mlr: Machine Learning in R

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.

Version: 2.8
Depends: R (≥ 3.0.2), BBmisc (≥ 1.9), ggplot2, ParamHelpers (≥ 1.7), stats
Imports: checkmate (≥ 1.7.1), ggvis, methods, parallelMap (≥ 1.3), plyr, reshape2, shiny, survival
Suggests: ada, adabag, bartMachine, brnn, bst, care, caret (≥ 6.0-57), class, clue, cluster, clusterSim, clValid, cmaes, CoxBoost, crs, Cubist, deepnet, DiceKriging, DiceOptim, DiscriMiner, e1071, earth, elasticnet, elmNN, emoa, extraTrees, flare, fields, FNN, fpc, frbs, FSelector, gbm, GenSA, glmnet, Hmisc, irace (≥ 1.0.7), kernlab, kknn, klaR, knitr, kohonen, laGP, LiblineaR, lqa, MASS, mboost, mco, mda, mlbench, modeltools, mRMRe, nnet, nodeHarvest (≥ 0.7-3), neuralnet, numDeriv, pamr, party, penalized, pls, PMCMR, pROC (≥ 1.8), randomForest, randomForestSRC (≥ 2.0.5), ranger (≥ 0.3.0), RCurl, rFerns, rjson, rknn, rmarkdown, robustbase, ROCR, rotationForest, rpart, rrlda, rsm, RSNNS, RWeka, sda, sparsediscrim, sparseLDA, stepPlr, SwarmSVM, testthat, tgp,, xgboost, XML
Published: 2016-02-13
Author: Bernd Bischl [aut, cre], Michel Lang [aut], Jakob Richter [aut], Jakob Bossek [aut], Leonard Judt [aut], Tobias Kuehn [aut], Erich Studerus [aut], Lars Kotthoff [aut], Zachary Jones [ctb], Schiffner Julia [aut]
Maintainer: Bernd Bischl <bernd_bischl at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
Materials: NEWS
In views: MachineLearning
CRAN checks: mlr results


Reference manual: mlr.pdf
Vignettes: mlr
Package source: mlr_2.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: mlr_2.8.tgz, r-oldrel: mlr_2.3.tgz
OS X Mavericks binaries: r-release: mlr_2.8.tgz
Old sources: mlr archive

Reverse dependencies:

Reverse depends: llama, RBPcurve, unbalanced
Reverse suggests: bnclassify, flacco