caret: Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Version: 6.0-73
Depends: R (≥ 2.10), lattice (≥ 0.20), ggplot2
Imports: car, foreach, methods, plyr, ModelMetrics (≥ 1.1.0), nlme, reshape2, stats, stats4, utils, grDevices
Suggests: BradleyTerry2, e1071, earth (≥ 2.2-3), fastICA, gam, ipred, kernlab, klaR, MASS, ellipse, mda, mgcv, mlbench, MLmetrics, nnet, party (≥ 0.9-99992), pls, pROC, proxy, randomForest, RANN, spls, subselect, pamr, superpc, Cubist, testthat (≥ 0.9.1)
Published: 2016-11-10
Author: Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan, and Tyler Hunt.
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_6.0-73.tar.gz
Windows binaries: r-devel: caret_6.0-73.zip, r-release: caret_6.0-73.zip, r-oldrel: caret_6.0-73.zip
OS X Mavericks binaries: r-release: caret_6.0-73.tgz, r-oldrel: caret_6.0-73.tgz
Old sources: caret archive

Reverse dependencies:

Reverse depends: adabag, AntAngioCOOL, conformal, fscaret, hsdar, ordBTL, textmining
Reverse imports: aLFQ, arulesCBA, blkbox, caretEnsemble, crtests, DamiaNN, darch, datafsm, DecisionCurve, dtwSat, ensembleR, ESKNN, fitcoach, kinn, LOGIT, NoiseFiltersR, parboost, preprocomb, preprosim, preproviz, quantable, RStoolbox, specmine, SSL, TLBC, WRTDStidal
Reverse suggests: AppliedPredictiveModeling, aVirtualTwins, biomod2, Cubist, data.table, deepboost, discSurv, doParallel, doSNOW, emil, gmum.r, GSIF, idm, Infusion, kernDeepStackNet, lulcc, mlr, NeuralNetTools, opera, pdp, ssc, subsemble, SuperLearner

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