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: 2.13
Depends: R (≥ 3.0.2), ParamHelpers (≥ 1.10)
Imports: BBmisc (≥ 1.11), backports (≥ 1.1.0), ggplot2, stats, stringi, checkmate (≥ 1.8.2), data.table, methods, parallelMap (≥ 1.3), survival, utils, XML
Suggests: ada, adabag, bartMachine, batchtools, brnn, bst, C50, care, caret (≥ 6.0-57), class, clue, cluster, clusterSim (≥ 0.44-5), clValid, cmaes, cowplot, CoxBoost, crs, Cubist, deepnet, DiceKriging, DiscriMiner, e1071, earth, elasticnet, emoa, evtree, extraTrees, fda.usc, FDboost, flare, FNN, fpc, frbs, FSelector, gbm, GenSA, glmnet, hrbrthemes, h2o (≥ 3.6.0.8), GPfit, Hmisc, irace (≥ 2.0), kernlab, kknn, klaR, knitr, laGP, LiblineaR, lintr (≥ 1.0.0.9001), MASS, mboost, mco, mda, mlbench, mldr, mlrMBO, mmpf, modeltools, mRMRe, nnet, nodeHarvest (≥ 0.7-3), neuralnet, numDeriv, pamr, party, penalized (≥ 0.9-47), pls, PMCMR (≥ 4.1), purrr, randomForest, randomForestSRC (≥ 2.7.0), ranger (≥ 0.8.0), refund, rex, rFerns, rknn, rmarkdown, ROCR, rotationForest, rpart, RRF, rgenoud, rrlda, rsm, RSNNS, RWeka, sda, sf, smoof, sparseLDA, stepPlr, survAUC, svglite, SwarmSVM, testthat, tgp, TH.data, wavelets, xgboost (≥ 0.6-2)
Published: 2018-08-28
Author: Bernd Bischl ORCID iD [aut, cre], Michel Lang ORCID iD [aut], Lars Kotthoff [aut], Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio ORCID iD [aut], Mason Gallo [aut], Patrick Schratz ORCID iD [aut], Jakob Bossek ORCID iD [ctb], Erich Studerus ORCID iD [ctb], Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke ORCID iD [ctb], Florian Fendt [ctb], Philipp Probst ORCID iD [ctb], Xudong Sun ORCID iD [ctb], Janek Thomas ORCID iD [ctb], Bruno Vieira [ctb], Laura Beggel ORCID iD [ctb], Quay Au ORCID iD [ctb], Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb]
Maintainer: Bernd Bischl <bernd_bischl at gmx.net>
BugReports: https://github.com/mlr-org/mlr/issues
License: BSD_2_clause + file LICENSE
URL: https://github.com/mlr-org/mlr
NeedsCompilation: yes
Citation: mlr citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: mlr results

Downloads:

Reference manual: mlr.pdf
Vignettes: mlr
Package source: mlr_2.13.tar.gz
Windows binaries: r-devel: mlr_2.13.zip, r-release: mlr_2.13.zip, r-oldrel: mlr_2.13.zip
OS X binaries: r-release: mlr_2.13.tgz, r-oldrel: mlr_2.13.tgz
Old sources: mlr archive

Reverse dependencies:

Reverse depends: llama, mlrCPO, mlrMBO, OOBCurve, OpenML, RBPcurve, spFSR, unbalanced
Reverse imports: aslib, flacco, live, tuneRanger
Reverse suggests: bnclassify, featurefinder, iml, lime, plotmo
Reverse enhances: liquidSVM

Linking:

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