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.
learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be
Depends: ParamHelpers (>= 1.10), R (>= 3.0.2)
data.table, ggplot2, methods, parallelMap (>= 1.3), stats,
frbs, FSelector, FSelectorRcpp (>= 0.2.1), gbm, GenSA, ggpubr,
glmnet, GPfit, h2o (>= 3.6.0.8), Hmisc, hrbrthemes, irace (>=
2.0), kernlab, kknn, klaR, knitr, laGP, LiblineaR, lintr (>=
1.0.0.9001), liquidSVM, MASS, mboost, mco, mda, memoise,
mlbench, mldr, mlrMBO, mmpf, modeltools, mRMRe, neuralnet,
nnet, nodeHarvest (>= 0.7-3), numDeriv, pander, pamr, party,
rappdirs, refund, rex, rFerns, rgenoud, rknn, rmarkdown, ROCR,
rotationForest, rpart, RRF, rrlda, rsm, RSNNS, RWeka, sda, sf,
smoof, sparseLDA, stepPlr, survAUC, svglite, SwarmSVM,
Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>),

caret (>= 6.0-57), class, clue, cluster, ClusterR, clusterSim
(>= 0.44-5), clValid, cmaes, cowplot, CoxBoost, crs, Cubist,
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 (>=
randomForestSRC (>= 2.7.0), ranger (>= 0.8.0), rappdirs,
refund, rex, rFerns, rgenoud, rknn, rmarkdown, ROCR,

data.table (>= 1.12.4), ggplot2, methods, parallelMap (>= 1.3),
caret (>= 6.0-57), class, classiFunc, clue, cluster, ClusterR,
clusterSim (>= 0.44-5), clValid, cmaes, cowplot, CoxBoost, crs,
Cubist, deepnet, DiceKriging, DiscriMiner, e1071, earth,
elasticnet, emoa, evtree, extraTrees, fda.usc, FDboost, flare,
FNN, forecast (>= 8.3), fpc, frbs, FSelector, FSelectorRcpp (>=
0.2.1), gbm, GenSA, ggpubr, glmnet, GPfit, h2o (>= 3.6.0.8),
Hmisc, hrbrthemes, 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, tsfeatures,
Packaged: 2020-01-10 16:20:32 UTC; patrickschratz