The data used in Chapter 13, Exercise 25 | data.frame | 30 | 3 |
C13E7 | AMCP | The data used in Chapter 13, Exercise 7 | data.frame | 5 | 4 |
C13T1 | AMCP | The data used in Chapter 13, Table 1 | data.frame | 5 | 2 |
C13T12 | AMCP | The data used in Chapter 13, Table 12 | data.frame | 8 | 2 |
C13T14 | AMCP | The data used in Chapter 13, Table 14 | data.frame | 8 | 2 |
C13T2 | AMCP | The data used in Chapter 13, Table 2 | data.frame | 8 | 3 |
C13T5 | AMCP | The data used in Chapter 13, Table 5 | data.frame | 12 | 4 |
C14E10 | AMCP | The data used in Chapter 14, Exercise 10 | data.frame | 10 | 4 |
C14E14 | AMCP | The data used in Chapter 14, Exercise 14 | data.frame | 30 | 5 |
C14E15 | AMCP | The data used in Chapter 14, Exercise 15 | data.frame | 10 | 3 |
C14E21 | AMCP | The data used in Chapter 14, Exercise 21 | data.frame | 14 | 5 |
C14E22 | AMCP | The data used in Chapter 14, Exercise 22 | data.frame | 47 | 6 |
C14T1 | AMCP | The data used in Chapter 14, Table 1 | data.frame | 10 | 4 |
C14T10 | AMCP | The data used in Chapter 14, Table 10 | data.frame | 20 | 4 |
C14T3 | AMCP | The data used in Chapter 14, Table 3 | data.frame | 10 | 3 |
C14T4 | AMCP | The data used in Chapter 14, Table 4 | data.frame | 10 | 6 |
C14T5 | AMCP | The data used in Chapter 14, Table 5 | data.frame | 10 | 5 |
C14T8 | AMCP | The data used in Chapter 14, Table 8 | data.frame | 20 | 3 |
C15E16 | AMCP | The data used in Chapter 15, Exercise 16 | data.frame | 14 | 4 |
C15E17 | AMCP | The data used in Chapter 15, Exercise 17 | data.frame | 56 | 4 |
C15E18 | AMCP | The data used in Chapter 15, Exercise 18 | data.frame | 24 | 4 |
C15E18U | AMCP | The data used in Chapter 15, Exercise 18 (Univariate) | data.frame | 72 | 3 |
C15E19 | AMCP | The data used in Chapter 15, Exercise 19 | data.frame | 24 | 4 |
C15E19U | AMCP | The data used in Chapter 15, Exercise 19 (Univariate) | data.frame | 72 | 3 |
C15T1 | AMCP | The data used in Chapter 15, Table 1 | data.frame | 12 | 4 |
C16E5 | AMCP | The data used in Chapter 16, Exercise 5 | data.frame | 24 | 3 |
C16E7 | AMCP | The data used in Chapter 16, Exercise 7 | data.frame | 29 | 6 |
C16E9 | AMCP | The data used in Chapter 16, Exercise 9 | data.frame | 29 | 6 |
C16T1 | AMCP | The data used in Chapter 16, Table 1 | data.frame | 24 | 3 |
C16T4 | AMCP | The data used in Chapter 16, Table 4 | data.frame | 29 | 6 |
C1E18 | AMCP | The data used in Chapter 1, Exercise 18 | data.frame | 4 | 3 |
C1E19 | AMCP | The data used in Chapter 1, Exercise 19 | data.frame | 30 | 2 |
C1E21 | AMCP | The data used in Chapter 1, Exercise 21 | data.frame | 12 | 2 |
C1E22 | AMCP | The data used in Chapter 1, Exercise 22 | data.frame | 11 | 3 |
C1E23 | AMCP | The data used in Chapter 1, Exercise 23 | data.frame | 12 | 3 |
C1T1 | AMCP | The data used in Chapter 1, Table 1 | data.frame | 10 | 3 |
C3E10 | AMCP | The data used in Chapter 3, Exercise 10 | data.frame | 36 | 3 |
C3E11 | AMCP | The data used in Chapter 3, Exercise 11 | data.frame | 24 | 2 |
C3E19 | AMCP | The data used in Chapter 3, Exercise 19 | data.frame | 155 | 3 |
C3E20 | AMCP | The data used in Chapter 3, Exercise 20 | data.frame | 72 | 2 |
C3E21 | AMCP | The data used in Chapter 3, Exercise 21 | data.frame | 192 | 2 |
C3E22 | AMCP | The data used in Chapter 3, Exercise 22 | data.frame | 310 | 5 |
C3E9 | AMCP | The data used in Chapter 3, Exercise 9 | data.frame | 12 | 2 |
C3T1 | AMCP | The data used in Chapter 3, Table 1 | data.frame | 6 | 1 |
C3T3 | AMCP | The data used in Chapter 3, Table 3 | data.frame | 30 | 2 |
C3T7R | AMCP | The data used for Chapter 3, Table 7 (raw data to produce the summary measures) | data.frame | 88 | 3 |
C3T9R | AMCP | The data used for Chapter 3, Table 9 (raw data to produce the summary measures) | data.frame | 88 | 3 |
C4E11 | AMCP | The data used in Chapter 4, Exercise 11 | data.frame | 24 | 2 |
C4E12 | AMCP | The data used in Chapter 4, Exercise 12 | data.frame | 18 | 2 |
C4E13 | AMCP | The data used in Chapter 4, Exercise 13 | data.frame | 20 | 2 |
C4T1 | AMCP | The data used in Chapter 4, Table 1 | data.frame | 20 | 2 |
C4T7 | AMCP | The data used in Chapter 4, Table 7 | data.frame | 15 | 2 |
C5E10 | AMCP | The data used in Chapter 5, Exercise 10 | data.frame | 24 | 2 |
C5E16 | AMCP | The data used in Chapter 5, Exercise 16 | data.frame | 18 | 2 |
C5E5 | AMCP | The data used in Chapter 5, Exercise 5 | data.frame | 20 | 2 |
C5T4 | AMCP | The data used in Chapter 5, Table 4 | data.frame | 24 | 2 |
C6E10 | AMCP | The data used in Chapter 6, Exercise 10 | data.frame | 45 | 2 |
C6E14 | AMCP | The data used in Chapter 6, Exercise 14 | data.frame | 48 | 2 |
C6E16 | AMCP | The data used in Chapter 6, Exercise 16 | data.frame | 91 | 5 |
C6T1 | AMCP | The data used in Chapter 6, Table 1 | data.frame | 24 | 2 |
C7E12 | AMCP | The data used in Chapter 7, Exercise 12 | data.frame | 32 | 3 |
C7E13 | AMCP | The data used in Chapter 7, Exercise 13 | data.frame | 48 | 3 |
C7E14 | AMCP | The data used in Chapter 7, Exercise 14 | data.frame | 28 | 3 |
C7E15 | AMCP | The data used in Chapter 7, Exercise 15 | data.frame | 36 | 3 |
C7E18 | AMCP | The data used in Chapter 7, Exercise 18 | data.frame | 22 | 3 |
C7E19 | AMCP | The data used in Chapter 7, Exercise 19 | data.frame | 40 | 3 |
C7E22 | AMCP | The data used in Chapter 7, Exercise 22 | data.frame | 28 | 4 |
C7E23 | AMCP | The data used in Chapter 7, Exercise 23 | data.frame | 68 | 4 |
C7E24 | AMCP | The data used in Chapter 7, Exercise 24 | data.frame | 56 | 4 |
C7E25 | AMCP | The data used in Chapter 7, Exercise 25 | data.frame | 60 | 4 |
C7E6 | AMCP | The data used in Chapter 7, Exercise 6 | data.frame | 45 | 3 |
C7E9 | AMCP | The data used in Chapter 7, Exercise 9 | data.frame | 48 | 3 |
C7T1 | AMCP | The data used in Chapter 7, Table 1 | data.frame | 20 | 2 |
C7T11 | AMCP | The data used in Chapter 7, Table 11 | data.frame | 45 | 3 |
C7T15 | AMCP | The data used in Chapter 7, Table 15 | data.frame | 22 | 3 |
C7T23 | AMCP | The data used in Chapter 7, Table 23 | data.frame | 45 | 3 |
C7T5 | AMCP | The data used in Chapter 7, Table 5 | data.frame | 30 | 3 |
C7T9 | AMCP | The data used in Chapter 7, Table 9 | data.frame | 36 | 3 |
C8E15 | AMCP | The data used in Chapter 8, Exercise 15 | data.frame | 48 | 4 |
C8E16 | AMCP | The data used in Chapter 8, Exercise 16 | data.frame | 96 | 4 |
C8E17 | AMCP | The data used in Chapter 8, Exercise 17 | data.frame | 54 | 4 |
C8E18 | AMCP | The data used in Chapter 8, Exercise 18 | data.frame | 80 | 5 |
C8E19 | AMCP | The data used in Chapter 8, Exercise 19 | data.frame | 80 | 5 |
C8T12 | AMCP | The data used in Chapter 8, Table 12 | data.frame | 72 | 4 |
C9E14 | AMCP | The data used in Chapter 9, Exercise 14 | data.frame | 155 | 4 |
C9E15 | AMCP | The data used in Chapter 9, Exercise 15 | data.frame | 310 | 6 |
C9E16 | AMCP | The data used in Chapter 9, Exercise 16 | data.frame | 310 | 6 |
C9E4 | AMCP | The data used in Chapter 9, Exercise 4 | data.frame | 10 | 3 |
C9ExtE1 | AMCP | The data used in Chapter 9 Extension, Exercise 1 | data.frame | 140 | 6 |
C9ExtE2 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 168 | 6 |
C9ExtE3 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 310 | 6 |
C9ExtFigs4and5 | AMCP | The data used in Chapter 9 Extension Figures 4 and 5 | data.frame | 310 | 10 |
C9ExtT1 | AMCP | The data used in Chapter 9, Extension Table 1 | data.frame | 6 | 3 |
C9T1 | AMCP | The data used in Chapter 9, Table 1 | data.frame | 6 | 3 |
C9T11 | AMCP | The data used in Chapter 9, Table 11 | data.frame | 18 | 4 |
C9T7 | AMCP | The data used in Chapter 9, Table 7 | data.frame | 30 | 3 |
T1T1 | AMCP | The data used in Tutorial 1, Table 1 | data.frame | 102 | 1 |
T2T1 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 2 |
T2T2 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 4 |
T3AT1 | AMCP | The data used in Tutorial 3A, Table 1 | data.frame | 8 | 2 |
T3AT2 | AMCP | The data used in Tutorial 3A, Table 2 | data.frame | 8 | 4 |
T3AT4 | AMCP | The data used in Tutorial 3A, Table 4 | data.frame | 10 | 6 |
T3AT5 | AMCP | The data used in Tutorial 3A, Table 5 | data.frame | 10 | 6 |
chapter_10_exercise_14 | AMCP | The data used in Chapter 10, Exercise 14 | data.frame | 63 | 4 |
chapter_10_exercise_7 | AMCP | The data used in Chapter 10, Exercise 7 | data.frame | 45 | 3 |
chapter_10_exercise_9 | AMCP | The data used in Chapter 10, Exercise 9 | data.frame | 72 | 4 |
chapter_10_table_5 | AMCP | The data used in Chapter 10, Table 5 | data.frame | 40 | 3 |
chapter_10_table_9 | AMCP | The data used in Chapter 10, Table 9 | data.frame | 24 | 3 |
chapter_11_exercise_17 | AMCP | The data used in Chapter 11, Exercise 17 | data.frame | 14 | 4 |
chapter_11_exercise_18 | AMCP | The data used in Chapter 11, Exercise 18 | data.frame | 12 | 3 |
chapter_11_exercise_19 | AMCP | The data used in Chapter 11, Exercise 19 | data.frame | 14 | 4 |
chapter_11_exercise_21 | AMCP | The data used in Chapter 11, Exercise 21 | data.frame | 42 | 3 |
chapter_11_exercise_22 | AMCP | The data used in Chapter 11, Exercise 22 | data.frame | 19 | 7 |
chapter_11_exercise_23 | AMCP | The data used in Chapter 11, Exercise 23 | data.frame | 183 | 3 |
chapter_11_exercise_24 | AMCP | The data used in Chapter 11, Exercise 24 | data.frame | 90 | 3 |
chapter_11_exercise_3 | AMCP | The data used in Chapter 11, Exercise 3 | data.frame | 5 | 4 |
chapter_11_exercise_5 | AMCP | The data used in Chapter 11, Exercise 5 | data.frame | 5 | 3 |
chapter_11_table_1 | AMCP | The data used in Chapter 11, Table 1 | data.frame | 6 | 2 |
chapter_11_table_19 | AMCP | The data used in Chapter 11, Table 19 | data.frame | 24 | 3 |
chapter_11_table_20 | AMCP | The data used in Chapter 11, Table 20 | data.frame | 15 | 3 |
chapter_11_table_4 | AMCP | The data used in Chapter 11, Table 4 | data.frame | 10 | 4 |
chapter_11_table_5 | AMCP | The data used in Chapter 11, Table 5 | data.frame | 12 | 4 |
chapter_12_exercise_17 | AMCP | The data used in Chapter 12, Exercise 17 | data.frame | 14 | 5 |
chapter_12_exercise_18 | AMCP | The data used in Chapter 12, Exercise 18 | data.frame | 10 | 3 |
chapter_12_exercise_19 | AMCP | The data used in Chapter 12, Exercise 19 | data.frame | 47 | 6 |
chapter_12_exercise_21 | AMCP | The data used in Chapter 12, Exercise 21 | data.frame | 36 | 4 |
chapter_12_exercise_9 | AMCP | The data used in Chapter 12, Exercise 9 | data.frame | 10 | 4 |
chapter_12_table_1 | AMCP | The data used in Chapter 12, Table 1 | data.frame | 10 | 6 |
chapter_12_table_11 | AMCP | The data used in Chapter 12, Table 11 | data.frame | 10 | 3 |
chapter_12_table_15 | AMCP | The data used in Chapter 12, Table 15 | data.frame | 10 | 3 |
chapter_12_table_21 | AMCP | The data used in Chapter 12, Table 21 | data.frame | 18 | 5 |
chapter_12_table_7 | AMCP | The data used in Chapter 12, Table 7 | data.frame | 10 | 3 |
chapter_12_table_9 | AMCP | The data used in Chapter 12, Table 9 | data.frame | 10 | 2 |
chapter_13_exercise_10 | AMCP | The data used in Chapter 13, Exercise 10 | data.frame | 14 | 4 |
chapter_13_exercise_13 | AMCP | The data used in Chapter 13, Exercise 13 | data.frame | 14 | 4 |
chapter_13_exercise_14 | AMCP | The data used in Chapter 13, Exercise 14 | data.frame | 13 | 3 |
chapter_13_exercise_22 | AMCP | The data used in Chapter 13, Exercise 22 | data.frame | 5 | 3 |
chapter_13_exercise_23 | AMCP | The data used in Chapter 13, Exercise 23 | data.frame | 19 | 7 |
chapter_13_exercise_24 | AMCP | The data used in Chapter 13, Exercise 24 | data.frame | 183 | 3 |
chapter_13_exercise_25 | AMCP | The data used in Chapter 13, Exercise 25 | data.frame | 30 | 3 |
chapter_13_exercise_7 | AMCP | The data used in Chapter 13, Exercise 7 | data.frame | 5 | 4 |
chapter_13_table_1 | AMCP | The data used in Chapter 13, Table 1 | data.frame | 5 | 2 |
chapter_13_table_12 | AMCP | The data used in Chapter 13, Table 12 | data.frame | 8 | 2 |
chapter_13_table_14 | AMCP | The data used in Chapter 13, Table 14 | data.frame | 8 | 2 |
chapter_13_table_2 | AMCP | The data used in Chapter 13, Table 2 | data.frame | 8 | 3 |
chapter_13_table_5 | AMCP | The data used in Chapter 13, Table 5 | data.frame | 12 | 4 |
chapter_14_exercise_10 | AMCP | The data used in Chapter 14, Exercise 10 | data.frame | 10 | 4 |
chapter_14_exercise_14 | AMCP | The data used in Chapter 14, Exercise 14 | data.frame | 30 | 5 |
chapter_14_exercise_15 | AMCP | The data used in Chapter 14, Exercise 15 | data.frame | 10 | 3 |
chapter_14_exercise_21 | AMCP | The data used in Chapter 14, Exercise 21 | data.frame | 14 | 5 |
chapter_14_exercise_22 | AMCP | The data used in Chapter 14, Exercise 22 | data.frame | 47 | 6 |
chapter_14_table_1 | AMCP | The data used in Chapter 14, Table 1 | data.frame | 10 | 4 |
chapter_14_table_10 | AMCP | The data used in Chapter 14, Table 10 | data.frame | 20 | 4 |
chapter_14_table_3 | AMCP | The data used in Chapter 14, Table 3 | data.frame | 10 | 3 |
chapter_14_table_4 | AMCP | The data used in Chapter 14, Table 4 | data.frame | 10 | 6 |
chapter_14_table_5 | AMCP | The data used in Chapter 14, Table 5 | data.frame | 10 | 5 |
chapter_14_table_8 | AMCP | The data used in Chapter 14, Table 8 | data.frame | 20 | 3 |
chapter_15_exercise_16 | AMCP | The data used in Chapter 15, Exercise 16 | data.frame | 14 | 4 |
chapter_15_exercise_17 | AMCP | The data used in Chapter 15, Exercise 17 | data.frame | 56 | 4 |
chapter_15_exercise_18 | AMCP | The data used in Chapter 15, Exercise 18 | data.frame | 24 | 4 |
chapter_15_exercise_18_univariate | AMCP | The data used in Chapter 15, Exercise 18 (Univariate) | data.frame | 72 | 3 |
chapter_15_exercise_19 | AMCP | The data used in Chapter 15, Exercise 19 | data.frame | 24 | 4 |
chapter_15_exercise_19_univariate | AMCP | The data used in Chapter 15, Exercise 19 (Univariate) | data.frame | 72 | 3 |
chapter_15_table_1 | AMCP | The data used in Chapter 15, Table 1 | data.frame | 12 | 4 |
chapter_16_exercise_5 | AMCP | The data used in Chapter 16, Exercise 5 | data.frame | 24 | 3 |
chapter_16_exercise_7 | AMCP | The data used in Chapter 16, Exercise 7 | data.frame | 29 | 6 |
chapter_16_exercise_9 | AMCP | The data used in Chapter 16, Exercise 9 | data.frame | 29 | 6 |
chapter_16_table_1 | AMCP | The data used in Chapter 16, Table 1 | data.frame | 24 | 3 |
chapter_16_table_4 | AMCP | The data used in Chapter 16, Table 4 | data.frame | 29 | 6 |
chapter_1_exercise_18 | AMCP | The data used in Chapter 1, Exercise 18 | data.frame | 4 | 3 |
chapter_1_exercise_19 | AMCP | The data used in Chapter 1, Exercise 19 | data.frame | 30 | 2 |
chapter_1_exercise_21 | AMCP | The data used in Chapter 1, Exercise 21 | data.frame | 12 | 2 |
chapter_1_exercise_22 | AMCP | The data used in Chapter 1, Exercise 22 | data.frame | 11 | 3 |
chapter_1_exercise_23 | AMCP | The data used in Chapter 1, Exercise 23 | data.frame | 12 | 3 |
chapter_1_table_1 | AMCP | The data used in Chapter 1, Table 1 | data.frame | 10 | 3 |
chapter_3_exercise_10 | AMCP | The data used in Chapter 3, Exercise 10 | data.frame | 36 | 3 |
chapter_3_exercise_11 | AMCP | The data used in Chapter 3, Exercise 11 | data.frame | 24 | 2 |
chapter_3_exercise_19 | AMCP | The data used in Chapter 3, Exercise 19 | data.frame | 155 | 3 |
chapter_3_exercise_20 | AMCP | The data used in Chapter 3, Exercise 20 | data.frame | 72 | 2 |
chapter_3_exercise_21 | AMCP | The data used in Chapter 3, Exercise 21 | data.frame | 192 | 2 |
chapter_3_exercise_22 | AMCP | The data used in Chapter 3, Exercise 22 | data.frame | 310 | 5 |
chapter_3_exercise_9 | AMCP | The data used in Chapter 3, Exercise 9 | data.frame | 12 | 2 |
chapter_3_table_1 | AMCP | The data used in Chapter 3, Table 1 | data.frame | 6 | 1 |
chapter_3_table_3 | AMCP | The data used in Chapter 3, Table 3 | data.frame | 30 | 2 |
chapter_3_table_7_raw | AMCP | The data used for Chapter 3, Table 7 (raw data to produce the summary measures) | data.frame | 88 | 3 |
chapter_3_table_9_raw | AMCP | The data used for Chapter 3, Table 9 (raw data to produce the summary measures) | data.frame | 88 | 3 |
chapter_4_exercise_11 | AMCP | The data used in Chapter 4, Exercise 11 | data.frame | 24 | 2 |
chapter_4_exercise_12 | AMCP | The data used in Chapter 4, Exercise 12 | data.frame | 18 | 2 |
chapter_4_exercise_13 | AMCP | The data used in Chapter 4, Exercise 13 | data.frame | 20 | 2 |
chapter_4_table_1 | AMCP | The data used in Chapter 4, Table 1 | data.frame | 20 | 2 |
chapter_4_table_7 | AMCP | The data used in Chapter 4, Table 7 | data.frame | 15 | 2 |
chapter_5_exercise_10 | AMCP | The data used in Chapter 5, Exercise 10 | data.frame | 24 | 2 |
chapter_5_exercise_16 | AMCP | The data used in Chapter 5, Exercise 16 | data.frame | 18 | 2 |
chapter_5_exercise_5 | AMCP | The data used in Chapter 5, Exercise 5 | data.frame | 20 | 2 |
chapter_5_table_4 | AMCP | The data used in Chapter 5, Table 4 | data.frame | 24 | 2 |
chapter_6_exercise_10 | AMCP | The data used in Chapter 6, Exercise 10 | data.frame | 45 | 2 |
chapter_6_exercise_14 | AMCP | The data used in Chapter 6, Exercise 14 | data.frame | 48 | 2 |
chapter_6_exercise_16 | AMCP | The data used in Chapter 6, Exercise 16 | data.frame | 91 | 5 |
chapter_6_table_1 | AMCP | The data used in Chapter 6, Table 1 | data.frame | 24 | 2 |
chapter_7_exercise_12 | AMCP | The data used in Chapter 7, Exercise 12 | data.frame | 32 | 3 |
chapter_7_exercise_13 | AMCP | The data used in Chapter 7, Exercise 13 | data.frame | 48 | 3 |
chapter_7_exercise_14 | AMCP | The data used in Chapter 7, Exercise 14 | data.frame | 28 | 3 |
chapter_7_exercise_15 | AMCP | The data used in Chapter 7, Exercise 15 | data.frame | 36 | 3 |
chapter_7_exercise_18 | AMCP | The data used in Chapter 7, Exercise 18 | data.frame | 22 | 3 |
chapter_7_exercise_19 | AMCP | The data used in Chapter 7, Exercise 19 | data.frame | 40 | 3 |
chapter_7_exercise_22 | AMCP | The data used in Chapter 7, Exercise 22 | data.frame | 28 | 4 |
chapter_7_exercise_23 | AMCP | The data used in Chapter 7, Exercise 23 | data.frame | 68 | 4 |
chapter_7_exercise_24 | AMCP | The data used in Chapter 7, Exercise 24 | data.frame | 56 | 4 |
chapter_7_exercise_25 | AMCP | The data used in Chapter 7, Exercise 25 | data.frame | 60 | 4 |
chapter_7_exercise_6 | AMCP | The data used in Chapter 7, Exercise 6 | data.frame | 45 | 3 |
chapter_7_exercise_9 | AMCP | The data used in Chapter 7, Exercise 9 | data.frame | 48 | 3 |
chapter_7_table_1 | AMCP | The data used in Chapter 7, Table 1 | data.frame | 20 | 2 |
chapter_7_table_11 | AMCP | The data used in Chapter 7, Table 11 | data.frame | 45 | 3 |
chapter_7_table_15 | AMCP | The data used in Chapter 7, Table 15 | data.frame | 22 | 3 |
chapter_7_table_23 | AMCP | The data used in Chapter 7, Table 23 | data.frame | 45 | 3 |
chapter_7_table_5 | AMCP | The data used in Chapter 7, Table 5 | data.frame | 30 | 3 |
chapter_7_table_9 | AMCP | The data used in Chapter 7, Table 9 | data.frame | 36 | 3 |
chapter_8_exercise_15 | AMCP | The data used in Chapter 8, Exercise 15 | data.frame | 48 | 4 |
chapter_8_exercise_16 | AMCP | The data used in Chapter 8, Exercise 16 | data.frame | 96 | 4 |
chapter_8_exercise_17 | AMCP | The data used in Chapter 8, Exercise 17 | data.frame | 54 | 4 |
chapter_8_exercise_18 | AMCP | The data used in Chapter 8, Exercise 18 | data.frame | 80 | 5 |
chapter_8_exercise_19 | AMCP | The data used in Chapter 8, Exercise 19 | data.frame | 80 | 5 |
chapter_8_table_12 | AMCP | The data used in Chapter 8, Table 12 | data.frame | 72 | 4 |
chapter_9_exercise_14 | AMCP | The data used in Chapter 9, Exercise 14 | data.frame | 155 | 4 |
chapter_9_exercise_15 | AMCP | The data used in Chapter 9, Exercise 15 | data.frame | 310 | 6 |
chapter_9_exercise_16 | AMCP | The data used in Chapter 9, Exercise 16 | data.frame | 310 | 6 |
chapter_9_exercise_4 | AMCP | The data used in Chapter 9, Exercise 4 | data.frame | 10 | 3 |
chapter_9_extension_exercise_1 | AMCP | The data used in Chapter 9 Extension, Exercise 1 | data.frame | 140 | 6 |
chapter_9_extension_exercise_2 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 168 | 6 |
chapter_9_extension_exercise_3 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 310 | 6 |
chapter_9_extension_figures_4_and_5 | AMCP | The data used in Chapter 9 Extension Figures 4 and 5 | data.frame | 310 | 10 |
chapter_9_extension_table_1 | AMCP | The data used in Chapter 9, Extension Table 1 | data.frame | 6 | 3 |
chapter_9_table_1 | AMCP | The data used in Chapter 9, Table 1 | data.frame | 6 | 3 |
chapter_9_table_11 | AMCP | The data used in Chapter 9, Table 11 | data.frame | 18 | 4 |
chapter_9_table_7 | AMCP | The data used in Chapter 9, Table 7 | data.frame | 30 | 3 |
tutorial_1_table_1 | AMCP | The data used in Tutorial 1, Table 1 | data.frame | 102 | 1 |
tutorial_2_table_1 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 2 |
tutorial_2_table_2 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 4 |
tutorial_3a_table_1 | AMCP | The data used in Tutorial 3A, Table 1 | data.frame | 8 | 2 |
tutorial_3a_table_2 | AMCP | The data used in Tutorial 3A, Table 2 | data.frame | 8 | 4 |
tutorial_3a_table_4 | AMCP | The data used in Tutorial 3A, Table 4 | data.frame | 10 | 6 |
tutorial_3a_table_5 | AMCP | The data used in Tutorial 3A, Table 5 | data.frame | 10 | 6 |
cpSpecHpcMzXml | readBrukerFlexData | Mass spectrum generated by Bruker Daltonics CompassXport | list | | |
rmadataset | DFP | A sample ExpressionSet object | ExpressionSet | | |
test_container | scITD | Data container for testing tensor formation steps | environment | | |
ExampleDataBinary | pprof | Example data with binary outcomes | list | | |
ExampleDataLinear | pprof | Example data with continuous outcomes | list | | |
ecls_data | pprof | Early Childhood Longitudinal Study Dataset | tbl_df | 9101 | 5 |
beach_preferences | BayesMallows | Beach preferences | data.frame | 1442 | 3 |
bernoulli_data | BayesMallows | Simulated intransitive pairwise preferences | data.frame | 2280 | 3 |
cluster_data | BayesMallows | Simulated clustering data | matrix | 60 | |
potato_true_ranking | BayesMallows | True ranking of the weights of 20 potatoes. | numeric | | |
potato_visual | BayesMallows | Potato weights assessed visually | matrix | 12 | 20 |
potato_weighing | BayesMallows | Potato weights assessed by hand | matrix | 12 | 20 |
sushi_rankings | BayesMallows | Sushi rankings | matrix | 5000 | 10 |
data.mb01 | mdmb | Example Datasets for 'mdmb' Package | list | | |
data.mb02 | mdmb | Example Datasets for 'mdmb' Package | list | | |
data.mb03 | mdmb | Example Datasets for 'mdmb' Package | data.frame | 74 | 3 |
data.mb04 | mdmb | Example Datasets for 'mdmb' Package | data.frame | 500 | 4 |
data.mb05 | mdmb | Example Datasets for 'mdmb' Package | data.frame | 5001 | 13 |
clock_iso_weekdays | clock | Integer codes | environment | | |
clock_months | clock | Integer codes | environment | | |
clock_weekdays | clock | Integer codes | environment | | |
between.1 | bruceR | Demo data. | data.frame | 32 | 2 |
between.2 | bruceR | Demo data. | data.frame | 24 | 3 |
between.3 | bruceR | Demo data. | data.frame | 32 | 4 |
mixed.2_1b1w | bruceR | Demo data. | data.frame | 8 | 4 |
mixed.3_1b2w | bruceR | Demo data. | data.frame | 8 | 5 |
mixed.3_2b1w | bruceR | Demo data. | data.frame | 16 | 4 |
within.1 | bruceR | Demo data. | data.frame | 8 | 5 |
within.2 | bruceR | Demo data. | data.frame | 4 | 7 |
within.3 | bruceR | Demo data. | data.frame | 4 | 9 |
ihdp | bartcs | Infant Health and Development Program Data | data.frame | 747 | 30 |
pitprops | epca | Pitprops correlation data | matrix | 13 | 13 |
psid | bife | Female labor force participation | data.table | 13149 | 8 |
R_example | unusualprofile | An example correlation matrix | matrix | 8 | 8 |
d_example | unusualprofile | An example data.frame | tbl_df | 1 | 8 |
sepsis | phoenix | sepsis | data.frame | 20 | 27 |
dat.age | metamedian | Example data set: Comparing the age between COVID-19 survivors and nonsurvivors (cleaned version) | data.frame | 51 | 13 |
dat.age_raw | metamedian | Example data set: Comparing the age between COVID-19 survivors and nonsurvivors (raw version) | data.frame | 52 | 13 |
dat.asat | metamedian | Example data set: Comparing aspartate transaminase levels between COVID-19 survivors and nonsurvivors (cleaned version) | data.frame | 26 | 13 |
dat.asat_raw | metamedian | Example data set: Comparing aspartate transaminase levels between COVID-19 survivors and nonsurvivors (raw version) | data.frame | 27 | 13 |
dat.ck | metamedian | Example data set: Comparing creatine kinase levels between COVID-19 survivors and nonsurvivors (cleaned version) | data.frame | 17 | 13 |
dat.ck_raw | metamedian | Example data set: Comparing creatine kinase transaminase levels between COVID-19 survivors and nonsurvivors (raw version) | data.frame | 18 | 13 |
dat.phq9 | metamedian | Example data set: Patient Health Questionnaire-9 (PHQ-9) scores (processed version) | data.frame | 58 | 9 |
dat.phq9_raw | metamedian | Example data set: Patient Health Questionnaire-9 (PHQ-9) scores (raw version) | data.frame | 58 | 9 |
coral6 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a branching coral, identified hereon as โcoral6โ. | data.frame | 300 | 5 |
foram166shot7 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a planktonic foraminifera | data.frame | 144 | 8 |
foram174shot4 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a planktonic foraminifera | data.frame | 144 | 8 |
foram72shot3 | lablaster | Time resolved analysis by laser ablation inductively coupled plasma mass spectrometry of a planktonic foraminifera | data.frame | 144 | 8 |
case_study | countfitteR | Short version of the 'case_study_FITC' | data.frame | 117 | 16 |
case_study_APC | countfitteR | Case study for APC dye | data.frame | 117 | 120 |
case_study_FITC | countfitteR | Case study for FITC dye | data.frame | 117 | 120 |
case_study_all | countfitteR | Case study with two fluorescent dyes | data.frame | 117 | 240 |
sim_dat | countfitteR | Data created from simulation of NB Poiss | tbl_df | 24000 | 5 |
BITR | refsplitr | Data from the journal BioTropica (pulled from Web of Knowledge) | data.frame | 10 | 32 |
BITR_geocode | refsplitr | Georeferenced data from the journal BioTropica (pulled from Web of Science) | data.frame | 41 | 15 |
countries | refsplitr | Names of all the countries in the world | character | | |
data | gatoRs | Downloaded data from gators_download() for Galax urceolata with default settings and 'limit' set to 5: data <- gators_download(synonyms.list = c("Galax urceolata", "Galax aphylla"), limit = 5) | data.frame | 17 | 23 |
Dogs_MimicData | CureDepCens | Dogs_MimicData data set | data.frame | 400 | 13 |
AirPassengers_ts | timeSeriesDataSets | Monthly Airline Passenger Numbers from 1949 to 1960. | ts | | |
BJsales_ts | timeSeriesDataSets | Sales Data with Leading Indicator. | ts | | |
JohnsonJohnson_ts | timeSeriesDataSets | Quarterly Earnings per Johnson & Johnson Share (1960-1981). | ts | | |
LakeHuron_ts | timeSeriesDataSets | Lake Huron Water Level (1875-1972). | ts | | |
Nile_ts | timeSeriesDataSets | Flow of the River Nile | ts | | |
USgas_ts | timeSeriesDataSets | US Monthly Natural Gas Consumption | ts | | |
WWWusage_ts | timeSeriesDataSets | Internet Usage per Minute | ts | | |
a10_ts | timeSeriesDataSets | Monthly Anti-Diabetic Drug Subsidy in Australia from 1991 to 2008. | ts | | |
airpass_ts | timeSeriesDataSets | Monthly Airline Passenger Numbers from 1949 to 1960. | ts | | |
ausbeer_ts | timeSeriesDataSets | Quarterly Australian Beer Production. | ts | | |
auscafe_ts | timeSeriesDataSets | Monthly Expenditure on Eating Out in Australia. | ts | | |
beer_ts | timeSeriesDataSets | Monthly Beer Production. | ts | | |
books_mts | timeSeriesDataSets | Sales of Paperback and Hardcover Books. | mts | 30 | 2 |
bricksq_ts | timeSeriesDataSets | Quarterly Clay Brick Production. | ts | | |
co2_ts | timeSeriesDataSets | Mauna Loa Atmospheric CO2 Concentration. | ts | | |
discoveries_ts | timeSeriesDataSets | Yearly Numbers of Important Discoveries. | ts | | |
economics_df_ts | timeSeriesDataSets | US Economic Time Series. | spec_tbl_df | 574 | 6 |
elec_ts | timeSeriesDataSets | Electricity Production. | ts | | |
elecdaily_mts | timeSeriesDataSets | Half-Hourly and Daily Electricity Demand for Victoria, Australia, in 2014. | mts | 365 | 3 |
elecdemand_msts | timeSeriesDataSets | Half-Hourly and Daily Electricity Demand for Victoria, Australia, in 2014. | msts | 17520 | 3 |
elecequip_ts | timeSeriesDataSets | Electrical Equipment Manufactured in the Euro Area. | ts | | |
euretail_ts | timeSeriesDataSets | Quarterly Retail Trade in the Euro Area. | ts | | |
goog200_ts | timeSeriesDataSets | Daily Closing Stock Prices of Google Inc. (200 Days). | ts | | |
gtemp_both_ts | timeSeriesDataSets | Global Mean Land and Open Ocean Temperature Deviations (1850-2023). | ts | | |
gtemp_land_ts | timeSeriesDataSets | Global Mean Land Temperature Deviations (1850-2023). | ts | | |
gtemp_ocean_ts | timeSeriesDataSets | Global Mean Ocean Temperature Deviations (1850-2023). | ts | | |
h02_ts | timeSeriesDataSets | Monthly Corticosteroid Drug Subsidy in Australia (1991-2008). | ts | | |
hsales2_ts | timeSeriesDataSets | Sales of New One-Family Houses (1987-1996). | ts | | |
hyndsight_ts | timeSeriesDataSets | Daily Pageviews for the Hyndsight Blog (April 2014 - April 2015). | ts | | |
ibm_mts | timeSeriesDataSets | IBM Sales and Profit Data. | mts | 42 | 4 |
ibmclose_ts | timeSeriesDataSets | Daily Closing Stock Prices of IBM. | ts | | |
jcars_ts | timeSeriesDataSets | Motor Vehicle Production (1947-1989). | ts | | |
jj_ts | timeSeriesDataSets | Johnson & Johnson Quarterly Earnings Per Share (1960-1981). | ts | | |
ldeaths_ts | timeSeriesDataSets | Monthly Deaths from Lung Diseases in the UK (1974-1980). | ts | | |
livestock_ts | timeSeriesDataSets | Livestock (Sheep) in Asia, 1961-2007. | ts | | |
marathon_ts | timeSeriesDataSets | Boston Marathon Winning Times Since 1897 | ts | | |
maxtemp_ts | timeSeriesDataSets | Maximum Annual Temperatures at Moorabbin Airport, Melbourne | ts | | |
mdeaths_ts | timeSeriesDataSets | Monthly Deaths from Lung Diseases in the UK | ts | | |
mens400_ts | timeSeriesDataSets | Winning Times in Olympic Men's 400m Track Final | ts | | |
milk_ts | timeSeriesDataSets | Monthly Milk Production per Cow | ts | | |
nail_ts | timeSeriesDataSets | Nail Prices | ts | | |
pedestrian_tbl_ts | timeSeriesDataSets | Pedestrian Counts in the City of Melbourne | tbl_ts | 66037 | 5 |
qauselec_ts | timeSeriesDataSets | Quarterly Australian Electricity Production | ts | | |
qcement_ts | timeSeriesDataSets | Quarterly Australian Portland Cement Production | ts | | |
qgas_ts | timeSeriesDataSets | Quarterly Australian Gas Production | ts | | |
sunspotarea_ts | timeSeriesDataSets | Annual Average Sunspot Area | ts | | |
taylor_30_min_df_ts | timeSeriesDataSets | Half-Hourly Electricity Demand | tbl_df | 4032 | 2 |
tourism_tbl_ts | timeSeriesDataSets | Australian Domestic Overnight Trips | tbl_ts | 24320 | 5 |
uschange_mts | timeSeriesDataSets | Growth Rates of Personal Consumption and Personal Income in the USA | mts | 187 | 5 |
usmelec_ts | timeSeriesDataSets | Monthly Total Net Electricity Generation in the USA | ts | | |
uspop_ts | timeSeriesDataSets | US Census Population Data | ts | | |
wineind_ts | timeSeriesDataSets | Australian Total Wine Sales | ts | | |
wmurders_ts | timeSeriesDataSets | Annual Female Murder Rate in the USA | ts | | |
woolyrnq_ts | timeSeriesDataSets | Quarterly Production of Woollen Yarn in Australia | ts | | |
gregorius | genetics | Probability of Observing All Alleles with a Given Frequency in a Sample of a Specified Size. | data.frame | 16 | 4 |
Altitude_Cluster | WallomicsData | Altitude Cluster | factor | | |
Ecotype | WallomicsData | Ecotype | factor | | |
Genetic_Cluster | WallomicsData | Genetic Cluster | factor | | |
Metabolomics_Rosettes | WallomicsData | Metabolomics Rosettes | data.frame | 30 | 6 |
Metabolomics_Stems | WallomicsData | Metabolomics Stems | data.frame | 30 | 6 |
Metadata | WallomicsData | Metadata | data.frame | 474 | 4 |
Phenomics_Rosettes | WallomicsData | Phenomics Rosettes | data.frame | 30 | 5 |
Phenomics_Stems | WallomicsData | Phenomics Stems | data.frame | 30 | 4 |
Proteomics_Rosettes_CW | WallomicsData | Proteomics Rosettes Cell Wall | data.frame | 30 | 364 |
Proteomics_Stems_CW | WallomicsData | Proteomics Stems Cell Wall | data.frame | 30 | 414 |
Temperature | WallomicsData | Temperature | factor | | |
Transcriptomics_Rosettes | WallomicsData | Transcriptomics Rosettes | data.frame | 30 | 19763 |
Transcriptomics_Rosettes_CW | WallomicsData | Transcriptomics Rosettes Cell Wall | data.frame | 30 | 364 |
Transcriptomics_Stems | WallomicsData | Transcriptomics Stems | data.frame | 30 | 22570 |
Transcriptomics_Stems_CW | WallomicsData | Transcriptomics Stems Cell Wall | data.frame | 30 | 414 |
ga_topo | textures | Topographic image | list | | |
europeananews | nametagger | Tagged news paper articles from Europeana | data.frame | 533893 | 4 |
Conf_griffin | sequoia | Example output from estimating confidence probabilities: griffins | list | | |
FieldMums_griffin | sequoia | Example field-observed mothers: griffins | data.frame | 143 | 2 |
Geno_HSg5 | sequoia | Example genotype file: 'HSg5' | matrix | 920 | |
Geno_griffin | sequoia | Example genotype file: Griffins | matrix | 142 | |
Inherit_patterns | sequoia | Inheritance patterns | array | | |
LH_HSg5 | sequoia | Example life history file: 'HSg5' | data.frame | 1000 | 3 |
LH_griffin | sequoia | Example life history data: griffins | data.frame | 200 | 3 |
MaybeRel_griffin | sequoia | Example output from check for relatives: griffins | list | | |
Ped_HSg5 | sequoia | Example pedigree: 'HSg5' | data.frame | 1000 | 3 |
Ped_griffin | sequoia | Example pedigree: griffins | data.frame | 200 | 4 |
SeqOUT_HSg5 | sequoia | Example output from pedigree inference: 'HSg5' | list | | |
SeqOUT_griffin | sequoia | Example output from pedigree inference: griffins | list | | |
SimGeno_example | sequoia | Example genotype file: 'HSg5' | matrix | 214 | 200 |
Lob.bt.pe | nlraa | object for confidence bands vignette Lob.bt.pe | boot | | |
barley | nlraa | Barley response to nitrogen fertilizer | data.frame | 76 | 3 |
fm1.P.at.x.0.4 | nlraa | object for confidence bands vignette fm1.P.at.x.0.4 | boot | | |
fm1.P.bt | nlraa | object for confidence bands vignette fm1.P.bt | boot | | |
fm1.P.bt.ft | nlraa | object for confidence bands vignette fm1.P.bt.ft | boot | | |
fm2.Lob.bt | nlraa | object for confidence bands vignette fm2.Lob.bt | boot | | |
fmm1.bt | nlraa | object for confidence bands vignette fmm1.bt | boot | | |
lfmc | nlraa | Live fuel moisture content | data.frame | 247 | 6 |
maizeleafext | nlraa | Maize leaf extension rate as a response to temperature | data.frame | 10 | 2 |
sm | nlraa | Sorghum and Maize growth in Greece | data.frame | 236 | 5 |
swpg | nlraa | Water limitations for Soybean growth | data.frame | 20 | 2 |
ibd | ZIBR | Longitudinal human microbiome data | data.frame | 236 | 5 |
GermanIndustry | micEconCES | Aggregated Time Series Data for the West German Industry | data.frame | 34 | 33 |
MishraCES | micEconCES | Mishra's (2006) CES data | data.frame | 50 | 6 |
bivariate_missingness | dosearch | Systematic Analysis of Bivariate Missing Data Problems | data.frame | 6144 | 8 |
corn_data | maize | Synthetic Corn Dataset for Corny Example | tbl_df | 300 | 3 |
umaru | interactionRCS | UMARU IMPACT Study data | data.frame | 575 | 15 |
mockstudy | arsenal | Mock study data for examples | data.frame | 1499 | 14 |
scale_tabs | goeveg | Conversion tables for cover-abundance scales | list | | |
schedenenv | goeveg | Header data for Vegetation releves from Scheden | data.frame | 28 | 11 |
schedenveg | goeveg | Vegetation releves from Scheden | data.frame | 28 | 155 |
apacpnut | SWMPr | Example nutrient data for Apalachicola Bay Cat Point station. | swmpr | 215 | 13 |
apacpwq | SWMPr | Example water quality data for Apalachicola Bay Cat Point station. | swmpr | 70176 | 25 |
apadbwq | SWMPr | Example water quality data for Apalachicola Bay Dry Bar station. | swmpr | 70176 | 25 |
apaebmet | SWMPr | Example weather data for Apalachicola Bay East Bay station. | swmpr | 70176 | 23 |
stat_locs | SWMPr | Locations of NERRS sites | tbl_df | 178 | 4 |
adtte | ggsurvfit | Example phase III clinical trial data set | tbl_df | 2199 | 19 |
df_colon | ggsurvfit | Formatted Copy of 'survival::colon' | tbl_df | 929 | 14 |
df_lung | ggsurvfit | Formatted Copy of 'survival::lung' | tbl_df | 228 | 10 |
arxiv_cats | aRxiv | arXiv subject classifications | data.frame | 155 | 5 |
query_terms | aRxiv | arXiv query field terms | data.frame | 10 | 2 |
dat.aloe2013 | metadat | Studies on the Association Between Supervision Quality and Various Outcomes in Social, Mental Health, and Child Welfare Workers | data.frame | 5 | 5 |
dat.anand1999 | metadat | Studies on the Effectiveness of Oral Anticoagulants in Patients with Coronary Artery Disease | data.frame | 34 | 9 |
dat.assink2016 | metadat | Studies on the Association between Recidivism and Mental Health | escalc | 100 | 8 |
dat.axfors2021 | metadat | Mortality Outcomes with Hydroxychloroquine and Chloroquine in COVID-19 from an International Collaborative Meta-Analysis of Randomized Trials | data.frame | 33 | 13 |
dat.bakdash2021 | metadat | Dataset on Situation Awareness and Task Performance Associations | data.frame | 678 | 12 |
dat.baker2009 | metadat | Studies on Pharmacologic Treatments for Chronic Obstructive Pulmonary Disease | data.frame | 94 | 6 |
dat.bangertdrowns2004 | metadat | Studies on the Effectiveness of Writing-to-Learn Interventions | escalc | 48 | 16 |
dat.bartos2023 | metadat | Results of 350,757 Coin Flips to Examine Same-Side Bias | data.frame | 48 | 7 |
dat.baskerville2012 | metadat | Studies on the Effectiveness of Practice Facilitation Interventions | data.frame | 23 | 18 |
dat.bassler2004 | metadat | Studies on Ketotifen Alone or as Additional Medication for Long-Term Control of Asthma and Wheeze in Children | data.frame | 10 | 6 |
dat.bcg | metadat | Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis | data.frame | 13 | 9 |
dat.begg1989 | metadat | Studies on Bone-Marrow Transplantation versus Chemotherapy for the Treatment of Leukemia | escalc | 20 | 6 |
dat.berkey1998 | metadat | Studies on Treatments for Periodontal Disease | escalc | 10 | 9 |
dat.besson2016 | metadat | Dataset on How Maternal Diet Impacts Copying Styles in Rodents | data.frame | 473 | 67 |
dat.bonett2010 | metadat | Studies on the Reliability of the CES-D Scale | data.frame | 9 | 6 |
dat.bornmann2007 | metadat | Studies on Gender Differences in Grant and Fellowship Awards | data.frame | 66 | 13 |
dat.bourassa1996 | metadat | Studies on the Association between Handedness and Eye-Dominance | data.frame | 96 | 14 |
dat.cannon2006 | metadat | Studies on the Effectiveness of Intensive Versus Moderate Statin Therapy for Preventing Coronary Death or Myocardial Infarction | data.frame | 4 | 16 |
dat.cohen1981 | metadat | Studies on the Relationship between Course Instructor Ratings and Student Achievement | data.frame | 20 | 5 |
dat.colditz1994 | metadat | Studies on the Effectiveness of the BCG Vaccine Against Tuberculosis | data.frame | 13 | 9 |
dat.collins1985a | metadat | Studies on the Treatment of Upper Gastrointestinal Bleeding by a Histamine H2 Antagonist | data.frame | 27 | 14 |
dat.collins1985b | metadat | Studies on the Effects of Diuretics in Pregnancy | data.frame | 9 | 16 |
dat.craft2003 | metadat | Studies on the Relationship between the Competitive State Anxiety Inventory-2 and Sport Performance | data.frame | 60 | 6 |
dat.crede2010 | metadat | Studies on the Relationship between Class Attendance and Grades in College Students | data.frame | 97 | 8 |
dat.crisafulli2020 | metadat | Duchenne Muscular Dystrophy (DMD) Prevalence Data | data.frame | 26 | 7 |
dat.curtin2002 | metadat | Studies on Potassium Supplementation to Reduce Diastolic Blood Pressure | data.frame | 21 | 6 |
dat.curtis1998 | metadat | Studies on the Effects of Elevated CO2 Levels on Woody Plant Mass | data.frame | 102 | 20 |
dat.dagostino1998 | metadat | Studies on the Effectiveness of Antihistamines in Reducing Symptoms of the Common Cold | data.frame | 72 | 16 |
dat.damico2009 | metadat | Studies on Topical plus Systemic Antibiotics to Prevent Respiratory Tract Infections | data.frame | 16 | 8 |
dat.debruin2009 | metadat | Studies on Standard Care Quality and HAART-Adherence | data.frame | 13 | 10 |
dat.dogliotti2014 | metadat | Studies on Antithrombotic Treatments to Prevent Strokes | data.frame | 44 | 5 |
dat.dong2013 | metadat | Studies on Safety of Inhaled Medications for Chronic Obstructive Pulmonary Disease | data.frame | 99 | 4 |
dat.dorn2007 | metadat | Studies on Complementary and Alternative Medicine for Irritable Bowel Syndrome | data.frame | 19 | 12 |
dat.dumouchel1994 | metadat | Nitrogen dioxide data set | data.frame | 9 | 7 |
dat.egger2001 | metadat | Studies on the Effectiveness of Intravenous Magnesium in Acute Myocardial Infarction | data.frame | 16 | 7 |
dat.fine1993 | metadat | Studies on Radiation Therapy with or without Adjuvant Chemotherapy in Patients with Malignant Gliomas | data.frame | 17 | 11 |
dat.franchini2012 | metadat | Studies on Dopamine Agonists to Reduce "Off-Time" in Patients with Advanced Parkinson Disease | data.frame | 7 | 13 |
dat.frank2008 | metadat | Studies on the Association Between the CASP8 -652 6N del Promoter Polymorphism and Breast Cancer Risk | data.frame | 4 | 7 |
dat.furukawa2003 | metadat | Studies on Low Dosage Tricyclic Antidepressants for the Treatment of Depression | data.frame | 17 | 7 |
dat.gibson2002 | metadat | Studies on the Effectiveness of Self-Management Education and Regular Medical Review for Adults with Asthma | data.frame | 15 | 13 |
dat.graves2010 | metadat | Studies on the Effectiveness of Injected Cholera Vaccines | data.frame | 17 | 5 |
dat.gurusamy2011 | metadat | Studies on Interventions to Reduce Mortality after Liver Transplantation | data.frame | 29 | 4 |
dat.hackshaw1998 | metadat | Studies on the Risk of Lung Cancer in Women Exposed to Environmental Tobacco Smoke | escalc | 37 | 11 |
dat.hahn2001 | metadat | Studies on the Effectiveness of Different Rehydration Solutions for the Prevention of Unscheduled Intravenous Infusion in Children with Diarrhoea | data.frame | 12 | 5 |
dat.hannum2020 | metadat | Studies Comparing Objective and Subjective Olfactory Loss in COVID-19 Patients | data.frame | 35 | 11 |
dat.hart1999 | metadat | Studies on the Effectiveness of Warfarin for Preventing Strokes | data.frame | 6 | 12 |
dat.hartmannboyce2018 | metadat | Studies on the Effectiveness of Nicotine Replacement Therapy for Smoking Cessation | data.frame | 136 | 6 |
dat.hasselblad1998 | metadat | Studies on the Effectiveness of Counseling for Smoking Cessation | data.frame | 50 | 7 |
dat.hine1989 | metadat | Studies on Prophylactic Use of Lidocaine After a Heart Attack | data.frame | 6 | 6 |
dat.ishak2007 | metadat | Studies on Deep-Brain Stimulation in Patients with Parkinson's disease | escalc | 46 | 11 |
dat.kalaian1996 | metadat | Studies on the Effectiveness of Coaching for the SAT | data.frame | 67 | 12 |
dat.kearon1998 | metadat | Studies on the Accuracy of Venous Ultrasonography for the Diagnosis of Deep Venous Thrombosis | data.frame | 34 | 8 |
dat.knapp2017 | metadat | Studies on Differences in Planning Performance in Schizophrenia Patients versus Healthy Controls | data.frame | 66 | 14 |
dat.konstantopoulos2011 | metadat | Studies on the Effects of Modified School Calendars on Student Achievement | escalc | 56 | 6 |
dat.landenberger2005 | metadat | Studies on the Effectiveness of CBT for Reducing Recidivism | data.frame | 58 | 32 |
dat.laopaiboon2015 | metadat | Studies on the Effectiveness of Azithromycin for Treating Lower Respiratory Tract Infections | data.frame | 15 | 11 |
dat.lau1992 | metadat | Studies on Intravenous Streptokinase for Acute Myocardial Infarction | data.frame | 33 | 6 |
dat.lee2004 | metadat | Studies on Acupoint P6 Stimulation for Preventing Nausea | data.frame | 16 | 7 |
dat.lehmann2018 | metadat | The Effect of Red on Perceived Attractiveness | data.frame | 81 | 49 |
dat.li2007 | metadat | Studies on the Effectiveness of Intravenous Magnesium in Acute Myocardial Infarction | data.frame | 22 | 7 |
dat.lim2014 | metadat | Studies on the Association Between Maternal Size, Offspring Size, and Number of Offsprings | list | | |
dat.linde2005 | metadat | Studies on the Effectiveness of St. John's Wort for Treating Depression | data.frame | 26 | 17 |
dat.linde2015 | metadat | Studies on Classes of Antidepressants for the Primary Care Setting | data.frame | 66 | 24 |
dat.linde2016 | metadat | Studies on Antidepressants for the Primary Care Setting | data.frame | 124 | 5 |
dat.lopez2019 | metadat | Studies on the Effectiveness of CBT for Depression | data.frame | 172 | 23 |
dat.maire2019 | metadat | Studies on Temporal Trends in Fish Community Structures in French Rivers | list | | |
dat.mccurdy2020 | metadat | Studies on the Generation Effect | escalc | 1653 | 26 |
dat.mcdaniel1994 | metadat | Studies on the Validity of Employment Interviews | data.frame | 160 | 5 |
dat.michael2013 | metadat | The Non-Persuasive Power of a Brain Image | data.frame | 12 | 13 |
dat.molloy2014 | metadat | Studies on the Relationship between Conscientiousness and Medication Adherence | data.frame | 16 | 10 |
dat.moura2021 | metadat | Studies on Assortative Mating | list | | |
dat.nakagawa2007 | metadat | Assessing the Function of House Sparrows' Bib Size Using a Flexible Meta-Analysis Method | data.frame | 15 | 4 |
dat.nielweise2007 | metadat | Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections | data.frame | 18 | 7 |
dat.nielweise2008 | metadat | Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections | data.frame | 9 | 7 |
dat.normand1999 | metadat | Studies on the Length of Hospital Stay of Stroke Patients | data.frame | 9 | 8 |
dat.obrien2003 | metadat | Studies on the Relationship Between BMI and Risk of Preeclampsia | data.frame | 43 | 13 |
dat.pagliaro1992 | metadat | Studies on the Effectiveness of Nonsurgical Treatments in Cirrhosis | data.frame | 54 | 4 |
dat.pignon2000 | metadat | Studies on the Effectiveness of Locoregional Treatment plus Chemotherapy for Head and Neck Squamous-Cell Carcinoma | data.frame | 65 | 5 |
dat.pritz1997 | metadat | Studies on the Effectiveness of Hyperdynamic Therapy for Treating Cerebral Vasospasm | data.frame | 14 | 4 |
dat.raudenbush1985 | metadat | Studies on Assessing the Effects of Teacher Expectations on Pupil IQ | escalc | 19 | 10 |
dat.riley2003 | metadat | Studies on MYC-N as a Prognostic Marker for Neuroblastoma | escalc | 98 | 5 |
dat.roever2022 | metadat | Irinotecan / S-1 Toxicity Dataset | data.frame | 37 | 5 |
dat.senn2013 | metadat | Studies on the Effectiveness of Glucose-Lowering Agents | data.frame | 53 | 6 |
dat.spooner2002 | metadat | Studies on Nedocromil Sodium for Preventing Exercise-Induced Bronchoconstriction | data.frame | 17 | 9 |
dat.stowe2010 | metadat | Studies on Adjuvant Treatments to Levodopa Therapy for Parkinson disease | data.frame | 29 | 14 |
dat.tannersmith2016 | metadat | Studies on the Relationship between School Motivation and Criminal Behavior | data.frame | 113 | 8 |
dat.ursino2021 | metadat | Sorafenib Toxicity Dataset | data.frame | 49 | 5 |
dat.vanhowe1999 | metadat | Studies on the Association between Circumcision and HIV Infection | data.frame | 33 | 6 |
dat.viechtbauer2021 | metadat | Studies to Illustrate Model Checking Methods | data.frame | 20 | 6 |
dat.white2020 | metadat | Studies on the Relationship between Sexual Signal Expression and Individual Quality | data.frame | 186 | 15 |
dat.woods2010 | metadat | Studies on Treatments for Chronic Obstructive Pulmonary Disease | data.frame | 8 | 4 |
dat.yusuf1985 | metadat | Studies of Beta Blockers During and After Myocardial Infarction | data.frame | 130 | 7 |
ICU_data | stratamatch | Demographics and comorbidities of 10,157 ICU patients | tbl_df | 10157 | 13 |
pie | lulcc | Land use change dataset for Plum Island Ecosystem | list | | |
sibuyan | lulcc | Land use change dataset for Sibuyan Island | list | | |
students | Kernelheaping | Student0405 | data.frame | 690 | 7 |
nhanes_2010 | furniture | NHANES 2009-2010 | data.frame | 1417 | 24 |
isohedron | mapmisc | Country boundaries | matrix | 6762 | 2 |
meuse | mapmisc | Data from the Netherlands | PackedSpatVector | | |
nldCities | mapmisc | Data from the Netherlands | PackedSpatVector | | |
nldElev | mapmisc | Data from the Netherlands | PackedSpatRaster | | |
nldTiles | mapmisc | Data from the Netherlands | PackedSpatRaster | | |
worldMap | mapmisc | Country boundaries | PackedSpatVector | | |
nelson_arrivals | treat.sim | Time dependent arrival profile from Nelson (2013) | data.frame | 18 | 3 |
boundary | SurfaceTortoise | | SpatialPolygonsDataFrame | | |
davis1974 | chlorpromazineR | Chlorpromazine equivalent key from Davis 1974 data | list | | |
gardner2010 | chlorpromazineR | Chlorpromazine equivalent key from Gardner et al. 2010 data | list | | |
gardner2010_withsai | chlorpromazineR | Chlorpromazine equivalent key from Gardner et al. 2010 data | list | | |
leucht2016 | chlorpromazineR | Chlorpromazine equivalent key from Leucht et al. 2016 data | list | | |
leucht2020 | chlorpromazineR | Antipsychotic equivalent key from Leucht et al. 2020 data | list | | |
woods2003 | chlorpromazineR | Chlorpromazine equivalent key from Woods 2003 data | list | | |
mtscr_creativity | mtscr | Creativity assessment through semantic distance dataset | tbl_df | 4585 | 4 |
mtscr_self_rank | mtscr | Self-chosen best answers | tbl_df | 3225 | 4 |
breweries | mapview | Selected breweries in Franconia | sf | 224 | 9 |
franconia | mapview | Administrative district borders of Franconia | sf | 37 | 7 |
trails | mapview | Selected hiking trails in Franconia | sf | 543 | 4 |
standgrowth | forestGYM | Data for construction of stand growth model. | data.frame | 330 | 16 |
example.data | hettest | Example data | list | | |
data_corpus_news2014 | wordvector | Yahoo News summaries from 2014 | corpus | | |
duke | bestridge | Duke breast cancer data | data.frame | 46 | 7130 |
patient.data | bestridge | Lymphoma patients data set | list | | |
trim32 | bestridge | The Bardet-Biedl syndrome Gene expression data | data.frame | 120 | 501 |
meta16S | aIc | 16S rRNA tag-sequencing data | data.frame | 860 | 359 |
metaTscome | aIc | meta-transcriptome data | data.frame | 3647 | 17 |
selex | aIc | Selection-based differential sequence variant abundance dataset | data.frame | 1600 | 14 |
singleCell | aIc | single cell transcriptome data | data.frame | 1508 | 2000 |
transcriptome | aIc | Saccharomyces cerevisiae transcriptome | data.frame | 5892 | 96 |
sampleData | GIMMEgVAR | sampleData | list | | |
R_0 | prioGene | the vector of initial disease risk scores for all genes | numeric | | |
dise_gene | prioGene | a vector of disease related genes | matrix | 79 | 1 |
edge_weight | prioGene | weights of edges of a net | matrix | 25 | 3 |
genes_mat | prioGene | a one-to-many matrix of GO term and gene | matrix | 45 | |
metabolic_net | prioGene | a matrix, Human metabolic network | matrix | 589199 | 2 |
net | prioGene | a network of genes | matrix | 2000 | 2 |
net_disease | prioGene | a network of disease related genes | matrix | 26 | 2 |
net_disease_term | prioGene | GO terms for each pair of nodes in the network | matrix | 25 | 4 |
node_weight | prioGene | a matrix, genes and their weights | matrix | 45 | |
terms_mat | prioGene | a matrix, GO terms and GO genes | matrix | 1172 | |
DF2011 | stratEst | Data of Dal Bo and Frechette (2011) | data.frame | 7358 | 6 |
DFS2020 | stratEst | Data of Dvorak, Fischbacher and Schmelz (2020) | data.frame | 569 | 7 |
FRD2012 | stratEst | Data of Fudenberg, Rand, and Dreber (2012) | data.frame | 13126 | 9 |
WXZ2014 | stratEst | Data of the rock-paper-scissors game from Wang, Xu, and Zhou (2014) | data.frame | 21600 | 6 |
data.DF2011 | stratEst | Data of Dal Bo and Frechette (2011) | stratEst.data | 7358 | 7 |
data.DFS2020 | stratEst | Data of Dvorak, Fischbacher and Schmelz (2020) | stratEst.data | 569 | 8 |
data.FRD2012 | stratEst | Data of Fudenberg, Rand, and Dreber (2012) | stratEst.data | 13126 | 10 |
data.WXZ2014 | stratEst | Data of the rock-paper-scissors game from Wang, Xu, and Zhou (2014) | stratEst.data | 21600 | 7 |
strategies.DF2011 | stratEst | strategies.DF2011 | list | | |
strategies.DFS2020 | stratEst | strategies.DFS2020 | list | | |
strategies.FRD2012 | stratEst | strategies.FRD2012 | list | | |
strategies.PD | stratEst | strategies.PD | list | | |
strategies.RPS | stratEst | strategies.RPS | list | | |
runoff | PRSim | Sample runoff of a catchment | data.frame | 15706 | 4 |
runoff_multi_site_T | PRSim | Sample runoff and temperature data of two catchments with a similar discharge regime | list | | |
runoff_multi_sites | PRSim | Sample runoff of four catchments with a similar discharge regime | list | | |
simulations | PRSim | Simulated runoff | list | | |
simulations_multi_sites | PRSim | Simulated runoff for four catchments | list | | |
weather_multi_sites | PRSim | Sample temperature and precipitation of four catchments derived from the ERA5-Land gridded dataset | list | | |
weather_sim_multi_sites | PRSim | Simulated temperature and precipitation for two grid cells | list | | |
andes | tbea | Divergence-time estimation data for cis-trans-Andean pairs | data.frame | 54 | 3 |
laventa | tbea | Geochronology samples from the Honda Group in Colombia | data.frame | 87 | 7 |
cirdata | isocir | Random Circular Data. | numeric | | |
cirgenes | isocir | A set of angular measurements from cell-cycle experiments with genes. | matrix | 10 | 16 |
datareplic | isocir | Random Circular Data with Replications. | matrix | 8 | 10 |
colorPaletteNRIND | clinUtils | Color palette for a standard CDISC Normal/Reference Range Indicator. | character | | |
dataADaMCDISCP01 | clinUtils | Example of ADaM datasets from the CDISC original Pilot 01 study | list | | |
dataSDTMCDISCP01 | clinUtils | Example of SDTM datasets from the CDISC original Pilot 01 study | list | | |
shapePaletteNRIND | clinUtils | Shape palette for a standard CDISC Normal/Reference Range Indicator. | numeric | | |
motcon | RSA | Data set on motive congruence. | data.frame | 84 | 3 |
motcon2 | RSA | Another data set on motive congruence. | data.frame | 362 | 3 |
selfother | RSA | A fake data set on self-other agreement | data.frame | 800 | 9 |
flucyl | mem | Castilla y Leon influenza crude rates | data.frame | 33 | 8 |
flucylraw | mem | Castilla y Leon influenza standarised rates | data.frame | 267 | 3 |
Tasmania | aPCoA | Tasmania Dataset | list | | |
SEXE | mmeln | A two mixture example | factor | | |
Y | mmeln | A two mixture example | matrix | 30 | |
wafer40 | npregderiv | The data set on the n=7755 substrate's deflection measurements before and after the thin film deposition in two radial directions. | data.frame | 7755 | 5 |
candidate_module | ProgModule | candidate_module, candidate module | list | | |
final_candidate_module | ProgModule | final_candidate_module, Final candidate modules | list | | |
local_network | ProgModule | local_network, local network gene set | list | | |
maf_data | ProgModule | maf_data, MAF file | data.frame | 3745 | 10 |
module | ProgModule | module, gene set | character | | |
mut_status | ProgModule | mut_status, mutations matrix | matrix | 338 | 331 |
net | ProgModule | net, network | igraph | | |
plotMutInteract_moduledata | ProgModule | plotMutInteract_moduledata | list | | |
plotMutInteract_mutdata | ProgModule | plotMutInteract_mutdata | matrix | 89 | 430 |
subnet | ProgModule | subnet, network | igraph | | |
univarCox_result | ProgModule | univarCox_result | numeric | | |
rna | AutoPipe | rna egene expression of 48 meningiomas | data.frame | 200 | 48 |
welfare | hdpGLM | Fake data set with 2000 observations | data.frame | 2000 | 4 |
welfare2 | hdpGLM | Fake data set with 2000 observations | tbl_df | 3200 | 6 |
cytokine | varbvs | Cytokine signaling genes SNP annotation. | numeric | | |
leukemia | varbvs | Expression levels recorded in leukemia patients. | list | | |
exampleData | DiPALM | Example Data: Data for use with the DiPALM vignette | list | | |
testData | DiPALM | Test Data: Data for function testing | list | | |
reference | SeqNet | RNA-seq reference dataset | list | | |
mock | GenEst | A mock example data set | list | | |
solar_PV | GenEst | Photovoltaic Example Dataset | list | | |
solar_powerTower | GenEst | Power Tower Example Dataset | list | | |
solar_trough | GenEst | Trough-based solar thermal power simulated example | list | | |
wind_RP | GenEst | Wind Road and Pad (120m) Example | list | | |
wind_RPbat | GenEst | Wind Bat-Only Road and Pad (120m) Example | list | | |
wind_cleared | GenEst | Wind cleared plot (60m) Search Example | list | | |
alonso15 | island | Lakshadweep Archipelago coral fish community reassembly | list | | |
idaho | island | Mapped plant community time series, Dubois, ID | list | | |
lakshadweep | island | Lakshadweep Archipelago coral fish community reassembly data (expanded) | list | | |
lakshadweepPLUS | island | Lakshadweep Archipelago coral fish community reassembly data in a single data frame | list | | |
simberloff | island | Simberloff and Wilson original defaunation experiment data | list | | |
cities | nngeo | Point layer of the three largest cities in Israel | sf | 3 | 2 |
line | nngeo | Sample network dataset: lines | sf | 18 | 13 |
pnt | nngeo | Sample network dataset: points | sf | 6 | 7 |
towns | nngeo | Point layer of towns in Israel | sf | 193 | 5 |
water | nngeo | Polygonal layer of water bodies in Israel | sf | 4 | 2 |
APA | Rankcluster | Rank data: APA | list | | |
big4 | Rankcluster | Rank data: big4 | list | | |
eurovision | Rankcluster | Multidimensional partial rank data: eurovision | list | | |
quiz | Rankcluster | Multidimensional rank data: quiz | list | | |
sports | Rankcluster | Rank data: sports | list | | |
words | Rankcluster | Rank data: words | list | | |
pbcsample | LTRCforests | Sample Mayo Clinic Primary Biliary Cirrhosis Data | data.frame | 57 | 9 |
sex2 | logistf | Urinary Tract Infection in American College Students | data.frame | 239 | 7 |
sexagg | logistf | Urinary Tract Infection in American College Students | data.frame | 36 | 9 |
DM | apmx | DM | data.frame | 22 | 12 |
EX | apmx | EX | data.frame | 42 | 19 |
LB | apmx | LB | data.frame | 2159 | 16 |
PC | apmx | PC | data.frame | 420 | 21 |
VL | apmx | VL | data.frame | 66 | 4 |
LOPART.ROC | directlabels | ROC curve for LOPART algorithm and competitors | list | | |
LOPART100 | directlabels | Labeled Optimal Partitioning (LOPART) results | list | | |
SegCost | directlabels | Cost of segmentation models | data.frame | 560 | 5 |
iris.l1.cluster | directlabels | Clustering of the iris data with the l1 clusterpath | data.frame | 9643 | 8 |
normal.l2.cluster | directlabels | Clustering of some normal data in 2d with the l2 clusterpath | list | | |
odd_timings | directlabels | Odd timings | data.frame | 116 | 4 |
projectionSeconds | directlabels | Timings of projection algorithms | data.frame | 603 | 6 |
svmtrain | directlabels | False positive rates from several 1-SVM models | data.frame | 378 | 5 |
IVfeature | netSEM | IV features data | data.frame | 21 | 6 |
PVmodule | netSEM | Dataframe for PV module degradation under Damp Heat Exposure | spec_tbl_df | 16 | 4 |
acrylic | netSEM | A data frame of an acrylic polymer degradation experiment | data.frame | 357 | 6 |
backsheet | netSEM | Backsheet PET/PET/EVA Degradation | data.frame | 110 | 5 |
crack | netSEM | Crack Quantification for Photovoltaic Backsheets | tbl_df | 97 | 5 |
metal | netSEM | Aluminum Gradient Material for Metal's Design | data.frame | 72 | 6 |
pet | netSEM | A data frame of an degradation experiment of poly(ethylene-terephthalate) films | data.frame | 35 | 6 |
australia10 | comorbidity | Australian mortality data, 2010 | tbl_df | 3322 | 3 |
icd10_2009 | comorbidity | ICD-10 Diagnostic Codes, 2009 Version | tbl_df | 10817 | 4 |
icd10_2011 | comorbidity | ICD-10 Diagnostic Codes, 2011 Version | tbl_df | 10856 | 4 |
icd10cm_2017 | comorbidity | ICD-10-CM Diagnostic Codes, 2017 Version | spec_tbl_df | 71486 | 2 |
icd10cm_2018 | comorbidity | ICD-10-CM Diagnostic Codes, 2018 Version | spec_tbl_df | 71704 | 2 |
icd10cm_2022 | comorbidity | ICD-10-CM Diagnostic Codes, 2022 Version | data.frame | 72750 | 2 |
icd9_2015 | comorbidity | ICD-9 Diagnostic Codes, 2015 Version (v32) | tbl_df | 14567 | 3 |
nhds2010 | comorbidity | Adult same-day discharges, 2010 | tbl_df | 2210 | 15 |
bechdel | epoxy | Top 10 Highest-Rated, Bechdel-Passing Movies | tbl_df | 10 | 18 |
australiaGPCP | remote | Monthly GPCP precipitation data for Australia | RasterBrick | | |
pacificSST | remote | Monthly SSTs for the tropical Pacific Ocean | RasterBrick | | |
vdendool | remote | Mean seasonal (DJF) 700 mb geopotential heights | RasterBrick | | |
web | network.tools | Bipartite network | data.frame | 32 | 3 |
simtheopp | npde | Simulated data for the computation of normalised prediction distribution errors in the theophylline dataset | data.frame | 13200 | 3 |
simvirload | npde | Simulated HIV viral loads in HIV patients | data.frame | 150000 | 3 |
simwarfarinCov | npde | Pharmacokinetics of warfarin | data.frame | 247000 | 3 |
theopp | npde | Pharmacokinetics of theophylline | data.frame | 132 | 5 |
virload | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 5 |
virload20 | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 5 |
virload50 | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 5 |
virloadMDV20 | npde | Simulated HIV viral loads in HIV patients | data.frame | 300 | 6 |
warfarin | npde | Pharmacokinetics of warfarin | data.frame | 247 | 8 |
simulation | SimCorMultRes | Simulated Correlation Parameters | data.frame | 100 | 4 |
Users | Authenticate | Users Dataset | data.frame | 3 | 4 |
game_of_thrones_network | igraphwalshdata | Game of Thrones Social Network Data | igraph | | |
marvel_bimodal_network | igraphwalshdata | Marvel Bimodal Network Data | igraph | | |
marvel_network | igraphwalshdata | Marvel Unimodal Network Data | igraph | | |
mjp_crisis_network | igraphwalshdata | Modernist Journals Project *The Crisis* (1910-1922) Social Network Data | igraph | | |
mjp_marsden_network | igraphwalshdata | *The Freewoman* (1911-1912), *The New Freewoman* (1913), and *The Egoist* (1914-1919) Social Network Data | igraph | | |
mjp_plr_network | igraphwalshdata | Modernist Journals Project *Poetry* (1912-1922) and *The Little Review* (1914-1922) Social Network Data | igraph | | |
political_books_network | igraphwalshdata | Political Books Social Network Data | igraph | | |
quaker_network | igraphwalshdata | 17th Century Quakers Social Network Data | igraph | | |
trump_network | igraphwalshdata | Trump Social Network Data | igraph | | |
CmonsData | PAICE | Occurrence matrix of _Cistus monspeliensis_ in the Canary Islands | data.frame | 37 | 20 |
CmonsNetwork | PAICE | Genealogical relationship of _Cistus monspeliensis_ haplotypes | data.frame | 18 | 3 |
CmonsRare | PAICE | Simulated rarefaction curves of _Cistus monspeliensis_ | rarecol | | |
testAudioData | voiceR | voiceR test Audio Data | data.frame | 90 | 11 |
testAudioList | voiceR | voiceR test Audio List | list | | |
ami | BNSP | Amitriptyline dataset from Johnson and Wichern | data.frame | 17 | 7 |
simD | BNSP | Simulated dataset | data.frame | 300 | 3 |
simD2 | BNSP | Simulated dataset | data.frame | 300 | 4 |
dass | pleLMA | Dateframe of responses to items from depression, anxiety, and stress scales | data.frame | 1000 | 42 |
vocab | pleLMA | Dataframe of response to vocabulary items from the 2018 General Social Survey | data.frame | 1309 | 10 |
BES_panel | OrthoPanels | Responses from the 2010 British Election Study | data.frame | 5535 | 11 |
abond_panel | OrthoPanels | UK Company Data Panel | data.frame | 813 | 16 |
subtype_data | riskclustr | Simulated subtype data | data.frame | 2000 | 38 |
BTflow | exdqlm | Monthly time-series of water flow at Big Tree water gauge. | ts | | |
ELIanoms | exdqlm | Daily time-series of ELI anomalies. | ts | | |
nino34 | exdqlm | Monthly Niรฑo 3.4 Index. | ts | | |
scIVTmag | exdqlm | Time series of daily average magnitude IVT in Santa Cruz, CA. | ts | | |
breastcancer | PPCI | Discrimination of Cancerous and Non-Cancerous Breast Masses | list | | |
dermatology | PPCI | Eryhemato-Squamous Disease Identification | list | | |
optidigits | PPCI | Optical Recognition of Handwritten Digits | list | | |
pendigits | PPCI | Pen-based Recognition of Handwritten Digits | list | | |
phoneme | PPCI | Speech Recognition through Phoneme Identification | list | | |
yale | PPCI | Face Recognition | list | | |
model.output | RLumModel | Example data (TL curve) simulated with parameter set from Pagonis 2007 | RLum.Analysis | | |
BNhold | EPX | AID348 hold-out data using Burden Numbers for testing the EPX package | data.frame | 3946 | 25 |
BNsample | EPX | AID348 sample (training) data with Burden Numbers for testing the EPX package | data.frame | 1000 | 25 |
harvest | EPX | Simulated dataset for testing the EPX package | data.frame | 190 | 4 |
area_titles | blscrapeR | Dataset containing FIPS codes for counties, states and MSAs. | data.frame | 4723 | 2 |
county_fips | blscrapeR | Return a dataframe of county FIPS codes by state. | data.frame | 3235 | 5 |
cu_main | blscrapeR | Dataset containing All items in U.S. city average, all urban consumers, seasonally adjusted CUSR0000SA0. | tbl_df | 924 | 4 |
naics | blscrapeR | Dataset containing NIACS codes for industry lookups. | data.frame | 2469 | 2 |
series_ids | blscrapeR | Dataset containing BLS series ids and descriptions. | data.frame | 97813 | 4 |
size_titles | blscrapeR | Dataset containing size codes for US industries by size. | data.frame | 10 | 2 |
state_fips | blscrapeR | Dataset with the lat. / long. of county FIPS codes used for mapping. | data.frame | 57 | 4 |
NSDUH_female | twangMediation | A dataset containing the substance use condition and sexual orientation of 40293 women respondents to the 2017 & 2018 National Survey of Drug Use and Health. | data.frame | 40293 | 14 |
tMdat | twangMediation | Simulated data for twangMediation | data.frame | 500 | 7 |
PANAS_november | cosinor2 | Self-reported mood | data.frame | 19 | 30 |
PANAS_time | cosinor2 | Measurement times of self-reported mood | numeric | | |
PA_extraverts | cosinor2 | Self-reported positive affect of extraverts | data.frame | 24 | 6 |
PA_introverts | cosinor2 | Self-reported positive affect of introverts | data.frame | 29 | 6 |
PA_time | cosinor2 | Measurement times of self-reported positive affect | numeric | | |
temperature_zg | cosinor2 | Daily air temperature in Zagreb | data.frame | 48 | 2 |
Data_sample | compindPCA | Sample data for the PCA based compositive index. | data.frame | 30 | 11 |
icils_conf | intsvy | Config files for intsvy studies | list | | |
llece_conf | intsvy | Config files for intsvy studies | list | | |
pasec_conf | intsvy | Config files for intsvy studies | list | | |
piaac_conf | intsvy | Config files for intsvy studies | list | | |
pirls_conf | intsvy | Config files for intsvy studies | list | | |
pisa_conf | intsvy | Config files for intsvy studies | list | | |
sea_conf | intsvy | Config files for intsvy studies | list | | |
timss4_conf | intsvy | Config files for intsvy studies | list | | |
timss8_conf | intsvy | Config files for intsvy studies | list | | |
ECG | Rlibeemd | Electrocardiogram Data Example ECG data from MIT-BIH Normal Sinus Rhythm Database, ECG1 of record 16265, first 2049 observations (0 to 16 seconds with sampling interval of 0.0078125 seconds) | ts | | |
float | Rlibeemd | Float Data The data are a position record from an acoustically tracked subsurface oceanographic float, used as an example data in Rilling et al (2007). | data.frame | 549 | 2 |
acidata1 | plantecophys | An example A-Ci curve | data.frame | 10 | 5 |
manyacidat | plantecophys | An example dataset with multiple A-Ci curves | data.frame | 390 | 6 |
ghibli_palettes | ghibli | Complete list of available ghibli palettes | list | | |
bootstrap_moby | poweRlaw | Example bootstrap results for the full Moby Dick data set | bs_xmin | | |
bootstrap_p_moby | poweRlaw | Example bootstrap results for the full Moby Dick data set | bs_p_xmin | | |
moby | poweRlaw | Moby Dick word count | integer | | |
moby_sample | poweRlaw | Moby Dick word count | integer | | |
native_american | poweRlaw | Casualties in the American Indian Wars (1776 and 1890) | data.frame | 1297 | 2 |
population | poweRlaw | City boundaries and the universality of scaling laws | numeric | | |
swiss_prot | poweRlaw | Word frequency in the Swiss-Prot database | data.frame | 10745 | 2 |
us_american | poweRlaw | Casualties in the American Indian Wars (1776 and 1890) | data.frame | 1232 | 2 |
lorenz.ts | tseriesChaos | Lorenz simulated time series, without noise | ts | | |
rossler.ts | tseriesChaos | Roessler simulated time series, without noise | ts | | |
centers | spDates | Coordinates of 9 sites considered as potential centers of origin of the Neolithic expansion. Modified from Pinhasi et al. (2005). | SpatialPointsDataFrame | | |
land | spDates | Land polygons. | SpatialPolygonsDataFrame | | |
neof | spDates | Radiocarbon dates and coordinates of 717 Neolithic sites in the Near East and Europe. Modified from Pinhasi et al. (2005). Only the earliest dates per site are included. | SpatialPointsDataFrame | | |
gdp_2014_admin_districts | leafdown | GPD for administrative districts of Germany for 2014. | data.frame | 402 | 2 |
gdp_2014_federal_states | leafdown | GPD for federal states of Germany for 2014. | data.frame | 16 | 2 |
us_election_counties | leafdown | Results of the 2016 US Presidential Election - County Level | tbl_df | 3143 | 17 |
us_election_states | leafdown | Results of the 2016 US Presidential Election - State Level | tbl_df | 51 | 15 |
europe_100km | rN2000 | 100-km grid of Europe | sf | 5712 | 4 |
europe_10km | rN2000 | 10-km grid of Europe | sf | 5712 | 4 |
europe_countries_lowres | rN2000 | European country administrative boundaries | sf | 67 | 64 |
europe_countries_midres | rN2000 | European country administrative boundaries | sf | 80 | 64 |
linkdata | CropScapeR | A dataset documenting the correspondence between crop names and crop names for CDL | data.table | 256 | 2 |
edhec | PerformanceAnalytics | EDHEC-Risk Hedge Fund Style Indices | xts | 293 | 13 |
managers | PerformanceAnalytics | Hypothetical Alternative Asset Manager and Benchmark Data | xts | 132 | 10 |
portfolio_bacon | PerformanceAnalytics | Bacon(2008) Data | xts | 24 | 2 |
prices | PerformanceAnalytics | Selected Price Series Example Data | zoo | 2011 | 1 |
test_returns | PerformanceAnalytics | Sample sector returns for use by unit tests | xts | 5 | 10 |
test_weights | PerformanceAnalytics | Sample sector weights for use by unit tests | xts | 5 | 10 |
weights | PerformanceAnalytics | Selected Portfolio Weights Data | xts | 8 | 11 |
economiccomplexity_output | economiccomplexity | Example Outputs of the Functions within the Package | list | | |
world_gdp_avg_1998_to_2000 | economiccomplexity | World Trade Per-Capita GDP for the Period 1998-2000 | tbl_df | 240 | 2 |
world_trade_avg_1998_to_2000 | economiccomplexity | World Trade Averages for the Period 1998-2000 | tbl_df | 124336 | 3 |
ext_linear | xrnet | Simulated external data | matrix | 50 | |
x_linear | xrnet | Simulated example data for hierarchical regularized linear regression | matrix | 200 | |
y_linear | xrnet | Simulated outcome data | numeric | | |
AddHealth | heplots | Adolescent Mental Health Data | data.frame | 4344 | 3 |
Adopted | heplots | Adopted Children | data.frame | 62 | 6 |
Bees | heplots | Captive and maltreated bees | data.frame | 246 | 6 |
Diabetes | heplots | Diabetes Dataset | data.frame | 145 | 6 |
FootHead | heplots | Head measurements of football players | data.frame | 90 | 7 |
Headache | heplots | Treatment of Headache Sufferers for Sensitivity to Noise | data.frame | 98 | 6 |
Hernior | heplots | Recovery from Elective Herniorrhaphy | data.frame | 32 | 9 |
Iwasaki_Big_Five | heplots | Personality Traits of Cultural Groups | tbl_df | 203 | 7 |
MockJury | heplots | Effects Of Physical Attractiveness Upon Mock Jury Decisions | data.frame | 114 | 17 |
NLSY | heplots | National Longitudinal Survey of Youth Data | data.frame | 243 | 6 |
NeuroCog | heplots | Neurocognitive Measures in Psychiatric Groups | data.frame | 242 | 10 |
Oslo | heplots | Oslo Transect Subset Data | data.frame | 332 | 14 |
Overdose | heplots | Overdose of Amitriptyline | data.frame | 17 | 7 |
Parenting | heplots | Father Parenting Competence | data.frame | 60 | 4 |
Plastic | heplots | Plastic Film Data | data.frame | 20 | 5 |
Pottery2 | heplots | Chemical Analysis of Romano-British Pottery | data.frame | 48 | 12 |
Probe1 | heplots | Response Speed in a Probe Experiment | data.frame | 11 | 5 |
Probe2 | heplots | Response Speed in a Probe Experiment | data.frame | 20 | 6 |
RatWeight | heplots | Weight Gain in Rats Exposed to Thiouracil and Thyroxin | data.frame | 27 | 6 |
ReactTime | heplots | Reaction Time Data | data.frame | 10 | 6 |
Rohwer | heplots | Rohwer Data Set | data.frame | 69 | 10 |
RootStock | heplots | Growth of Apple Trees from Different Root Stocks | data.frame | 48 | 5 |
Sake | heplots | Taste Ratings of Japanese Rice Wine (Sake) | data.frame | 30 | 10 |
Skulls | heplots | Egyptian Skulls | data.frame | 150 | 5 |
SocGrades | heplots | Grades in a Sociology Course | data.frame | 40 | 10 |
SocialCog | heplots | Social Cognitive Measures in Psychiatric Groups | data.frame | 139 | 5 |
TIPI | heplots | Data on the Ten Item Personality Inventory | data.frame | 1799 | 16 |
VocabGrowth | heplots | Vocabulary growth data | data.frame | 64 | 4 |
WeightLoss | heplots | Weight Loss Data | data.frame | 34 | 7 |
dogfood | heplots | Dogfood Preferences | data.frame | 16 | 3 |
mathscore | heplots | Math scores for basic math and word problems | data.frame | 12 | 3 |
oral | heplots | Effect of Delay in Oral Practice in Second Language Learning | tbl_df | 56 | 5 |
peng | heplots | Size measurements for penguins near Palmer Station, Antarctica | tbl_df | 333 | 8 |
schooldata | heplots | School Data | data.frame | 70 | 8 |
meta_meat | multibiasmeta | Meta-analysis about meat consumption | tbl_df | 100 | 4 |
lipcancer | eCAR | Number of recorded lip cancer cases in the 56 districts of Scotland. | list | | |
ACTG175 | LongCART | Converted AIDS Clinical Trials Group Study 175 (source: speff2trial package) | data.frame | 6417 | 24 |
GBSG2 | LongCART | German Breast Cancer Study Group 2 (source: TH.data package) | data.frame | 686 | 10 |
example_performance_results | eiExpand | Example performance analysis results | data.frame | 12 | 7 |
example_rpvDF | eiExpand | Example RPV analysis results in Washington State | tbl_df | 72 | 13 |
mt_block_data | eiExpand | Example block-level population data from Montana for split precinct analysis | sf | 3880 | 2 |
planShp | eiExpand | Example district plan shape for split precinct analysis | sf | 1 | 18 |
south_carolina | eiExpand | Example election and demographic data from South Carolina 2020 General Elections | data.frame | 750 | 42 |
vtd | eiExpand | Example vtd-level sf dataframe with election results for split precinct analysis | sf | 19 | 12 |
wa_block_data | eiExpand | Example block-level population data from Washington for BISG | sf | 821 | 8 |
wa_geocoded | eiExpand | Example geocoded voter file from Washington for BISG | data.frame | 1000 | 6 |
washington | eiExpand | Example election data with BISG demographics from Washington 2020 General Presidential Election | data.frame | 637 | 27 |
countries | PWIR | Index of Countries. | data.frame | 200 | 1 |
test_data | PSW | Test data | data.frame | 500 | 7 |
wine | bootcluster | Wine Data Set | data.frame | 178 | 14 |
pakistan | endorse | Pakistan Survey Experiment on Support for Militant Groups | data.frame | 5212 | 25 |
fs_prices | piar | Price data | data.frame | 40 | 5 |
fs_weights | piar | Price data | data.frame | 5 | 2 |
ms_prices | piar | Price data | data.frame | 40 | 4 |
ms_weights | piar | Price data | data.frame | 5 | 3 |
pplace | BoSSA | A placement object as obtained with the read_sqlite function | pplace | | |
alphaltc | gpbStat | Line x Tester data (only Crosses) in Alpha Lattice design. | data.frame | 60 | 5 |
alphaltcchk | gpbStat | Line x Tester data (Crosses and Checks) in Alpha Lattice | data.frame | 54 | 6 |
alphaltcmt | gpbStat | Line x Tester data (only Crosses) in Alpha Lattice design. | spec_tbl_df | 60 | 7 |
alphaltcs | gpbStat | Line x Tester data (only Crosses) with single plant observations laid in Alpha Lattice design. | tbl_df | 240 | 6 |
datdti | gpbStat | Data of estimating drought tolerance indices without replication | tbl_df | 30 | 8 |
datrdti | gpbStat | Data of estimating drought tolerance indices with replication | tbl_df | 60 | 9 |
dm2alpha | gpbStat | Diallel Method 2 data in Alpha Lattice. | data.frame | 240 | 5 |
dm2rcbd | gpbStat | Diallel Method 2 data in RCBD | tbl_df | 240 | 4 |
rcbdltc | gpbStat | Line x Tester data in RCBD | tbl_df | 60 | 4 |
rcbdltcchk | gpbStat | Line x Tester data (Crosses and Checks) in RCBD | tbl_df | 72 | 5 |
rcbdltcmt | gpbStat | Line x Tester data (only Crosses) in Randomized Complete Block design. | spec_tbl_df | 60 | 5 |
rcbdltcs | gpbStat | Line x Tester data (only Crosses) with single plant observations laid in RCBD design. | spec_tbl_df | 240 | 5 |
data_abundance | specieschrom | Abundance of 14 pseudo-species. A dataset containing the abundance values of 14 pseudo-species in 100 samples. The environmental conditions associated with each samples are described in the environment dataset. Pseudo-species v1 and v2, v3 and v4, v5 and v6, v7 and v8, v9 and v10, v11 and v12, v13 and v14 have the same niche. | data.frame | 100 | 14 |
environment | specieschrom | Three fictive environmental variables A dataset containing the values of three fictive envionemental variables in 100 samples. The corresponding pseudo-species abundance in each sample are available in the data_abundance dataset. | data.frame | 100 | 3 |
tropicalsound | soundecology | tropicalsound sound example | Wave | | |
prodelec | dynBiplotGUI | Electric production | data.frame | 55 | 7 |
geninvitro | NMTox | Genetic toxicity in vitro dataset | tbl_df | 4161 | 17 |
nm400 | NMTox | NM-400 in vitro dataset | tbl_df | 253 | 17 |
Bundesliga | perryExamples | Austrian Bundesliga football player data | data.frame | 123 | 20 |
TopGearMPG | perryExamples | Top Gear fuel consumption data | data.frame | 255 | 11 |
press | jcext | Mean Sea Level pressure files | list | | |
ccData | ICODS | Toy Example for Case-Cohort Design with Interval-Censored Data | data.frame | 500 | 6 |
odsData | ICODS | Toy Example for ODS Design with Interval-Censored Data | data.frame | 501 | 6 |
TMT10 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT10ETD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT10HCD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT11 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT11HCD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT16 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT16HCD | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT6 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT6b | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT7 | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
TMT7b | MSnbase | TMT 6/10-plex sets | ReporterIons | | |
iTRAQ4 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
iTRAQ5 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
iTRAQ8 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
iTRAQ9 | MSnbase | iTRAQ 4-plex set | ReporterIons | | |
itraqdata | MSnbase | Example 'MSnExp' and 'MSnSet' data sets | MSnExp | | |
msnset | MSnbase | Example 'MSnExp' and 'MSnSet' data sets | MSnSet | | |
msnset2 | MSnbase | Example 'MSnExp' and 'MSnSet' data sets | MSnSet | | |
naset | MSnbase | Quantitative proteomics data imputation | MSnSet | | |
exp.m | SightabilityModel | Experimental (test trials) data set used to estimate detection probabilities for moose in MN | data.frame | 124 | 4 |
g.fit | SightabilityModel | Mountain Goat Sightability Model Information | list | | |
gdat | SightabilityModel | Mountain Goat Survey Data from Olympic National park | data.frame | 77 | 6 |
obs.m | SightabilityModel | MN moose survey data | data.frame | 805 | 10 |
sampinfo.m | SightabilityModel | Data set containing sampling information for observation survey of moose in MN | data.frame | 12 | 4 |
AddHealth | gscaLCA | Add Health data about substance use | data.frame | 5114 | 8 |
TALIS | gscaLCA | Teaching and Learning International Survey | data.frame | 2560 | 6 |
data_fake_county | attrib | Fake data for mortality in Norway | data.table | 6314 | 12 |
data_fake_nation | attrib | Fake data for mortality in Norway nationally | data.table | 574 | 11 |
beet | agriTutorial | Beet data for Example 2 | data.frame | 15 | 3 |
greenrice | agriTutorial | Rice data for Example 3 | data.frame | 64 | 5 |
rice | agriTutorial | Rice data for Example 1 | data.frame | 135 | 8 |
sorghum | agriTutorial | Sorghum data for Example 4 | data.frame | 100 | 7 |
turnip | agriTutorial | Turnip data for Example 5 | data.frame | 60 | 6 |
lambs | kfino | a dataset containing the WoW weighing for 4 animals of 1296 observations, https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 1296 | 5 |
merinos1 | kfino | a dataset containing the WoW weighing for one animal (merinos lamb) of 397 observations. https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 397 | 5 |
merinos2 | kfino | a dataset containing the WoW weighing for one animal (merinos lamb) of 345 observations, difficult to model. https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 345 | 5 |
spring1 | kfino | a dataset containing the WoW weighing for one animal of 203 observations. https://doi.org/10.1016/j.compag.2018.08.022 | grouped_df | 203 | 5 |
daily_data | dsa | Exemplary time series | xts | 3430 | 6 |
dsa_examples | dsa | Exemplary dsa outputs | list | | |
holidays | dsa | Data set for frequently used regressors | xts | 46021 | 131 |
simulation.1 | htetree | A Simulated Dataset | data.frame | 500 | 12 |
toyexample | wcep | Toy example | data.frame | 104 | 4 |
aga_15.ref | childsds | Parameters from recommendations of the German Adiposity Association (2015, AGA) | RefGroup | | |
belgium.ref | childsds | Parameters derived from Flandern population | RefGroup | | |
bone.ref | childsds | Parameters for different bone parameters | RefGroup | | |
bp_wuehl_age.ref | childsds | Parameters from Wuehl et al. blood pressure reference values Germany according to age, from version 0.7.3 unplausible values are replaced by interpolated ones. For the original values check out earlier versions | RefGroup | | |
bp_wuehl_height.ref | childsds | Parameters from Wuehl et al. blood pressure reference values Germany according to height from version 0.7.3 unplausible values are replaced by interpolated ones. For the original values check out earlier versions | RefGroup | | |
cdc.ref | childsds | LMS Parameters for the Centers for Disease Control and Prevention 2000 Growth Charts, contains bmi, height, head cirumference, weight, weight for length, | RefGroup | | |
cn.ref | childsds | Parameters for height of normal weight and obese children from the CrescNet database dependent on height | RefGroup | | |
colombia_sf.ref | childsds | Parameters of skinfold measures derived from Colombian population | RefGroup | | |
doyon_age.ref | childsds | Parameters for different carotid artery intima-media thickness and distensibility dependent on age | RefGroup | | |
doyon_height.ref | childsds | Parameters for different carotid artery intima-media thickness and distensibility dependent on height | RefGroup | | |
duran_bf.ref | childsds | Parameters for bodyfat ( for Whites, Blacks, and Mexican-Americans | RefGroup | | |
ethiop.ref | childsds | Parameters derived Ethiopian children | RefGroup | | |
fredriks05.ref | childsds | Parameters derived from Dutch children (additional to nl4.ref) | RefGroup | | |
international_lab.ref | childsds | International Laboratory Parameters Tables | RefGroup | | |
iron.ref | childsds | Parameters for iron-related blood parameters in children | RefGroup | | |
italian.ref | childsds | Parameters derived from Italian children | RefGroup | | |
japan_lab.ref | childsds | Parameters of serum insulin-like growth factor-I (IGF-I) | RefGroup | | |
japanese.ref | childsds | Parameters derived from Japanese children | RefGroup | | |
kawel_boehm.ref | childsds | Parameters for Cardiovascular Magnetic Resonance | RefGroup | | |
kiggs.ref | childsds | LMS Parameters for German reference data (KiGGS, 2003-2006) for height, weight, bmi, hip, whr, whtr, bodyfat, skinfold sum, triceps skinfold, subscapular skinfold, and waist circumference | RefGroup | | |
kiggs_bp.ref | childsds | Parameters derived from the German KiGGS cohort | RefGroup | | |
kro.ref | childsds | LMS Parameters for German reference data (Kromeyer Hauschild, 2001) for height, weight, bmi, and waist circumference, including preterm correction (Voigt) | RefGroup | | |
life_circ.ref | childsds | Parameters for different circumferences and whr and whtr | RefGroup | | |
life_cysc.ref | childsds | Parameters for different metabolom parameters from the LIFE Child cohort | RefGroup | | |
life_fibroscan.ref | childsds | Parameters for fibroscan from the LIFE Child cohort | RefGroup | | |
life_heart.ref | childsds | hs-Troponin T and NT-proBNP from the LIFE Child cohort | RefGroup | | |
life_igf.ref | childsds | IGF-I and IGF-BP3 from the LIFE Child cohort | RefGroup | | |
life_liver.ref | childsds | Parameters for serum liver enzymes | RefGroup | | |
life_skinfold.ref | childsds | Parameters for different skinfolds | RefGroup | | |
life_thyr.ref | childsds | Parameters for TSH, FT3, FT4 from the LIFE Child cohort | RefGroup | | |
lipids.ref | childsds | Parameters for serum lipids in children | RefGroup | | |
metabolom.ref | childsds | Parameters for different metabolom parameters from the LIFE Child cohort | RefGroup | | |
momo.ref | childsds | Parameters for the German MoMo study (sports test) | RefGroup | | |
motor.ref | childsds | Parameters for 5 subtests of the KiGGS Motorik Module | RefGroup | | |
nl3.ref | childsds | Parameters of skinfold measures derived from Colombian population | RefGroup | | |
nl4.ref | childsds | Parameters derived from the 4th Dutch growth study | RefGroup | | |
portug.ref | childsds | Parameters derived from Portuguese children | RefGroup | | |
preterm.ref | childsds | Parameters Preterm and Intrauterine | RefGroup | | |
saudi.ref | childsds | Parameters derived from Saudi children | RefGroup | | |
turkish.ref | childsds | Parameters derived from Turkish children | RefGroup | | |
uk1990.ref | childsds | Parameters from the 1990 UK growth study | RefGroup | | |
ukwho.ref | childsds | LMS Parameters for UK-WHO growth charts for height, weight, bmi, head circumference | RefGroup | | |
us.ref | childsds | Parameters derived from US children (additional to the cdc.ref) | RefGroup | | |
who.ref | childsds | LMS Parameters for UK-WHO growth charts for height, weight, bmi, head circumference,arm mid upper arm circumference, subscapular and triceps skinfold, weight for height | RefGroup | | |
who2007.ref | childsds | Parameters of skinfold measures derived from Colombian population | RefGroup | | |
zong13.ref | childsds | Parameters derived from Chinese children (additional to nl4.ref) | RefGroup | | |
CuHallData | complexlm | AC (Complex) Hall effect data measured from a thin-film copper sample. | data.frame | 240 | 12 |
pmcode_00041 | ie2miscdata | 00041 Weather | data.frame | 54 | 3 |
pmcode_00067 | ie2miscdata | 00067 Tide stage, code | data.frame | 315 | 3 |
pmcode_00115 | ie2miscdata | 00115 Sample treatment | data.frame | 4 | 3 |
pmcode_01300 | ie2miscdata | 01300 Oil-Grease (Severity) | data.frame | 5 | 3 |
pmcode_01305 | ie2miscdata | 01305 Detergent Suds (Severity) | data.frame | 5 | 3 |
pmcode_01310 | ie2miscdata | 01310 Gas Bubbles (Severity) | data.frame | 5 | 3 |
pmcode_01315 | ie2miscdata | 01315 Sludge: Floating (Severity) | data.frame | 5 | 3 |
pmcode_01320 | ie2miscdata | 01320 Garbage, Floating (Severity) | data.frame | 5 | 3 |
pmcode_01325 | ie2miscdata | 01325 Algae, Floating Mats (Severity) | data.frame | 5 | 3 |
pmcode_01330 | ie2miscdata | 01330 Odor, Atmospheric (Severity) | data.frame | 5 | 3 |
pmcode_01335 | ie2miscdata | 01335 Sewage Solids, Fresh, Floating (Severity) | data.frame | 5 | 3 |
pmcode_01340 | ie2miscdata | 01340 Fish, Dead (Severity) | data.frame | 5 | 3 |
pmcode_01345 | ie2miscdata | 01345 Debris, Floating (Severity) | data.frame | 5 | 3 |
pmcode_01350 | ie2miscdata | 01350 Turbidity (Severity) | data.frame | 5 | 3 |
pmcode_01351 | ie2miscdata | 01351 Streamflow (Severity) | data.frame | 5 | 3 |
pmcode_01355 | ie2miscdata | 01355 Ice Cover, Floating Or Solid (Severity) | data.frame | 5 | 3 |
pmcode_04117 | ie2miscdata | 04117 Tether Line Used For Collecting Sample (Yes=1) Codes | data.frame | 2 | 3 |
pmcode_31678 | ie2miscdata | 31678 Streptocci, Fecal, Tube Configuration | data.frame | 8 | 3 |
pmcode_49986 | ie2miscdata | 49986 Degree Of Decomposition, Soil, Code | data.frame | 3 | 3 |
pmcode_50276 | ie2miscdata | 50276 Filter Type, Code | data.frame | 14 | 3 |
pmcode_50280 | ie2miscdata | 50280 Water Samples, Code | data.frame | 28 | 3 |
pmcode_62955 | ie2miscdata | 62955 Sample Matrix, Code | data.frame | 3 | 3 |
pmcode_71995 | ie2miscdata | 71995 Water Use, Primary (Codes) | data.frame | 131 | 3 |
pmcode_71996 | ie2miscdata | 71996 Water Use, Secondary (Codes) | data.frame | 131 | 3 |
pmcode_71997 | ie2miscdata | 71997 Water Use, Tertiary (Codes) | data.frame | 131 | 3 |
pmcode_71998 | ie2miscdata | 71998 Water Use, Quaternary (Codes) | data.frame | 131 | 3 |
pmcode_71999 | ie2miscdata | 71999 Sample Purpose (Codes) | data.frame | 28 | 3 |
pmcode_72005 | ie2miscdata | 72005 Sample Source (Codes) | data.frame | 101 | 3 |
pmcode_72006 | ie2miscdata | 72006 Sampling Condition (Codes) | data.frame | 46 | 3 |
pmcode_74200 | ie2miscdata | 74200 Sample Preservation Method, (Codes) | data.frame | 119 | 3 |
pmcode_82305 | ie2miscdata | 82305 Atmospheric Deposition Type Bulk, (Codes) | data.frame | 5 | 3 |
pmcode_82309 | ie2miscdata | 82309 Contamination Source, Possible (Codes) | data.frame | 22 | 3 |
pmcode_82398 | ie2miscdata | 82398 Sampling Method (Codes) | data.frame | 63 | 3 |
pmcode_82923 | ie2miscdata | 82923 Atmospheric Deposition Type Wet, (Codes) | data.frame | 7 | 3 |
pmcode_84060 | ie2miscdata | 84060 Topography, Physiographic Setting (Codes) | data.frame | 18 | 3 |
pmcode_84143 | ie2miscdata | 84143 Well Purging Condition (Codes) | data.frame | 12 | 3 |
pmcode_84144 | ie2miscdata | 84144 Well Selection Criteria (Codes) | data.frame | 2 | 3 |
pmcode_84145 | ie2miscdata | 84145 Project Component (Codes) | data.frame | 7 | 3 |
pmcode_84146 | ie2miscdata | 84146 Land Use, Predominant, Within 200 Feet Of Well, (Codes) | data.frame | 15 | 3 |
pmcode_84147 | ie2miscdata | 84147 Land Use, Predominant, Within 0.25 Mile Of Well (Codes) | data.frame | 15 | 3 |
pmcode_84148 | ie2miscdata | 84148 Land Use, Predominant Fraction, Within 0.25 Mile Of Well (Codes) | data.frame | 4 | 3 |
pmcode_84149 | ie2miscdata | 84149 Land-Use Changes Within Last 10 Years, Within 0.25 Mile Of Well (Codes) | data.frame | 4 | 3 |
pmcode_84164 | ie2miscdata | 84164 Sampler Type, (Codes) | data.frame | 121 | 3 |
pmcode_84171 | ie2miscdata | 84171 Sample Splitter Type, Field Code | data.frame | 12 | 3 |
pmcode_84172 | ie2miscdata | 84172 Air Sampler Filter Type, Code | data.frame | 4 | 3 |
pmcode_84173 | ie2miscdata | 84173 Air Sample Trap Sorbent Type, Code | data.frame | 6 | 3 |
pmcode_91112 | ie2miscdata | 91112 Latitude/Longitude Horizontal Datum | data.frame | 7 | 3 |
pmcode_91113 | ie2miscdata | 91113 Latitude/Longitude Measurement Method | data.frame | 9 | 3 |
pmcode_91114 | ie2miscdata | 91114 Latitude/Longitude Coordinate Accuracy | data.frame | 6 | 3 |
pmcode_99100 | ie2miscdata | 99100 Blank, Type Of Solution, Fixed Value Code | data.frame | 13 | 3 |
pmcode_99101 | ie2miscdata | 99101 Blank, Source Of Solution, Fixed Value Code | data.frame | 25 | 3 |
pmcode_99102 | ie2miscdata | 99102 Blank, Type Of Sample, Fixed Value Code | data.frame | 13 | 3 |
pmcode_99103 | ie2miscdata | 99103 Reference Material, Source, Fixed Value Code | data.frame | 12 | 3 |
pmcode_99105 | ie2miscdata | 99105 Replicate, Type, Fixed Value Code | data.frame | 6 | 3 |
pmcode_99106 | ie2miscdata | 99106 Spike, Type, Fixed Value Code | data.frame | 5 | 3 |
pmcode_99107 | ie2miscdata | 99107 Spike, Source, Fixed Value Code | data.frame | 14 | 3 |
pmcode_99111 | ie2miscdata | 99111 Quality Assurance Data Type Associated With Sample, Code | data.frame | 9 | 3 |
pmcode_99112 | ie2miscdata | 99112 Sulfide, Water, Filtered, Field, milligrams Per Liter | data.frame | 10 | 3 |
pmcode_99329 | ie2miscdata | 99329 Coliphage, somatic, E. coli C-host, 2-stepenrichment presence/absence per 1 liter | data.frame | 2 | 3 |
pmcode_99335 | ie2miscdata | 99335 ColipgeF-spec, famp, 2-step,pres(1) abs(2)/ 1 L | data.frame | 2 | 3 |
pmcode_99595 | ie2miscdata | 99595 Total coliform, ColilrtPA, wtrprs/abs /1L | data.frame | 2 | 3 |
pmcode_99596 | ie2miscdata | 99596 E. coliColilrt PA, water prs/abs /1 L | data.frame | 2 | 3 |
pmcode_99766 | ie2miscdata | 99766 Entero- virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99767 | ie2miscdata | 99767 Reo- virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99768 | ie2miscdata | 99768 Rota virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99769 | ie2miscdata | 99769 Hepatitis-A virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99770 | ie2miscdata | 99770 Norwalk virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
pmcode_99771 | ie2miscdata | 99771 Calici- virus, RT pcrpres(1) abs(2) / 50 L | data.frame | 2 | 3 |
tz_codes | ie2miscdata | Timezone (tz) codes | data.table | 54 | 7 |
weather_results | ie2miscdata | Global Engineering Weather Data | data.table | 805 | 7 |
Edgescore | EGRNi | Edge score obtained from 4 different methods for Ensemble Gene Regulatory Network Inference | data.frame | 4950 | 6 |
gene_exp | EGRNi | Gene expression data for Ensemble Gene Regulatory Network Inference | spec_tbl_df | 100 | 47 |
pvalue | EGRNi | Probability values for Ensemble Gene Regulatory Network Inference | data.frame | 4950 | 6 |
weight | EGRNi | Weights for Ensemble Gene Regulatory Network Inference | data.frame | 1 | 4 |
freq_study | freqtables | Simulated study data. | tbl_df | 100 | 6 |
Data_potato | stlARIMA | Normalized Monthly Average Potato Price of India | ts | 79 | 1 |
data_gaussian | saturnin | Gaussian data. | matrix | 50 | |
data_multinomial | saturnin | Multinomial data. | matrix | 100 | |
Abstracts | deepMOU | Abstracts dataset | matrix | 379 | 606 |
CNAE2 | deepMOU | CNAE dataset on classes 4 and 9 | matrix | 240 | 357 |
cl_CNAE | deepMOU | Classification labels of the CNAE2 data set | integer | | |
content | fcr | Example dataset | data.frame | 3967 | 9 |
amd | clusrank | CARMS scores | data.frame | 283 | 7 |
crd | clusrank | Clustered Non-Stratified Data for Testing Clustered Rank Sum Test | data.frame | 340 | 3 |
crdStr | clusrank | Clustered Stratified Data for Testing the Clustered Rank Sum Test | data.frame | 956 | 4 |
crsd | clusrank | Difference Between Pre and Post Treatment Scores with Clustering Structure: Balanced | data.frame | 40 | 2 |
crsdUnb | clusrank | Difference between Pre and Post Treatment Scores with Clustering Structure: Unbalanced | data.frame | 748 | 2 |
cabrera | MetaLandSim | Modified patch occupancy data of Cabrera vole | metapopulation | | |
landscape_change | MetaLandSim | Landscape loosing 5% of patches per time step | list | | |
mc_df | MetaLandSim | Modified patch occupancy data of Cabrera vole as a data frame | data.frame | 685 | 5 |
occ.landscape | MetaLandSim | Sample landscape with one simulated occupancy snapshot | metapopulation | | |
occ.landscape2 | MetaLandSim | Sample landscape with 10 simulated occupancy snapshots | metapopulation | | |
param1 | MetaLandSim | Sample parameter data frame number 1 | data.frame | 4 | 1 |
param2 | MetaLandSim | Sample parameter data frame number 2 | data.frame | 4 | 1 |
rg_exp | MetaLandSim | List with range.expansion output | data.frame | 100 | 4 |
rland | MetaLandSim | Random landscape | landscape | | |
sim.area | MetaLandSim | Vector of the areas for each site; here, 100 sites | numeric | | |
sim.det.20 | MetaLandSim | Array corresponding to nsites x nyears x nvisits | array | | |
sim.distance | MetaLandSim | Distance matrix between sampling sites (nsite x nsite). | matrix | 100 | |
z.sim | MetaLandSim | Occupancy data generated with perfect detection. | matrix | 100 | |
z.sim.20 | MetaLandSim | Occupancy data generated with perfect detection with approximately 20% of data missing at random. | matrix | 100 | |
z.sim.20.fa | MetaLandSim | Occupancy data containing false absences | matrix | 100 | |
milazzese | TaxicabCA | Counts of archeological objects | data.frame | 31 | 19 |
rodent | TaxicabCA | Rodent species abundance | data.frame | 28 | 9 |
colon | sdwd | simplified gene expression data from Alon et al. (1999) | list | | |
wav | signal | Example wav file | Sample | | |
disorders | configural | Meta-analytic correlations among Big Five personality traits and psychological disorders | list | | |
gre | configural | Meta-analytic correlations of Graduate Record Examination subtests with graduate grade point average | list | | |
hrm | configural | Meta-analytic correlations of HRM practices with organizational financial performance | list | | |
jobchar | configural | Meta-analytic correlations of job characteristics with performance and satisfaction | list | | |
mindfulness | configural | Meta-analytic correlations among Big Five personality traits and trait mindfulness | list | | |
prejudice | configural | Correlations between study design moderators and effect sizes for prejudice reduction following intergroup contact | list | | |
team | configural | Meta-analytic correlations among team processes and team effectiveness | list | | |
AP | TopicScore | Associated Press data | simple_triplet_matrix | | |
cement | ImpShrinkage | Hald's Cement Data | data.frame | 13 | 5 |
ranks_antifragility | MSmix | Antifragility Data (complete rankings with covariates) | data.frame | 99 | 17 |
ranks_beers | MSmix | Beers Data (partial rankings with covariate) | data.frame | 105 | 21 |
ranks_horror | MSmix | Arkham Horror Data (complete rankings) | data.frame | 421 | 5 |
ranks_read_genres | MSmix | Reading Genres Data (partial rankings with covariates) | data.frame | 507 | 18 |
ranks_sports | MSmix | Sports Data (complete rankings with covariates) | data.frame | 647 | 39 |
eeg | VARDetect | EEG signal data | data.frame | 4063 | 20 |
weekly | VARDetect | weekly stock price data | matrix | 824 | 20 |
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 |
bathymetry | TrackReconstruction | Bathymetry data for the Eastern Bering Sea | data.frame | 1814400 | 3 |
georef1min01 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 6681 | 6 |
georef1min02 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 9147 | 6 |
georef1min03 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 6724 | 6 |
georef1min26 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 430 | 6 |
georef1min95 | TrackReconstruction | GeoReferenced fur seal track | data.frame | 6566 | 6 |
gpsdata01 | TrackReconstruction | GPS raw data | data.frame | 233 | 3 |
gpsdata02 | TrackReconstruction | GPS raw data | data.frame | 276 | 3 |
gpsdata03 | TrackReconstruction | GPS raw data | data.frame | 57 | 3 |
gpsdata26 | TrackReconstruction | GPS raw data | data.frame | 9 | 3 |
gpsdata95 | TrackReconstruction | GPS raw data | data.frame | 93 | 3 |
rawdata | TrackReconstruction | Raw triaxial magnetomater and accelerometer data | data.frame | 133100 | 9 |
rawdatagap | TrackReconstruction | Raw biologger data with a gap | data.frame | 13738 | 9 |
square | TrackReconstruction | Raw triaxial magnetomater and accelerometer data | data.frame | 100 | 10 |
cisbpTFcat | TFutils | cisbpTFcat: data.frame with information on CISBP TFs for human, retained for reproducibility support; see cisbpTFcat_2.0 for a more recent catalog | data.frame | 7592 | 28 |
cisbpTFcat_2.0 | TFutils | cisbpTFcat_2.0: data.frame with information on CISBP TFs for human, described in PMID 31133749 | data.frame | 5861 | 28 |
demo_fimo_granges | TFutils | a list of GRanges instances with TF FIMO scores returned by 'fimo_granges' | list | | |
encode690 | TFutils | encode690: DataFrame extending AnnotationHub metadata about ENCODE cell line x TF ranges | DFrame | | |
fimo16 | TFutils | fimo16: GenomicFiles instance to AWS S3-resident FIMO bed for 16 TFs | GenomicFiles | | |
fimoMap | TFutils | fimoMap: table with Mnnnn (motif PWM tags) and HGNC symbols for TFs | data.frame | 689 | 2 |
gwascat_hg19_chr17 | TFutils | gwascat_hg19: GRanges of march 21 2018 EBI gwascat, limit to chr17 | GRanges | | |
hocomoco.mono | TFutils | hocomoco.mono: data.frame with information on HOCOMOCO TFs for human | data.frame | 771 | 9 |
hocomoco.mono.sep2018 | TFutils | hocomoco.mono.sep2018: data.frame with information on HOCOMOCO TFs for human, Sept 2018 download | data.frame | 769 | 9 |
lambert_snps | TFutils | lambert_snps is Table S3 of Lambert et al PMID 29425488 | data.frame | 143864 | 6 |
metadata_tf | TFutils | metadata_tf: list with metadata (motif_if and hgnc_symbol) about all the CISBP FIMO scan TF bed files | list | | |
named_tf | TFutils | named_tf: named list with the names being the hgnc_symbol of the motif_id | list | | |
seqinfo_hg19_chr17 | TFutils | a Seqinfo instance for a chr17 in hg19 | Seqinfo | | |
tfhash | TFutils | tfhash: data.frame with MSigDb TFs, TF targets as symbol or ENTREZ | data.frame | 164130 | 3 |
tftColl | TFutils | tftColl: GSEABase GeneSetCollection for transcription factor targets | GeneSetCollection | | |
tftCollMap | TFutils | tftCollMap: data.frame with information on MSigDb TFs for human | data.frame | 615 | 3 |
fit_US_cities | distributionsrd | Fitted distributions to the US Census 2000 city size distribution. | tbl_df | 52 | 7 |
example_markblue | AgroTech | Dataset: Example markblue | tbl_df | 20 | 5 |
example_markbluecurve | AgroTech | Dataset: Example markbluecurve | tbl_df | 26 | 4 |
example_markmet | AgroTech | Dataset: Example markmet | tbl_df | 60 | 3 |
example_meteorological | AgroTech | Dataset: Example meteorological | tbl_df | 152 | 4 |
USCrimes | TeachingDemos | US Crime Statistics | array | | |
ccc | TeachingDemos | Sample data downloaded and converted from a GPS unit | data.frame | 89 | 13 |
coin.faces | TeachingDemos | Designs for coin faces for use with plot.rgl.coin | list | | |
evap | TeachingDemos | Data on soil evaporation. | data.frame | 46 | 14 |
h2h | TeachingDemos | Sample data downloaded and converted from a GPS unit | data.frame | 131 | 16 |
ldsgrowth | TeachingDemos | Growth of The Church of Jesus Christ of Latter-day Saints. | data.frame | 179 | 6 |
outliers | TeachingDemos | Outliers data | numeric | | |
steps | TeachingDemos | Steps data | data.frame | 331 | 79 |
stork | TeachingDemos | Neyman's Stork data | data.frame | 54 | 6 |
towork | TeachingDemos | Sample data downloaded and converted from a GPS unit | data.frame | 211 | 16 |
nhanes | pomcheckr | National Health and Nutrition Examination Survey 2011-2012 | tbl_df | 9756 | 16 |
ologit | pomcheckr | Simulated data for ordinal logistic regression example. | tbl_df | 400 | 4 |
German_Credit | CollapseLevels | German Credit data set | data.frame | 1000 | 21 |
line73 | r4lineups | line73 | data.frame | 42 | 1 |
mickwick | r4lineups | Confidence & Accuracy data (Mickes & Wixted) | tbl_df | 100 | 2 |
mockdata | r4lineups | mockdata | tbl_df | 94 | 3 |
nortje2012 | r4lineups | nortje2012 | tbl_df | 133 | 3 |
jan | pa | Edited Barra data set in Jan. 2010. | data.frame | 3000 | 15 |
quarter | pa | Edited Barra data set for Q1, 2010. | data.frame | 9000 | 15 |
test | pa | A sample portfolio edited based on Barra data set in Jan. 2010. | data.frame | 3000 | 6 |
year | pa | Edited Barra data set in year 2010. | data.frame | 36000 | 15 |
data_TRY_15160 | rtry | Sample TRY data (Request 15160) | data.table | 1782 | 28 |
data_TRY_15161 | rtry | Sample TRY data (Request 15161) | data.table | 4627 | 28 |
data_coordinates | rtry | Sample coordinates data | data.table | 20 | 2 |
data_locations | rtry | Sample locations data | data.table | 20 | 3 |
IV_4K | ddiv | Damp Heat Plus Dynamic Mechanical Load Indoor Accelerated Test I-V Curve. | data.frame | 3637 | 2 |
IV_5M_1 | ddiv | I-V Curves from External Solar Testing Laboratory. | data.frame | 478 | 2 |
IV_5M_2 | ddiv | I-V Curves from External Solar Testing Laboratory. | data.frame | 476 | 2 |
IV_daystar | ddiv | Outdoor Time Series I-V Curve Data from SDLE SunFarm. | data.frame | 48 | 2 |
IV_step1 | ddiv | A data frame of IV curve with 1 step. | data.frame | 41 | 2 |
IV_step2 | ddiv | A data frame of IV curve with 2 step. | data.frame | 41 | 2 |
IV_step3 | ddiv | A data frame of IV curve with 3 step. | data.frame | 41 | 2 |
IV_timeseries | ddiv | Outdoor Time Series I-V Curve Data from SDLE SunFarm. | data.frame | 60 | 2 |
bread_mixture | stepjglm | Bread-making problem data | data.frame | 90 | 6 |
injection_molding | stepjglm | Data from Injection molding experiment | data.frame | 32 | 11 |
Enron | SBMSplitMerge | The Enron data set as extracted from 'igraph' using the script in data-raw | list | | |
Macaque | SBMSplitMerge | The Macaque data set as extracted from 'igraph' using the script in data-raw | edges | | |
StackOverflow | SBMSplitMerge | The Stack-Overflow data set as extracted from 'igraph' using the script in data-raw Extracted on 27/8/2019 from Kaggle (login required) using: 'library(rvest)' 'read_html("https://www.kaggle.com/stackoverflow/stack-overflow-tag-network/downloads/stack_network_links.csv/1")' | edges | | |
Example_IDEAM | ideamdb | A dataset with fictitious values of no real IDEAM's Stations. The text file keeps IDEAM's text format. | character | | |
GROAN.AI | GROAN | Example data for pea AI lines | list | | |
GROAN.KI | GROAN | Example data for pea KI lines | list | | |
GROAN.pea.SNPs | GROAN | [DEPRECATED] | data.frame | 103 | 647 |
GROAN.pea.kinship | GROAN | [DEPRECATED] | data.frame | 103 | 103 |
GROAN.pea.yield | GROAN | [DEPRECATED] | numeric | | |
aspre_emulation | OptHoldoutSize | Emulation-based OHS estimation for ASPRE | list | | |
aspre_parametric | OptHoldoutSize | Parametric-based OHS estimation for ASPRE | list | | |
ci_cover_a_yn | OptHoldoutSize | Data for example on asymptotic confidence interval for OHS. | matrix | 11 | |
ci_cover_cost_a_yn | OptHoldoutSize | Data for example on asymptotic confidence interval for min cost. | matrix | 11 | |
ci_cover_cost_e_yn | OptHoldoutSize | Data for example on empirical confidence interval for min cost. | matrix | 11 | |
ci_cover_e_yn | OptHoldoutSize | Data for example on empirical confidence interval for OHS. | matrix | 11 | |
data_example_simulation | OptHoldoutSize | Data for vignette showing general example | list | | |
data_nextpoint_em | OptHoldoutSize | Data for 'next point' demonstration vignette on algorithm comparison using emulation algorithm | list | | |
data_nextpoint_par | OptHoldoutSize | Data for 'next point' demonstration vignette on algorithm comparison using parametric algorithm | list | | |
ohs_array | OptHoldoutSize | Data for vignette on algorithm comparison | array | | |
ohs_resample | OptHoldoutSize | Data for vignette on algorithm comparison | matrix | 1000 | 4 |
params_aspre | OptHoldoutSize | Parameters of reported ASPRE dataset | list | | |
HPK_SampleData | HEDA | HPK_SampleData | data.frame | 30000 | 3 |
anmu | quickNmix | Ancient Murrelet Chick Counts | matrix | 6 | |
eagles | quickNmix | Golden Eagle Counts Data | data.frame | 28 | 11 |
x_multiPop | popPCR | dPCR sample w/ >=3 populations | numeric | | |
x_onePop | popPCR | dPCR sample w/ 1 population | numeric | | |
x_twoPop | popPCR | dPCR sample w/ 2 populations | numeric | | |
ktx | kidney.epi | Sample dataset with kidney transplant patients. | data.frame | 10 | 13 |
symptom | simfit | Responses to symptoms from a sample of the general population of Pakistan. | data.frame | 151 | 10 |
Brennan.3.2 | gtheory | Brennan's (2001) Table 3.2 | data.frame | 120 | 4 |
Rajaratnam.2 | gtheory | Rajaratnam, Cronbach and Gleser's (1965) Table 2 | data.frame | 64 | 4 |
rain | ensemblepp | Precipitation Observations and Forecasts for Innsbruck | data.frame | 2749 | 12 |
temp | ensemblepp | Minimum Temperature Observations and Forecasts for Innsbruck | data.frame | 2749 | 12 |
cccma | MBC | Sample CanESM2 and CanRCM4 data | list | | |
transmat | DRaWR | Sample transition matrix. | dgCMatrix | | |
lalonde.exp | causalsens | Experimental data from the job training program first studied by LaLonde (1986) | data.frame | 445 | 12 |
lalonde.psid | causalsens | Non-experimental data from Lalonde (1986) | data.frame | 2675 | 12 |
bmigrowth | eggla | BMI Measurements For 100 Individuals From 0 To 17 Years. | data.frame | 1050 | 6 |
sim_sce_test | smartid | scRNA-seq test data of 4 groups simulated by 'splatter'. | SingleCellExperiment | | |
nsw | causalSLSE | Lalonde Subsample of the National Supported Work Demonstration Data (NSW) | data.frame | 722 | 9 |
simDat1 | causalSLSE | Simulated Data | data.frame | 300 | 9 |
simDat2 | causalSLSE | Simulated Data | data.frame | 300 | 11 |
simDat3 | causalSLSE | Simulated Data | data.frame | 300 | 16 |
simDat4 | causalSLSE | Simulated Data. | data.frame | 500 | 7 |
simDat5 | causalSLSE | Simulated Data | data.frame | 300 | 6 |
UKmortality | rprev | General population survival data. | data.table | 109575 | 3 |
prevsim | rprev | Simulated patient dataset. | data.frame | 1000 | 6 |
UScrime_data | PEPBVS | US Crime Data | data.frame | 47 | 15 |
arctic_2019 | puls | NOAA's Arctic Sea Daily Ice Extend Data | spec_tbl_df | 13391 | 6 |
smoothed_arctic | puls | Discrete Form of Smoothed Functional Form of Arctic Data | tbl_df | 39 | 366 |
data_example1 | SLEMI | Exemplary data set I | data.frame | 1500 | 3 |
data_example2 | SLEMI | Exemplary data set II | data.frame | 1500 | 4 |
data_nfkb | SLEMI | Data from experiment with NFkB pathway | data.frame | 15632 | 6 |
dFactors | nFactors | Eigenvalues from classical studies | list | | |
MLB2016 | pinnacle.data | MLB2016. | tbl_df | 2462 | 11 |
USA_Election_2016 | pinnacle.data | USA_Election_2016 | tbl_df | 1443 | 5 |
WineData | NetDA | Network-Based Discriminant Analysis Subject to Multi-Label Classes | data.frame | 178 | 14 |
especies | cncaGUI | Species data | data.frame | 28 | 12 |
variables | cncaGUI | Environmental variables data | data.frame | 28 | 6 |
Bean | ZeBook | Bean gene-based models dataset | list | | |
Sunflower_Phomopsis | ZeBook | Phomopsis stem canker observations for Sunflower | data.frame | 43 | 2 |
WheatYieldGreece | ZeBook | National Wheat Yield evolution for Greece from FAO | data.frame | 50 | 2 |
Wheat_GPC | ZeBook | Grain Protein Contents in Wheat Grains | data.frame | 43 | 13 |
carcass_data | ZeBook | Data of growth of beef cattle for Carcass model | list | | |
chicks_data | ZeBook | Data of growth of chicks | data.frame | 600 | 4 |
maize.data_EuropeEU | ZeBook | maize biomass and leaf area data | data.frame | 40 | 6 |
maize.data_MetaModelling | ZeBook | dataset of simulation for maize final biomass | data.frame | 680 | 9 |
seedweight.data | ZeBook | Wheat grain weight measurements after anthesis | data.frame | 31 | 3 |
watbal.simobsdata | ZeBook | Soil water content measurements and associated simulations with WaterBalance model | data.frame | 123 | 10 |
weather_EuropeEU | ZeBook | Weather serie for Europe EU from NASA POWER agroclimatology | data.frame | 292160 | 8 |
weather_FranceWest | ZeBook | Weather series for western France from NASA POWER agroclimatology | data.frame | 248360 | 10 |
weather_GNS | ZeBook | Weather series for Gainesville (FL, USA) years 1982 and 1983 | data.frame | 730 | 11 |
weather_SouthAsia | ZeBook | Weather series for southern Asia from NASA POWER agroclimatology | data.frame | 496808 | 9 |
cardealers1 | adea | A data set about car dealers, 1 of 4, to be used in DEA | data.frame | 4 | 2 |
cardealers2 | adea | A data set about cardealers, 2 of 4, to be used in DEA | data.frame | 6 | 3 |
cardealers3 | adea | A data set about car dealers, 3 of 4, to be used in DEA | data.frame | 6 | 3 |
cardealers4 | adea | A data set about car dealers, 4 of 4, to be used in DEA | data.frame | 6 | 4 |
spanishuniversities2018 | adea | A data set of Spanish public universities | data.frame | 47 | 9 |
tokyo_libraries | adea | A data set of Tokyo libraries | data.frame | 23 | 6 |
col | RpeakChrom | Parameters data frame for columnar measurements. | data.frame | 50 | 9 |
parameters_col_metoxi | RpeakChrom | Parameters data frame for sulphadimetoxine columnar measurements. | data.frame | 12 | 9 |
parameters_dead | RpeakChrom | Parameters data frame for dead marker measurements. | data.frame | 13 | 9 |
parameters_ext | RpeakChrom | Parameters data frame for Kbr extracolumnar measurements. | data.frame | 13 | 9 |
peak | RpeakChrom | Peak read using readChrom function. | data.frame | 12000 | 2 |
nlsy27 | LAWBL | National Longitudinal Survey of Youth 1997 | list | | |
sim18ccfa40 | LAWBL | Simulated CCFA data with LI and missingness | list | | |
sim18ccfa41 | LAWBL | Simulated CCFA data with LD and missingness | list | | |
sim18cfa0 | LAWBL | Simulated CFA data with LI | list | | |
sim18cfa1 | LAWBL | Simulated CFA data with LD | list | | |
sim18mcfa41 | LAWBL | Simulated MCFA data with LD and Missingness | list | | |
sim24ccfa21 | LAWBL | Simulated CCFA data (dichotomous) with LD and a minor factor/trait | list | | |
sicri2018 | LearningStats | SICRI: information system on risk-taking behaviour | data.frame | 7853 | 18 |
Earthquake | spherepc | Earthquake | data.frame | 77 | 22 |
emp_agr | TSsmoothing | Employment in agriculture | ts | | |
ltable | TSsmoothing | Lambda values table. | array | | |
trade | TSsmoothing | Annual Trade for USA and Mexico | matrix | 49 | 2 |
simdata | mme | Dataset for Model 1 | data.frame | 15 | 9 |
simdata2 | mme | Dataset for Model 2 | data.frame | 20 | 9 |
simdata3 | mme | Dataset for Model 3 | data.frame | 40 | 9 |
world | MoLE | Model parameters | list | | |
disabData | addhaz | Example of disability data | data.frame | 6294 | 7 |
ex2PL | PsyControl | Example data set based on a simulated 2PL model. | matrix | 200 | |
exGRM | PsyControl | Example data set based on a simulated GRM model. | matrix | 100 | |
gh | PsyControl | Example data set based on a simulated GRM model. | list | | |
FC | ThurMod | Paired comparisons of $N=15$ items from one factor/trait (Thurstonian modeling) | data.frame | 1000 | 105 |
FC12 | ThurMod | Paired comparisons of $N=12$ items from one factor/trait (Thurstonian modeling) | data.frame | 1000 | 66 |
FC_raw | ThurMod | Raw ranking data of $N=15$ items from three factors/traits (Thurstonian modeling) | matrix | 1000 | 15 |
FC_scores | ThurMod | Scores of the data set 'FC' from Mplus. | data.frame | 1000 | 111 |
sucra | OrigamiPlot | SUCRA | data.frame | 8 | 5 |
CL_ACTIVITY_ANZSIC06 | statcodelists | Codelist Activity - ISIC, Revision 4 | data.frame | 825 | 5 |
CL_ACTIVITY_ISIC4 | statcodelists | Codelist Activity - ISIC, Revision 4 | data.frame | 766 | 5 |
CL_ACTIVITY_NACE2 | statcodelists | Codelist Activity - NACE, Revision 2 | data.frame | 996 | 5 |
CL_AGE | statcodelists | Codelist Age | data.frame | 5 | 5 |
CL_AREA | statcodelists | Reference Area Code List | data.frame | 899 | 5 |
CL_CIVIL_STATUS | statcodelists | Codelist Civil (or Marital) Status | data.frame | 8 | 5 |
CL_COFOG_1999 | statcodelists | Classification of the Functions of Government | data.frame | 188 | 5 |
CL_CONF_STATUS | statcodelists | Codelist Confidentiality Status | data.frame | 11 | 5 |
CL_COPNI_1999 | statcodelists | Classification of the Purposes of Non-Profit Institutions Serving Households | data.frame | 65 | 5 |
CL_COPP_1999 | statcodelists | Classification of the Outlays of Producers According to Purpose | data.frame | 51 | 5 |
CL_DECIMALS | statcodelists | Codelist Decimals | data.frame | 16 | 5 |
CL_DEG_URB | statcodelists | Codelist Degree of Urbanization | data.frame | 14 | 5 |
CL_FREQ | statcodelists | Codelist Frequency | data.frame | 34 | 5 |
CL_OBS_STATUS | statcodelists | Codelist Observation status | data.frame | 20 | 5 |
CL_OCCUPATION | statcodelists | Codelist Occupation | data.frame | 619 | 5 |
CL_SEASONAL_ADJUST | statcodelists | Codelist Seasonal Adjustment | data.frame | 11 | 5 |
CL_SEX | statcodelists | Codelist Sex | data.frame | 7 | 5 |
CL_TIMETRANS | statcodelists | Codelist Time Transformation | data.frame | 47 | 5 |
CL_TIMETRANS_PER | statcodelists | Codelist Time Transformation Period | data.frame | 12 | 5 |
CL_TIMETRANS_TYPE | statcodelists | Codelist Time Transformation Type | data.frame | 13 | 5 |
CL_TIME_FORMAT | statcodelists | Codelist Time Format | data.frame | 21 | 5 |
CL_TIME_PER_COLLECT | statcodelists | Codelist Time Period - Collection | data.frame | 8 | 5 |
CL_UNIT_MULT | statcodelists | Codelist Unit multiplier | data.frame | 31 | 5 |
codebooks | statcodelists | Available codelists by concept | data.frame | 21 | 3 |
Democratization | qcauchyreg | Estimation of Democratization Index | data.frame | 138 | 4 |
Poverty | qcauchyreg | Percentage of extremely poor. | data.frame | 5501 | 4 |
campsites | rleafmap | French Campsites | SpatialPolygonsDataFrame | | |
hotels | rleafmap | French Hotels | SpatialPolygonsDataFrame | | |
velov | rleafmap | Velo'v stations | SpatialPointsDataFrame | | |
lung | pvclust | DNA Microarray Data of Lung Tumors | data.frame | 916 | 73 |
genomeData | derfinder | Genome samples processed data | list | | |
genomeDataRaw | derfinder | Genome samples processed data | list | | |
genomeFstats | derfinder | F-statistics for the example data | Rle | | |
genomeInfo | derfinder | Genome samples information | data.frame | 31 | 5 |
genomeRegions | derfinder | Candidate DERs for example data | list | | |
genomicState | derfinder | Genomic State for Hsapiens.UCSC.hg19.knownGene | CompressedGRangesList | | |
data_cls | etree | Classification toy dataset | list | | |
data_reg | etree | Regression toy dataset | list | | |
Eyam | MultiBD | Eyam plague. | data.frame | 8 | 4 |
cantilever | mistral | A function calculating the deviation of a cantilever beam. | function | | |
kiureghian | mistral | A limit-state-function defined by Der Kiureghian | function | | |
oscillator_d6 | mistral | A limit-state-function defined with a non-linear oscillator in dimension 6. | function | | |
oscillator_d8 | mistral | A limit-state-function defined with a two degrees of freedom damped oscillator | function | | |
rackwitz | mistral | A limit-state-function defined by Rackwitz | function | | |
twodof | mistral | A limit-state-function defined with a two degrees of freedom damped oscillator | function | | |
waarts | mistral | A limit-state-function defined by Waarts | function | | |
chldat | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay | data.frame | 452 | 4 |
daydat | WRTDStidal | Daily chlorophyll, salinity, and discharge time series for the Upper Patuxent River Estuary | data.frame | 3506 | 9 |
tidfit | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object | tidal | 452 | 15 |
tidfitmean | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object for the conditional mean model | tidalmean | 452 | 13 |
tidobj | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object | tidal | 452 | 9 |
tidobjmean | WRTDStidal | Monthly chlorophyll time series for Hillsborough Bay as a tidal object, conditional mean model | tidalmean | 452 | 9 |
MathAnxiety | likert | Pre-summarized results from an administration of the Math Anxiety Scale Survey. | data.frame | 14 | 6 |
MathAnxietyGender | likert | Pre-summarized results from an administration of the Math Anxiety Scale Survey grouped by gender. | data.frame | 28 | 7 |
gap | likert | Fictitious dataset with importance and satisfaction results across five different offices. | data.frame | 68 | 11 |
mass | likert | Results from an administration of the Math Anxiety Scale Survey. | data.frame | 20 | 15 |
pisaitems | likert | Programme of International Student Assessment | data.frame | 66690 | 81 |
sasr | likert | Results from the Survey of Academic Self-Regulation (SASR). | data.frame | 860 | 63 |
Wheat_IBCF | IBCF.MTME | Wheat Data | data.frame | 3000 | 4 |
Year_IBCF | IBCF.MTME | Year_IBCF Data | data.frame | 720 | 4 |
corona_data_all | hystReet | Downloaded data for the vignette | data.frame | 18988 | 5 |
data_73_74 | hystReet | Downloaded data for the vignette | data.frame | 62 | 3 |
location_71 | hystReet | Downloaded data for the vignette | list | | |
locations | hystReet | Downloaded data for the vignette | data.frame | 188 | 3 |
ratio | hystReet | Downloaded data for the vignette | data.frame | 188 | 3 |
myGOs | ViSEAGO | myGOs dataset | GO_SS | | |
gs.data | odk | 'Google Sheets' Data for 'odk.frame' | Workbook | | |
odk.frame | odk | 'Google Sheets' or 'XLSForm' Dummy 'ODK' Frame | Workbook | | |
Crossdata | ComparisonSurv | The Data with Survival Curves Crossed | data.frame | 200 | 3 |
PHdata | ComparisonSurv | The Data Satisfied Proportional Hazard Assumption | data.frame | 200 | 3 |
SMBassWB1 | RFishBC | Fish-specific data for West Bearskin Lake Smallmouth Bass. | data.frame | 445 | 6 |
SMBassWB2 | RFishBC | Radial measurements for for West Bearskin Lake Smallmouth Bass. | data.frame | 181 | 13 |
StdIntLit | RFishBC | Standard intercepts for Fraser-Lee model by species. | data.frame | 12 | 3 |
school23 | influence.ME | Math test performance in 23 schools | data.frame | 519 | 15 |
BMI | wec | Data on BMI of Dutch citizens | data.frame | 3314 | 7 |
PUMS | wec | Public Use Microdata Sample files (PUMS) 2013 | data.table | 10000 | 4 |
newdata | fpa | Fixation probability data generated by ft2fp() function | data.frame | 3264 | 8 |
pattern | fpa | Summary of fixation pattern generated by get_pattern() function | cast_df | 32 | 53 |
rawdata | fpa | Fixation time data of an eye movement experiment | data.frame | 1603 | 7 |
pd1.count.matrix | robustrao | pubdata1 | matrix | 249 | 5 |
pd1.similarity | robustrao | pubdata1 | matrix | 249 | 249 |
pd1.uncat.refs | robustrao | pubdata1 | numeric | | |
pd2.count.matrix | robustrao | pubdata2 | matrix | 249 | 2 |
pd2.similarity | robustrao | pubdata2 | matrix | 249 | 249 |
pd2.uncat.refs | robustrao | pubdata2 | numeric | | |
synthetic.sub35 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2858 | 10 |
synthetic.sub40 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2500 | 10 |
synthetic.sub42 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2381 | 10 |
synthetic.sub50 | deadband | Samples subset of 10 pesudo periodic signals | data.frame | 2000 | 10 |
map | PAS | beef data | data.frame | 300 | 2 |
x | PAS | beef data | matrix | 836 | |
y | PAS | beef data | matrix | 836 | |
baltimore | dyn | Baltimore energy data | zoo | 96 | 6 |
BCJ | astsa | Daily Returns of Three Banks | mts | 3243 | 3 |
EBV | astsa | Entire Epstein-Barr Virus (EBV) Nucleotide Sequence | character | | |
ENSO | astsa | El Nino - Southern Oscillation Index | ts | | |
EQ5 | astsa | Seismic Trace of Earthquake number 5 | ts | | |
EQcount | astsa | EQ Counts | ts | | |
EXP6 | astsa | Seismic Trace of Explosion number 6 | ts | | |
GDP | astsa | Quarterly U.S. GDP - updated to 2023 | ts | | |
GNP | astsa | Quarterly U.S. GNP - updated to 2023 | ts | | |
HCT | astsa | Hematocrit Levels | ts | | |
Hare | astsa | Snowshoe Hare | ts | | |
Lynx | astsa | Canadian Lynx | ts | | |
MEI | astsa | Multivariate El Nino/Southern Oscillation Index (version 1) | ts | | |
Months | astsa | Month Labels | character | | |
PLT | astsa | Platelet Levels | ts | | |
USpop | astsa | U.S. Population - 1900 to 2010 | ts | | |
UnempRate | astsa | U.S. Unemployment Rate | ts | | |
WBC | astsa | White Blood Cell Levels | ts | | |
ar1miss | astsa | AR with Missing Values | ts | | |
arf | astsa | Simulated ARFIMA | ts | | |
beamd | astsa | Infrasonic Signal from a Nuclear Explosion | data.frame | 2048 | 3 |
birth | astsa | U.S. Monthly Live Births | ts | | |
blood | astsa | Daily Blood Work with Missing Values | mts | 91 | 3 |
bnrf1ebv | astsa | Nucleotide sequence - BNRF1 Epstein-Barr | ts | | |
bnrf1hvs | astsa | Nucleotide sequence - BNRF1 of Herpesvirus saimiri | ts | | |
cardox | astsa | Monthly Carbon Dioxide Levels at Mauna Loa | ts | | |
chicken | astsa | Monthly price of a pound of chicken | ts | | |
climhyd | astsa | Lake Shasta inflow data | data.frame | 454 | 6 |
cmort | astsa | Cardiovascular Mortality from the LA Pollution study | ts | | |
cpg | astsa | Hard Drive Cost per GB | ts | | |
djia | astsa | Dow Jones Industrial Average | xts | 2518 | 5 |
econ5 | astsa | Five Quarterly Economic Series | mts | 161 | 5 |
eqexp | astsa | Earthquake and Explosion Seismic Series | data.frame | 2048 | 17 |
flu | astsa | Monthly pneumonia and influenza deaths in the U.S., 1968 to 1978. | ts | | |
fmri | astsa | fMRI - complete data set | list | | |
fmri1 | astsa | fMRI Data Used in Chapter 1 | mts | 128 | 9 |
gas | astsa | Gas Prices | ts | | |
gdp | astsa | Quarterly U.S. GDP | ts | | |
gnp | astsa | Quarterly U.S. GNP | ts | | |
gtemp_both | astsa | Global mean land and open ocean temperature deviations, 1850-2023 | ts | | |
gtemp_land | astsa | Global mean land temperature deviations, 1850-2023 | ts | | |
gtemp_ocean | astsa | Global mean ocean temperature deviations, 1850-2023 | ts | | |
hor | astsa | Hawaiian occupancy rates | ts | | |
jj | astsa | Johnson and Johnson Quarterly Earnings Per Share | ts | | |
lap | astsa | LA Pollution-Mortality Study | mts | 508 | 11 |
lead | astsa | Leading Indicator | ts | | |
nyse | astsa | Returns of the New York Stock Exchange | ts | | |
oil | astsa | Crude oil, WTI spot price FOB | ts | | |
part | astsa | Particulate levels from the LA pollution study | ts | | |
polio | astsa | Poliomyelitis cases in US | ts | | |
prodn | astsa | Monthly Federal Reserve Board Production Index | ts | | |
qinfl | astsa | Quarterly Inflation | ts | | |
qintr | astsa | Quarterly Interest Rate | ts | | |
rec | astsa | Recruitment (number of new fish index) | ts | | |
sales | astsa | Sales | ts | | |
salmon | astsa | Monthly export price of salmon | ts | | |
salt | astsa | Salt Profiles | ts | | |
saltemp | astsa | Temperature Profiles | ts | | |
sleep1 | astsa | Sleep State and Movement Data - Group 1 | list | | |
sleep2 | astsa | Sleep State and Movement Data - Group 2 | list | | |
so2 | astsa | SO2 levels from the LA pollution study | ts | | |
soi | astsa | Southern Oscillation Index | ts | | |
soiltemp | astsa | Spatial Grid of Surface Soil Temperatures | matrix | 64 | |
sp500.gr | astsa | Returns of the S&P 500 | ts | | |
sp500w | astsa | Weekly Growth Rate of the Standard and Poor's 500 | xts | 509 | 1 |
speech | astsa | Speech Recording | ts | | |
star | astsa | Variable Star | ts | | |
sunspotz | astsa | Biannual Sunspot Numbers | ts | | |
tempr | astsa | Temperatures from the LA pollution study | ts | | |
unemp | astsa | U.S. Unemployment | ts | | |
varve | astsa | Annual Varve Series | ts | | |
xA_readme | astsa | SCRIPTS MARKED WITH AN 'x' ARE SCHEDULED TO BE PHASED OUT | character | | |
xglobtemp | astsa | Superseded by 'gtemp_both' - Global mean land-ocean temperature deviations. | ts | | |
xglobtempl | astsa | Superseded by 'gtemp_land' - Global mean land temperature deviations. | ts | | |
xgtemp | astsa | Superseded by 'gtemp_both' - Global mean land-ocean temperature deviations. | ts | | |
xgtemp2 | astsa | Superseded by 'gtemp_land' - Global Mean Surface Air Temperature Deviations | ts | | |
mayo | APtools | Mayo Marker data | data.frame | 312 | 4 |
data2 | RPEXE.RPEXT | RPEXE_fitting | data.frame | 118 | 3 |
df | RPEXE.RPEXT | JAMA Breast cancer | data.frame | 508 | 10 |
loopcut_onestep_data | RPEXE.RPEXT | Example data for loopcut_onestep | matrix | 178 | 2 |
loopcuts_cut | RPEXE.RPEXT | Example data for loopcuts_cuttimes | numeric | | |
loopcuts_t_c | RPEXE.RPEXT | Example data for loopcut_times_censoring | matrix | 178 | 2 |
loopcuts_umbrella_cuttimes_mono | RPEXE.RPEXT | Example data for loopcut_umbrella | matrix | 11 | 2 |
pava_dfrd | RPEXE.RPEXT | Example data for pava | matrix | 139 | 3 |
pexeest_times_censoring | RPEXE.RPEXT | Example data for pexeest_times_censoring | matrix | 178 | 2 |
simple | RPEXE.RPEXT | None Small Cell Lung cancer data | data.frame | 178 | 2 |
t100 | RPEXE.RPEXT | Example data for pexeest_tx | numeric | | |
Farms | sfadv | Data set of farm accountancy data | data.frame | 2500 | 14 |
Pistoia | LPM | Dataset of Pistoia (Italy) | data.frame | 744 | 2 |
hourly.rainfall.series | LPM | hourly rainfall series | data.frame | 41094 | 1 |
milano | LPM | Maximum annual rainfall series for different durations | data.frame | 30 | 11 |
series.rainfall | LPM | Daily Rainfall Series | matrix | 5475 | 5 |
series.runoff | LPM | Daily Runoff Series | numeric | | |
BWHCitationReport | hindexcalculator | WoS exported citation report for search AD=(brigham same anes*) OR AD=(brigham same anaes*) | data.frame | 3075 | 92 |
CHROM | ADPF | Data frame of Chromatogram values | data.frame | 201 | 6 |
CCU12_Precip | UStatBookABSC | Precipitation for June-September 2012 recorded in Kolkata | data.frame | 51 | 4 |
exampleHiCDOCDataSet | HiCDOC | Example HiCDOCDataSet. | HiCDOCDataSet | | |
exampleHiCDOCDataSetProcessed | HiCDOC | Example HiCDOCDataSet, filtered, normalized and with compartements detected. | HiCDOCDataSet | | |
Brazil_epiflows | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | epiflows | | |
YF_Brazil | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | list | | |
YF_coordinates | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | data.frame | 15 | 3 |
YF_flows | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | data.frame | 100 | 3 |
YF_locations | epiflows | Yellow Fever Data from Brazil; 2016-12 to 2017-05 | data.frame | 15 | 6 |
Owls | glmmTMB | Begging by Owl Nestlings | data.frame | 599 | 8 |
Salamanders | glmmTMB | Repeated counts of salamanders in streams | data.frame | 644 | 9 |
epil2 | glmmTMB | Seizure Counts for Epileptics - Extended | data.frame | 236 | 12 |
spider_long | glmmTMB | Spider data from CANOCO, long format | data.frame | 336 | 9 |
rotif.env | fuzzySim | Rotifers and environmental variables on TDWG level 4 regions of the world | data.frame | 291 | 47 |
rotifers | fuzzySim | Rotifer species on TDWG level 4 regions of the world | data.frame | 3865 | 2 |
cells_citeseq_mtx | dsb | small example CITE-seq protein dataset for 87 surface protein in 2872 cells | matrix | 87 | 2872 |
empty_drop_citeseq_mtx | dsb | small example CITE-seq protein dataset for 87 surface protein in 8005 empty droplets | matrix | 87 | 8005 |
academic_awards | gorica | Academic awards data | data.frame | 200 | 4 |
hox_2010 | gorica | Sesame Street data based on Hox (2010) | data.frame | 2000 | 6 |
nederhof_2014 | gorica | Data based on Nederhof, Ormel, and Oldehinkel (2014) | data.frame | 310 | 4 |
reading_ach | gorica | Reading achievement data | data.frame | 10320 | 5 |
school_admissions | gorica | High School Admissions Data | data.frame | 30 | 3 |
stevens_1999 | gorica | Sesame Street data based on Stevens (1999) | data.frame | 240 | 14 |
wechsler | gorica | Wechsler intelligence test data | data.frame | 1680 | 10 |
kunnat1865_2021 | sorvi | Municipality dataset | data.frame | 1337 | 10 |
polygons1909_2009 | sorvi | Municipality geometries | sf | 861 | 3 |
amd | eyedata | Twelve years neovascular AMD survival data | tbl_df | 118255 | 11 |
amd2 | eyedata | Real life data of patients with neovascular AMD | tbl_df | 40764 | 7 |
amd3 | eyedata | Ten year neovascular AMD survival data | tbl_df | 6696 | 23 |
amdoct | eyedata | Real life OCT segmentation data of patients with AMD | tbl_df | 2966 | 24 |
dme | eyedata | Real life data of patients with diabetic macular edema | tbl_df | 40281 | 8 |
VADIR_fake | sampleVADIR | Fake VADIR data | data.frame | 200000 | 10 |
rankDat | sampleVADIR | Rank to pay grade data | data.frame | 114 | 6 |
petersen | multiwayvcov | Simulation of clustering with firm and time effects. | data.frame | 5000 | 4 |
boxly_adeg | boxly | An example ADEG dataset | tbl_df | 32139 | 35 |
boxly_adlb | boxly | An example ADLB dataset | tbl_df | 24746 | 24 |
boxly_adsl | boxly | A Subject Level Demographic Dataset | data.frame | 254 | 49 |
boxly_advs | boxly | An example ADVS dataset | tbl_df | 32139 | 34 |
WachusettReservoir | qqtest | Storage, in millions of gallons daily per square mile of net land area, at the Wachusett Reservoir in Massacusetts - storage computed for each of several rates of draft (draft being a determined maintainable flow in 1,000s of gallons per square mile daily). | data.frame | 15 | 6 |
bacteria | qqtest | Bacteria from Delaware River water entering the Torresdale Filter of the Philadelphia water supply 1913. | data.frame | 22 | 2 |
penicillin | qqtest | 31 contrast sums from a 32 run 2^(5-0) factorial experiment on penicillin production. | data.frame | 31 | 1 |
primer | qqtest | Automobile primer paint thickness quality control measurements. | data.frame | 20 | 14 |
pullstrength | qqtest | Strength of pull for 519 males aged 23-26. | data.frame | 7 | 5 |
sittingHeights | qqtest | Sitting height in inches of female adults (aged 23-50). | data.frame | 9 | 8 |
stacklossDistances | qqtest | Mahalanobis squared distances of Brownlee's stack loss plant operation data based only on the explanatory variates (air flow, water temperature, and acid concentration). | data.frame | 21 | 2 |
advert | fma | Sales and advertising expenditure | mts | 24 | 2 |
advsales | fma | Sales volume and advertising expenditure | mts | 36 | 2 |
airpass | fma | Monthly Airline Passenger Numbers 1949-1960 | ts | | |
auto | fma | Attributes of some US and Japanese automobiles | data.frame | 45 | 4 |
bank | fma | Mutual savings bank deposits | data.frame | 60 | 3 |
beer | fma | Monthly beer production | ts | | |
bicoal | fma | Annual bituminous coal production | ts | | |
books | fma | Sales of paperback and hardcover books | mts | 30 | 2 |
boston | fma | Monthly dollar volume of sales | mts | 35 | 2 |
bricksq | fma | Quarterly clay brick production | ts | | |
canadian | fma | Canadian unemployment rate | ts | | |
capital | fma | Quarterly capital expenditure and appropriations | mts | 88 | 2 |
cement | fma | Cement composition and heat data | data.frame | 10 | 4 |
chicken | fma | Price of chicken | ts | | |
condmilk | fma | Condensed milk | ts | | |
copper | fma | Copper price | ts | | |
copper1 | fma | Copper prices | ts | | |
copper2 | fma | Copper prices | ts | | |
copper3 | fma | Copper prices | ts | | |
cowtemp | fma | Temperature of a cow | ts | | |
cpimel | fma | Consumer price index | ts | | |
dexter | fma | Dexterity test and production ratings | data.frame | 20 | 2 |
dj | fma | Dow-Jones index | ts | | |
dole | fma | Unemployment benefits in Australia | ts | | |
dowjones | fma | Dow-Jones index | ts | | |
econsumption | fma | Electricity consumption and temperature | data.frame | 12 | 2 |
eggs | fma | Price of eggs | ts | | |
eknives | fma | Sales of electric knives | ts | | |
elco | fma | Sales of Elco's laser printers | ts | | |
elec | fma | Electricity production | ts | | |
expenditure | fma | Expenditure | numeric | | |
fancy | fma | Sales for a souvenir shop | ts | | |
french | fma | Industry index | ts | | |
housing | fma | Housing data | mts | 82 | 3 |
hsales | fma | Sales of one-family houses | ts | | |
hsales2 | fma | Sales of new one-family houses | ts | | |
huron | fma | Level of Lake Huron | ts | | |
ibm | fma | IBM sales and profit | mts | 42 | 4 |
ibmclose | fma | Closing IBM stock price | ts | | |
input | fma | Input series | ts | | |
internet | fma | Number of internet users | ts | | |
invent15 | fma | Inventory demand | ts | | |
jcars | fma | Motor vehicle production | ts | | |
kkong | fma | King Kong data | data.frame | 21 | 2 |
labour | fma | Civilian labour force | ts | | |
lynx | fma | Annual Canadian Lynx trappings 1821-1934 | ts | | |
milk | fma | Monthly milk production per cow | ts | | |
mink | fma | Number of minks trapped | ts | | |
mortal | fma | Mortality | data.frame | 156 | 2 |
motel | fma | Total accommodation at hotel, motel and guest house | mts | 186 | 2 |
motion | fma | Employment figures in the motion picture industry | ts | | |
nail | fma | Nail prices | ts | | |
oilprice | fma | Oil prices | ts | | |
olympic | fma | Men's 400 m final winning times in each Olympic Games | data.frame | 23 | 2 |
ozone | fma | Ozone depletion and melanoma rates | data.frame | 11 | 2 |
paris | fma | Average temperature | ts | | |
pcv | fma | GDP | data.frame | 19 | 2 |
petrol | fma | Sales of petroleum and related product | mts | 252 | 4 |
pigs | fma | Number of pigs slaughtered | ts | | |
plastics | fma | Sales of plastic product | ts | | |
pollution | fma | Shipment of pollution equipment | ts | | |
productC | fma | Sales of product C | ts | | |
pulpprice | fma | Pulp price and shipments | data.frame | 25 | 2 |
qelec | fma | Electricity production | ts | | |
qsales | fma | Sales data | ts | | |
running | fma | Running times and maximal aerobic capacity | data.frame | 14 | 2 |
sales | fma | Sales data | ts | | |
schizo | fma | Perceptual speed scores | ts | | |
shampoo | fma | Sales of shampoo | ts | | |
sheep | fma | Sheep population | ts | | |
ship | fma | Electric can opener shipments | ts | | |
shipex | fma | Shipments | ts | | |
strikes | fma | Number of strikes | ts | | |
telephone | fma | Telephone cost | ts | | |
texasgas | fma | Price and consumption of natural gas | data.frame | 20 | 2 |
ukdeaths | fma | Total deaths and serious injuries | ts | | |
usdeaths | fma | Accidental deaths in USA | ts | | |
uselec | fma | Total generation of electricity | ts | | |
ustreas | fma | Treasury bill contracts | ts | | |
wagesuk | fma | Real daily wages | ts | | |
wheat | fma | Wheat prices | ts | | |
wn | fma | White noise series | ts | | |
wnoise | fma | White noise time series | ts | | |
writing | fma | Sales of printing and writing paper | ts | | |
ppendemic_tab13 | ppendemic | ppendemic_tab: Endemic Plant Database of Peru | tbl_df | 7815 | 14 |
LR_dataset | TDCor | Lateral root transcriptomic dataset | matrix | 15240 | 18 |
TF | TDCor | Table of 1834 Arabidopsis Transcription factors | data.frame | 1834 | 2 |
l_genes | TDCor | l_genes | character | | |
l_names | TDCor | l_names | character | | |
l_prior | TDCor | l_prior | integer | | |
times | TDCor | The 'times' vector to use with the lateral root dataset | numeric | | |
rice_normal | dhga | The gene expression data of rice under control or normal condition | data.frame | 200 | 20 |
rice_salt | dhga | The gene expression data of rice under salinity stress condition | data.frame | 200 | 20 |
db1rl | LearnSL | Test Database 1 | data.frame | 20 | 5 |
db2 | LearnSL | Test Database 6 | data.frame | 10 | 4 |
db3 | LearnSL | Test Database 7 | data.frame | 12 | 4 |
db_flowers | LearnSL | Test Database 5 | data.frame | 20 | 5 |
db_per_and | LearnSL | Test Database 2 | data.frame | 8 | 4 |
db_per_or | LearnSL | Test Database 3 | data.frame | 8 | 4 |
db_per_xor | LearnSL | Test Database 4 | data.frame | 8 | 4 |
db_tree_struct | LearnSL | Test Database 8 | tree_struct | | |
fitPoly_data | fitPoly | Small fitPoly input datasets for testing and examples | list | | |
apg_families | taxize | MOBOT family names | tbl_df | 1705 | 6 |
apg_orders | taxize | MOBOT order names | tbl_df | 576 | 5 |
plantGenusNames | taxize | Vector of plant genus names from ThePlantList | character | | |
plantNames | taxize | Vector of plant species (genus - specific epithet) names from ThePlantList | character | | |
rank_ref | taxize | Lookup-table for IDs of taxonomic ranks | data.frame | 46 | 2 |
rank_ref_zoo | taxize | Lookup-table for IDs of taxonomic ranks (WoRMS) | data.frame | 47 | 2 |
species_plantarum_binomials | taxize | Species names from Species Plantarum | data.frame | 5940 | 3 |
theplantlist | taxize | Lookup-table for family, genus, and species names for ThePlantList | data.frame | 10000 | 3 |
worrms_ranks | taxize | WORMS ranks | spec_tbl_df | 216 | 2 |
Ex1 | APFr | Example dataset 1 | numeric | | |
Ex2 | APFr | Example 2 | list | | |
BR_LatSq | scidesignR | BR_LatSq | spec_tbl_df | 16 | 4 |
CSectdat | scidesignR | CSectdat | tbl_df | 7779 | 2 |
agedata | scidesignR | agedata | tbl_df | 50 | 1 |
chemplant | scidesignR | chemplant | data.frame | 16 | 5 |
cookies | scidesignR | cookies | data.frame | 8 | 5 |
covid19_trial | scidesignR | covid19_trial | tbl_df | 20 | 4 |
fertdat | scidesignR | fertdat | tbl_df | 12 | 2 |
hsvdat | scidesignR | hsvdat | data.frame | 32 | 8 |
leafspring | scidesignR | leafspring | data.frame | 16 | 8 |
lifesat_childmort | scidesignR | lifesat_childmort | spec_tbl_df | 22595 | 7 |
nhefs9282 | scidesignR | nhefs9282 | spec_tbl_df | 9281 | 34 |
painstudy | scidesignR | painstudy | data.frame | 150 | 2 |
painstudy2 | scidesignR | painstudy2 | data.frame | 120 | 2 |
rtdat | scidesignR | rtdat | tbl_df | 100 | 2 |
shoedat_obs | scidesignR | shoedat_obs | data.frame | 10 | 5 |
silkdat | scidesignR | silkdat | tbl_df | 48 | 11 |
wtlossdat | scidesignR | wtlossdat | spec_tbl_df | 8 | 5 |
cghs | RDHonest | Oreopoulos (2006) UK general household survey dataset | data.frame | 73954 | 2 |
headst | RDHonest | Head Start data from Ludwig and Miller (2007) | data.frame | 3127 | 18 |
lee08 | RDHonest | Lee (2008) US House elections dataset | data.frame | 6558 | 2 |
rcp | RDHonest | Battistin, Brugiavini, Rettore, and Weber (2009) retirement consumption puzzle dataset | data.frame | 30006 | 8 |
rebp | RDHonest | Austrian unemployment duration data from Lalive (2008) | data.frame | 29371 | 4 |
msmdata | WeightIt | Simulated data for a 3 time point sequential study | data.frame | 7500 | 10 |
greta_deps_tf_tfp | greta | Suggested valid Python dependencies for greta | tbl_df | 63 | 5 |
brfss | jointVIP | 2015 Behavioral Risk Factor Surveillance System | data.frame | 5000 | 15 |
abdom | lmls | Abdominal circumference data | data.frame | 610 | 2 |
BaetenEtAl2013 | bayesmeta | Ankylosing spondylitis example data | data.frame | 8 | 4 |
BucherEtAl1997 | bayesmeta | Direct and indirect comparison example data | data.frame | 22 | 7 |
Cochran1954 | bayesmeta | Fly counts example data | data.frame | 7 | 3 |
CrinsEtAl2014 | bayesmeta | Pediatric liver transplant example data | data.frame | 6 | 19 |
GoralczykEtAl2011 | bayesmeta | Liver transplant example data | data.frame | 19 | 17 |
HinksEtAl2010 | bayesmeta | JIA example data | data.frame | 3 | 8 |
KarnerEtAl2014 | bayesmeta | COPD example data | data.frame | 22 | 27 |
NicholasEtAl2019 | bayesmeta | Multiple sclerosis disability progression example data | data.frame | 28 | 4 |
Peto1980 | bayesmeta | Aspirin after myocardial infarction example data | data.frame | 6 | 11 |
RobergeEtAl2017 | bayesmeta | Aspirin during pregnancy example data | data.frame | 45 | 14 |
Rubin1981 | bayesmeta | 8-schools example data | data.frame | 8 | 4 |
SchmidliEtAl2017 | bayesmeta | Historical variance example data | data.frame | 6 | 4 |
SidikJonkman2007 | bayesmeta | Postoperative complication odds example data | data.frame | 29 | 7 |
SnedecorCochran | bayesmeta | Artificial insemination of cows example data | data.frame | 6 | 4 |
countData | SurfR | countData | matrix | 2500 | 4 |
enrichedList | SurfR | enrichedList | list | | |
ind_deg | SurfR | ind_deg | list | | |
metadata | SurfR | metadata | data.frame | 4 | 3 |
state_tbl | statebins | "State" abbreviation to name data frame | data.frame | 54 | 2 |
friends | friends | The transcript of Friends | tbl_df | 67359 | 6 |
friends_emotions | friends | Emotions for transcript of Friends | tbl_df | 12606 | 5 |
friends_entities | friends | Character Entities for transcript of Friends | tbl_df | 10557 | 5 |
friends_info | friends | Episode Information | tbl_df | 236 | 8 |
jackolantern_surreal_data | surreal | Jack-o'-Lantern Surreal Data | data.frame | 5395 | 7 |
r_logo_image_data | surreal | R Logo Pixel Data | data.frame | 2000 | 2 |
diabetes | glmxdiag | Diabetes | data.frame | 43 | 3 |
moons | glmxdiag | Moons of the 13 planets of the Solar System | data.frame | 13 | 5 |
stopping | glmxdiag | Stopping | data.frame | 63 | 2 |
anole | patternator | Dorsal pattern image of a female brown anole lizard | data.table | 1675 | 2 |
data | NegativeControlOutcomeAdjustment | Data for examples | data.frame | 1000 | 5 |
E2grades | ProfessR | Examination grades from Test 2 in 2007 | numeric | | |
QBANK1 | ProfessR | Example Question Bank | list | | |
QBANK2 | ProfessR | Example Question Bank | list | | |
catage.long | TAF | Catch at Age in Long Format | data.frame | 32 | 3 |
catage.taf | TAF | Catch at Age in TAF Format | data.frame | 8 | 5 |
catage.xtab | TAF | Catch at Age in Crosstab Format | data.frame | 8 | 4 |
summary.taf | TAF | Summary Results in TAF Format | data.frame | 3 | 16 |
taf.blue | TAF | TAF Colors | character | | |
taf.dark | TAF | TAF Colors | character | | |
taf.green | TAF | TAF Colors | character | | |
taf.light | TAF | TAF Colors | character | | |
taf.orange | TAF | TAF Colors | character | | |
bottle.df | Hotelling | Bottle data | data.frame | 120 | 6 |
container.df | Hotelling | Container data | data.frame | 20 | 10 |
manova1.df | Hotelling | manova1 data | data.frame | 18 | 4 |
carabidae | sinar | Counts of arthropods in a grid-sampled wheat field | matrix | 9 | 7 |
nematodes | sinar | A matrix of counting data with 15 rows and 15 columns. | matrix | 15 | 15 |
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 |
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 |
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 |
bladder | frailtyHL | Bladder Cancer Data | data.frame | 396 | 13 |
bladder0 | frailtyHL | Bladder cancer data | data.frame | 410 | 5 |
cgd | frailtyHL | Chronic Granulomatous Disease (CGD) Infection Data | data.frame | 203 | 16 |
kidney | frailtyHL | Kidney Infection Data | data.frame | 76 | 10 |
rats | frailtyHL | Rats data | data.frame | 150 | 4 |
ren | frailtyHL | Mammary tumor data | data.frame | 254 | 6 |
renal | frailtyHL | Renal transplant data | data.frame | 1395 | 9 |
test | frailtyHL | Simulated data with clustered competing risks | data.frame | 250 | 6 |
babies | rbiom | Longitudinal Stool Samples from Infants (n = 2,684) | rbiom | | |
gems | rbiom | Global Enteric Multicenter Study (n = 1,006) | rbiom | | |
hmp50 | rbiom | Human Microbiome Project - demo dataset (n = 50) | rbiom | | |
for_bus_gtfs | GTFSwizard | GTFS Data for Fortaleza (Bus System), Brazil. | wizardgtfs | | |
for_rail_gtfs | GTFSwizard | GTFS Data for Fortaleza (Rail System), Brazil | wizardgtfs | | |
global_regions | sdmtools | Global regions | long_tibble | 249 | 6 |
raster_to_terra | sdmtools | 'raster' to 'terra' equivalence table | long_tibble | 42 | 3 |
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 | 5 |
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 | | |
margex | prediction | Artificial data for margins, copied from Stata | tbl_df | 3000 | 11 |
stations | traveltime | Singapore MRT and LRT data | matrix | 563 | 2 |
bdv.connection | archeofrag | Dataset: Refitting relationships between lithic fragments from the Bout des Vergnes site | matrix | 3931 | 2 |
bdv.fragments | archeofrag | Dataset: Refitting relationships between lithic fragments from the Bout des Vergnes site | data.frame | 3666 | 9 |
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 |
stations | h3r | Stations | data.frame | 219 | 4 |
WYcond | FIESTA | FIA data. Condition-level data from FIA public database. | data.frame | 3224 | 26 |
WYp2veg_subp_structure | FIESTA | FIA data. P2 vegetation structure data from FIA public database. | data.frame | 57200 | 6 |
WYp2veg_subplot_spp | FIESTA | FIA data. P2 vegetation species data from FIA public database. | data.frame | 8265 | 9 |
WYplt | FIESTA | FIA data. Plot-level data from FIA public database. | data.frame | 3047 | 20 |
WYpltassgn | FIESTA | FIA data. Plot assignment data from FIA public database. | data.frame | 3047 | 24 |
WYseed | FIESTA | FIA data. Seedling data from FIA public database. | data.frame | 1607 | 10 |
WYstratalut | FIESTA | FIA data. Post-stratification data from FIA public database. | data.frame | 35 | 7 |
WYsubp_cond | FIESTA | FIA data. Subplot condition data from FIA public database. | data.frame | 12214 | 6 |
WYsubplot | FIESTA | FIA data. Subplot data from FIA public database. | data.frame | 12188 | 6 |
WYtree | FIESTA | FIA data. Tree-level data from FIA public database. | data.frame | 18574 | 19 |
WYunitarea | FIESTA | FIA data. Acres data from FIA public database. | data.frame | 23 | 4 |
WYunitzonal | FIESTA | Zonal data. Zonal means for auxiliary data in counties in Wyoming. | data.frame | 23 | 9 |
GDT_NAMES | FIESTAutils | Reference tables - gdal data types. | character | | |
kindcd3old | FIESTAutils | Reference table - List of RMRS plots that have fallen out of inventory because they were not found or they were in the wrong place. | data.frame | 38 | 8 |
ref_codes | FIESTAutils | Reference tables - Code definitions. | data.frame | 739 | 7 |
ref_cond | FIESTAutils | Reference table - Metadata for cond default variables output from DBgetPlots() | data.frame | 97 | 3 |
ref_conversion | FIESTAutils | Reference table - for conversion factors. | data.frame | 7 | 6 |
ref_diacl2in | FIESTAutils | Reference table - diameter 2-inch class codes (DIA). | data.frame | 40 | 3 |
ref_domain | FIESTAutils | Reference table - for generating tables. | data.frame | 32 | 3 |
ref_estimators | FIESTAutils | Reference table - FIESTA estimators. | data.frame | 8 | 9 |
ref_estvar | FIESTAutils | Reference table - for generating estimates | data.frame | 178 | 11 |
ref_evaltyp | FIESTAutils | Reference table - for generating tables. | data.frame | 14 | 3 |
ref_plt | FIESTAutils | Reference table - Metadata for plt default variables output from DBgetPlots() | data.frame | 59 | 3 |
ref_popType | FIESTAutils | Reference table - popType codes. | data.frame | 15 | 2 |
ref_shp | FIESTAutils | Reference table - Metadata for shp_* default variables output from DBgetPlots() | data.frame | 63 | 4 |
ref_species | FIESTAutils | Reference table - Code definitions. | data.frame | 2677 | 20 |
ref_statecd | FIESTAutils | Reference table - state codes (STATECD). | data.frame | 59 | 7 |
ref_titles | FIESTAutils | Reference table - Variable titles. | data.frame | 70 | 2 |
ref_tree | FIESTAutils | Reference table - Metadata for tree default variables output from DBgetPlots() | data.frame | 117 | 3 |
ref_units | FIESTAutils | Reference table - for variable units. | data.frame | 47 | 5 |
stunitco | FIESTAutils | SpatialPolygonsDataFrame with FIA state, unit, county codes and names | sf | 3233 | 8 |
data_example_intelligibility_by_length | wisclabmisc | Simulated intelligibility scores by utterance length | tbl_df | 694 | 5 |
data_fake_intelligibility | wisclabmisc | Fake intelligibility data | tbl_df | 200 | 2 |
data_fake_rates | wisclabmisc | Fake speaking rate data | tbl_df | 200 | 2 |
data_features_consonants | wisclabmisc | Phonetic features of consonants and vowels | tbl_df | 24 | 10 |
data_features_vowels | wisclabmisc | Phonetic features of consonants and vowels | tbl_df | 17 | 12 |
kid | kyotil | Dataset from Cowling et al. | data.frame | 736 | 10 |
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 | | |
spikeins | iq | An example dataset of 12 spike-in proteins | data.frame | 18189 | 9 |
disgust | bayestestR | Moral Disgust Judgment | data.frame | 150 | 2 |
MOats | emmeans | Oats data in multivariate form | data.frame | 18 | 3 |
auto.noise | emmeans | Auto Pollution Filter Noise | data.frame | 36 | 4 |
feedlot | emmeans | Feedlot data | data.frame | 67 | 4 |
fiber | emmeans | Fiber data | data.frame | 15 | 3 |
neuralgia | emmeans | Neuralgia data | data.frame | 60 | 5 |
nutrition | emmeans | Nutrition data | data.frame | 107 | 4 |
oranges | emmeans | Sales of oranges | data.frame | 36 | 6 |
pigs | emmeans | Effects of dietary protein on free plasma leucine concentration in pigs | data.frame | 29 | 3 |
ubds | emmeans | Unbalanced dataset | data.frame | 100 | 5 |
Argentina | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 93 | 25 |
Asia | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 98 | 39 |
Austria | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 66 | 17 |
BosniaHerz | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 63 | 17 |
China | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 67 | 71 |
Europe | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 97 | 24 |
Japan | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 82 | 71 |
USA | mispitools | STRs allelic frequencies from specified country. | spec_tbl_df | 97 | 30 |
YRI_summarized_ibd_datatxt | mispitools | | data.frame | 11945 | 4 |
dmbp | tsgarch | Deutschemark/British pound Exchange Rate | data.frame | 1974 | 2 |
nikkei | tsgarch | Japanese NIKKEI Stock Index | data.frame | 4246 | 2 |
dataEnrichmentMeans | rpact | Enrichment Dataset of Means | data.frame | 12 | 14 |
dataEnrichmentMeansStratified | rpact | Stratified Enrichment Dataset of Means | data.frame | 24 | 14 |
dataEnrichmentRates | rpact | Enrichment Dataset of Rates | data.frame | 6 | 10 |
dataEnrichmentRatesStratified | rpact | Stratified Enrichment Dataset of Rates | data.frame | 12 | 10 |
dataEnrichmentSurvival | rpact | Enrichment Dataset of Survival Data | data.frame | 9 | 8 |
dataEnrichmentSurvivalStratified | rpact | Stratified Enrichment Dataset of Survival Data | data.frame | 12 | 10 |
dataMeans | rpact | One-Arm Dataset of Means | data.frame | 3 | 7 |
dataMultiArmMeans | rpact | Multi-Arm Dataset of Means | data.frame | 2 | 25 |
dataMultiArmRates | rpact | Multi-Arm Dataset of Rates | data.frame | 2 | 13 |
dataMultiArmSurvival | rpact | Multi-Arm Dataset of Survival Data | data.frame | 2 | 13 |
dataRates | rpact | One-Arm Dataset of Rates | data.frame | 4 | 5 |
dataSurvival | rpact | One-Arm Dataset of Survival Data | data.frame | 3 | 7 |
rawDataTwoArmNormal | rpact | Raw Dataset Of A Two Arm Continuous Outcome With Covariates | data.frame | 522 | 8 |
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 |
. | clifford | Class "dot" | dot | | |
erddapList | PAMmisc | A list of edinfo objects from ERDDAP data sources | list | | |
hycomList | PAMmisc | A list of edinfo objects from HYCOM data sources | hycomList | | |
Jurkat293T | jackstraw | A Jurkat:293T equal mixture dataset from Zheng et al. (2017) | matrix | 3381 | 10 |
distruct_colours | tidypopgen | Distruct colours | character | | |
cfc | handwriterRF | Cluster Fill Counts for 1200 CSAFE Handwriting Database Samples | tbl_df | 1200 | 43 |
random_forest | handwriterRF | A 'ranger' Random Forest and Data Frame of Distances | list | | |
ref_scores | handwriterRF | Reference Similarity Scores | list | | |
templateK40 | handwriterRF | Cluster Template with 40 Clusters | list | | |
test | handwriterRF | A Test Set of Cluster Fill Rates | tbl_df | 332 | 43 |
train | handwriterRF | A Training Set of Cluster Fill Rates | tbl_df | 800 | 43 |
validation | handwriterRF | A Validation Set of Cluster Fill Rates | tbl_df | 1200 | 43 |
BF_sim | SimDesign | Example simulation from Brown and Forsythe (1974) | SimDesign | 28 | 12 |
BF_sim_alternative | SimDesign | (Alternative) Example simulation from Brown and Forsythe (1974) | SimDesign | 16 | 29 |
testdataLong | RHRT | Long term data | numeric | | |
testdataLong_Ann | RHRT | Long term data annotations | character | | |
morrowplots | morrowplots | Morrow Plots Yield and Treatment Data | spec_tbl_df | 3216 | 26 |
fishdf | bioregion | Spatial distribution of fish in Europe (data.frame) | data.frame | 2703 | 3 |
fishmat | bioregion | Spatial distribution of fish in Europe (co-occurrence matrix) | matrix | 338 | 195 |
fishsf | bioregion | Spatial distribution of fish in Europe | sf | 338 | 2 |
vegedf | bioregion | Spatial distribution of Mediterranean vegetation (data.frame) | data.frame | 460878 | 3 |
vegemat | bioregion | Spatial distribution of Mediterranean vegetation (co-occurrence matrix) | matrix | 715 | 3697 |
vegesf | bioregion | Spatial distribution of Mediterranean vegetation (spatial grid) | sf | 728 | 2 |
additional.residency.results | actel | Example residency results | list | | |
example.results | actel | Example migration results | list | | |
dogfish | sdmTMB | Example fish survey data | tbl_df | 1458 | 9 |
hbll_s_grid | sdmTMB | Example fish survey data | data.frame | 2802 | 3 |
pcod | sdmTMB | Example fish survey data | tbl_df | 2143 | 12 |
pcod_2011 | sdmTMB | Example fish survey data | tbl_df | 969 | 12 |
pcod_mesh_2011 | sdmTMB | Example fish survey data | sdmTMBmesh | | |
qcs_grid | sdmTMB | Example fish survey data | data.frame | 7314 | 5 |
wcvi_grid | sdmTMB | Example fish survey data | data.frame | 2689 | 3 |
yelloweye | sdmTMB | Example fish survey data | data.frame | 1559 | 9 |
X.class | caretEnsemble | data for classification | matrix | 150 | 6 |
X.reg | caretEnsemble | data for classification | matrix | 150 | 6 |
Y.class | caretEnsemble | data for classification | factor | | |
Y.reg | caretEnsemble | data for regression | numeric | | |
models.class | caretEnsemble | caretList of classification models | caretList | | |
models.reg | caretEnsemble | caretList of regression models | caretList | | |
csafe | handwriter | Cursive written word: csafe | matrix | 111 | |
example_analysis | handwriter | Example of writership analysis | list | | |
example_cluster_template | handwriter | Example cluster template | list | | |
example_model | handwriter | Example of a hierarchical model | list | | |
london | handwriter | Cursive written word: London | matrix | 148 | |
message | handwriter | Full page image of the handwritten London letter. | matrix | 1262 | |
nature1 | handwriter | Full page image of the 4th sample (nature) of handwriting from the first writer. | matrix | 811 | |
templateK40 | handwriter | Cluster Template with 40 Clusters | list | | |
twoSent | handwriter | Two sentence printed example handwriting | matrix | 396 | |
bad_link_text | shinyGovstyle | Lookup for bad link text | data.frame | 53 | 1 |
pbmc_facs | fastTopics | Mixture of 10 FACS-purified PBMC Single-Cell RNA-seq data | list | | |
GGDC10S | collapse | Groningen Growth and Development Centre 10-Sector Database | data.frame | 5027 | 16 |
wlddev | collapse | World Development Dataset | data.frame | 13176 | 13 |
scc_rain | epicmodel | Rain example SCC model | epicmodel_scc | | |
steplist_party | epicmodel | Birthday party example steplist | epicmodel_steplist | | |
steplist_rain | epicmodel | Rain example steplist | epicmodel_steplist | | |
CRM001 | eCerto | An example set of data collected for a CRM. | list | | |
LTS001 | eCerto | An example set of data collected for a LTS monitoring. | list | | |
cvals_Dixon | eCerto | Dixon critical values table. | matrix | 29 | 16 |
cvals_Grubbs2 | eCerto | Grubbs2 critical values table. | matrix | 97 | 13 |
category_conc_n | vprr | A binned data frame of concentration data per category | data.frame | 212 | 22 |
ctd_dat_combine | vprr | VPR CTD data | data.frame | 1000 | 15 |
ctd_roi_merge | vprr | VPR CTD data combined with tabulated ROIs | data.frame | 1000 | 28 |
ctd_roi_oce | vprr | VPR data including CTD and ROI information | ctd | | |
roi_dat_combine | vprr | VPR ROI data | data.frame | 1000 | 13 |
roimeas_dat_combine | vprr | VPR measurement data calculated by Visual Plankton | data.frame | 1000 | 12 |
size_df_f | vprr | VPR size information dataframe | data.frame | 14 | 14 |
dfr | lares | Results for AutoML Predictions | list | | |
dft | lares | Titanic Dataset | data.frame | 891 | 11 |
baro | bridgr | Swiss Economic Indicators | tbl_df | 228 | 2 |
fcurve | bridgr | Swiss Economic Indicators | tbl_df | 4875 | 2 |
gdp | bridgr | Swiss Economic Indicators | tbl_df | 76 | 2 |
wea | bridgr | Swiss Economic Indicators | tbl_df | 939 | 2 |
metadata_forestdata | forestdata | Metadata for 'forestdata' functions | list | | |
NLD_dist | tmap | Netherlands datasets | sf | 3340 | 19 |
NLD_muni | tmap | Netherlands datasets | sf | 345 | 19 |
NLD_prov | tmap | Netherlands datasets | sf | 12 | 3 |
World | tmap | World dataset | sf | 177 | 18 |
land | tmap | Spatial data of global land cover | stars | | |
metro | tmap | Spatial data of metropolitan areas | sf | 436 | 13 |
rivers | tmap | Spatial data of rivers | sf | 1616 | 5 |
lat.lon.meuse | loa | Example data for use with loa | data.frame | 155 | 14 |
roadmap.meuse | loa | Example data for use with loa | staticMap | | |
aml | survival | Acute Myelogenous Leukemia survival data | data.frame | 23 | 3 |
bladder | survival | Bladder Cancer Recurrences | data.frame | 340 | 7 |
bladder1 | survival | Bladder Cancer Recurrences | data.frame | 294 | 11 |
bladder2 | survival | Bladder Cancer Recurrences | data.frame | 178 | 8 |
braking | survival | Reliability data sets | tmerge | 83 | 5 |
cancer | survival | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
capacitor | survival | Reliability data sets | data.frame | 64 | 5 |
cgd | survival | Chronic Granulotamous Disease data | data.frame | 203 | 16 |
cgd0 | survival | Chronic Granulotomous Disease data | data.frame | 128 | 20 |
colon | survival | Chemotherapy for Stage B/C colon cancer | data.frame | 1858 | 16 |
cracks | survival | Reliability data sets | data.frame | 8 | 2 |
diabetic | survival | Ddiabetic retinopathy | data.frame | 394 | 8 |
flchain | survival | Assay of serum free light chain for 7874 subjects. | data.frame | 7874 | 11 |
gbsg | survival | Breast cancer data sets used in Royston and Altman (2013) | data.frame | 686 | 11 |
genfan | survival | Reliability data sets | data.frame | 70 | 2 |
heart | survival | Stanford Heart Transplant data | data.frame | 172 | 8 |
hoel | survival | Mouse cancer data | data.frame | 181 | 4 |
ifluid | survival | Reliability data sets | data.frame | 41 | 2 |
imotor | survival | Reliability data sets | data.frame | 40 | 3 |
jasa | survival | Stanford Heart Transplant data | data.frame | 103 | 14 |
jasa1 | survival | Stanford Heart Transplant data | data.frame | 170 | 8 |
kidney | survival | Kidney catheter data | data.frame | 76 | 7 |
leukemia | survival | Acute Myelogenous Leukemia survival data | data.frame | 23 | 3 |
logan | survival | Data from the 1972-78 GSS data used by Logan | data.frame | 838 | 4 |
lung | survival | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
mgus | survival | Monoclonal gammopathy data | data.frame | 241 | 12 |
mgus1 | survival | Monoclonal gammopathy data | data.frame | 305 | 14 |
mgus2 | survival | Monoclonal gammopathy data | data.frame | 1384 | 11 |
myeloid | survival | Acute myeloid leukemia | data.frame | 646 | 9 |
myeloma | survival | Survival times of patients with multiple myeloma | data.frame | 3882 | 5 |
nafld1 | survival | Non-alcoholic fatty liver disease | data.frame | 17549 | 9 |
nafld2 | survival | Non-alcoholic fatty liver disease | data.frame | 400123 | 4 |
nafld3 | survival | Non-alcoholic fatty liver disease | data.frame | 34327 | 3 |
nwtco | survival | Data from the National Wilm's Tumor Study | data.frame | 4028 | 9 |
ovarian | survival | Ovarian Cancer Survival Data | data.frame | 26 | 6 |
pbc | survival | Mayo Clinic Primary Biliary Cholangitis Data | data.frame | 418 | 20 |
pbcseq | survival | Mayo Clinic Primary Biliary Cirrhosis, sequential data | data.frame | 1945 | 19 |
rats | survival | Rat treatment data from Mantel et al | data.frame | 300 | 5 |
rats2 | survival | Rat data from Gail et al. | data.frame | 253 | 6 |
retinopathy | survival | Diabetic Retinopathy | data.frame | 394 | 9 |
rhDNase | survival | rhDNASE data set | data.frame | 767 | 8 |
rotterdam | survival | Breast cancer data set used in Royston and Altman (2013) | data.frame | 2982 | 15 |
solder | survival | Data from a soldering experiment | data.frame | 900 | 6 |
stanford2 | survival | More Stanford Heart Transplant data | data.frame | 184 | 5 |
survexp.mn | survival | Census Data Sets for the Expected Survival and Person Years Functions | ratetable | | |
survexp.us | survival | Census Data Sets for the Expected Survival and Person Years Functions | ratetable | | |
survexp.usr | survival | Census Data Sets for the Expected Survival and Person Years Functions | ratetable | | |
tobin | survival | Tobin's Tobit data | data.frame | 20 | 3 |
transplant | survival | Liver transplant waiting list | data.frame | 815 | 6 |
turbine | survival | Reliability data sets | data.frame | 11 | 3 |
udca | survival | Data from a trial of usrodeoxycholic acid | data.frame | 170 | 15 |
udca1 | survival | Data from a trial of usrodeoxycholic acid | data.frame | 170 | 7 |
udca2 | survival | Data from a trial of usrodeoxycholic acid | data.frame | 1360 | 8 |
uspop2 | survival | Projected US Population | array | | |
valveSeat | survival | Reliability data sets | data.frame | 89 | 3 |
veteran | survival | Veterans' Administration Lung Cancer study | data.frame | 137 | 8 |
admiral_adlb | admiral | Lab Analysis Dataset | tbl_df | 3779 | 111 |
admiral_adsl | admiral | Subject Level Analysis Dataset | tbl_df | 306 | 54 |
atoxgr_criteria_ctcv4 | admiral | Metadata Holding Grading Criteria for NCI-CTCAEv4 | tbl_df | 40 | 13 |
atoxgr_criteria_ctcv5 | admiral | Metadata Holding Grading Criteria for NCI-CTCAEv5 | tbl_df | 37 | 13 |
atoxgr_criteria_daids | admiral | Metadata Holding Grading Criteria for DAIDs | tbl_df | 63 | 15 |
ex_single | admiral | Single Dose Exposure Dataset | tbl_df | 22439 | 17 |
example_qs | admiral | Example 'QS' Dataset | tbl_df | 161 | 11 |
queries | admiral | Queries Dataset | tbl_df | 15 | 8 |
queries_mh | admiral | Queries MH Dataset | tbl_df | 14 | 8 |
KETPch4 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
KETPex481 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
KETPex497 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
KETPex498 | dvir | Data used in the book Kling et al. (2021) | dviData | | |
example1 | dvir | DVI dataset: Generational trio | dviData | | |
example2 | dvir | DVI dataset: Two reference families | dviData | | |
exclusionExample | dvir | Dataset: Exclusion example | dviData | | |
fire | dvir | DVI dataset: Family of fire victims | dviData | | |
grave | dvir | DVI dataset: Family grave | dviData | | |
icmp | dvir | DVI dataset: A large reference pedigree | dviData | | |
planecrash | dvir | DVI dataset: Simulated plane crash | dviData | | |
symmetricSibs | dvir | Dataset: Symmetry examples | dviData | | |
mcmc.her | FLRef | | data.frame | 4997 | 328 |
ss3her | FLRef | | list | | |
DS3 | dbscan | DS3: Spatial data with arbitrary shapes | data.table | 8000 | 2 |
Dataset_1 | dbscan | DBCV Paper Datasets | data.frame | 925 | 3 |
Dataset_2 | dbscan | DBCV Paper Datasets | data.frame | 1863 | 3 |
Dataset_3 | dbscan | DBCV Paper Datasets | data.frame | 1500 | 3 |
Dataset_4 | dbscan | DBCV Paper Datasets | data.frame | 885 | 3 |
moons | dbscan | Moons Data | data.frame | 100 | 2 |
addhealth | slca | Adolescent Depression Data from the Add Health Study | data.frame | 2061 | 18 |
gss7677 | slca | GSS 1976-1977 Data on Social Status and Tolerance towards Minorities | data.frame | 2942 | 14 |
nlsy97 | slca | NLSY97 Substance Use Data | data.frame | 1004 | 38 |
nlsy_jlcpa | slca | JLCPA Model Estimated with NLSY97 Data | slcafit | | |
nlsy_jlta | slca | JLCPA Model Estimated with NLSY97 Data | slcafit | | |
specimens | CompareTests | Fictitious data on specimens tested by two methods | data.frame | 402 | 3 |
Country_data | FPDclustering | Unsupervised Learning on Country Data | spec_tbl_df | 167 | 10 |
Star | FPDclustering | Star dataset to predict star types | tbl_df | 233 | 7 |
Students | FPDclustering | Statistics 1 students | tbl_df | 253 | 10 |
ais | FPDclustering | Australian institute of sport data | data.frame | 202 | 13 |
asymmetric20 | FPDclustering | Asymmetric data set shape 20 | data.frame | 800 | 101 |
asymmetric3 | FPDclustering | Asymmetric data set shape 3 | data.frame | 800 | 101 |
outliers | FPDclustering | Data set with outliers | data.frame | 960 | 101 |
lac_operon_net | pastboon | The lactose operon Boolean network | BooleanNetwork | | |
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 | | |
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 |
swimmers | MagmaClustR | French swimmers performances data on 100m freestyle events | data.frame | 76832 | 4 |
weight | MagmaClustR | Weight follow-up data of children in Singapore | data.frame | 3629 | 4 |
Lobo.data | TreeTools | Data from Zhang et al. 2016 | list | | |
Lobo.phy | TreeTools | Data from Zhang et al. 2016 | phyDat | | |
brewer | TreeTools | Brewer palettes | list | | |
nRootedShapes | TreeTools | Number of rooted / unrooted tree shapes | integer64 | | |
nUnrootedShapes | TreeTools | Number of rooted / unrooted tree shapes | integer64 | | |
unrootedKeys | TreeTools | Integer representing shape of a tree | list | | |
albatross | distantia | Flight Path Time Series of Albatrosses in The Pacific | sf | 536 | 8 |
cities_coordinates | distantia | Coordinates of 100 Major Cities | sf | 20 | 5 |
cities_temperature | distantia | Long Term Monthly Temperature in 20 Major Cities | data.frame | 8420 | 3 |
covid_coordinates | distantia | County Coordinates of the Covid Prevalence Dataset | sf | 36 | 4 |
covid_polygons | distantia | County Polygons of the Covid Prevalence Dataset | sf | 36 | 2 |
covid_prevalence | distantia | Time Series of Covid Prevalence in California Counties | data.frame | 7092 | 3 |
distances | distantia | Distance Methods | data.frame | 12 | 5 |
eemian_coordinates | distantia | Site Coordinates of Nine Interglacial Sites in Central Europe | sf | 9 | 4 |
eemian_pollen | distantia | Pollen Counts of Nine Interglacial Sites in Central Europe | data.frame | 376 | 24 |
fagus_coordinates | distantia | Site Coordinates of Fagus sylvatica Stands | sf | 3 | 4 |
fagus_dynamics | distantia | Time Series Data from Three Fagus sylvatica Stands | data.frame | 648 | 5 |
honeycomb_climate | distantia | Rainfall and Temperature in The Americas | tbl_df | 9432 | 4 |
honeycomb_polygons | distantia | Hexagonal Grid | sf | 72 | 2 |
data_dictionary | pacta.multi.loanbook | Data dictionary | tbl_df | 193 | 5 |
Ames | coursekata | Ames, Iowa housing data | tbl_df | 185 | 21 |
Fingers | coursekata | Data from introductory statistics students at a university. | data.frame | 157 | 17 |
FingersMessy | coursekata | Raw data from introductory statistics students at a university. | data.frame | 210 | 17 |
Smallville | coursekata | Simulated housing data | data.table | 32 | 4 |
Survey | coursekata | Students at a university were asked to enter a random number between 1-20 into a survey. | data.frame | 211 | 1 |
Tables | coursekata | Tables data | data.frame | 44 | 2 |
TipExperiment | coursekata | Data from an experiment about smiley faces and tips | data.frame | 44 | 5 |
World | coursekata | Data on countries from the Happy Planet Index project. | tbl_df | 130 | 14 |
class_data | coursekata | Generated "class data" for exploring pairwise tests | tbl_df | 105 | 2 |
er | coursekata | Emergency room canine therapy | tbl_df | 84 | 53 |
fevdata | coursekata | Forced Expiratory Volume (FEV) Data | tbl_df | 654 | 5 |
game_data | coursekata | Simulated math game data. | data.frame | 105 | 2 |
penguins | coursekata | A modified form of the 'palmerpenguins::penguins' data set. | tbl_df | 333 | 7 |
tip_exp | coursekata | Simulated data for an experiment about smiley faces and tips | tbl_df | 89 | 3 |
wine | clusterMI | Chemical analysis of wines from three different cultivars | data.frame | 178 | 14 |
england | footBayes | English league results 1888-2022 | data.frame | 203956 | 12 |
italy | footBayes | Italy league results 1934-2022 | data.frame | 27684 | 8 |
nsher | FLCore | FLCore datasets | FLSR | | |
ple4 | FLCore | FLCore datasets | FLStock | | |
ple4.biol | FLCore | FLCore datasets | FLBiol | | |
ple4.index | FLCore | FLCore datasets | FLIndex | | |
ple4.indices | FLCore | FLCore datasets | FLIndices | | |
ple4sex | FLCore | FLCore datasets | FLStock | | |
W | spfilteR | Synthetic Dataset | matrix | 100 | 100 |
fakedataset | spfilteR | Synthetic Dataset | data.frame | 100 | 8 |
OBDAPpoint | PublicWorksFinanceIT | Soil Defense Public Work for the Molise. | data.frame | 550 | 22 |
OCpoint | PublicWorksFinanceIT | Soil Defense Public works for the Umbria Region | data.frame | 82 | 44 |
RENDISpoint | PublicWorksFinanceIT | Soil Defense Public Works for the Basilicata Region. | data.frame | 210 | 27 |
abstracts | oolong | Abstracts of communication journals dataset | tbl_df | 2500 | 1 |
abstracts_btm | oolong | Topic models trained with the abstracts dataset. | BTM | | |
abstracts_dfm | oolong | Abstracts of communication journals dataset | dfm | | |
abstracts_dictionary | oolong | Abstracts of communication journals dataset | list | | |
abstracts_seededlda | oolong | Topic models trained with the abstracts dataset. | textmodel_lda | | |
afinn | oolong | AFINN dictionary | dictionary2 | | |
newsgroup_nb | oolong | Naive Bayes model trained on 20 newsgroups data | textmodel_nb | | |
trump2k | oolong | Trump's tweets dataset | character | | |
dynbenchmark_data | funkyheatmap | The results data frame from dynbenchmark. | list | | |
scib_summary | funkyheatmap | Summary results from the scIB project | tbl_df | 20 | 27 |
biomee_gs_leuning_drivers | rsofun | rsofun BiomeE driver data (Leuning photosynthesis model) | tbl_df | 1 | 8 |
biomee_gs_leuning_output | rsofun | rsofun BiomeE (gs_leuning) output data | tbl_df | 1 | 2 |
biomee_p_model_drivers | rsofun | rsofun BiomeE driver data (P-model photosynthesis model) | tbl_df | 1 | 8 |
biomee_p_model_output | rsofun | rsofun BiomeE (P-model) output data | tbl_df | 1 | 2 |
biomee_validation | rsofun | rsofun BiomeE targets validation data | tbl_df | 1 | 2 |
p_model_drivers | rsofun | rsofun P-model driver data | tbl_df | 1 | 4 |
p_model_drivers_vcmax25 | rsofun | rsofun P-model driver data (for leaf traits) | tbl_df | 4 | 4 |
p_model_output | rsofun | rsofun P-model output data | tbl_df | 1 | 3 |
p_model_output_vcmax25 | rsofun | rsofun P-model output data (using vcmax25 drivers) | tbl_df | 4 | 3 |
p_model_validation | rsofun | rsofun P-model GPP validation data | tbl_df | 1 | 2 |
p_model_validation_vcmax25 | rsofun | rsofun P-model Vcmax25 validation data | grouped_df | 4 | 2 |
telework_data | OPSR | Telework data | data.frame | 1584 | 35 |
timeuse_data | OPSR | Data from TimeUse+ | data.frame | 824 | 40 |
Table1.1.1 | LearnNonparam | Sodium Contents | numeric | | |
Table1.2.1 | LearnNonparam | Cycles Until Failure | numeric | | |
Table2.1.1 | LearnNonparam | Test Scores | list | | |
Table2.3.1 | LearnNonparam | Runoff Minutes | data.frame | 8 | 2 |
Table2.6.1 | LearnNonparam | Hours Until Recharge | data.frame | 4 | 2 |
Table2.6.2 | LearnNonparam | Cerium Amounts | data.frame | 6 | 2 |
Table2.8.1 | LearnNonparam | Ounces Of Beverage | data.frame | 5 | 2 |
Table3.1.2 | LearnNonparam | Normal Samples | data.frame | 5 | 3 |
Table3.2.2 | LearnNonparam | Logarithms of Bacteria Counts | list | | |
Table3.2.3 | LearnNonparam | Saltiness Scores | list | | |
Table3.3.1 | LearnNonparam | Percentages of Clay | data.frame | 6 | 4 |
Table3.4.1 | LearnNonparam | Phosphorus Contents | data.frame | 6 | 4 |
Table4.1.1 | LearnNonparam | Caloric Intake | data.frame | 5 | 2 |
Table4.1.3 | LearnNonparam | Cholesterol Reduction | data.frame | 17 | 2 |
Table4.4.3 | LearnNonparam | Yield Data | data.frame | 4 | 6 |
Table4.5.3 | LearnNonparam | Randomized Complete Block with Ties | data.frame | 4 | 3 |
Table5.1.2 | LearnNonparam | Heterophils and Lymphocytes | data.frame | 18 | 2 |
Table5.2.2 | LearnNonparam | Scores of Projects | data.frame | 10 | 2 |
Table5.4.2 | LearnNonparam | Satisfaction with Pain-Relief Treatment | data.frame | 2 | 3 |
data1 | MethodCompare | Simulated dataset 1 | tbl_df | 1468 | 3 |
data2 | MethodCompare | Simulated dataset 2 | tbl_df | 1680 | 3 |
data3 | MethodCompare | Simulated dataset 3 | tbl_df | 1682 | 3 |
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 65 | data.frame | 537946 | 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 | 19 | 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 65 | sf | 615 | 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 |
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 | 733 | 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 |
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 Rรฉunion 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 |
clc_codes | clc | CLC Codes | character | | |
design | phoenics | Dataset "MTBLS422" | data.frame | 11 | 4 |
pathways | phoenics | Dataset "MTBLS422" | data.frame | 11 | 4 |
quantif | phoenics | Dataset "MTBLS422" | data.frame | 10 | 11 |
holes | cocons | Holes Data Set | list | | |
holes_bm | cocons | Holes with trend + multiple realizations Data Set | list | | |
stripes | cocons | Stripes Data Set | list | | |
beets | pbkrtest | Sugar beets data | data.frame | 30 | 5 |
budworm | pbkrtest | Budworm data | data.frame | 12 | 4 |
GPvam.benchmark | GPvam | Benchmarks of the program using simulated data. | data.frame | 160 | 9 |
vam_data | GPvam | Simulated Data | data.frame | 3750 | 5 |
Jahn_CellReports_2018 | WeightedTreemaps | Data from the publication of Jahn et al., CellReports, 2018 | data.frame | 19790 | 13 |
rounded_rect | WeightedTreemaps | Coordinates to draw a rounded rectangle as parent cell for treemaps | data.frame | 50 | 2 |
airstrikes | geocausal | airstrikes | data.frame | 3938 | 4 |
airstrikes_base | geocausal | airstrikes_base | data.frame | 808 | 3 |
insurgencies | geocausal | insurgencies | data.frame | 68573 | 4 |
iraq_window | geocausal | iraq_window | owin | | |
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 |
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 |
boundaries_ccg21 | geographr | Clinical Commissioning Groups (April 2021) | sf | 106 | 3 |
boundaries_countries20 | geographr | Countries (December 2020) | sf | 4 | 3 |
boundaries_dz11 | geographr | Data Zones (2011) | sf | 6976 | 3 |
boundaries_hb19 | geographr | Health Boards (2019) | sf | 14 | 3 |
boundaries_icb22 | geographr | Integrated Care Boards (2022) | sf | 42 | 3 |
boundaries_iz11 | geographr | Intermediate Zones (2011) | sf | 1279 | 3 |
boundaries_lhb20 | geographr | Wales Local Health Boards (2020) | sf | 7 | 3 |
boundaries_lhb22 | geographr | Wales Local Health Boards (2022) | sf | 7 | 3 |
boundaries_lsoa11 | geographr | Lower Layer Super Output Areas (2011) | sf | 34753 | 3 |
boundaries_ltla19 | geographr | Local Authority Districts (December 2019) | sf | 382 | 3 |
boundaries_ltla20 | geographr | Local Authority Districts (December 2020) | sf | 379 | 3 |
boundaries_ltla21 | geographr | Local Authority Districts (December 2021) | sf | 374 | 3 |
boundaries_ltla24 | geographr | Local Authority Districts (July 2024) | sf | 361 | 3 |
boundaries_msoa11 | geographr | Middle Layer Super Output Areas (2011) | sf | 7201 | 3 |
boundaries_msoa21 | geographr | Middle Layer Super Output Areas (2021) | sf | 7264 | 3 |
boundaries_msoa21_iz11_sdz21 | geographr | Middle Layer Super Output Areas equivalents for the UK | sf | 9393 | 3 |
boundaries_pfa20 | geographr | Police Force Areas (2020) | sf | 43 | 3 |
boundaries_region21 | geographr | Regions (December 2021) | sf | 9 | 3 |
boundaries_sdz21 | geographr | Super Data Zones (2021) | sf | 850 | 3 |
boundaries_soa11 | geographr | Super Output Areas (2011) | sf | 890 | 3 |
boundaries_stp21 | geographr | Sustainability and Transformation Partnerships (April 2021) | sf | 42 | 3 |
boundaries_trusts_ni18 | geographr | Northern Ireland Health and Social Care Trusts (2017) | sf | 5 | 3 |
boundaries_utla19 | geographr | Counties and Unitary Authorities (December 2019) | sf | 216 | 3 |
boundaries_utla20 | geographr | Counties and Unitary Authorities (December 2020) | sf | 216 | 3 |
boundaries_utla21 | geographr | Counties and Unitary Authorities (May 2021) | sf | 217 | 3 |
boundaries_vcsep22 | geographr | VCSEP regions (2022). | sf | 5 | 2 |
boundaries_wards21 | geographr | Wards / Electoral Divisions (December 2021) | sf | 8694 | 3 |
boundaries_wards22 | geographr | Wards / Electoral Divisions (December 2022) | sf | 8483 | 3 |
lookup_dz01_dz11 | geographr | Data Zone (2001) to Data Zone (2011) Lookup | tbl_df | 10437 | 2 |
lookup_dz11_iz11_ltla20 | geographr | Data Zone (2011) to Intermediate Zone (2011) to LAD (December 2019) Lookup | tbl_df | 6976 | 6 |
lookup_dz11_ltla19_hb19 | geographr | Data Zone (2011) to Local Authority (2019) to Health Board (2019) Lookup | tbl_df | 6976 | 3 |
lookup_dz21_sdz21_dea14_lgd14 | geographr | Data Zone (2021) to Super Data Zone (2021) to District Electoral Areas (2019) to Local Government Districts (2014) Lookup | tbl_df | 3780 | 8 |
lookup_lsoa11_ccg21_stp21_ltla21 | geographr | Lower Layer Super Output Area (2011) to Clinical Commissioning Group (2021) to Sustainability and Transformation Plan (2021) to Lower Tier Local Authority District (2021) Lookup | tbl_df | 32844 | 8 |
lookup_lsoa11_lsoa21_ltla22 | geographr | Lower Layer Super Output Area (2011) to Lower Layer Super Output Area (2021) to LAD (December 2022) Lookup | tbl_df | 35795 | 7 |
lookup_lsoa11_ltla18 | geographr | Lower Layer Super Output Area (2011) to LAD (December 2018) Lookup | tbl_df | 32844 | 4 |
lookup_lsoa11_ltla19 | geographr | Lower Layer Super Output Area (2011) to LAD (December 2019) Lookup | tbl_df | 34753 | 4 |
lookup_lsoa11_ltla20 | geographr | Lower Layer Super Output Area (2011) to LAD (December 2020) Lookup | tbl_df | 34753 | 4 |
lookup_lsoa11_ltla21 | geographr | Lower Layer Super Output Area (2011) to LAD (December 2021) Lookup | tbl_df | 34753 | 4 |
lookup_lsoa11_msoa11 | geographr | Lower Layer Super Output Area (2011) to Middle Layer Super Output Area (2011) Lookup | tbl_df | 41729 | 4 |
lookup_lsoa11_sicbl22_icb22_ltla22 | geographr | LSOA (2011) to SICBL (2022) to ICB (2022) to LTLA (2022) Lookup | tbl_df | 32844 | 10 |
lookup_lsoa11_ward17 | geographr | LSOA (2011) to Ward (2017) Lookup | tbl_df | 34753 | 2 |
lookup_ltla19_brc | geographr | Local Authority District (2019) to British Red Cross area lookup | spec_tbl_df | 382 | 2 |
lookup_ltla19_cauth19 | geographr | Local Authority Districts to Combined Authorities lookup (2019). | tbl_df | 53 | 4 |
lookup_ltla19_region19 | geographr | Local Authority District to Region (December 2019) Lookup in England | tbl_df | 317 | 4 |
lookup_ltla19_utla19 | geographr | Local Authority Districts to County and Unitary Authorities lookup (2019). | tbl_df | 339 | 4 |
lookup_ltla20_cauth20 | geographr | Local Authority Districts to Combined Authorities lookup (2020). | tbl_df | 53 | 4 |
lookup_ltla20_region20 | geographr | Local Authority District to Region (December 2020) Lookup in England | tbl_df | 314 | 4 |
lookup_ltla20_utla20 | geographr | Local Authority Districts to County and Unitary Authorities lookup (2020). | tbl_df | 336 | 4 |
lookup_ltla21_brc | geographr | Local Authority District (2021) to British Red Cross area lookup | tbl_df | 382 | 2 |
lookup_ltla21_cauth21 | geographr | Local Authority Districts to Combined Authorities lookup (2021). | tbl_df | 53 | 4 |
lookup_ltla21_fra21 | geographr | Local Authority Districts to Fire and Rescue Authorities lookup (2021). | tbl_df | 331 | 4 |
lookup_ltla21_hsct18 | geographr | Local Government District (2021) to Health and Social Care Trust (2018) Lookup | tbl_df | 11 | 4 |
lookup_ltla21_lhb22 | geographr | Wales Local Authority Districts (2021) to Local Health Boards (2022) Lookup | tbl_df | 22 | 4 |
lookup_ltla21_region21 | geographr | Local Authority District to Region (April 2021) Lookup in England | tbl_df | 309 | 4 |
lookup_ltla21_utla21 | geographr | Local Authority Districts to County and Unitary Authorities lookup (2021). | tbl_df | 331 | 4 |
lookup_ltla21_vcsep22 | geographr | Local Authority Districts to VCSEP regions lookup (2021). | tbl_df | 309 | 3 |
lookup_ltla22_ltla23 | geographr | Local Authority Districts (2022) to Local Authority Districts (2023). | tbl_df | 374 | 4 |
lookup_ltla23_cauth23 | geographr | Local Authority Districts to Combined Authorities lookup (2023). | tbl_df | 53 | 4 |
lookup_ltla23_region23 | geographr | Local Authority District to Region (April 2023) Lookup in England | tbl_df | 296 | 4 |
lookup_ltla23_utla23 | geographr | Lower Tier Local Authority (2023) to Upper Tier Local Authority (2023). | tbl_df | 318 | 4 |
lookup_ltla_ltla | geographr | Changes to England Local Authority codes over time (from 2019). | tbl_df | 393 | 16 |
lookup_msoa11_ltla19 | geographr | Middle Layer Super Output Area (2011) to LAD (December 2019) Lookup | tbl_df | 7201 | 4 |
lookup_msoa11_ltla20 | geographr | Middle Layer Super Output Area (2011) to LAD (December 2020) Lookup | tbl_df | 7201 | 4 |
lookup_msoa11_ltla21 | geographr | Middle Layer Super Output Area (2011) to LAD (December 2021) Lookup | tbl_df | 7201 | 4 |
lookup_msoa11_msoa21_ltla22 | geographr | Middle Layer Super Output Area (2011) to Middle Layer Super Output Area (2021) to LAD (December 2022) Lookup | tbl_df | 7201 | 6 |
lookup_msoa11_ward17 | geographr | MSOA (2011) to Ward (2017) Lookup | tbl_df | 13873 | 2 |
lookup_msoa21_ward17 | geographr | MSOA (2021) to Ward (2017) Lookup | tbl_df | 13865 | 2 |
lookup_nhs_trusts22_icb22 | geographr | England NHS Trusts (2022) to ICB (2022) Lookup | tbl_df | 1321 | 3 |
lookup_nhs_trusts22_ltla21 | geographr | England NHS Trusts (2022) to LTLA (2021) Lookup | tbl_df | 5670 | 3 |
lookup_nhs_trusts22_msoa11 | geographr | England NHS Trusts (2022) to MSOA (2011) Lookup | tbl_df | 574104 | 4 |
lookup_nhs_trusts22_nhs_region21 | geographr | NHS Trusts (February 2022) to NHS Regions (April 2021) Lookup | tbl_df | 214 | 6 |
lookup_nhs_trusts22_stp21 | geographr | England NHS Trusts (2022) to STP/ICS (2021) Lookup | tbl_df | 209 | 4 |
lookup_postcode_dz21_sdz21_dea14_lgd14 | geographr | Postcode to Data Zone to Super DataZone to District Electoral Areas to Local Government District lookup for Northern Ireland | tbl_df | 59995 | 9 |
lookup_postcode_oa11_lsoa11_msoa11_ltla20 | geographr | Postcode to Output Area (2011) to Lower Layer Super Output Area (2011) to Middle Layer Super Output Area (2011) to Local Authority District (2020) Lookup | tbl_df | 2568780 | 5 |
lookup_postcode_oa21_lsoa21_msoa21_ltla22 | geographr | Postcode to Output Area (2021) to Lower Layer Super Output Area (2021) to Middle Layer Super Output Area (2021) to Local Authority District (2022) Lookup | tbl_df | 2600236 | 5 |
lookup_sa11_soa11_lgd18 | geographr | Small Areas (2011) to SOAs to Local Government Districts (December 2018) Lookup with Area Classifications in Northern Ireland | tbl_df | 4537 | 5 |
lookup_soa01_lgd14 | geographr | SOA (2001) to LGD (2014) Lookup | tbl_df | 890 | 3 |
lookup_trust_trust | geographr | NHS Trust and Orginisation Changes | tbl_df | 25480 | 3 |
lookup_ward21_ltla21 | geographr | Ward (December 2021) to LTLA (December 2021) Lookup | tbl_df | 8694 | 4 |
names_codes_hb19 | geographr | Names and codes for NHS Scotland Health Boards (2014 - 2019) | tbl_df | 18 | 4 |
points_hospitals22 | geographr | NHS England Hopsitals (July 2022) | sf | 1061 | 2 |
points_nhs_trusts22 | geographr | NHS Trusts (February 2022) | sf | 218 | 4 |
ruc11_lsoa11 | geographr | Rural-Urban Classifications for Lower Layer Super Output Areas (2011) in England and Wales | tbl_df | 34753 | 4 |
ruc11_msoa11 | geographr | Rural-Urban Classifications for Middle Layer Super Output Areas (2011) in England and Wales | tbl_df | 7201 | 4 |
ruc11_wards11 | geographr | Rural-Urban Classifications for wards (2011) in England and Wales | tbl_df | 8570 | 4 |
ruc16_dz11 | geographr | Rural-Urban Classifications for Data Zones (2011) in Scotland | tbl_df | 6976 | 4 |
ipi_c_eu | RJDemetra | Industrial Production Indices in manufacturing industry in the European Union | mts | 372 | 34 |
TMB1964r | superb | Data of Tulving, Mandler, & Baumal, 1964 (reproduction of 2021) | data.frame | 58 | 10 |
dataFigure1 | superb | Data for Figure 1 | data.frame | 50 | 3 |
dataFigure2 | superb | Data for Figure 2 | data.frame | 25 | 3 |
dataFigure3 | superb | Data for Figure 3 | data.frame | 50 | 3 |
dataFigure4 | superb | Data for Figure 4 | data.frame | 25 | 2 |
dep_wor_data | topics | Example data about mental health descirptions . | tbl_df | 500 | 12 |
pool | bvq | Pool of words | tbl_df | 1590 | 14 |
diamonds | ggplot2 | Prices of over 50,000 round cut diamonds | tbl_df | 53940 | 10 |
economics | ggplot2 | US economic time series | spec_tbl_df | 574 | 6 |
economics_long | ggplot2 | US economic time series | tbl_df | 2870 | 4 |
faithfuld | ggplot2 | 2d density estimate of Old Faithful data | tbl_df | 5625 | 3 |
luv_colours | ggplot2 | 'colors()' in Luv space | data.frame | 657 | 4 |
midwest | ggplot2 | Midwest demographics | tbl_df | 437 | 28 |
mpg | ggplot2 | Fuel economy data from 1999 to 2008 for 38 popular models of cars | tbl_df | 234 | 11 |
msleep | ggplot2 | An updated and expanded version of the mammals sleep dataset | tbl_df | 83 | 11 |
presidential | ggplot2 | Terms of 12 presidents from Eisenhower to Trump | tbl_df | 12 | 4 |
seals | ggplot2 | Vector field of seal movements | tbl_df | 1155 | 4 |
txhousing | ggplot2 | Housing sales in TX | tbl_df | 8602 | 9 |
engagement | tna | Example data on student engagement | stslist | 200 | 20 |
engagement_mmm | tna | Example mixed Markov model fitted to the 'engagement' data | mhmm | | |
group_regulation | tna | Example data on group regulation | data.frame | 2000 | 26 |
MNImap_hip | VertexWiseR | Hippocampal surface in MNI space | MNIsurface | | |
ROImap_fs5 | VertexWiseR | Atlas parcellations of fsaverage5 | ROImap | | |
ROImap_fs6 | VertexWiseR | Atlas parcellations of fsaverage6 | ROImap | | |
ROImap_fslr32k | VertexWiseR | Atlas parcellations of FS_LR32k | ROImap | | |
ROImap_hip | VertexWiseR | Atlas parcellations of the hippocampus (CITI168) | ROImap | | |
edgelist_hip | VertexWiseR | List of edges for the hippocampal template | edgelist | | |
fs6_to_fs5_map | VertexWiseR | fsaverage6 template object for nearest neighbor conversion in fs6_to_fs5() | numeric | | |
hip_points_cells | VertexWiseR | points and cells data required to build the hippocampus surface template | list | | |
Hadza | SQMtools | Hadza hunter-gatherer gut metagenomes | SQM | | |
MGKOs | SQMtools | Single Copy Phylogenetic Marker Genes from Sunagawa's group (KOs) | character | | |
MGOGs | SQMtools | Single Copy Phylogenetic Marker Genes from Sunagawa's group (OGs) | character | | |
RecA | SQMtools | RecA/RadA recombinase | character | | |
USiCGs | SQMtools | Universal Single-Copy Genes | character | | |
api29 | OTrecod | Student performance in California schools: the results of the county 29 | data.frame | 418 | 12 |
api35 | OTrecod | Student performance in California schools: the results of the county 35 | data.frame | 362 | 12 |
ncds_14 | OTrecod | National Child Development Study: a sample of the first four waves of data collection | data.frame | 5476 | 6 |
ncds_5 | OTrecod | National Child Development Study: a sample of the fifth wave of data collection | data.frame | 365 | 6 |
simu_data | OTrecod | A simulated dataset to test the functions of the OTrecod package | data.frame | 700 | 8 |
tab_test | OTrecod | A simulated dataset to test the library | data.frame | 10000 | 6 |
pbmc.rna.mat | SPECK | Single cell RNA-sequencing (scRNA-seq) peripheral blood (PBMC) data sample. | dgCMatrix | | |
target.malt.rna.mat | STREAK | Single cell RNA-sequencing (scRNA-seq) target subset of the 10X Genomics MALT counts. | dgCMatrix | | |
train.malt.adt.mat | STREAK | Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) training subset of the 10X Genomics MALT counts. | dgeMatrix | | |
train.malt.rna.mat | STREAK | Single cell RNA-sequencing (scRNA-seq) training subset of the 10X Genomics MALT counts. | dgCMatrix | | |
LDL | Markovchart | Aggregated low-density-lipoprotein patient data for control chart applications | data.frame | 1 | 12 |
diabetes | Markovchart | Pseudonymised and randomised time series dataset of diabetes patients for control chart applications | data.frame | 87598 | 11 |
simdata1 | markophylo | Simulated data. | list | | |
simdata2 | markophylo | Simulated data. | list | | |
simdata3 | markophylo | Simulated data. | list | | |
simdata4 | markophylo | Simulated data. | list | | |
simdata5 | markophylo | Simulated data. | list | | |
ExCWM | flexCWM | dataset ExCWM | data.frame | 200 | 8 |
students | flexCWM | dataset students | data.frame | 270 | 4 |
Australia | TestDimorph | Australia | data.frame | 94 | 9 |
Cremains_measurements | TestDimorph | Measurements from calcined postcranial materials. | data.frame | 21 | 8 |
FT | TestDimorph | Heuristic data | data.frame | 24 | 3 |
Howells | TestDimorph | The Howells' craniometric data | data.frame | 441 | 10 |
Howells_R | TestDimorph | Pooled within group correlation matrix for Howells' data | matrix | 8 | 8 |
Howells_V | TestDimorph | Pooled within-group variance-covariance matrix for Howells' data | matrix | 8 | 8 |
Howells_summary | TestDimorph | Summary of the Howells' craniometric data | data.frame | 32 | 8 |
Howells_summary_list | TestDimorph | List format of Howells_summary for multivariate analysis | list | | |
NHANES_1999 | TestDimorph | NHANES 1999 | data.frame | 1430 | 5 |
SMO | TestDimorph | Hypothetical set of unbalanced data | data.frame | 11 | 3 |
baboon.parms_R | TestDimorph | Pooled within group correlation matrix for baboon data | matrix | 4 | |
baboon.parms_df | TestDimorph | data frame format for the baboon.parms_df for multivariate analysis | data.frame | 12 | 8 |
baboon.parms_list | TestDimorph | List format for the baboon.parms_df for multivariate analysis | list | | |
dd | EHR | dd | data.frame | 10000 | 1505 |
dd.baseline | EHR | dd.baseline | data.frame | 10000 | 1505 |
dd.baseline.small | EHR | dd.baseline.small | data.frame | 2000 | 55 |
dd.small | EHR | dd.small | data.frame | 2000 | 55 |
lam_metadata | EHR | Example of Metadata for Lamotrigine Data | data.frame | 5 | 4 |
lam_mxr_parsed | EHR | Example of Lamotrigine Output from 'parseMedExtractR' | data.table | 10 | 9 |
tac_lab | EHR | Example of Lab Time Data for Tacrolimus | data.frame | 2 | 3 |
tac_metadata | EHR | Example of Metadata for Tacrolimus Data | data.frame | 3 | 4 |
tac_mxr_parsed | EHR | Example of Tacrolimus Output from 'parseMedExtractR' | data.table | 7 | 9 |
all_genes | TPEA | All human protein coding genes | data.frame | 20949 | 1 |
gene2ec | TPEA | The relationship of genes and EC | data.frame | 3521 | 2 |
gene2ko | TPEA | The relationship of genes and KO | data.frame | 9730 | 2 |
keggGene2gene | TPEA | KeggGene to genes | data.frame | 21796 | 2 |
node_gene | TPEA | The relationship between nodes and genes | list | | |
num_node_gene_score | TPEA | The score of each node in a certain pathway | list | | |
pathway_names | TPEA | Pathway names in KEGG Database | data.frame | 109 | 2 |
Samusik_01_subset | hypergate | 2000 events randomly sampled from the 'Samusik_01' dataset | list | | |
lognormAssay | rADA | Simulated Lognormal Dataset | data.frame | 100 | 20 |
adapt | borrowr | Data set used in the package vignette | data.frame | 180 | 5 |
demo_DMRfinder_DMRs | metevalue | DMRfinder Output Demo Dataset | data.frame | 757 | 8 |
demo_DMRfinder_rate_combine | metevalue | DMRfinder Methyrate Demo Dataset | data.frame | 46073 | 6 |
demo_biseq_DMR | metevalue | BiSeq Output Demo Dataset | data.frame | 14 | 9 |
demo_biseq_methyrate | metevalue | BiSeq Methyrate Demo Dataset | data.frame | 10502 | 12 |
demo_desq_out | metevalue | DESeq Output Dataset | matrix | 8166 | 7 |
demo_methylkit_met_all | metevalue | Methyrate output dataset from methylKit | data.frame | 24 | 7 |
demo_methylkit_methyrate | metevalue | Methyrate Dataset | data.frame | 963 | 6 |
demo_metilene_input | metevalue | Metilene Methyrate Demo Dataset | data.frame | 86723 | 18 |
demo_metilene_out | metevalue | Metilene Demo Output Dataset | data.frame | 723 | 10 |
higlasso.df | higlasso | Synthetic Example Data For Higlasso | data.frame | 300 | 8 |
events.fishes | BAMMtools | BAMMtools datasets | data.frame | 2313 | 8 |
events.primates | BAMMtools | BAMMtools datasets | data.frame | 16860 | 6 |
events.whales | BAMMtools | BAMMtools datasets | data.frame | 4509 | 8 |
fishes | BAMMtools | BAMMtools datasets | phylo | | |
mass.primates | BAMMtools | BAMMtools datasets | data.frame | 233 | 2 |
mcmc.primates | BAMMtools | BAMMtools datasets | data.frame | 4000 | 6 |
mcmc.whales | BAMMtools | BAMMtools datasets | data.frame | 2000 | 6 |
primates | BAMMtools | BAMMtools datasets | phylo | | |
traits.fishes | BAMMtools | BAMMtools datasets | numeric | | |
whales | BAMMtools | BAMMtools datasets | phylo | | |
gsz | ivDiag | Data from GSZ (2016) | data.frame | 5357 | 11 |
gsz_south | ivDiag | Data from GSZ (2016): Subsample | data.frame | 2175 | 11 |
rueda | ivDiag | Data from Rueda (2017) | data.frame | 4352 | 6 |
indSex | MoNAn | Example Data for the MoNAn Package | numeric | | |
mobilityEdgelist | MoNAn | Example Data for the MoNAn Package | matrix | 742 | |
myAlg | MoNAn | Exemplary Outcome Objects for the MoNAn Package | algorithm.monan | | |
myEffects | MoNAn | Exemplary Outcome Objects for the MoNAn Package | effectsList.monan | | |
myResDN | MoNAn | Exemplary Outcome Objects for the MoNAn Package | result.monan | | |
mySimDN | MoNAn | Exemplary Outcome Objects for the MoNAn Package | sims.monan | | |
myState | MoNAn | Exemplary Outcome Objects for the MoNAn Package | processState.monan | | |
orgRegion | MoNAn | Example Data for the MoNAn Package | numeric | | |
orgSize | MoNAn | Example Data for the MoNAn Package | numeric | | |
chirps_monthly | SeaVal | Monthly mean precipitation | data.table | 209040 | 6 |
ecmwf_monthly | SeaVal | Monthly mean precipitation forecast example dataset | data.table | 37224 | 9 |
Tropheus | vcvComp | Tropheus dataset | data.frame | 723 | 57 |
Tropheus.IK.coord | vcvComp | Tropheus IK coord dataset | data.frame | 511 | 58 |
SimulatedData | NVCSSL | Simulated data for illustration | data.frame | 436 | 103 |
coc | isni | A data set for Psychiatric Drug Treatment | data.frame | 869 | 10 |
qolef | isni | A data set for Quality of Life Emontional Functioning outcome. | data.frame | 2860 | 10 |
skquit | isni | A randomzied trial data set for Smoking cessation | data.frame | 1861 | 6 |
sos | isni | Dataset for a survey of sexual behavior | data.frame | 6136 | 3 |
srilanka | NiLeDAM | An example data set: electron microprobe data. | data.frame | 32 | 6 |
Mrk421 | RobPer | Data: Light curve from Mrk 421 | data.frame | 655 | 3 |
Mrk501 | RobPer | Data: Light curve from Mrk 501 | data.frame | 210 | 3 |
star_groj0422.32 | RobPer | Data: Light curve from GROJ0422+32 | matrix | 729 | |
ML_ex_dat | GPSeqClus | Sample Data for Sequential Clustering Routine | data.frame | 2138 | 4 |
iccdata1 | irrICC | Scores assigned by 4 judges to 5 targets/subjects. | data.frame | 12 | 5 |
iccdata2 | irrICC | Scores assigned by 4 judges to 5 targets/subjects distributed in 2 groups A and B. | data.frame | 15 | 6 |
iccdata3 | irrICC | Scores assigned by 3 raters to 4 subjects. | data.frame | 4 | 4 |
FlowcytometricData | opdisDownsampling | Example data of hematologic marker expression. | data.frame | 111686 | 7 |
GMMartificialData | opdisDownsampling | Example data an artificial Gaussian mixture. | data.frame | 30000 | 11 |
toy_data | TrumpetPlots | Toy dataset | data.table | 8000 | 7 |
LASERI | ICSNP | Cardiovascular Responses to Head-up Tilt | data.frame | 223 | 32 |
pulmonary | ICSNP | Change in Pulmonary Response after Exposure to Cotton Dust | data.frame | 12 | 3 |
med_dat | hdmed | Mediation Example Dataset | list | | |
Simulated_data | GSSE | Simulated Parkinson's disease data | data.frame | 268 | 4 |
p0G_data | GSSE | Data Set for Illustration of the 'p0G' Calculation | data.frame | 20 | 2 |
biasMatrix | falconx | Bias Matrix | data.frame | 1000 | 2 |
pos | falconx | Position (bp) | integer | | |
readMatrix | falconx | Reads Matrix | data.frame | 1000 | 4 |
tauhat | falconx | Estimated Break Points | numeric | | |
plasma | qrjoint | Plasma Concentration of Beta-Carotene and Retinol | data.frame | 315 | 14 |
redmaple | qrjoint | Basal Areas of Red Maple Trees | data.frame | 608 | 8 |
simX | FourWayHMM | Simulated Data | array | | |
I_sig | SLIDE | Protein Expression Levels in an Infected Cell Sample | data.frame | 38 | 7 |
UN_sig | SLIDE | Protein Expression Levels in an Uninfected Cell Population | data.frame | 781 | 7 |
dataIEA | multilevLCA | Data for understanding of good citizenship behaviour | data.frame | 90221 | 28 |
dataTOY | multilevLCA | Artificial data set | data.frame | 3000 | 13 |
Q | LTCDM | Data Set Q | data.frame | 40 | 4 |
cep | LTCDM | Data Set cep | list | | |
dat0 | LTCDM | Data Set dat0 | data.frame | 719 | 40 |
dat1 | LTCDM | Data Set dat1 | data.frame | 2005 | 82 |
step3.output | LTCDM | Data Set step3.output | list | | |
strength | mdscore | Impact Strength an Insulating Material | data.frame | 30 | 3 |
CPPdata | JADE | Cocktail Party Problem Data | data.frame | 50000 | 4 |
dental | geesmv | A Data Set of Orthodontic Measurements on Children | data.frame | 27 | 6 |
seizure | geesmv | Epiliptic seizure counts from the Randomized Progabide Trial | data.frame | 59 | 7 |
toenail | geesmv | Toenail infection data from a multicenter study | data.frame | 1908 | 5 |
dataset | CeRNASeek | Data for Examples | list | | |
gutten | FAwR | von Guttenberg's Norway spruce (Picea abies [L.] Karst) tree measurement data. | data.frame | 1200 | 9 |
herbdata | FAwR | Herbicide trial seedling data | data.frame | 960 | 8 |
leuschner | FAwR | Leuschner harvest schedule yield data | data.frame | 48 | 4 |
stage | FAwR | Stage's Grand fir (Abies grandis (Dougl) Lindl.) tree measurement data | data.frame | 542 | 11 |
sweetgum | FAwR | Lenhart's sweetgum (Liquidambar styraciflua L.) tree measurement data. | data.frame | 39 | 8 |
ufc | FAwR | Upper Flat Creek forest cruise tree data | data.frame | 336 | 5 |
binarydb | RWsearch | CRAN Matrix Of Available Binary Packages (archivedb.rda) | matrix | 94 | 17 |
crandb | RWsearch | CRAN Packages (crandb.rda) | data.frame | 94 | 69 |
tvdb | RWsearch | Task Views (tvdb.rda) | list | | |
Xmat | icRSF | A covariate matrix | matrix | 300 | 1000 |
pheno | icRSF | A longitudinal data with diagnostic results for pre-determined time | data.frame | 629 | 3 |
bologna | SpatEntropy | Bologna urban data. | matrix | 135 | |
bolognaTess | SpatEntropy | Municipalities' administrative borders for Bologna urban data. | list | | |
bolognaW | SpatEntropy | Observation window for Bologna urban data. | owin | | |
raintrees | SpatEntropy | Rainforest tree data 1. | ppp | | |
raintrees2 | SpatEntropy | Rainforest tree data 2. | ppp | | |
raintreesCOV | SpatEntropy | Covariates for the rainforest tree data. | imlist | | |
turin | SpatEntropy | Turin urban data. | matrix | 111 | |
turinTess | SpatEntropy | Municipalities' administrative borders for Turin urban data. | list | | |
turinW | SpatEntropy | Observation window for Turin urban data. | owin | | |
khan2001 | sda | Childhood Cancer Study of Khan et al. (2001) | list | | |
singh2002 | sda | Prostate Cancer Study of Singh et al. (2002) | list | | |
agri_studies | NaileR | Agribusiness studies survey | data.frame | 53 | 42 |
atomic_habit | NaileR | Atomic habits survey | data.frame | 167 | 50 |
atomic_habit_clust | NaileR | Atomic habits survey | data.frame | 167 | 51 |
beard | NaileR | Beard descriptions | tbl_df | 494 | 2 |
beard_cont | NaileR | Beard descriptions | data.frame | 8 | 335 |
beard_wide | NaileR | Beard descriptions | data.frame | 8 | 24 |
boss | NaileR | Ideal boss survey | data.frame | 73 | 39 |
car_alone | NaileR | Atomic habits survey | data.frame | 158 | 2 |
fabric | NaileR | Car seat fabrics | data.frame | 567 | 4 |
glossophobia | NaileR | Glossophobia survey | data.frame | 139 | 41 |
local_food | NaileR | Local food systems survey | data.frame | 573 | 63 |
nutriscore | NaileR | Nutri-score survey | data.frame | 112 | 36 |
quality | NaileR | Perception of food quality | tbl_df | 55 | 9 |
rorschach | NaileR | Rorschach inkblots | data.frame | 600 | 5 |
waste | NaileR | Food waste survey | data.frame | 180 | 77 |
Abrasion | MLGdata | Abrasion loss | data.frame | 30 | 3 |
Aids | MLGdata | Aids mortality | data.frame | 14 | 2 |
Alligators | MLGdata | Alligator food choice data | data.frame | 40 | 4 |
Ants | MLGdata | Ants and sandwiches | data.frame | 48 | 5 |
Aziende | MLGdata | Number of closed businesses | data.frame | 16 | 4 |
Bartlett | MLGdata | Bartlett data on plum root cuttings | table | | |
Bartlett2 | MLGdata | Bartlett data on plum root cuttings | data.frame | 4 | 4 |
Beetles | MLGdata | Deaths of flour beetles | data.frame | 8 | 3 |
Beetles10 | MLGdata | Deaths of flour beetles | data.frame | 481 | 2 |
Bioassay | MLGdata | Biological experiment | data.frame | 10 | 3 |
Biochemists | MLGdata | article production by graduate students in biochemistry Ph.D. programs | data.frame | 915 | 6 |
Britishdoc | MLGdata | British doctors study | data.frame | 10 | 4 |
Calcium | MLGdata | Calcium Uptake Data | data.frame | 27 | 2 |
Cement | MLGdata | Tensile strength of cement | data.frame | 21 | 2 |
Chimps | MLGdata | Chimpanzee Learning Data | data.frame | 40 | 3 |
Chlorsulfuron | MLGdata | Chlorsulfuron Data | data.frame | 51 | 3 |
Clotting | MLGdata | Blood clotting times | data.frame | 18 | 3 |
Credit | MLGdata | Credit Score Data From a South German Bank | data.frame | 1000 | 8 |
Customer | MLGdata | Bus customer satisfaction | data.frame | 12231 | 2 |
Customer3 | MLGdata | Bus customer satisfaction | data.frame | 4 | 6 |
Dogs | MLGdata | Dogs data | data.frame | 40 | 4 |
Drugs | MLGdata | Student Substance Use | data.frame | 8 | 4 |
Drugs2 | MLGdata | Student Substance Use | data.frame | 4 | 5 |
Drugs3 | MLGdata | Student Substance Use | data.frame | 32 | 6 |
Esito | MLGdata | Recreational activities and university performance | data.frame | 18 | 4 |
Germination | MLGdata | Seed Germination | data.frame | 21 | 4 |
Heart | MLGdata | Creatinine kinase and heart attacks | data.frame | 13 | 4 |
Homicide | MLGdata | Homicide data | data.frame | 1308 | 2 |
Infant | MLGdata | Infant survival | data.frame | 16 | 5 |
Kyphosis | MLGdata | Data on Children who have had Corrective Spinal Surgery | data.frame | 81 | 4 |
Malaria | MLGdata | Malaria Transmission in the Western Kenyan Highlands | data.frame | 8204 | 13 |
Mental | MLGdata | Mental impairment | data.frame | 40 | 3 |
Neonati | MLGdata | Weight at birth | data.frame | 32 | 3 |
Ohio | MLGdata | Ohio Children Wheeze Status | data.frame | 2148 | 4 |
Orthodont | MLGdata | Growth curve data on an orthdontic measurement | data.frame | 27 | 5 |
Orthodont1 | MLGdata | Growth curve data on an orthdontic measurement | data.frame | 108 | 4 |
Pneu | MLGdata | Pneumoconiosis amongst Coalminers | data.frame | 8 | 4 |
Rats | MLGdata | Teratology study | data.frame | 58 | 5 |
Seed | MLGdata | Seed germination | data.frame | 20 | 2 |
Snore | MLGdata | Snoring and heart disease | data.frame | 8 | 3 |
Spending | MLGdata | Opinions about government spending | data.frame | 81 | 5 |
Stroke | MLGdata | Stroke data | data.frame | 24 | 10 |
Stroke1 | MLGdata | Stroke data | data.frame | 192 | 4 |
Testingresso | MLGdata | University admission test | data.frame | 630 | 3 |
Vehicle | MLGdata | Preferred vehicle | data.frame | 2067 | 4 |
Wool | MLGdata | Wool data | data.frame | 27 | 4 |
drugdata | WCE | Simulated dataset to illustrate the use of WCE models | data.frame | 77038 | 7 |
AutumnLab | MLDS | Difference Scale Judgement Data Set | mlds.df | 210 | 5 |
Transparency | MLDS | Difference Scaling of Transparency | data.frame | 2520 | 6 |
kk1 | MLDS | Difference Scale Judgment Data Sets | mlds.df | 330 | 5 |
kk2 | MLDS | Difference Scale Judgment Data Sets | mlds.df | 330 | 5 |
kk3 | MLDS | Difference Scale Judgment Data Sets | mlds.df | 330 | 5 |
kktriad | MLDS | Difference Scale Judgment Data Sets | mlbs.df | 165 | 4 |
mark0 | SOFIA | Genetic maps to be used as examples for generating Circos figures | data.frame | 10000 | 6 |
dji30retw | tsmarch | Dow Jones 30 Constituents Closing Value log Weekly Return | data.frame | 1141 | 30 |
globalindices | tsmarch | Global Financial Indices Closing Value log Weekly Return | data.frame | 1698 | 34 |
fred_md | BVAR | FRED-MD and FRED-QD: Databases for Macroeconomic Research | data.frame | 788 | 118 |
fred_qd | BVAR | FRED-MD and FRED-QD: Databases for Macroeconomic Research | data.frame | 262 | 233 |
virtualSp | SDMtune | Virtual Species | list | | |
allGalaxies | admix | Measurements of heliocentric velocities in four galaxies | data.frame | 8862 | 3 |
milkyWay | admix | Heliocentric velocity for the Milky Way | data.frame | 170601 | 1 |
mortality_sample | admix | Deaths statistics in 11 european countries | list | | |
stmf_small | admix | Short-term Mortality Fluctuations (STMF) data | data.frame | 15252 | 19 |
monitoring | squeacr | Routine CMAM monitoring data from Sudan | tbl_df | 8234 | 16 |
muac_admission | squeacr | MUAC at admission | list | | |
muac_admission_tidy | squeacr | MUAC at admission in tidy format | tbl_df | 506 | 3 |
otp_beneficiaries | squeacr | Outpatient Therapeutic Care Programme (OTP) beneficiaries data | tbl_df | 405 | 13 |
seasonal_calendar | squeacr | Seasonal calendar data for Sudan | tbl_df | 28 | 4 |
time_to_travel | squeacr | Time-to-travel to health facilities for beneficiaries and volunteers | tbl_df | 165 | 9 |
eterna_wavegroups | earthtide | Hartmann and Wenzel (1995) (ETERNA 3.4) wavegroups | data.frame | 149 | 4 |
tnbc | ranktreeEnsemble | Gene expression profiles in triple-negative breast cancer cell | data.frame | 215 | 337 |
Abortion | vcdExtra | Abortion Opinion Data | table | | |
Accident | vcdExtra | Traffic Accident Victims in France in 1958 | data.frame | 80 | 5 |
AirCrash | vcdExtra | Air Crash Data | data.frame | 439 | 5 |
Alligator | vcdExtra | Alligator Food Choice | data.frame | 80 | 5 |
Asbestos | vcdExtra | Effect of Exposure to Asbestos | matrix | 5 | 4 |
Bartlett | vcdExtra | Bartlett Data on Plum Root Cuttings | table | | |
Burt | vcdExtra | Burt (1950) Data on Hair, Eyes, Head and Stature | data.frame | 36 | 5 |
Caesar | vcdExtra | Risk Factors for Infection in Caesarian Births | table | | |
Cancer | vcdExtra | Survival of Breast Cancer Patients | table | | |
Cormorants | vcdExtra | Advertising Behavior by Males Cormorants | data.frame | 343 | 8 |
CyclingDeaths | vcdExtra | London Cycling Deaths | data.frame | 208 | 2 |
DaytonSurvey | vcdExtra | Dayton Student Survey on Substance Use | data.frame | 32 | 6 |
Depends | vcdExtra | Dependencies of R Packages | table | | |
Detergent | vcdExtra | Detergent preference data | table | | |
Donner | vcdExtra | Survival in the Donner Party | data.frame | 90 | 5 |
Draft1970 | vcdExtra | USA 1970 Draft Lottery Data | data.frame | 366 | 3 |
Draft1970table | vcdExtra | USA 1970 Draft Lottery Table | table | 12 | 3 |
Dyke | vcdExtra | Sources of Knowledge of Cancer | table | | |
Fungicide | vcdExtra | Carcinogenic Effects of a Fungicide | array | | |
GSS | vcdExtra | General Social Survey- Sex and Party affiliation | data.frame | 6 | 3 |
Geissler | vcdExtra | Geissler's Data on the Human Sex Ratio | data.frame | 90 | 4 |
Gilby | vcdExtra | Clothing and Intelligence Rating of Children | table | 6 | 4 |
Glass | vcdExtra | British Social Mobility from Glass(1954) | data.frame | 25 | 3 |
HairEyePlace | vcdExtra | Hair Color and Eye Color in Caithness and Aberdeen | array | | |
Hauser79 | vcdExtra | Hauser (1979) Data on Social Mobility | data.frame | 25 | 3 |
Heart | vcdExtra | Sex, Occupation and Heart Disease | table | | |
Heckman | vcdExtra | Labour Force Participation of Married Women 1967-1971 | table | | |
HospVisits | vcdExtra | Hospital Visits Data | table | 3 | 3 |
HouseTasks | vcdExtra | Household Tasks Performed by Husbands and Wives | table | 13 | 4 |
Hoyt | vcdExtra | Minnesota High School Graduates | table | | |
ICU | vcdExtra | ICU data set | data.frame | 200 | 22 |
JobSat | vcdExtra | Cross-classification of job satisfaction by income | table | 4 | 4 |
Mammograms | vcdExtra | Mammogram Ratings | matrix | 4 | 4 |
Mental | vcdExtra | Mental Impairment and Parents SES | data.frame | 24 | 3 |
Mice | vcdExtra | Mice Depletion Data | data.frame | 30 | 4 |
Mobility | vcdExtra | Social Mobility data | table | 5 | 5 |
PhdPubs | vcdExtra | Publications of PhD Candidates | data.frame | 915 | 6 |
ShakeWords | vcdExtra | Shakespeare's Word Type Frequencies | data.frame | 100 | 2 |
TV | vcdExtra | TV Viewing Data | array | | |
Titanicp | vcdExtra | Passengers on the Titanic | data.frame | 1309 | 6 |
Toxaemia | vcdExtra | Toxaemia Symptoms in Pregnancy | data.frame | 60 | 5 |
Vietnam | vcdExtra | Student Opinion about the Vietnam War | data.frame | 40 | 4 |
Vote1980 | vcdExtra | Race and Politics in the 1980 Presidential Vote | data.frame | 28 | 4 |
WorkerSat | vcdExtra | Worker Satisfaction Data | data.frame | 8 | 4 |
Yamaguchi87 | vcdExtra | Occupational Mobility in Three Countries | data.frame | 75 | 4 |
china_city | leafletZH | city data for China | sf | 476 | 10 |
china_province | leafletZH | province data for China | sf | 34 | 10 |
bcd | prozor | Data frame as produced by COMET-MS search engine | data.frame | 4557 | 18 |
fdrSample | prozor | Data frame score and proteinID | data.frame | 40000 | 3 |
masses | prozor | MS masses A dataset containing approx 150000 MS1 precursor masses | numeric | | |
mm | prozor | | dgCMatrix | | |
pepprot | prozor | Table containing peptide information | data.frame | 4938 | 4 |
protpepmetashort | prozor | Small version of pepprot dataset to speed up computation | data.frame | 423 | 7 |
Angeville | Guerry | Data from d'Angeville (1836) on the population of France | data.frame | 86 | 16 |
Guerry | Guerry | Data from A.-M. Guerry, "Essay on the Moral Statistics of France" | data.frame | 86 | 23 |
gfrance | Guerry | Map of France in 1830 with the Guerry data | SpatialPolygonsDataFrame | | |
gfrance85 | Guerry | Map of France in 1830 with the Guerry data, excluding Corsica | SpatialPolygonsDataFrame | | |
propensity | Guerry | Distribution of crimes against persons at different ages | tbl_df | 124 | 4 |
AA_knowledge_test | mycaas | Example of a test to showcase the Adaptive Assessment tools | assessment | | |
cwna_data | ggsmc | Data generated from a constant velocity (or continuous white noise acceleration, CWNA) model for 20 time steps. | data.frame | 20 | 4 |
lv_output | ggsmc | 10000 simulations from a stochastic Lotka-Volterra model, assigned weights according to a Gaussian approximate Bayesian computation kernel with tolerance equal to 50. | data.frame | 320000 | 15 |
mixture_25_particles | ggsmc | The output of an SMC sampler where the initial distribution is a Gaussian and the final target is a mixture of Gaussians. 25 particles were used, with an adaptive method to determine the sequence of targets, and a Metropolis-Hastings move to move the particles at each step. | data.frame | 175 | 13 |
sir_cwna_model | ggsmc | The output of a bootstrap particle filter on the 'cwna_data'. The output consists of 100 particles over 20 time steps. | data.frame | 4000 | 13 |
daxreturns | VineCopula | Major German Stocks | data.frame | 1158 | 15 |
ge_macro_trial01 | beeca | Output from the Ge et al (2011) SAS macro applied to the trial01 dataset | tbl_df | 1 | 6 |
margins_trial01 | beeca | Output from the Margins SAS macro applied to the trial01 dataset | tbl_df | 1 | 11 |
trial01 | beeca | Example trial dataset 01 | tbl_df | 268 | 9 |
trial02_cdisc | beeca | Example CDISC Clinical Trial Dataset in ADaM Format | tbl_df | 254 | 13 |
housing | fasterElasticNet | Housing data from kaggle | data.frame | 10153 | 140 |
my_data | windsoraiR | Sample data from the Windsor API. | data.frame | 1677 | 6 |
elements | rfordummies | Periodic table of elements. | data.frame | 118 | 9 |
clothianidin | melt | Clothianidin concentration in maize plants | data.frame | 102 | 3 |
thiamethoxam | melt | Thiamethoxam applications in squash crops | data.frame | 165 | 11 |
T47D | RNAseqQC | The T47D cell line data of RNA-seq experiment GSE89888 | DESeqDataSet | | |
T47D_diff_testing | RNAseqQC | Differential expression results corresponding to the T47D data set. | DESeqResults | | |
egk_destinations | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_fire | steps | Eastern Grey Kangaroo example data | RasterBrick | | |
egk_hab | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_k | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_mat | steps | Eastern Grey Kangaroo example data | matrix | 3 | 3 |
egk_mat_stoch | steps | Eastern Grey Kangaroo example data | matrix | 3 | 3 |
egk_origins | steps | Eastern Grey Kangaroo example data | RasterLayer | | |
egk_pop | steps | Eastern Grey Kangaroo example data | RasterStack | | |
egk_road | steps | Eastern Grey Kangaroo example data | RasterBrick | | |
egk_sf | steps | Eastern Grey Kangaroo example data | RasterStack | | |
chinook | zoid | Data from Satterthwaite, W.H., Ciancio, J., Crandall, E., Palmer-Zwahlen, M.L., Grover, A.M., OโFarrell, M.R., Anson, E.C., Mohr, M.S. & Garza, J.C. (2015). Stock composition and ocean spatial distribution from California recreational chinook salmon fisheries using genetic stock identification. Fisheries Research, 170, 166โ178. The data genetic data collected from port-based sampling of recreationally-landed Chinook salmon in California from 1998-2002. | data.frame | 60 | 10 |
coddiet | zoid | Data from Magnussen, E. 2011. Food and feeding habits of cod (Gadus morhua) on the Faroe Bank. โ ICES Journal of Marine Science, 68: 1909โ1917. The data here are Table 3 from the paper, with sample proportions (columns w) multiplied by total weight to yield total grams (g) for each sample-diet item combination. Dashes have been replaced with 0s. | data.frame | 10 | 32 |
namesDE | mapping | Statistical Unit Names | list | | |
namesEU | mapping | Statistical Unit Names | list | | |
namesFR | mapping | Statistical Unit Names | list | | |
namesIT | mapping | Statistical Unit Names | list | | |
namesUK | mapping | Statistical Unit Names | list | | |
namesUS | mapping | Statistical Unit Names | list | | |
namesWR | mapping | Statistical Unit Names | data.frame | 251 | 19 |
popDE | mapping | German Population | tbl_df | 16 | 4 |
popEU | mapping | European population | data.frame | 2252 | 5 |
popEUnuts2 | mapping | European population | data.frame | 327 | 5 |
popFR | mapping | French Population | tbl_df | 13 | 2 |
popIT | mapping | Italian Population | data.frame | 107 | 4 |
popUK | mapping | United Kingdom Population | tbl_df | 420 | 3 |
popUS | mapping | USA population | data.frame | 52 | 2 |
popWR | mapping | World population | data.frame | 269 | 5 |
tax_wedge_ocde | mapping | OCDE tax wedge | data.frame | 74 | 7 |
usa_election | mapping | Usa Election | data.frame | 51 | 19 |
data_cr | mvord | Simulated credit ratings | data.frame | 690 | 10 |
data_cr_panel | mvord | Simulated panel of credit ratings | data.frame | 11320 | 9 |
data_mvord | mvord | Simulated credit ratings | data.frame | 3000 | 9 |
data_mvord2 | mvord | Simulated credit ratings | data.frame | 1000 | 10 |
data_mvord_panel | mvord | Simulated panel of credit ratings | data.frame | 10000 | 9 |
data_mvord_toy | mvord | Data set toy example | data.frame | 100 | 6 |
essay_data | mvord | Essay data | data.frame | 198 | 6 |
Example | dSVA | Example data for dSVA | list | | |
exampleTCGA | seAMLess | TCGA-LAML bulk RNA-seq data downloaded from GDC | data.frame | 60483 | 21 |
exampleTCGAmeta | seAMLess | TCGA-LAML example data meta file downloaded from GDC | data.frame | 20 | 34 |
grch38 | seAMLess | Grch38 | tbl_df | 67495 | 3 |
minRes | seAMLess | A minimal seAMLess result list object | list | | |
venoModel | seAMLess | Trained RF model on Venetoclax Resistance | randomForest | | |
disch | CoSMoS | Daily streamflow data data | data.table | 23315 | 2 |
precip | CoSMoS | Hourly station precipitation data | data.table | 79633 | 2 |
data1 | DSWE | Wind Energy data set containing 47,542 data points | data.frame | 47542 | 7 |
data2 | DSWE | Wind Energy data set containing 48,068 data points | data.frame | 48068 | 7 |
fake_questionnaire_data | longmixr | Fake questionnaire data | data.frame | 400 | 20 |
RateTable_Means_1p_Clades | EvoPhylo | Mean clock rates by node and clade (single clock) | data.frame | 79 | 3 |
RateTable_Means_3p_Clades | EvoPhylo | Mean clock rates by node and clade (3 clock partitions) | data.frame | 79 | 5 |
characters | EvoPhylo | A morphological phylogenetic data matrix | matrix | 178 | 43 |
post_trees | EvoPhylo | Multiple phylogenetic clock trees | treedataList | | |
posterior1p | EvoPhylo | Posterior parameter samples (single clock) | data.frame | 10000 | 21 |
posterior3p | EvoPhylo | Posterior parameter samples (3 clock partions) | data.frame | 10000 | 28 |
tree1p | EvoPhylo | Phylogenetic tree with a single clock partition | treedata | | |
tree3p | EvoPhylo | Phylogenetic tree with 3 clock partitions | treedata | | |
tree_clock1 | EvoPhylo | BEAST2 phylogenetic tree with clock rates from partition 1 | treedata | | |
tree_clock2 | EvoPhylo | BEAST2 phylogenetic tree with clock rates from partition 2 | treedata | | |
example_data | ccml | The input data for example | data.frame | 10 | 5 |
anscombe_tidy | cassowaryr | Data from Anscombe's famous example in tidy format | tbl_df | 44 | 3 |
datasaurus_dozen | cassowaryr | datasaurus_dozen data | tbl_df | 1846 | 3 |
datasaurus_dozen_wide | cassowaryr | datasaurus_dozen data | tbl_df | 142 | 26 |
features | cassowaryr | Simulated data with special features | tbl_df | 1913 | 3 |
numbat | cassowaryr | A toy data set with a numbat shape hidden among noise variables | tbl_df | 2100 | 10 |
pk | cassowaryr | Parkinsons data from UCI machine learning archive | spec_tbl_df | 195 | 24 |
df1 | ptmixed | Example dataset with longitudinal counts | data.frame | 18 | 5 |
sample_inputs | strand | Sample security inputs for examples and testing | data.frame | 31980 | 7 |
sample_pricing | strand | Sample security pricing data for examples and testing | data.frame | 31980 | 8 |
sample_secref | strand | Sample security reference data for examples and testing | data.frame | 492 | 4 |
DataExam2.1 | eda4treeR | Data for Example 2.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 16 | 2 |
DataExam2.2 | eda4treeR | Data for Example 2.2 from Experimental Design and Analysis for Tree Improvement | data.frame | 16 | 4 |
DataExam3.1 | eda4treeR | Data for Example 3.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 80 | 6 |
DataExam3.1.1 | eda4treeR | Data for Example 3.1.1 from Experimental Design and Analysis for Tree Improvement | tbl_df | 10 | 6 |
DataExam4.3 | eda4treeR | Data for Example 4.3 from Experimental Design and Analysis for Tree Improvement | data.frame | 72 | 8 |
DataExam4.3.1 | eda4treeR | Data for Example 4.3.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 72 | 8 |
DataExam4.4 | eda4treeR | Data for Example 4.4 from Experimental Design and Analysis for Tree Improvement | data.frame | 32 | 5 |
DataExam5.1 | eda4treeR | Data for Example 5.1 from Experimental Design and Analysis for Tree Improvement | data.frame | 108 | 4 |
DataExam5.2 | eda4treeR | Data for Example 5.2 from Experimental Design and Analysis for Tree Improvement | tbl_df | 222 | 4 |
DataExam6.2 | eda4treeR | Data for Example 6.2 from Experimental Design and Analysis for Tree Improvement | data.frame | 192 | 9 |
DataExam8.1 | eda4treeR | Data for Example 8.1 from Experimental Design and Analysis for Tree Improvement | tidytable | 236 | 7 |
DataExam8.2 | eda4treeR | Data for Example 8.2 from Experimental Design and Analysis for Tree Improvement | tbl_df | 300 | 13 |
london_area | zonebuilder | Region representing London in projected coordinate system | sfc_POLYGON | | |
london_area_lonlat | zonebuilder | Region representing London in projected coordinate system | sfc_POLYGON | | |
london_cent | zonebuilder | Region representing London in projected coordinate system | sfc_POINT | | |
london_cent_lonlat | zonebuilder | Region representing London in projected coordinate system | sfc_POINT | | |
zb_100_triangular_numbers | zonebuilder | The first 100 triangular numbers | integer | | |
version | tgver | Version of the tgvejs npm package bundled in 'tgver' | character | | |
locations | helminthR | Table of geographic location names, and associated coordinates | data.frame | 498 | 3 |
GriffingData1 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 1 | data.frame | 256 | 4 |
GriffingData2 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 2 | data.frame | 144 | 4 |
GriffingData3 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 3 | data.frame | 224 | 4 |
GriffingData4 | DiallelAnalysisR | Data for Diallel Analysis using Griffing Approach Method 4 | data.frame | 112 | 4 |
HaymanData | DiallelAnalysisR | Data for Diallel Analysis using Hayman's Approach | data.frame | 256 | 4 |
PartialDiallelData | DiallelAnalysisR | Data for Partial Diallel Analysis | data.frame | 160 | 4 |
pfms_dna | ggseqlogo | List of position frequency matrices for transcription factors | list | | |
seqs_aa | ggseqlogo | List of aligned kinase-substrate binding sequences | list | | |
seqs_dna | ggseqlogo | List of aligned transcription factor binding sequences | list | | |
SAAD | ADMUR | Radiocarbon dataset for South American Arid Diagonal (SAAD) | data.frame | 1527 | 10 |
bluhm2421 | ADMUR | Radiocarbon dataset from Bluhm and Surovell 2018 | data.frame | 2421 | 4 |
bryson1848 | ADMUR | Radiocarbon dataset from Bryson et al. 2006 | data.frame | 1848 | 6 |
data1 | ADMUR | Toy radiocarbon dataset | data.frame | 100 | 4 |
data2 | ADMUR | Toy radiocarbon dataset | data.frame | 100 | 4 |
data3 | ADMUR | Toy radiocarbon dataset | data.frame | 100 | 4 |
data4 | ADMUR | Toy radiocarbon dataset | data.frame | 300 | 4 |
intcal13 | ADMUR | Northern hemisphere 2013 calibration curve | data.frame | 5141 | 3 |
intcal20 | ADMUR | Northern hemisphere 2020 calibration curve | data.frame | 9501 | 3 |
shcal13 | ADMUR | Southern hemisphere 2013 calibration curve | data.frame | 5141 | 3 |
shcal20 | ADMUR | Southern hemisphere 2020 calibration curve | data.frame | 9501 | 3 |
toy | ADMUR | Toy population model | data.frame | 4 | 2 |
IGT | hmmr | Iowa Gambling Task data | data.frame | 3000 | 8 |
MAR_simulation_results | hmmr | Missing at random (MAR) simulation results. | data.frame | 21 | 9 |
MNAR_simulation_results | hmmr | Missing not at random (MNAR) simulation results. | data.frame | 21 | 9 |
SEsamples | hmmr | Bootstrap Samples for Simple 2-State Model | matrix | 1000 | |
WPT | hmmr | Weather Prediction Task Data | data.frame | 9600 | 11 |
balance8 | hmmr | Repeated measures on the balance scale | data.frame | 8032 | 23 |
balance8pars | hmmr | Parameter estimates of models for the balance8 data set | list | | |
confint | hmmr | Confidence intervals Visser et al (2000) | data.frame | 10 | 9 |
conservation | hmmr | Conservation of liquid | data.frame | 101 | 6 |
dccs | hmmr | Dimensional Change Card Sort Task Data | data.frame | 93 | 15 |
dccs_boot_LR | hmmr | dccs boot LR | boot | | |
dccslong | hmmr | Dimensional Change Card Sort Task Data | data.frame | 558 | 5 |
disc42 | hmmr | Discrimination Learning Data | data.frame | 274 | 1 |
discrimination | hmmr | Discrimination Learning Data | data.frame | 3139 | 1 |
perth | hmmr | Perth dams water levels. | data.frame | 107 | 3 |
simplehmm | hmmr | Hmm toy data set from Visser et al (2000) | data.frame | 2000 | 1 |
speed1 | hmmr | Speed Accuracy Switching Data | data.frame | 168 | 3 |
speed_boot_LR | hmmr | speed boot LR | boot | | |
speed_boot_LR_extra | hmmr | speed boot LR | boot | | |
speed_boot_par | hmmr | speed boot par | boot | | |
Battig | WordPools | Battig - Montague Categorized Word Norms | data.frame | 5231 | 9 |
CatProp | WordPools | Joelson-Hermann Category Properties | data.frame | 56 | 24 |
Paivio | WordPools | Paivio, Yuille & Madigan Word Pool | data.frame | 925 | 9 |
TWP | WordPools | The Toronto Word Pool | data.frame | 1093 | 12 |
DTcv | mixtox | critical value for Dunnett's test | matrix | 1050 | 8 |
antibiotox | mixtox | Toxicity of Seven Antibiotics on Photobacteria | list | | |
cytotox | mixtox | Cytotoxicity of Heavy Metal Ions and Ionic Liquids on MCF-7 | list | | |
hormesis | mixtox | Non-monotonic Concentration-response Data | list | | |
staval | mixtox | Starting Values for 13 Sigmoidal and 4 Hormetic Models | list | | |
fivenets | ergmito | Example of a group of small networks | list | | |
cobre32 | fcaR | Data for Differential Diagnosis for Schizophrenia | matrix | 105 | 32 |
cobre61 | fcaR | Data for Differential Diagnosis for Schizophrenia | data.frame | 105 | 61 |
planets | fcaR | Planets data | matrix | 9 | 7 |
vegas | fcaR | Data for Tourist Destination in Las Vegas | matrix | 504 | 25 |
grp_cor | visualizationQualityControl | 10 sample to sample correlations | matrix | 10 | 10 |
grp_cor_data | visualizationQualityControl | grp_cor_data | list | | |
grp_data | visualizationQualityControl | | matrix | 100 | |
grp_exp_data | visualizationQualityControl | grp_exp_data | list | | |
grp_info | visualizationQualityControl | 10 sample meta-data | data.frame | 10 | 2 |
cbm_spatial | SWMPrExtension | Spatial Data from Chesapeake Bay - Maryland | sf | 1 | 1 |
counties_4269 | SWMPrExtension | US County Map | sf | 3220 | 4 |
elk_spatial | SWMPrExtension | Spatial Data from Elkhorn Slough | sf | 1 | 1 |
elknmnut | SWMPrExtension | Nutrient Data from Elkhorn Slough - North Marsh Station | swmpr | 214 | 13 |
elksmwq | SWMPrExtension | Water Quality Data from Elkhorn Slough - South Marsh Station | swmpr | 140256 | 25 |
sampling_stations | SWMPrExtension | Detailed NERRS site data | data.frame | 356 | 17 |
sampling_stations_backup | SWMPrExtension | A Backup of Detailed NERRS site data | data.frame | 355 | 17 |
us_4269 | SWMPrExtension | US State Map | sf | 52 | 3 |
OVENdata | MigConnectivity | Ovenbird light-level geolocator and GPS necessary data | list | | |
abundExamples | MigConnectivity | Example relative abundance estimates from simulated data | list | | |
projections | MigConnectivity | Map projections | list | | |
sampleOriginDist | MigConnectivity | Example origin and target site positions and distances on a 2-D plane | list | | |
sampleOriginN | MigConnectivity | Example origin site abundances and relative abundances | list | | |
sampleOriginPos | MigConnectivity | Example origin and target site positions and distances on a 2-D plane | list | | |
sampleOriginRelN | MigConnectivity | Example origin site abundances and relative abundances | list | | |
samplePsis | MigConnectivity | Example transition probabilities (psis) between origin and target sites | list | | |
sampleTargetDist | MigConnectivity | Example origin and target site positions and distances on a 2-D plane | list | | |
sampleTargetPos | MigConnectivity | Example origin and target site positions and distances on a 2-D plane | list | | |
fit_200 | brokenstick | Broken stick model with nine lines for 200 children | brokenstick | | |
fit_200_light | brokenstick | Broken stick model with nine lines for 200 children (light) | brokenstick | | |
smocc_200 | brokenstick | Infant growth of 0-2 years, SMOCC data extract | tbl_df | 1942 | 7 |
weightloss | brokenstick | Weight loss self-measurement data | data.frame | 756 | 6 |
ARG_MAZ | sapfluxnetr | ARG_MAZ sapfluxnet site | sfn_data | | |
ARG_TRE | sapfluxnetr | ARG_TRE sapfluxnet site | sfn_data | | |
AUS_CAN_ST2_MIX | sapfluxnetr | AUS_CAN_ST2_MIX sapfluxnet site | sfn_data | | |
sfn_metadata_ex | sapfluxnetr | sfn_metadata cache file for example data (ARG_MAZ, ARG_TRE and AUS_CAN_ST2_MIX) | list | | |
index | argoFloats | A Sample Index of Profiles | argoFloats | | |
indexBgc | argoFloats | A Sample Index of Biogeochemical-Argo Profiles | argoFloats | | |
indexDeep | argoFloats | A Sample Index of Deep Argo | argoFloats | | |
indexSynthetic | argoFloats | A Sample Index of Synthetic Profiles | argoFloats | | |
myotis | bioacoustics | Audio recording of myotis species from United-Kingdom | Wave | | |
zc | bioacoustics | Audio recording of myotis species from United-Kingdom | zc | | |
EDH | sdam | Epigraphic Database Heidelberg dataset | dataset | | |
retn | sdam | Roman Empire transport network and Mediterranean region | list | | |
rp | sdam | Roman province names and acronyms as in EDH dataset | list | | |
rpd | sdam | Roman provinces dates from EDH dataset | list | | |
rpmcd | sdam | Caption maps and affiliation dates of Roman provinces | list | | |
rpmp | sdam | Maps of ancient Roman provinces and Italian regions | list | | |
STEHE | VIC5 | Sample datasets of the Stehekin for the running of the VIC model provided by UW Hydro | list | | |
veglib_IGBP | VIC5 | IGBP vegetation library for VIC model | data.table | 17 | 58 |
weight_behavior | BayesianMediationA | Weight_Behavior Data Set | data.frame | 691 | 15 |
simuGene | geeVerse | A Simulated Genetic Data from HapGen2 | matrix | 1000 | 500 |
yeastG1 | geeVerse | A Subset of Yeast Cell Cycle Gene Expression Data (G1 Phase) | data.frame | 1132 | 99 |
barbiturates | hyperSpec | | list | | |
flu | hyperSpec | | hyperSpec | | |
laser | hyperSpec | | hyperSpec | | |
palette_colorblind | hyperSpec | | character | | |
paracetamol | hyperSpec | | hyperSpec | | |
LAB_DETAILS | labNorm | Available lab names | data.frame | 93 | 8 |
creatinine_data | labNorm | Example values of Hemoglobin and Creatinine | data.frame | 1000 | 3 |
hemoglobin_data | labNorm | Example values of Hemoglobin and Creatinine | data.frame | 1000 | 3 |
covid19swiss | covid19swiss | The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Switzerland Outbreak Dataset | data.frame | 62200 | 7 |
lawyers.adjacency.advice | MEclustnet | Adjacency matrix detailing the presence or absence of advice links between the 'Lazega Lawyers'. | matrix | 71 | 71 |
lawyers.adjacency.coworkers | MEclustnet | Adjacency matrix detailing the presence or absence of coworker links between the 'Lazega Lawyers'. | matrix | 71 | 71 |
lawyers.adjacency.friends | MEclustnet | Adjacency matrix detailing the presence or absence of friendship links between the 'Lazega Lawyers'. | matrix | 71 | 71 |
lawyers.covariates | MEclustnet | A matrix of covariates of the 'Lazega Lawyers'. | data.frame | 71 | 8 |
us.twitter.adjacency | MEclustnet | Directed adjacency matrix detailing the presence or absence of Twitter friend/follower links between US politicians. | matrix | 69 | |
us.twitter.covariates | MEclustnet | A matrix of covariates of the US politicians. | data.frame | 69 | 8 |
oisst | palr | Sea surface temperature (SST). | matrix | 160 | |
annualDB | BioCro | Miscanthus dry biomass data. | data.frame | 5 | 7 |
annualDB2 | BioCro | Miscanthus dry biomass data. | data.frame | 5 | 6 |
catm_data | BioCro | Global annual mean atmopspheric CO2 levels | data.frame | 45 | 3 |
default_ode_solvers | BioCro | Default ODE solver settings | list | | |
miscanthus_x_giganteus | BioCro | Miscanthus model definition | list | | |
obsBea | BioCro | Miscanthus assimilation field data | data.frame | 27 | 4 |
obsBeaC | BioCro | Complete Miscanthus assimilation field data | data.frame | 35 | 6 |
obsNaid | BioCro | Miscanthus assimilation data | data.frame | 16 | 4 |
soil_parameters | BioCro | Soil properties | list | | |
soybean | BioCro | Soybean-BioCro model definition | list | | |
soybean_clock | BioCro | Soybean-BioCro circadian clock model definition | list | | |
soybean_weather | BioCro | Champaign, IL weather data for Soybean-BioCro | list | | |
weather | BioCro | Champaign, IL weather data | list | | |
willow | BioCro | Willow model definition | list | | |
dsl | IOHanalyzer | Example DataSetList used in tests / examples | DataSetList | | |
dsl_large | IOHanalyzer | Larger example DataSetList used in tests / examples | DataSetList | | |
cascades | NetworkInference | Example cascades | cascade | | |
policies | NetworkInference | US State Policy Adoption (SPID) | data.frame | 17835 | 3 |
policies_metadata | NetworkInference | US State Policy Adoption (SPID) | data.frame | 728 | 7 |
sim_validation | NetworkInference | Larger simulated validation network. | list | | |
validation | NetworkInference | Validation output from netinf source. | data.frame | 5 | 6 |
ecuador | sperrorest | J. Muenchow's Ecuador landslide data set | data.frame | 751 | 13 |
maipo | sperrorest | Fruit-tree crop classification: the Maipo dataset | data.frame | 7713 | 68 |
Johnston_Flight_heights_SOSS | stochLAB | Summarized flight height profiles from Johnston et al (2014) | data.frame | 30000 | 3 |
band_spreadsheet_dt | stochLAB | Parameter values and outputs from Band's Collision Risk spreadsheet ("Final_Report_SOSS02_BandSpreadSheetWorkedExampl1.xlsm") | list | | |
band_spreadsheet_dt_2 | stochLAB | Parameter values and outputs from Band's Collision Risk spreadsheet, example nr. 2 | list | | |
bird_pars_wide_example | stochLAB | Example of bird parameters stored in wide format | data.frame | 3 | 16 |
chord_prof_5MW | stochLAB | Rotor blade chord profile | data.frame | 21 | 2 |
dens_tnorm_wide_example | stochLAB | Example of Truncated Normal parameters for monthly estimates of bird density | data.frame | 3 | 25 |
generic_fhd_bootstraps | stochLAB | Bootstrap samples of generic FHDs of 25 seabird species | list | | |
rotor_grids_test | stochLAB | Sample rotor grids for generated_rotor_grids unit test | list | | |
turb_pars_wide_example | stochLAB | Example of turbine and windfarm parameters stored in wide format | data.frame | 3 | 51 |
wndspd_rtn_ptch_example | stochLAB | Example of data with relationship between wind speed, rotation speed and blade pitch | data.frame | 30 | 3 |
deltadelta_data | dabestr | Data to produce a delta2 Dabest plot | tbl_df | 40 | 5 |
minimeta_data | dabestr | Data to produce a mini-meta Dabest plot | tbl_df | 120 | 4 |
non_proportional_data | dabestr | Non-proportional data for Estimation plots. | tbl_df | 180 | 4 |
proportional_data | dabestr | Numerical Binary data for Proportion Plots | tbl_df | 400 | 4 |
drug_regimen_list | genieBPC | List of Drug Regimen Names by Cohort | tbl_df | 1490 | 4 |
genie_panels | genieBPC | Genomic Panels Included in GENIE BPC Data | data.frame | 12 | 3 |
nsclc_test_data | genieBPC | Simulated fake GENIE BPC data for function examples and tests | list | | |
regimen_abbreviations | genieBPC | List of Drug Regimen Abbreviations | data.frame | 12 | 2 |
synapse_tables | genieBPC | 'Synapse' table IDs | tbl_df | 186 | 5 |
AlgaeCO2 | abd | Carbon Dioxide and Growth Rate in Algae | data.frame | 14 | 2 |
Antilles | abd | Antilles Bird Immigration Dates | data.frame | 37 | 1 |
Aspirin | abd | Effects of Aspirin on Cancer Rates | data.frame | 39876 | 2 |
BeeGenes | abd | Foraging Gene Expression | data.frame | 6 | 3 |
BeeLifespans | abd | Bee Lifespans | data.frame | 33 | 1 |
Beetles | abd | Beetle Wings and Horns | data.frame | 19 | 2 |
BirdSexRatio | abd | Sex Ratios in Birds | data.frame | 15 | 1 |
Blackbirds | abd | Testosterone Levels in Blackbirds | data.frame | 13 | 6 |
BodyFatHeatLoss | abd | Heat Loss and Body Fat | data.frame | 12 | 2 |
BrainExpression | abd | Proteolipid Protein 1 Gene Expression | data.frame | 45 | 2 |
BrookTrout | abd | Salmon Survival in the Presence of Brook Trout | data.frame | 12 | 4 |
BrookTrout2 | abd | Salmon Survival in the Presence of Brook Trout | data.frame | 12 | 9 |
Cavalry | abd | Deaths from Horse Kicks | data.frame | 5 | 2 |
Chickadees | abd | Alarm Calls in Chickadees | data.frame | 13 | 3 |
ChimpBrains | abd | Brodmann's Area 44 in Chimps | data.frame | 20 | 3 |
Cichlids | abd | Cichlid Mating Preference | data.frame | 53 | 2 |
CichlidsGnRH | abd | GnRH Levels in Cichlids | data.frame | 11 | 2 |
Clearcuts | abd | Biomass Change in Rainforests near Clearcuts | data.frame | 36 | 1 |
CocaineDopamine | abd | Effects of Cocaine on Dopamine Receptors | data.frame | 34 | 2 |
Convictions | abd | Frequency of Convictions for a Cohort of English Boys | data.frame | 15 | 2 |
ConvictionsAndIncome | abd | Convictions and Income Level in a Cohort of English Boys | data.frame | 395 | 2 |
Crickets | abd | Immunity and Sperm Viability in Crickets | data.frame | 41 | 2 |
DEET | abd | DEET and Mosquito Bites | data.frame | 52 | 2 |
DaphniaLongevity | abd | Daphnia Longevity | data.frame | 32 | 2 |
DaphniaResistance | abd | Daphnia Resistance to Cyanobacteria | data.frame | 32 | 2 |
DayOfBirth | abd | Day of Birth | data.frame | 7 | 2 |
DesertBirds | abd | Desert Bird Census Data | data.frame | 43 | 2 |
Dioecy | abd | Dioecy vs. Monomorphism in Plants | data.frame | 28 | 3 |
Dolphins | abd | Dolphin Swimming Behavior | data.frame | 8 | 1 |
DungBeetles | abd | Heritability of Body Condition in Dung Beetles | data.frame | 36 | 2 |
Earthworms | abd | Earthworm Diversity and Soil Nitrogen Levels | data.frame | 39 | 2 |
Earwigs | abd | Earwig Density and Forceps | data.frame | 7 | 2 |
Eelgrass | abd | Eelgrass Genotypes | data.frame | 32 | 2 |
ElVerde | abd | Diet Breadth in a Rainforest Community | data.frame | 38 | 2 |
ElectricFish | abd | Electric Fish | data.frame | 12 | 3 |
EndangeredSpecies | abd | Endangered and Threatened Species | data.frame | 11 | 2 |
FingerRatio | abd | 2D:4D Finger Ratio | data.frame | 46 | 2 |
Fireflies | abd | Spermatophore Mass in Fireflies | data.frame | 35 | 1 |
FireflyFlash | abd | Firefly Flash Duration | data.frame | 35 | 1 |
FlyTestes | abd | Testes Size in Flies | data.frame | 8 | 2 |
FlycatcherPatch | abd | Forehead Patch Size in Collared Flycatachers | data.frame | 30 | 2 |
GeneRegulation | abd | Gene Regulation in Saccharomyces | data.frame | 26 | 2 |
GlidingSnakes | abd | GlidingSnakes | data.frame | 8 | 1 |
GodwitArrival | abd | Godwit Arrival Dates | data.frame | 10 | 2 |
Grassland | abd | Grassland Diversity | data.frame | 10 | 2 |
GreatTitMalaria | abd | Malaria in Populations of Great Tit | data.frame | 65 | 2 |
Greenspace | abd | Diversity in Urban Green Space | data.frame | 15 | 6 |
Guppies | abd | Ornamentation and Attractiveness in Guppies | data.frame | 36 | 2 |
Hemoglobin | abd | Hemoglobin Levels in High Altitude Populations | data.frame | 40 | 3 |
HippocampusLesions | abd | Memory and the Hippocampus | data.frame | 57 | 2 |
HornedLizards | abd | Horn Length and Predation Status of Horned Lizards | data.frame | 185 | 2 |
HumanBodyTemp | abd | Human Body Temperature | data.frame | 25 | 1 |
HumanGeneLengths | abd | Human Gene Lengths | data.frame | 20290 | 1 |
Hurricanes | abd | Intense Hurricanes | data.frame | 4 | 2 |
Iguanas | abd | Iguana Body Length Changes | data.frame | 64 | 1 |
IntertidalAlgae | abd | Intertidal Algae | data.frame | 64 | 3 |
JetLagKnees | abd | Circadian Rhythm Phase Shift | data.frame | 22 | 2 |
KenyaFinches | abd | Body Mass and Beak Length in Three Species of Finches in Kenya | data.frame | 45 | 3 |
LanguageBrains | abd | Brain Structure in Bilingual Humans | data.frame | 22 | 2 |
LarvalFish | abd | Exploited Larval Fish | data.frame | 28 | 3 |
Lefthanded | abd | Left-handedness and Rates of Violence | data.frame | 8 | 2 |
LionCubs | abd | Time to Reproduction in Female Lions | data.frame | 14 | 2 |
LionNoses | abd | Lion Age and Nose Coloration | data.frame | 32 | 2 |
LiverPreparation | abd | Liver Preparation | data.frame | 5 | 2 |
LizardBite | abd | Bite Force in Collard Lizards | data.frame | 11 | 2 |
LizardSprint | abd | Sprint Speeds in Canyon Lizards | data.frame | 68 | 2 |
Lobsters | abd | Lobster Orientation | data.frame | 15 | 1 |
LodgepolePines | abd | Lodgepole Pine Cone Masses | data.frame | 16 | 4 |
LupusMice | abd | Autoimmune Reactivity in Lupus-prone Mice | data.frame | 20 | 2 |
Lynx | abd | Population Cycles of Lynx in Canada 1752-1819 | data.frame | 68 | 2 |
MarineReserve | abd | Marine Reserve Biomass | data.frame | 32 | 1 |
MassExtinctions | abd | Mass Extinction Frequency | data.frame | 21 | 2 |
MoleRats | abd | Energy Expenditure in Mole Rats | data.frame | 35 | 3 |
Mosquitoes | abd | Body Size in Anopheles Mosquitoes | data.frame | 20 | 2 |
MouseEmpathy | abd | Mouse Empathy | data.frame | 42 | 3 |
NeanderthalBrains | abd | Cranial Capacity in Neanderthals and Modern Humans | data.frame | 39 | 3 |
NematodeLifespan | abd | Effects of Trimethadione on Lifespan in Nematodes | data.frame | 200 | 2 |
NeotropicalTrees | abd | Photosynthesis in Neotropical Trees | data.frame | 9 | 2 |
Newts | abd | Tetrodotoxin Resistance in Garter Snakes | data.frame | 12 | 2 |
NoSmokingDay | abd | No Smoking Day | data.frame | 10 | 3 |
NorthSeaCod | abd | Atlantic Cod Recruits | data.frame | 39 | 1 |
OstrichTemp | abd | Ostrich Body and Brain Temperatures | data.frame | 6 | 3 |
Penguins | abd | Penguin Heart Rate | data.frame | 24 | 2 |
PlantPersistence | abd | Population Persistence Times | data.frame | 16 | 2 |
Pollen | abd | Sterility in Hybrid Pollens | data.frame | 23 | 2 |
Powerball | abd | Powerball Tickets Sold | data.frame | 7 | 2 |
PrimateMetabolism | abd | Primate Metabolic Rates | data.frame | 17 | 2 |
PrimateWBC | abd | Primate White Blood Cell Counts and Promiscuity | data.frame | 9 | 2 |
ProgesteroneExercise | abd | Progesterone and Exercise | data.frame | 30 | 2 |
Pseudoscorpions | abd | Multiple Mating in Pseudoscorpions | data.frame | 36 | 2 |
Pufferfish | abd | Pufferfish Mimicry | data.frame | 20 | 2 |
Rattlesnakes | abd | Temperature Change and Meal Size in Rattlesnakes | data.frame | 17 | 2 |
Rigormortis | abd | Rigormortis and Time of Death | data.frame | 12 | 2 |
RopeTrick | abd | Indian Rope Trick | data.frame | 21 | 2 |
SagebrushCrickets | abd | Sagebrush Cricket Mating Times | data.frame | 24 | 2 |
SalmonColor | abd | Pacific Salmon Color | data.frame | 35 | 2 |
Seedlings | abd | Number of Seedlings Per Quadrat | data.frame | 8 | 2 |
Selection | abd | Data for Meta-analysis | data.frame | 814 | 8 |
SexualSelection | abd | Sexual Conflict | data.frame | 25 | 4 |
ShadParasites | abd | Shad Parasites | data.frame | 7 | 2 |
ShrinkingSeals | abd | Seal Body Lengths and Age | data.frame | 9665 | 2 |
ShuttleDisaster | abd | Ambient Temperature and O-Ring Failures | data.frame | 23 | 2 |
Silversword | abd | Rate of Speciation in Silverswords | data.frame | 21 | 1 |
SleepAndPerformance | abd | Sleep and Learning | data.frame | 10 | 2 |
SockeyeFemales | abd | Body Masses of Female Sockeye Salmon | data.frame | 228 | 1 |
Sparrows | abd | Lifetime Reproductive Success in House Sparrows | data.frame | 9 | 3 |
SpiderColonies | abd | Social Spiders | data.frame | 17 | 3 |
SpiderSpeed | abd | Spider Running Speeds after Amputation | data.frame | 16 | 2 |
Stalkies1 | abd | Eye Widths in Stalk-Eyed Flies | data.frame | 9 | 1 |
Stalkies2 | abd | Stalk-eyed Fly Eyespan | data.frame | 45 | 2 |
SticklebackPlates | abd | Number of Lateral Plates in Sticklebacks | data.frame | 344 | 2 |
SticklebackPreference | abd | Mating Preferences in Sticklebacks | data.frame | 9 | 1 |
Sumo | abd | Sumo Wrestling Wins | data.frame | 16 | 2 |
SyrupSwimming | abd | Syrup Swimming | data.frame | 18 | 1 |
TeenDeaths | abd | Causes of Teenage Deaths | data.frame | 11 | 2 |
Telomeres | abd | Telomere Shortening | data.frame | 39 | 2 |
TimeOfDeath | abd | Hypoxanthine and Time Since Death | data.frame | 48 | 2 |
Toads | abd | Right-handed Toads | data.frame | 19 | 2 |
Tobacco | abd | Flower Length in Tobacco Plants | data.frame | 13 | 3 |
Tobacco2 | abd | Flower Length in Tobacco Plants | data.frame | 617 | 2 |
ToothAge | abd | Radioactive Teeth | data.frame | 20 | 2 |
TreeSeedlings | abd | Tree Seedlings and Sunflecks | data.frame | 21 | 2 |
Trematodes | abd | Frequencies of Fish Eaten by Trematode Infection Level | data.frame | 141 | 2 |
Trillium | abd | Trillium Recruitment near Clearcuts | data.frame | 8 | 3 |
Truffles | abd | Truffle Distribution | data.frame | 5 | 2 |
TsetseLearning | abd | Dietary Learning in Tsetse Flies | data.frame | 13 | 2 |
TwoKids | abd | Number of Boys in Two-Child Families | data.frame | 3 | 2 |
VampireBites | abd | Vampire Bat Bites | data.frame | 4 | 3 |
VasopressinVoles | abd | Vasopressin Manipulation in the Meadow Vole | data.frame | 31 | 2 |
Vines | abd | Climbing Vines | data.frame | 48 | 2 |
VoleDispersal | abd | Home Range Size in Field Voles | data.frame | 5 | 3 |
WalkingStickFemurs | abd | Walking Stick Femur Length | data.frame | 50 | 2 |
WalkingStickHeads | abd | Walking Stick Head Width | data.frame | 50 | 2 |
WeddellSeals | abd | Energetic Cost of Diving | data.frame | 10 | 3 |
WillsDebates | abd | Presidential "Wills" | data.frame | 8 | 6 |
WillsPresidents | abd | Presidential "Wills" | data.frame | 16 | 6 |
WolfTeeth | abd | Wolf Tooth Measurements | data.frame | 35 | 1 |
Wolves | abd | Inbreeding in Wolves | data.frame | 24 | 2 |
WorldCup | abd | World Cup Goals | data.frame | 7 | 2 |
WrasseSexes | abd | Distribution of Wrasses | data.frame | 3 | 3 |
YeastGenes | abd | Yeast Regulatory Genes | data.frame | 6 | 2 |
ZebraFinchBeaks | abd | Mate Preference in Zebra Finches | data.frame | 10 | 1 |
ZebraFinches | abd | Zebra Finch Carotenoids | data.frame | 20 | 3 |
ZooMortality | abd | Home Range Size and Mortality | data.frame | 20 | 2 |
Zooplankton | abd | Zooplankton Depredation | data.frame | 15 | 3 |
dataInfo | abd | 'abd' Data Sets | data.frame | 143 | 5 |
discharge_df | streamDepletr | Streamflow for Sixmile Creek and Dorn Creek. | data.frame | 1460 | 3 |
stream_lines | streamDepletr | Stream network for Sixmile Creek Watershed, Wisconsin, USA. Extracted from US NHDPlus v2.1 national seamless dataset. | sf | 49 | 3 |
all_mpas | dafishr | Marine Protected Areas (MPAs) of Mexico | sf | 24 | 6 |
mpas_buffers | dafishr | Buffer around remote Marine Protected Areas, MPAs, of Mexico | sf | 5 | 3 |
mx_coastline | dafishr | Mexican coastline | sf | 177 | 4 |
mx_coastline_buffer | dafishr | Buffer around the Mexican coastline | sf | 1 | 4 |
mx_eez | dafishr | Mexico shape | sf | 1 | 3 |
mx_eez_pacific | dafishr | Economic Exclusive Zone (EEZ) of the Pacific side of Mexico | sf | 1 | 2 |
mx_inland | dafishr | Area inland of Mexico | sf | 1 | 3 |
mx_ports | dafishr | Ports and Marinas of Mexico | sf | 237 | 3 |
mx_shape | dafishr | Mexico mainland | sf | 1 | 3 |
pacific_landings | dafishr | Catch data from the vessels in Mexico | grouped_df | 23231 | 6 |
pelagic_vessels_permits | dafishr | List of vessels with pelagic fishing permits | tbl_df | 719 | 2 |
remote_mpas | dafishr | Remote Marine Protected Areas (MPAs) of Mexico | sf | 5 | 3 |
sample_dataset | dafishr | Vessel Monitoring System, VMS, sample dataset from Mexican fishery commission | spec_tbl_df | 10000 | 9 |
excalibdata | inctools | The dataset 'excalibdata.Rdata' contains example data from an evaluation of an assay measuring recency of infection. At an assay result of <10, the specimen is considered to be recently infected. It further contains viral load data, which is commonly used to reduce false recency. For example, when recency is defined as assay result <10 and viral load > 1000, the FRR is substantially lower (but the MDRI is also reduced). | data.frame | 1460 | 7 |
example_filtered_SNV_df | vivaldi | Example Dataframe The DF_filt_SNVs dataframe created in the vignette | tbl_df | 735 | 57 |
data1 | JAGStree | Simple Tree Data 1 | data.frame | 4 | 7 |
data2 | JAGStree | Simple Tree Data 2 | data.frame | 21 | 2 |
data3 | JAGStree | Simple Tree Data 3 | data.frame | 5 | 7 |
abundance | cxr | Abundance measurements | data.frame | 5508 | 4 |
glm_example_coefs | cxr | Generalized linear model coefficients | matrix | 8 | 4 |
metapopulation_example_param | cxr | Metapopulation dynamics coefficients | list | | |
neigh_list | cxr | neighbours and fitness observations | list | | |
salinity_list | cxr | Salinity measurements | list | | |
spatial_sampling | cxr | spatial arrangement of the observations | list | | |
species_rates | cxr | Species germination and survival rates | data.frame | 17 | 4 |
UnitTable | caffsim | Unit data of PK parameters | data.frame | 16 | 2 |
conceptual_diagram_data | MIMSunit | The input accelerometer data used to generate the conceptual diagram (Figure 1) in the manuscript. | data.frame | 1704 | 5 |
cv_different_algorithms | MIMSunit | Coefficient of variation values for different acceleration data summary algorithms | data.frame | 30 | 3 |
edge_case | MIMSunit | A short snippet of raw accelerometer signal from a device that has ending data maxed out. | data.frame | 20001 | 4 |
measurements_different_devices | MIMSunit | The mean and standard deviation of accelerometer summary measure for different acceleration data summary algorithms and for different devices. | data.frame | 235 | 8 |
rest_on_table | MIMSunit | A short snippet of raw accelerometer signal from a device resting on a table. | data.frame | 4999 | 4 |
sample_raw_accel_data | MIMSunit | Sample raw accelerometer data | data.frame | 480 | 4 |
package_rss | newscatcheR | RSS table from python package newscatcher | spec_tbl_df | 4505 | 7 |
simdata | LPWC | Example datasets for LPC | data.frame | 200 | 10 |
carrot | LinkageMapView | a carrot comparative linkage map data frame kindly provided by Massimo Iorizzo: Cavagnaro et al. BMC Genomics 2014, 15:1118 | data.frame | 126 | 3 |
oat | LinkageMapView | oat consensus map data frame | data.frame | 16668 | 3 |
example_bundles1 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_bundles2 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_bundles3 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_bundles4 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_bundles5 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_bundles6 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_bundles7 | fhircrackr | Toy example bundles for multiple entries | fhir_bundle_list | | |
example_resource1 | fhircrackr | Toy examples to POST/PUT on a server | fhir_resource_serialized | | |
example_resource2 | fhircrackr | Toy examples to POST/PUT on a server | fhir_resource_serialized | | |
example_resource3 | fhircrackr | Toy examples to POST/PUT on a server | fhir_resource_serialized | | |
medication_bundles | fhircrackr | Exemplary FHIR bundles | fhir_bundle_list | | |
patient_bundles | fhircrackr | Exemplary FHIR bundles | fhir_bundle_list | | |
transaction_bundle_example | fhircrackr | Toy examples to POST/PUT on a server | fhir_bundle_serialized | | |
sample_df | rise | RISE analysis sample data | data.frame | 7 | 3 |
tree.ppp | forestSAS | Sample data for analizing the forest spatial structure. | ppp | | |
treecom_example | forestSAS | Example data for analizing the forest community. | data.frame | 100 | 11 |
treedata | forestSAS | Sample data for analizing the forest spatial structure. | data.frame | 41 | 11 |
Npop | forestPSD | Data for forest population number of within different age class. | data.frame | 11 | 3 |
normforest | geodiv | NDVI errors for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
orelevation | geodiv | SRTM elevation for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
orforest | geodiv | NDVI for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
epu | sentometrics | Monthly U.S. Economic Policy Uncertainty index | data.frame | 403 | 4 |
list_lexicons | sentometrics | Built-in lexicons | list | | |
list_valence_shifters | sentometrics | Built-in valence word lists | list | | |
usnews | sentometrics | Texts (not) relevant to the U.S. economy | data.frame | 4145 | 7 |
keyATM_data_bills | keyATM | Bills data | list | | |
stocks | RTransferEntropy | Daily stock data for 10 stocks from 2000-2017 | data.table | 46940 | 4 |
Weights | vibass | Weights of children | data.frame | 100 | 6 |
easy_adtte | easysurv | Formatted Copy of ggsurvfit::adtte | tbl_df | 2199 | 19 |
easy_bc | easysurv | Formatted Copy of flexsurv::bc | data.frame | 686 | 4 |
easy_lung | easysurv | Formatted Copy of survival::lung | data.frame | 228 | 10 |
berlin | segmentr | Daily temperatures from weather stations in Berlin | matrix | 7 | 730 |
ikeogu.2017 | waves | Example vis-NIRS and reference dataset | tbl_df | 175 | 2155 |
winequality | sgd | Wine quality data of white wine samples from Portugal | data.frame | 4898 | 12 |
db_function_all_patients_table_template | ReviewR | Database Table Function: All Patients Table Template | character | | |
db_function_subject_table_template | ReviewR | Database Table Function: Subject Table Template | character | | |
db_module_template | ReviewR | Database Module Template | character | | |
redcap_survey_complete | ReviewR | REDCap Survey Complete | tbl_df | 3 | 2 |
redcap_widget_map | ReviewR | REDCap Widget Map | tbl_df | 9 | 3 |
supported_data_models | ReviewR | Supported Data Model Schemas | grouped_df | 12 | 4 |
synPUF | ReviewR | synPUF | tbl_df | 23 | 2 |
directors | soc.ca | Directors dataset | data.frame | 100 | 204 |
moschidis | soc.ca | Moschidis example | data.frame | 68 | 4 |
pe13 | soc.ca | The Field of the Danish Power Elite | tbl_df | 423 | 46 |
political_space97 | soc.ca | French Political Space example | tbl_df | 2980 | 22 |
taste | soc.ca | Taste dataset | data.frame | 1253 | 9 |
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 |
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 |
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 |
Survey_WHO2007 | anthroplus | Sample Survey Data for the WHO 2007 References | data.frame | 933 | 12 |
EFM | ATbounds | EFM | data.frame | 14484 | 6 |
RHC | ATbounds | RHC | data.frame | 5735 | 74 |
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 | | |
legumesIds | geneHummus | NCBI taxonomy ids for the legume family | numeric | | |
my_legumes | geneHummus | ARF proteins per legume specie | list | | |
gp | encryptr | General Practioner (family doctor) practices in Scotland 2018 | spec_tbl_df | 1212 | 12 |
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 |
HSImetadata | ecorest | Habitat suitability index (HSI) model metadata | data.frame | 351 | 85 |
HSImodels | ecorest | Habitat suitability index (HSI) models | list | | |
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 |
anole.data | phytools | Phylogenetic datasets | data.frame | 82 | 6 |
anoletree | phytools | Phylogenetic datasets | simmap | | |
ant.geog | phytools | Phylogenetic datasets | factor | | |
ant.tree | phytools | Phylogenetic datasets | phylo | | |
bat.tree | phytools | Phylogenetic datasets | phylo | | |
bat_virus.data | phytools | Phylogenetic datasets | data.frame | 17 | 2 |
betaCoV.tree | phytools | Phylogenetic datasets | phylo | | |
bonyfish.data | phytools | Phylogenetic datasets | data.frame | 90 | 2 |
bonyfish.tree | phytools | Phylogenetic datasets | phylo | | |
butterfly.data | phytools | Phylogenetic datasets | data.frame | 287 | 1 |
butterfly.tree | phytools | Phylogenetic datasets | phylo | | |
cordylid.data | phytools | Phylogenetic datasets | data.frame | 28 | 3 |
cordylid.tree | phytools | Phylogenetic datasets | phylo | | |
darter.tree | phytools | Phylogenetic datasets | phylo | | |
eel.data | phytools | Phylogenetic datasets | data.frame | 61 | 2 |
eel.tree | phytools | Phylogenetic datasets | phylo | | |
elapidae.tree | phytools | Phylogenetic datasets | phylo | | |
flatworm.data | phytools | Phylogenetic datasets | data.frame | 28 | 1 |
flatworm.tree | phytools | Phylogenetic datasets | phylo | | |
liolaemid.data | phytools | Phylogenetic datasets | data.frame | 258 | 3 |
liolaemid.tree | phytools | Phylogenetic datasets | phylo | | |
mammal.data | phytools | Phylogenetic datasets | data.frame | 49 | 2 |
mammal.geog | phytools | Phylogenetic datasets | matrix | 72 | 2 |
mammal.tree | phytools | Phylogenetic datasets | phylo | | |
primate.data | phytools | Phylogenetic datasets | data.frame | 90 | 6 |
primate.tree | phytools | Phylogenetic datasets | phylo | | |
salamanders | phytools | Phylogenetic datasets | phylo | | |
sunfish.data | phytools | Phylogenetic datasets | data.frame | 28 | 3 |
sunfish.tree | phytools | Phylogenetic datasets | simmap | | |
tortoise.geog | phytools | Phylogenetic datasets | data.frame | 15 | 2 |
tortoise.tree | phytools | Phylogenetic datasets | phylo | | |
tropidurid.data | phytools | Phylogenetic datasets | data.frame | 76 | 2 |
tropidurid.tree | phytools | Phylogenetic datasets | simmap | | |
vertebrate.data | phytools | Phylogenetic datasets | data.frame | 11 | 3 |
vertebrate.tree | phytools | Phylogenetic datasets | phylo | | |
wasp.data | phytools | Phylogenetic datasets | data.frame | 15 | 2 |
wasp.trees | phytools | Phylogenetic datasets | multiPhylo | | |
whale.tree | phytools | Phylogenetic datasets | phylo | | |
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 |
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 |
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 | | |
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 | | |
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 | | |
tokyo2005 | gwpcormapper | Tokyo 2005 Census Data. | sf | 3134 | 229 |
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 Sรฃo Paulo | tbl_df | 6 | 9 |
sao_paulo_activity_df_20 | abstr | Example Activity data for Sรฃo Paulo | tbl_df | 88 | 9 |
sao_paulo_activity_sf_2 | abstr | Example Activity data for Sรฃo Paulo | sf | 6 | 5 |
sao_paulo_activity_sf_20 | abstr | Example Activity data for Sรฃo 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 |
Census2000 | SGDinference | Census2000 | data.frame | 26120 | 3 |
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 |
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 |
community | traitstrap | Community data | tbl_df | 110 | 4 |
trait | traitstrap | Trait data | tbl_df | 705 | 6 |
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 |
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 |
bgd_msna | tidyrgee | A subset of question responses from the 2019 Host Community MSNA in Bangladesh | tbl_df | 1374 | 15 |
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 |
geneLists | diffEnrich | geneLists | list | | |
kegg | diffEnrich | kegg | list | | |
county | TDLM | Spatial distribution of US Kansas counties in 2000 | sf | 105 | 5 |
distance | TDLM | Great-circle distances between US Kansas counties | matrix | 105 | 105 |
mass | TDLM | Population and number of out- and in-commuters by US Kansas county in 2000 | data.frame | 105 | 3 |
od | TDLM | Origin-Destination commuting matrix between US Kansas counties in 2000 | matrix | 105 | 105 |
useR2016 | forwards | Data From useR! 2016 Survey | data.frame | 455 | 47 |
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 | | |
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 |
beaked_whale | tagtools | Set of sensor lists for a beaked_whale | animaltag | | |
harbor_seal | tagtools | Set of sensor lists for a harbor seal | animaltag | | |
geocod_base | utilsIPEA | Brazilian address | data.table | 5 | 5 |
CRANdf | collidr | CRAN Package and Function Data: 1 May 2019 | data.frame | 294190 | 2 |
data.sol | clidamonger | Data.Sol | data.frame | 3153 | 460 |
data.ta.hd | clidamonger | Data.TA.HD | data.frame | 11651 | 460 |
list.station.ta | clidamonger | List.Station.TA | data.frame | 831 | 10 |
tab.estim.sol.orient | clidamonger | Tab.Estim.Sol.Orient | data.frame | 33 | 13 |
tab.stationmapping | clidamonger | Tab.StationMapping | data.frame | 16336 | 17 |
votes | sejmRP | Votes from 7th Office of Polish Sejm | data.frame | 2890479 | 9 |
test_df | PVplr | DOE RTC Sample PV System Data | data.frame | 85380 | 5 |
example_data | epiomics | Example data with multiple exposures, multiple outcomes, | data.frame | 400 | 116 |
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 |
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 | | |
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 |
LM_eukaryotes | LifemapR | Transformation in a LifemapR format of NCBI information for 1000 eukaryotes | lifemap_obj | | |
eukaryotes_1000 | LifemapR | NCBI information for 1000 eukaryotes | data.frame | 1000 | 19 |
eukaryotes_80 | LifemapR | NCBI information for 80 eukaryotes | data.frame | 80 | 19 |
gen_res | LifemapR | Genomic results | data.frame | 808 | 3 |
kraken_res | LifemapR | Kraken results | data.frame | 4427 | 6 |
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 | | |
example_data | eventstudyr | Sample dataset obtained from the replication archive for Freyaldenhoven et al. (2021) | tbl_df | 2000 | 12 |
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 | | |
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 |
cran_inventory | CRANsearcher | CRAN inventory snapshot | data.frame | 10902 | 7 |
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 |
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 |
survey_ghl | ggFishPlots | Greenland halibut measurements from IMR surveys | tbl_df | 618779 | 5 |
DSR_data | PHEindicatormethods | SII test datasets - DSR | data.frame | 30 | 6 |
LE_data | PHEindicatormethods | SII test datasets - Life Expectancy | data.frame | 7222 | 7 |
esp2013 | PHEindicatormethods | European Standard Population 2013 | numeric | | |
prevalence_data | PHEindicatormethods | SII test datasets - Prevalence | data.frame | 240 | 10 |
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 |
choicedata | RSGHB | A synthetic discrete choice dataset | data.frame | 10242 | 7 |
test_smet | smetlite | Test SMET data | smet | 47 | 5 |
cal_res | enmpa | Example of results obtained from GLM calibration using enmpa | enmpa_calibration | | |
enm_data | enmpa | Example data used to run model calibration exercises | data.frame | 5627 | 3 |
sel_fit | enmpa | Example of selected models fitted | enmpa_fitted_models | | |
test | enmpa | Example data used to test models | data.frame | 100 | 3 |
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 | | |
dplace | glottospace | This is an internally stored version of D-PLACE data. Use glottoget("dplace", download = TRUE) to download the latest version | sf | 1988 | 2384 |
glottolog | glottospace | This is an internally stored version of glottolog data. Use glottoget("glottolog", download = TRUE) to download the latest version | data.frame | 26285 | 13 |
grambank | glottospace | This is an internally stored version of Grambank data. Use glottoget("grambank", download = TRUE) to download the latest version | data.frame | 2467 | 207 |
phoible_raw | glottospace | This is an internally stored version of raw PHOIBLE data. Use glottoget("phoible_raw", download = TRUE) to download the latest version | data.frame | 3020 | 3192 |
wals | glottospace | WALS data | sf | 2627 | 208 |
worldpol | glottospace | This is an internally stored version of political boundaries of the world (obtained from rnaturalearth). | sf | 265 | 7 |
sb_cat | LPDynR | Standing Biomass | RasterBrick | | |
sbd_cat | LPDynR | Season Beginning Day | RasterBrick | | |
sl_cat | LPDynR | Season Length | RasterBrick | | |
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 |
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 |
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 | | |
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 |
my_tosr_load | tosr | A list with data to create ToS | list | | |
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 |
COL | openintro | OpenIntro Statistics colors | matrix | 7 | 13 |
IMSCOL | openintro | Introduction to Modern Statistics (IMS) Colors | matrix | 8 | 13 |
LAhomes | openintro | LAhomes | tbl_df | 1594 | 8 |
absenteeism | openintro | Absenteeism from school in New South Wales | tbl_df | 146 | 5 |
acs12 | openintro | American Community Survey, 2012 | tbl_df | 2000 | 13 |
age_at_mar | openintro | Age at first marriage of 5,534 US women. | tbl_df | 5534 | 1 |
ames | openintro | Housing prices in Ames, Iowa | tbl_df | 2930 | 82 |
ami_occurrences | openintro | Acute Myocardial Infarction (Heart Attack) Events | tbl_df | 365 | 1 |
antibiotics | openintro | Pre-existing conditions in 92 children | tbl_df | 92 | 1 |
arbuthnot | openintro | Male and female births in London | tbl_df | 82 | 3 |
ask | openintro | How important is it to ask pointed questions? | tbl_df | 219 | 3 |
association | openintro | Simulated data for association plots | tbl_df | 121 | 15 |
assortative_mating | openintro | Eye color of couples | tbl_df | 204 | 2 |
assortive_mating | openintro | Eye color of couples | tbl_df | 204 | 2 |
avandia | openintro | Cardiovascular problems for two types of Diabetes medicines | tbl_df | 227571 | 2 |
babies | openintro | The Child Health and Development Studies | tbl_df | 1236 | 8 |
babies_crawl | openintro | Crawling age | tbl_df | 12 | 5 |
bac | openintro | Beer and blood alcohol content | tbl_df | 16 | 3 |
ball_bearing | openintro | Lifespan of ball bearings | tbl_df | 75 | 1 |
bdims | openintro | Body measurements of 507 physically active individuals. | tbl_df | 507 | 25 |
biontech_adolescents | openintro | Efficacy of Pfizer-BioNTech COVID-19 vaccine on adolescents | tbl_df | 2260 | 2 |
birds | openintro | Aircraft-Wildlife Collisions | tbl_df | 19302 | 17 |
births | openintro | North Carolina births, 100 cases | tbl_df | 150 | 9 |
births14 | openintro | US births | tbl_df | 1000 | 13 |
blizzard_salary | openintro | Blizzard Employee Voluntary Salary Info. | spec_tbl_df | 466 | 9 |
books | openintro | Sample of books on a shelf | tbl_df | 95 | 2 |
burger | openintro | Burger preferences | tbl_df | 500 | 2 |
cancer_in_dogs | openintro | Cancer in dogs | tbl_df | 1436 | 2 |
cards | openintro | Deck of cards | tbl_df | 52 | 4 |
cars04 | openintro | cars04 | spec_tbl_df | 428 | 19 |
cars93 | openintro | cars93 | tbl_df | 54 | 6 |
cchousing | openintro | Community college housing (simulated data) | tbl_df | 75 | 1 |
census | openintro | Random sample of 2000 U.S. Census Data | tbl_df | 500 | 8 |
cherry | openintro | Summary information for 31 cherry trees | tbl_df | 31 | 3 |
children_gender_stereo | openintro | Gender Stereotypes in 5-7 year old Children | list | | |
china | openintro | Child care hours | tbl_df | 9788 | 3 |
cia_factbook | openintro | CIA Factbook Details on Countries | tbl_df | 259 | 11 |
classdata | openintro | Simulated class data | tbl_df | 164 | 2 |
cle_sac | openintro | Cleveland and Sacramento | tbl_df | 500 | 8 |
climate70 | openintro | Temperature Summary Data, Geography Limited | tbl_df | 197 | 7 |
climber_drugs | openintro | Climber Drugs Data. | tbl_df | 211 | 6 |
coast_starlight | openintro | Coast Starlight Amtrak train | tbl_df | 16 | 3 |
comics | openintro | comics | spec_tbl_df | 21821 | 11 |
corr_match | openintro | Sample datasets for correlation problems | tbl_df | 121 | 9 |
country_iso | openintro | Country ISO information | tbl_df | 249 | 4 |
cpr | openintro | CPR dataset | tbl_df | 90 | 2 |
cpu | openintro | CPU's Released between 2010 and 2020. | tbl_df | 875 | 12 |
credits | openintro | College credits. | tbl_df | 100 | 1 |
daycare_fines | openintro | Daycare fines | tbl_df | 200 | 7 |
diabetes2 | openintro | Type 2 Diabetes Clinical Trial for Patients 10-17 Years Old | tbl_df | 699 | 2 |
dream | openintro | Survey on views of the DREAM Act | tbl_df | 910 | 2 |
drone_blades | openintro | Quadcopter Drone Blades | tbl_df | 2000 | 2 |
drug_use | openintro | Drug use of students and parents | tbl_df | 445 | 2 |
duke_forest | openintro | Sale prices of houses in Duke Forest, Durham, NC | tbl_df | 98 | 13 |
earthquakes | openintro | Earthquakes | spec_tbl_df | 123 | 7 |
ebola_survey | openintro | Survey on Ebola quarantine | tbl_df | 1042 | 1 |
elmhurst | openintro | Elmhurst College gift aid | tbl_df | 50 | 3 |
email | openintro | Data frame representing information about a collection of emails | tbl_df | 3921 | 21 |
email50 | openintro | Sample of 50 emails | tbl_df | 50 | 21 |
email_test | openintro | Data frame representing information about a collection of emails | tbl_df | 1252 | 21 |
env_regulation | openintro | American Adults on Regulation and Renewable Energy | tbl_df | 705 | 1 |
epa2012 | openintro | Vehicle info from the EPA for 2012 | tbl_df | 1129 | 28 |
epa2021 | openintro | Vehicle info from the EPA for 2021 | tbl_df | 1108 | 28 |
esi | openintro | Environmental Sustainability Index 2005 | tbl_df | 146 | 29 |
ethanol | openintro | Ethanol Treatment for Tumors Experiment | tbl_df | 24 | 2 |
evals | openintro | Professor evaluations and beauty | tbl_df | 463 | 23 |
exam_grades | openintro | Exam and course grades for statistics students | spec_tbl_df | 233 | 6 |
exams | openintro | Exam scores | tbl_df | 19 | 1 |
exclusive_relationship | openintro | Number of Exclusive Relationships | tbl_df | 218 | 1 |
fact_opinion | openintro | Can Americans categorize facts and opinions? | tbl_df | 5035 | 3 |
family_college | openintro | Simulated sample of parent / teen college attendance | tbl_df | 792 | 2 |
fastfood | openintro | Nutrition in fast food | tbl_df | 515 | 17 |
fcid | openintro | Summary of male heights from USDA Food Commodity Intake Database | tbl_df | 100 | 2 |
fheights | openintro | Female college student heights, in inches | tbl_df | 24 | 1 |
fish_age | openintro | Young fish in the North Sea. | spec_tbl_df | 300 | 3 |
fish_oil_18 | openintro | Findings on n-3 Fatty Acid Supplement Health Benefits | list | | |
flow_rates | openintro | River flow data | spec_tbl_df | 31 | 3 |
friday | openintro | Friday the 13th | tbl_df | 61 | 6 |
full_body_scan | openintro | Poll about use of full-body airport scanners | tbl_df | 1137 | 2 |
gdp_countries | openintro | GDP Countries Data. | data.frame | 654 | 9 |
gear_company | openintro | Fake data for a gear company example | tbl_df | 2000 | 2 |
gender_discrimination | openintro | Bank manager recommendations based on gender | tbl_df | 48 | 2 |
get_it_dunn_run | openintro | Get it Dunn Run, Race Times | tbl_df | 978 | 10 |
gifted | openintro | Analytical skills of young gifted children | tbl_df | 36 | 8 |
global_warming_pew | openintro | Pew survey on global warming | tbl_df | 2253 | 2 |
goog | openintro | Google stock data | tbl_df | 98 | 7 |
gov_poll | openintro | Pew Research poll on government approval ratings | tbl_df | 4223 | 2 |
gpa | openintro | Survey of Duke students on GPA, studying, and more | tbl_df | 55 | 5 |
gpa_iq | openintro | Sample of students and their GPA and IQ | tbl_df | 78 | 5 |
gpa_study_hours | openintro | gpa_study_hours | tbl_df | 193 | 2 |
gradestv | openintro | Simulated data for analyzing the relationship between watching TV and grades | tbl_df | 25 | 2 |
gsearch | openintro | Simulated Google search experiment | tbl_df | 10000 | 2 |
gss2010 | openintro | 2010 General Social Survey | tbl_df | 2044 | 5 |
gss_wordsum_class | openintro | gss_wordsum_class | spec_tbl_df | 795 | 2 |
health_coverage | openintro | Health Coverage and Health Status | tbl_df | 20000 | 2 |
healthcare_law_survey | openintro | Pew Research Center poll on health care, including question variants | tbl_df | 1503 | 2 |
heart_transplant | openintro | Heart Transplant Data | tbl_df | 103 | 8 |
helium | openintro | Helium football | tbl_df | 39 | 3 |
helmet | openintro | Socioeconomic status and reduced-fee school lunches | tbl_df | 12 | 2 |
hfi | openintro | Human Freedom Index | tbl_df | 1458 | 123 |
house | openintro | United States House of Representatives historical make-up | tbl_df | 116 | 12 |
housing | openintro | Simulated dataset on student housing | tbl_df | 75 | 1 |
hsb2 | openintro | High School and Beyond survey | tbl_df | 200 | 11 |
husbands_wives | openintro | Great Britain: husband and wife pairs | tbl_df | 199 | 7 |
immigration | openintro | Poll on illegal workers in the US | tbl_df | 910 | 2 |
infmortrate | openintro | Infant Mortality Rates, 2012 | tbl_df | 222 | 2 |
iowa | openintro | iowa | spec_tbl_df | 1386 | 5 |
ipo | openintro | Facebook, Google, and LinkedIn IPO filings | list | | |
ipod | openintro | Length of songs on an iPod | tbl_df | 3000 | 1 |
iran | openintro | iran | spec_tbl_df | 366 | 9 |
jury | openintro | Simulated juror dataset | tbl_df | 275 | 1 |
kobe_basket | openintro | Kobe Bryant basketball performance | tbl_df | 133 | 6 |
labor_market_discrimination | openintro | Are Emily and Greg More Employable Than Lakisha and Jamal? | tbl_df | 4870 | 63 |
law_resume | openintro | Gender, Socioeconomic Class, and Interview Invites | tbl_df | 316 | 3 |
lecture_learning | openintro | Lecture Delivery Method and Learning Outcomes | tbl_df | 552 | 8 |
leg_mari | openintro | Legalization of Marijuana Support in 2010 California Survey | tbl_df | 119 | 1 |
lego_population | openintro | Population of Lego Sets for Sale between Jan. 1, 2018 and Sept. 11, 2020. | tbl_df | 1304 | 14 |
lego_sample | openintro | Sample of Lego Sets | tbl_df | 75 | 14 |
life_exp | openintro | life_exp | tbl_df | 3142 | 4 |
lizard_habitat | openintro | Field data on lizards observed in their natural habitat | tbl_df | 332 | 2 |
lizard_run | openintro | Lizard speeds | spec_tbl_df | 48 | 3 |
loan50 | openintro | Loan data from Lending Club | tbl_df | 50 | 18 |
loans_full_schema | openintro | Loan data from Lending Club | tbl_df | 10000 | 55 |
london_boroughs | openintro | London Borough Boundaries | tbl_df | 45341 | 3 |
london_murders | openintro | London Murders, 2006-2011 | tbl_df | 838 | 5 |
mail_me | openintro | Influence of a Good Mood on Helpfulness | tbl_df | 42 | 4 |
major_survey | openintro | Survey of Duke students and the area of their major | tbl_df | 218 | 2 |
malaria | openintro | Malaria Vaccine Trial | tbl_df | 20 | 2 |
male_heights | openintro | Sample of 100 male heights | tbl_df | 100 | 1 |
male_heights_fcid | openintro | Random sample of adult male heights | tbl_df | 100 | 1 |
mammals | openintro | Sleep in Mammals | tbl_df | 62 | 11 |
mammogram | openintro | Experiment with Mammogram Randomized | tbl_df | 89835 | 2 |
manhattan | openintro | manhattan | spec_tbl_df | 20 | 1 |
marathon | openintro | New York City Marathon Times (outdated) | tbl_df | 59 | 3 |
mariokart | openintro | Wii Mario Kart auctions from Ebay | tbl_df | 143 | 12 |
mcu_films | openintro | Marvel Cinematic Universe films | tbl_df | 23 | 7 |
midterms_house | openintro | President's party performance and unemployment rate | tbl_df | 31 | 5 |
migraine | openintro | Migraines and acupuncture | tbl_df | 89 | 2 |
military | openintro | US Military Demographics | tbl_df | 1414593 | 6 |
mlb | openintro | Salary data for Major League Baseball (2010) | tbl_df | 828 | 4 |
mlb_players_18 | openintro | Batter Statistics for 2018 Major League Baseball (MLB) Season | tbl_df | 1270 | 19 |
mlb_teams | openintro | Major League Baseball Teams Data. | data.frame | 2784 | 41 |
mlbbat10 | openintro | Major League Baseball Player Hitting Statistics for 2010 | tbl_df | 1199 | 19 |
mn_police_use_of_force | openintro | Minneapolis police use of force data. | data.frame | 12925 | 13 |
movies | openintro | movies | spec_tbl_df | 140 | 5 |
mtl | openintro | Medial temporal lobe (MTL) and other data for 26 participants | tbl_df | 35 | 23 |
murders | openintro | Data for 20 metropolitan areas | tbl_df | 20 | 4 |
nba_finals | openintro | NBA Finals History | tbl_df | 73 | 9 |
nba_finals_teams | openintro | NBA Finals Team Summary | tbl_df | 33 | 7 |
nba_heights | openintro | NBA Player heights from 2008-9 | tbl_df | 435 | 4 |
nba_players_19 | openintro | NBA Players for the 2018-2019 season | tbl_df | 494 | 7 |
ncbirths | openintro | North Carolina births, 1000 cases | tbl_df | 1000 | 13 |
nuclear_survey | openintro | Nuclear Arms Reduction Survey | tbl_df | 1028 | 1 |
nyc | openintro | nyc | tbl_df | 168 | 6 |
nyc_marathon | openintro | New York City Marathon Times | tbl_df | 108 | 7 |
nycflights | openintro | Flights data | tbl_df | 32735 | 16 |
offshore_drilling | openintro | California poll on drilling off the California coast | tbl_df | 828 | 2 |
openintro_colors | openintro | OpenIntro colors | character | | |
openintro_palettes | openintro | OpenIntro palettes | list | | |
opportunity_cost | openintro | Opportunity cost of purchases | tbl_df | 150 | 2 |
orings | openintro | 1986 Challenger disaster and O-rings | tbl_df | 23 | 4 |
oscars | openintro | Oscar winners, 1929 to 2018 | tbl_df | 184 | 11 |
outliers | openintro | Simulated datasets for different types of outliers | tbl_df | 50 | 5 |
paralympic_1500 | openintro | Race time for Olympic and Paralympic 1500m. | tbl_df | 82 | 9 |
penelope | openintro | Guesses at the weight of Penelope (a cow) | tbl_df | 17184 | 1 |
penetrating_oil | openintro | What's the best way to loosen a rusty bolt? | tbl_df | 30 | 2 |
penny_ages | openintro | Penny Ages | tbl_df | 648 | 2 |
pew_energy_2018 | openintro | Pew Survey on Energy Sources in 2018 | list | | |
photo_classify | openintro | Photo classifications: fashion or not | tbl_df | 1822 | 2 |
piracy | openintro | Piracy and PIPA/SOPA | tbl_df | 534 | 8 |
playing_cards | openintro | Table of Playing Cards in 52-Card Deck | tbl_df | 52 | 3 |
pm25_2011_durham | openintro | Air quality for Durham, NC | tbl_df | 449 | 20 |
pm25_2022_durham | openintro | Air quality for Durham, NC | tbl_df | 356 | 19 |
poker | openintro | Poker winnings during 50 sessions | tbl_df | 50 | 1 |
possum | openintro | Possums in Australia and New Guinea | tbl_df | 104 | 8 |
ppp_201503 | openintro | US Poll on who it is better to raise taxes on | tbl_df | 691 | 2 |
present | openintro | Birth counts | tbl_df | 63 | 3 |
president | openintro | United States Presidental History | tbl_df | 67 | 5 |
prison | openintro | Prison isolation experiment | tbl_df | 14 | 6 |
prius_mpg | openintro | User reported fuel efficiency for 2017 Toyota Prius Prime | tbl_df | 19 | 5 |
race_justice | openintro | Yahoo! News Race and Justice poll results | tbl_df | 1059 | 2 |
reddit_finance | openintro | Reddit Survey on Financial Independence. | tbl_df | 1998 | 65 |
res_demo_1 | openintro | Simulated data for regression | tbl_df | 100 | 3 |
res_demo_2 | openintro | Simulated data for regression | tbl_df | 300 | 3 |
resume | openintro | Which resume attributes drive job callbacks? | tbl_df | 4870 | 30 |
rosling_responses | openintro | Sample Responses to Two Public Health Questions | tbl_df | 278 | 3 |
russian_influence_on_us_election_2016 | openintro | Russians' Opinions on US Election Influence in 2016 | tbl_df | 506 | 1 |
sa_gdp_elec | openintro | Sustainability and Economic Indicators for South Africa. | tbl_df | 16 | 7 |
salinity | openintro | Salinity in Bimini Lagoon, Bahamas | spec_tbl_df | 30 | 2 |
sat_improve | openintro | Simulated data for SAT score improvement | tbl_df | 30 | 1 |
satgpa | openintro | SAT and GPA data | tbl_df | 1000 | 6 |
scotus_healthcare | openintro | Public Opinion with SCOTUS ruling on American Healthcare Act | tbl_df | 1012 | 1 |
seattlepets | openintro | Names of pets in Seattle | tbl_df | 52519 | 7 |
sex_discrimination | openintro | Bank manager recommendations based on sex | tbl_df | 48 | 2 |
simpsons_paradox_covid | openintro | Simpson's Paradox: Covid | tbl_df | 268166 | 3 |
simulated_dist | openintro | Simulated datasets, not necessarily drawn from a normal distribution. | list | | |
simulated_normal | openintro | Simulated datasets, drawn from a normal distribution. | list | | |
simulated_scatter | openintro | Simulated data for sample scatterplots | tbl_df | 2033 | 3 |
sinusitis | openintro | Sinusitis and antibiotic experiment | tbl_df | 166 | 2 |
sleep_deprivation | openintro | Survey on sleep deprivation and transportation workers | tbl_df | 1087 | 2 |
smallpox | openintro | Smallpox vaccine results | tbl_df | 6224 | 2 |
smoking | openintro | UK Smoking Data | tbl_df | 1691 | 12 |
snowfall | openintro | Snowfall at Paradise, Mt. Rainier National Park | spec_tbl_df | 100 | 3 |
socialexp | openintro | Social experiment | tbl_df | 45 | 2 |
soda | openintro | soda | data.frame | 60 | 2 |
solar | openintro | Energy Output From Two Solar Arrays in San Francisco | tbl_df | 284 | 3 |
sowc_child_mortality | openintro | SOWC Child Mortality Data. | data.frame | 195 | 18 |
sowc_demographics | openintro | SOWC Demographics Data. | data.frame | 202 | 18 |
sowc_maternal_newborn | openintro | SOWC Maternal and Newborn Health Data. | data.frame | 202 | 18 |
sp500 | openintro | Financial information for 50 S&P 500 companies | tbl_df | 50 | 12 |
sp500_1950_2018 | openintro | Daily observations for the S&P 500 | tbl_df | 17346 | 7 |
sp500_seq | openintro | S&P 500 stock data | tbl_df | 2948 | 1 |
speed_gender_height | openintro | Speed, gender, and height of 1325 students | tbl_df | 1325 | 3 |
ssd_speed | openintro | SSD read and write speeds | spec_tbl_df | 54 | 7 |
starbucks | openintro | Starbucks nutrition | tbl_df | 77 | 7 |
stats_scores | openintro | Final exam scores for twenty students | tbl_df | 20 | 1 |
stem_cell | openintro | Embryonic stem cells to treat heart attack (in sheep) | tbl_df | 18 | 3 |
stent30 | openintro | Stents for the treatment of stroke | tbl_df | 451 | 2 |
stent365 | openintro | Stents for the treatment of stroke | tbl_df | 451 | 2 |
stocks_18 | openintro | Monthly Returns for a few stocks | tbl_df | 36 | 4 |
student_housing | openintro | Community college housing (simulated data, 2015) | tbl_df | 175 | 1 |
student_sleep | openintro | Sleep for 110 students (simulated) | tbl_df | 110 | 1 |
sulphinpyrazone | openintro | Treating heart attacks | tbl_df | 1475 | 2 |
supreme_court | openintro | Supreme Court approval rating | tbl_df | 976 | 1 |
teacher | openintro | Teacher Salaries in St. Louis, Michigan | tbl_df | 71 | 8 |
textbooks | openintro | Textbook data for UCLA Bookstore and Amazon | tbl_df | 73 | 7 |
thanksgiving_spend | openintro | Thanksgiving spending, simulated based on Gallup poll. | tbl_df | 436 | 1 |
tips | openintro | Tip data | tbl_df | 95 | 5 |
toohey | openintro | Simulated polling dataset | tbl_df | 500 | 1 |
tourism | openintro | Turkey tourism | tbl_df | 47 | 3 |
toy_anova | openintro | Simulated dataset for ANOVA | tbl_df | 140 | 2 |
transplant | openintro | Transplant consultant success rate (fake data) | tbl_df | 62 | 1 |
twins | openintro | twins | tbl_df | 27 | 2 |
ucb_admit | openintro | ucb_admit | data.frame | 4526 | 3 |
ucla_f18 | openintro | UCLA courses in Fall 2018 | tbl_df | 3950 | 14 |
ucla_textbooks_f18 | openintro | Sample of UCLA course textbooks for Fall 2018 | tbl_df | 201 | 20 |
ukdemo | openintro | United Kingdom Demographic Data | tbl_df | 12 | 6 |
unempl | openintro | Annual unemployment since 1890 | tbl_df | 121 | 3 |
unemploy_pres | openintro | President's party performance and unemployment rate | tbl_df | 29 | 5 |
us_temperature | openintro | US temperatures in 1950 and 2022 | tbl_df | 18759 | 9 |
winery_cars | openintro | Time Between Gondola Cars at Sterling Winery | tbl_df | 52 | 2 |
world_pop | openintro | World Population Data. | data.frame | 216 | 62 |
xom | openintro | Exxon Mobile stock data | tbl_df | 98 | 7 |
yawn | openintro | Contagiousness of yawning | tbl_df | 50 | 2 |
yrbss | openintro | Youth Risk Behavior Surveillance System (YRBSS) | tbl_df | 13583 | 13 |
yrbss_samp | openintro | Sample of Youth Risk Behavior Surveillance System (YRBSS) | tbl_df | 100 | 13 |
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 | | |
penobscot | pals | Seismic data horizon offshore of Nova Scotia. | matrix | 463 | 595 |
motifs_discords_small | matrixprofiler | Just a synthetic dataset for testing | numeric | | |
ExampleGDP | voronoiTreemap | ExampleGDP | data.frame | 42 | 6 |
canada | voronoiTreemap | canada | data.table | 247 | 6 |
isco_occupations_bundle | labourR | ISCO occupations bundle | data.table | 619 | 2 |
occupations_bundle | labourR | ESCO occupations bundle | data.table | 2942 | 5 |
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 |
decathlon88 | snha | Men Decathlon data from the 1988 Olympics | data.frame | 33 | 10 |
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 | | |
SampleData | SSplots | Sample time series of commercial fish landings of selected marine resources (2007-2021) | data.frame | 15 | 26 |
golub | clusterCons | Data sets for the clusterCons package | data.frame | 999 | 38 |
sim_class | clusterCons | Data sets for the clusterCons package | data.frame | 200 | 30 |
sim_profile | clusterCons | Data sets for the clusterCons package | data.frame | 120 | 4 |
testcmr | clusterCons | Data sets for the clusterCons package | list | | |
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 |
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 | | |
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 |
zip.test | tensorBSS | Handwritten Digit Recognition Data | matrix | 2007 | |
zip.train | tensorBSS | Handwritten Digit Recognition Data | matrix | 7291 | |
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 | | |
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 | | |
Safariland | bipartite | A pollination web from Argentina | matrix | 9 | 27 |
barrett1987 | bipartite | Individuals caught in a pollination web in boreal Canada. | matrix | 12 | 102 |
bezerra2009 | bipartite | Individuals observed in a flower-visitation network of oil-collecting bees in a Brazilian steppe. | matrix | 13 | 13 |
elberling1999 | bipartite | No. of visits in a pollination web of arctic-alpine Sweden | matrix | 23 | 118 |
inouye1988 | bipartite | A pollination network from the Snowy Mountains of New South Wales, Australia | data.frame | 41 | 83 |
junker2013 | bipartite | Flower visitation network | matrix | 56 | 257 |
kato1990 | bipartite | No. of individuals caught in a pollination web of a Japanese beech forest | matrix | 93 | 679 |
kevan1970 | bipartite | A pollination network from Northern Ellesmere Island, Canada | matrix | 30 | 115 |
memmott1999 | bipartite | Flower visitation network from a meadow near Bristol, UK | matrix | 25 | 79 |
mosquin1967 | bipartite | Flower visitation network from Melville Island, Northwest Territories, Canada | matrix | 11 | 18 |
motten1982 | bipartite | A spring flower visitation network from North Carolina, USA | matrix | 13 | 44 |
olesen2002aigrettes | bipartite | A flower visitation network from the Azores | matrix | 14 | 13 |
olesen2002flores | bipartite | Another flower visitation network from the Azores | matrix | 10 | 12 |
olito2015 | bipartite | A pollination network from the Canadian Rockies | data.frame | 41 | 125 |
ollerton2003 | bipartite | ollerton2003 | data.frame | 9 | 56 |
schemske1978 | bipartite | A flower visitation network from Urbana, IL, USA | matrix | 7 | 32 |
small1976 | bipartite | A flower visitation network from a peat bog in Ottawa, Canada | matrix | 13 | 34 |
vazarr | bipartite | A pollination network. | matrix | 10 | 29 |
vazcer | bipartite | A pollination network. | matrix | 9 | 33 |
vazllao | bipartite | A pollination network. | matrix | 10 | 29 |
vazmasc | bipartite | A pollination network. | matrix | 8 | 26 |
vazmasnc | bipartite | A pollination network. | matrix | 8 | 35 |
vazquec | bipartite | A pollination network. | matrix | 8 | 27 |
vazquenc | bipartite | A pollination network. | matrix | 7 | 24 |
cancer | BASSLINE | VA Lung Cancer Trial Dataset | matrix | 137 | 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 |
Cleveland | modgo | Cleveland Dataset ('Cleveland') | data.frame | 303 | 11 |
SCE | scCAN | SCE | list | | |
catalysis | iBART | Single-Atom Catalysis Data | list | | |
iBART_real_data | iBART | iBART Real Data Result | list | | |
iBART_sim | iBART | iBART Simulation Result | list | | |
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 |
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 |
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 |
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 | | |
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 | | |
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 |
micro_mctq | mctq | A fictional muMCTQ dataset | tbl_df | 50 | 19 |
shift_mctq | mctq | A fictional MCTQ Shift dataset | tbl_df | 50 | 135 |
std_mctq | mctq | A fictional standard MCTQ dataset | tbl_df | 50 | 39 |
nigeria | rr | Nigeria Randomized Response Survey Experiment on Social Connections to Armed Groups | data.frame | 2457 | 8 |
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 | | |
data_flu_ses | discord | Flu Vaccination and SES Data | data.frame | 12686 | 23 |
data_sample | discord | Sample Data from NLSY | tbl_df | 1200 | 9 |
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 |
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 |
BUPA | kerndwd | BUPA's liver disorders data | list | | |
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 | |