Interface to a large number of classification and
regression techniques, including machine-readable parameter
descriptions. There is also an experimental extension for survival
analysis, clustering and general, example-specific cost-sensitive
learning. Generic resampling, including cross-validation,
bootstrapping and subsampling.  Hyperparameter tuning with modern
optimization techniques, for single- and multi-objective problems.
Filter and wrapper methods for feature selection. Extension of basic
learners with additional operations common in machine learning, also
allowing for easy nested resampling.  Most operations can be
parallelized.
ada, adabag, bartMachine, batchtools, brnn, bst, C50, care,
caret (>= 6.0-57), class, clue, cluster, ClusterR, clusterSim
(>= 0.44-5), cmaes, cowplot, CoxBoost, crs, Cubist, deepnet,
DiceKriging, DiscriMiner, e1071, earth, elasticnet, emoa,
evtree, extraTrees, fda.usc, FDboost, FNN, forecast (>= 8.3),
fpc, frbs, FSelector, FSelectorRcpp (>= 0.2.1), gbm, GenSA,
ggpubr, glmnet, GPfit, h2o (>= 3.6.0.8), Hmisc, irace (>= 2.0),
kernlab, kknn, klaR, knitr, laGP, LiblineaR, lintr (>=
1.0.0.9001), MASS, mboost, mco, mda, memoise, mlbench, mldr,
mlrMBO, mmpf, modeltools, mRMRe, neuralnet, nnet, nodeHarvest
(>= 0.7-3), numDeriv, pamr, pander, party, penalized (>=
0.9-47), pls, PMCMR (>= 4.1), praznik (>= 5.0.0), randomForest,
randomForestSRC (>= 2.7.0), ranger (>= 0.8.0), rappdirs,
refund, rex, rFerns, rgenoud, rknn, rmarkdown, ROCR,
rotationForest, rpart, RRF, rrlda, rsm, RSNNS, rucrdtw, RWeka,
sda, sf, smoof, sparseLDA, stepPlr, survAUC, svglite, SwarmSVM,
testthat, tgp, TH.data, tidyr, tsfeatures, vdiffr, wavelets,
xgboost (>= 0.7)

ada, adabag, bartMachine, batchtools, bit64, brnn, bst, C50,
care, caret (>= 6.0-57), class, clue, cluster, ClusterR,
clusterSim (>= 0.44-5), cmaes, cowplot, crs, Cubist, deepnet,
DiceKriging, DiscriMiner, e1071, earth, elasticnet, emoa,
evtree, extraTrees, fda.usc, FDboost, FNN, forecast (>= 8.3),
fpc, frbs, FSelector, FSelectorRcpp (>= 0.3.5), gbm, GenSA,
ggpubr, glmnet, GPfit, h2o (>= 3.6.0.8), Hmisc, irace (>= 2.0),
kernlab, kknn, klaR, knitr, laGP, LiblineaR, lintr (>=
1.0.0.9001), MASS, mboost, mco, mda, memoise, mlbench, mldr,
mlrMBO, mmpf, modeltools, mRMRe, neuralnet, nnet, nodeHarvest
(>= 0.7-3), numDeriv, pamr, pander, party, pec, penalized (>=
0.9-47), pls, PMCMRplus, praznik (>= 5.0.0), randomForest,
randomForestSRC (>= 2.7.0), ranger (>= 0.8.0), rappdirs,
refund, rex, rFerns, rgenoud, rknn, rmarkdown, Rmpi, ROCR,
rotationForest, rpart, RRF, rrlda, rsm, RSNNS, rucrdtw, RWeka,
sda, sf, smoof, snow, sparseLDA, stepPlr, survAUC, svglite,
SwarmSVM, testthat, tgp, TH.data, tidyr, tsfeatures, vdiffr,
wavelets, xgboost (>= 0.7)
base_generateHyperParsEffect,
classif_bartMachine, tune_tuneIrace, featsel_filters,
learners_all*, regr_h2ogbm
gdal (optional), geos (optional), proj (optional),
udunits (optional), gsl (optional), gmp (optional), glu
(optional), jags (optional), mpfr (optional), openmpi
(optional)