caret: Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Version:	7.0-1
Depends:	ggplot2, lattice (≥ 0.20), R (≥ 3.2.0)
Imports:	e1071, foreach, grDevices, methods, ModelMetrics (≥ 1.2.2.2), nlme, plyr, pROC, recipes (≥ 0.1.10), reshape2, stats, stats4, utils, withr (≥ 2.0.0)
Suggests:	BradleyTerry2, covr, Cubist, dplyr, earth (≥ 2.2-3), ellipse, fastICA, gam (≥ 1.15), ipred, kernlab, klaR, knitr, MASS, Matrix, mda, mgcv, mlbench, MLmetrics, nnet, pamr, party (≥ 0.9-99992), pls, proxy, randomForest, RANN, rmarkdown, rpart, spls, superpc, testthat (≥ 0.9.1), themis (≥ 0.1.3)
Published:	2024-12-10
DOI:	10.32614/CRAN.package.caret
Author:	Max Kuhn [aut, cre], Jed Wing [ctb], Steve Weston [ctb], Andre Williams [ctb], Chris Keefer [ctb], Allan Engelhardt [ctb], Tony Cooper [ctb], Zachary Mayer [ctb], Brenton Kenkel [ctb], R Core Team [ctb], Michael Benesty [ctb], Reynald Lescarbeau [ctb], Andrew Ziem [ctb], Luca Scrucca [ctb], Yuan Tang [ctb], Can Candan [ctb], Tyler Hunt [ctb]
Maintainer:	Max Kuhn <mxkuhn at gmail.com>
BugReports:	https://github.com/topepo/caret/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/topepo/caret/
NeedsCompilation:	yes
Citation:	caret citation info
Materials:	NEWS
In views:	HighPerformanceComputing, MachineLearning
CRAN checks:	caret results

Documentation:

Reference manual:	caret.pdf
Vignettes:	A Short Introduction to the caret Package (source, R code)

Downloads:

Package source:	caret_7.0-1.tar.gz
Windows binaries:	r-devel: caret_7.0-1.zip, r-release: caret_7.0-1.zip, r-oldrel: caret_7.0-1.zip
macOS binaries:	r-release (arm64): caret_7.0-1.tgz, r-oldrel (arm64): caret_7.0-1.tgz, r-release (x86_64): caret_7.0-1.tgz, r-oldrel (x86_64): caret_7.0-1.tgz
Old sources:	caret archive

Reverse dependencies:

Reverse depends:	adabag, AntAngioCOOL, AutoStepwiseGLM, branchpointer, criticality, dbcsp, fscaret, iForecast, JQL, MantaID, manymodelr, maPredictDSC, MLSeq, MRReg, MSclassifR, natstrat, RandPro, SpatialML, stacking
Reverse imports:	AdaSampling, aggTrees, aLFQ, ampir, AnalysisLin, animalcules, assignPOP, autoBagging, BG2, BioM2, BLRShiny, BLRShiny2, bnviewer, bspcov, caretEnsemble, caretForecast, CAST, cdgd, CEEMDANML, classifierplots, ClinicalUtilityRecal, CMShiny, coca, combat.enigma, CondiS, ConfusionTableR, ContaminatedMixt, CopulaCenR, Coxmos, CSCNet, CTShiny, CTShiny2, CytoGLMM, cytominer, D2MCS, daltoolbox, DamiaNN, DaMiRseq, datafsm, DebiasInfer, DeepLearningCausal, dfr, dissever, DMTL, driveR, DSAM, dtComb, eclust, Ecume, EMgaussian, EpiSemble, evalITR, fairness, FAST.R, FastJM, FeatureTerminatoR, FFTrees, flevr, FLORAL, flowml, fmf, FuncNN, FunctanSNP, GB5mcPred, geomod, glmtrans, glmtree, GPCMlasso, GWAS.BAYES, HaploCatcher, hdcate, heimdall, HPiP, HTRX, hypervolume, icardaFIGSr, idiolect, iimi, imanr, ImFoR, ImML, immunaut, imt, ImVol, iNETgrate, ipd, iSFun, JMH, KCSKNNShiny, KCSNBShiny, kfa, KMEANS.KNN, KNNShiny, KnowSeq, l1spectral, LassoGEE, less, lilikoi, LncFinder, loadeR, LPRelevance, m2b, MAI, MAIT, mand, matrans, MBMethPred, mcca, metabCombiner, MetabolomicsBasics, metaEnsembleR, meteo, mgwrsar, MiDA, mikropml, MiMIR, mistyR, MLDataR, mlmts, mlquantify, MNLR, modelgrid, mosaicModel, mpae, MRFcov, MSiP, mtlgmm, multiclassPairs, MUVR2, mxnorm, NBShiny, NBShiny2, NBShiny3, nbTransmission, NEONiso, nestedcv, NeuralSens, noisemodel, nonet, NonProbEst, npcs, OddsPlotty, omu, OncoSubtype, oncrawlR, OOS, OpEnHiMR, outliers.ts.oga, panelWranglR, pathwayTMB, peptoolkit, pheble, planningML, POMA, pomodoro, pprof, preciseTAD, PredPsych, predtoolsTS, PriceIndices, pRoloc, promor, RadialVisGadgets, RankPCA, RaSEn, RCTrep, refitME, RelimpPCR, REMP, RGCCA, RISCA, rmda, robustcov, roseRF, rQSAR, RStoolbox, rTLsDeep, Rtropical, SAiVE, scAnnotatR, scGPS, scR, sentometrics, SGCP, sgs, skedastic, SLEMI, smdi, sMTL, SoftBart, SPONGE, squallms, sregsurvey, ssr, stabiliser, stepPenal, studyStrap, SubCellBarCode, supersigs, survcompare, survivalSL, SVMDO, swag, TOP, TrafficBDE, transcriptR, tRigon, TSEAL, TSGS, varEst, VIMPS, waterquality, WaveletGBM, WaveletKNN, WaveletLSTM, WaveletML, waves, WMAP, WRTDStidal
Reverse suggests:	AppliedPredictiveModeling, archetyper, aVirtualTwins, basemodels, biomod2, biplotEZ, breakDown, broom, bundle, butcher, cat2cat, cellity, ciu, cobalt, condvis2, counterfactuals, discSurv, DNAshapeR, doParallel, doSNOW, easyalluvial, ENMTools, EventDetectR, FACT, FastImputation, fdm2id, fmeffects, fullRankMatrix, fuseMLR, GAparsimony, gcplyr, ibawds, idm, iml, imputeR, Infusion, iprior, latrend, LKT, lulcc, metaforest, metamicrobiomeR, MLInterfaces, mlr, mlr3filters, mlr3spatiotempcv, mmb, moreparty, mshap, NeuralNetTools, NHSRdatasets, opera, pcvr, pdp, pmml, posterior, pre, predfairness, purgeR, r2pmml, randomForestSRC, regsem, rScudo, sageR, SAMtool, shapr, sits, SLOPE, SmartMeterAnalytics, SmCCNet, spatialEco, spectacles, spFSR, ssc, SSLR, strip, subsemble, SuperLearner, superml, SurvMetrics, tornado, UBayFS, varrank, vetiver, vivid, waywiser, wconf
Reverse enhances:	bestglm, prediction, vip

Linking:

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