CRAN Task View: Machine Learning & Statistical Learning

Maintainer:Torsten Hothorn
Contact:Torsten.Hothorn at
Contributions:Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.
Citation:Torsten Hothorn (2023). CRAN Task View: Machine Learning & Statistical Learning. Version 2023-07-20. URL
Installation:The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("MachineLearning", coreOnly = TRUE) installs all the core packages or ctv::update.views("MachineLearning") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.

Several add-on packages implement ideas and methods developed at the borderline between computer science and statistics - this field of research is usually referred to as machine learning. The packages can be roughly structured into the following topics:

CRAN packages

Core:abess, e1071, gbm, kernlab, mboost, nnet, randomForest, rpart.
Regular:adabag, ahaz, ALEPlot, arules, BART, bartMachine, BayesTree, BDgraph, Boruta, bst, C50, caret, CORElearn, Cubist, DALEX, deepnet, dipm, DoubleML, earth, effects, elasticnet, evclass, evreg, evtree, fastshap, frbs, gamboostLSS, glmertree, glmnet, glmpath, GMMBoost, grf, grplasso, grpreg, h2o, hda, hdi, hdm, iBreakDown, ICEbox, iml, ipred, islasso, joinet, kernelshap, klaR, lars, LiblineaR, lightgbm, lime, maptree, mlpack, mlr3, model4you, mpath, naivebayes, ncvreg, nestedcv, OneR, opusminer, pamr, party, partykit, pdp, penalized, picasso, plotmo, pre, qeML, quantregForest, quint, randomForestSRC, ranger, Rborist, rgenoud, RGF, RLT, Rmalschains, rminer, ROCR, RoughSets, RPMM, RSNNS, RWeka, RXshrink, sda, semtree, shapper, shapr, shapviz, SIS, splitTools, ssgraph, stabs, SuperLearner, svmpath, tensorflow, tgp, tidymodels, torch, tree, trtf, varSelRF, wsrf, xgboost.
Archived:penalizedLDA, RcppDL.

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