caret: Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Version: 6.0-86
Depends: R (≥ 3.2.0), lattice (≥ 0.20), ggplot2
Imports: foreach, methods, plyr, ModelMetrics (≥ 1.2.2.2), nlme, reshape2, stats, stats4, utils, grDevices, recipes (≥ 0.1.10), withr (≥ 2.0.0), pROC
Suggests: BradleyTerry2, e1071, earth (≥ 2.2-3), fastICA, gam (≥ 1.15), ipred, kernlab, knitr, klaR, MASS, ellipse, mda, mgcv, mlbench, MLmetrics, nnet, party (≥ 0.9-99992), pls, proxy, randomForest, RANN, spls, subselect, pamr, superpc, Cubist, testthat (≥ 0.9.1), rpart, dplyr, covr
Published: 2020-03-20
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
Materials: NEWS
In views: HighPerformanceComputing, MachineLearning, Multivariate
CRAN checks: caret results

Downloads:

Reference manual: caret.pdf
Vignettes: A Short Introduction to the caret Package
Package source: caret_6.0-86.tar.gz
Windows binaries: r-devel: caret_6.0-86.zip, r-release: caret_6.0-86.zip, r-oldrel: caret_6.0-86.zip
macOS binaries: r-release: caret_6.0-86.tgz, r-oldrel: caret_6.0-86.tgz
Old sources: caret archive

Reverse dependencies:

Reverse depends: adabag, AntAngioCOOL, AutoStepwiseGLM, branchpointer, dtree, fscaret, hsdar, JQL, manymodelr, maPredictDSC, MLSeq, MobileTrigger, MRReg, RandPro, SQB
Reverse imports: AdaSampling, aLFQ, ampir, animalcules, assignPOP, autoBagging, biomod2, BLRShiny, BLRShiny2, bnviewer, caretEnsemble, CAST, cat2cat, chemmodlab, ChIC, ChIC.data, classifierplots, ClinicalUtilityRecal, clustDRM, CMShiny, coca, ContaminatedMixt, CopulaCenR, crtests, CSUV, CTShiny, CTShiny2, cytominer, DamiaNN, DaMiRseq, datafsm, dissever, dtwSat, easyalluvial, eclust, ensembleR, fairness, fdm2id, featuretoolsR, fieldRS, fmf, FSinR, FuncNN, glmdisc, glmtree, GPCMlasso, healthcareai, interflex, JFE, KCSKNNShiny, KCSNBShiny, KNNShiny, KnowSeq, lilikoi, LncFinder, LPRelevance, m2b, MAIT, mand, mcca, MetabolomicsBasics, MetaClean, MiDA, MLDAShiny, MLDAShiny2, mlquantify, MNLR, modelgrid, mosaicModel, MRFcov, MSstatsSampleSize, multiclassPairs, NBShiny, NBShiny2, NBShiny3, nbTransmission, NeuralSens, nnGarrote, NoiseFiltersR, nonet, NonProbEst, OmicsMarkeR, oncrawlR, panelWranglR, parboost, Pi, PredPsych, predtoolsTS, pRoloc, quantable, RaSEn, RelimpPCR, REMP, RISCA, rmda, RMKL, rModeling, RStoolbox, scGPS, sentometrics, shinyr, SLEMI, smartR, soilassessment, Sojourn, Sojourn.Data, specmine, splitSelect, ssr, stepPenal, studyStrap, SubCellBarCode, TBSignatureProfiler, TCGAbiolinksGUI, TestDimorph, TLBC, TrafficBDE, transcriptR, varEst, waterquality, waves, WRTDStidal
Reverse suggests: AppliedPredictiveModeling, aurelius, aVirtualTwins, breakDown, broom, butcher, CBDA, cellity, condvis2, Cubist, deepboost, discSurv, DNAshapeR, doParallel, doSNOW, ENMTools, EventDetectR, FCBF, flashlight, GAparsimony, genefu, GSIF, idm, iml, imputeR, iprior, lulcc, metaforest, MLInterfaces, mlr, mlr3filters, mmb, modelplotr, moreparty, NeuralNetTools, opera, ordinalClust, pdp, pmml, r2pmml, randomForestSRC, regsem, RGCxGC, rScudo, SLOPE, SmartMeterAnalytics, spectacles, spFSR, ssc, SSLR, strip, SuperLearner, superml, TCGAbiolinksGUI.data, varrank, vip, xspliner
Reverse enhances: bestglm, prediction

Linking:

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