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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:2.19.3
Depends:ParamHelpers (≥ 1.10), R (≥ 3.0.2)
Imports:backports (≥ 1.1.0),BBmisc (≥ 1.11),checkmate (≥ 1.8.2),data.table (≥ 1.12.4),ggplot2, methods,parallelMap (≥ 1.3), stats,stringi,survival, utils,XML
Suggests:ada,adabag,batchtools,bit64,brnn,bst,C50,care,caret (≥ 6.0-57),class,clue,cluster,ClusterR,clusterSim (≥0.44-5),cmaes,cowplot,crs,Cubist,deepnet,DiceKriging,e1071,earth,elasticnet,emoa,evtree,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,modeltools,mRMRe,neuralnet,nnet,numDeriv,pamr,pander,party,pec,penalized (≥0.9-47),pls,PMCMRplus,praznik (≥ 5.0.0),randomForest,ranger (≥ 0.8.0),rappdirs,refund,rex,rFerns,rgenoud,rmarkdown,Rmpi,ROCR,rotationForest,rpart,RRF,rsm,RSNNS,rucrdtw,RWeka,sda,sf,smoof,sparseLDA,stepPlr,survAUC,svglite,testthat,tgp,TH.data,tidyr,tsfeatures,vdiffr,wavelets,xgboost (≥ 0.7)
Published:2025-08-22
DOI:10.32614/CRAN.package.mlr
Author:Bernd BischlORCID iD [aut], Michel LangORCID iD [aut], Lars Kotthoff [aut], Patrick SchratzORCID iD [aut], Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe CasalicchioORCID iD [aut], Mason Gallo [aut], Jakob BossekORCID iD [ctb], Erich StuderusORCID iD [ctb], Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal KerschkeORCID iD [ctb], Florian Fendt [ctb], Philipp ProbstORCID iD [ctb], Xudong SunORCID iD [ctb], Janek ThomasORCID iD [ctb], Bruno Vieira [ctb], Laura BeggelORCID iD [ctb], Quay AuORCID iD [ctb], Martin Binder [aut, cre], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]
Maintainer:Martin Binder <mlr.developer at mb706.com>
BugReports:https://github.com/mlr-org/mlr/issues
License:BSD_2_clause + fileLICENSE
URL:https://mlr.mlr-org.com,https://github.com/mlr-org/mlr
NeedsCompilation:yes
SystemRequirements:gdal (optional), geos (optional), proj (optional),udunits (optional), gsl (optional), gmp (optional), glu(optional), jags (optional), mpfr (optional), openmpi(optional)
Citation:mlr citation info
Materials:NEWS
CRAN checks:mlr results

Documentation:

Reference manual:mlr.html ,mlr.pdf
Vignettes:mlr (source,R code)

Downloads:

Package source: mlr_2.19.3.tar.gz
Windows binaries: r-devel:mlr_2.19.3.zip, r-release:mlr_2.19.3.zip, r-oldrel:mlr_2.19.3.zip
macOS binaries: r-release (arm64):mlr_2.19.3.tgz, r-oldrel (arm64):mlr_2.19.3.tgz, r-release (x86_64):mlr_2.19.3.tgz, r-oldrel (x86_64):mlr_2.19.3.tgz
Old sources: mlr archive

Reverse dependencies:

Reverse depends:llama,mlrCPO,mlrMBO,OOBCurve,RBPcurve
Reverse imports:aslib,EFAfactors,FactorHet,flacco,ipfr,latentFactoR,live,nsga3,RobustPrediction,roseRF,seqimpute,tramnet,tuneRanger,varycoef
Reverse suggests:binaryRL,bnclassify,ChemoSpec2D,condvis2,counterfactuals,DALEXtra,ecr,iml,lime,mlrintermbo,OpenML,plotmo,vivid
Reverse enhances:vip

Linking:

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