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.

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Description file content

Package
mlr
Title
Machine Learning in R
Description
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.
URL
BugReports
https://github.com/mlr-org/mlr/issues
License
BSD_2_clause + file LICENSE
Encoding
UTF-8
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)
LazyData
yes
ByteCompile
yes
Version
2.13
VignetteBuilder
knitr
RoxygenNote
6.1.0
NeedsCompilation
yes
Packaged
2018-08-28 11:14:36 UTC; bischl
Author
Bernd Bischl [aut, cre] (), Michel Lang [aut] (), Lars Kotthoff [aut], Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (), Mason Gallo [aut], Patrick Schratz [aut] (), Jakob Bossek [ctb] (), Erich Studerus [ctb] (), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (), Florian Fendt [ctb], Philipp Probst [ctb] (), Xudong Sun [ctb] (), Janek Thomas [ctb] (), Bruno Vieira [ctb], Laura Beggel [ctb] (), Quay Au [ctb] (), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb]
Maintainer
Bernd Bischl
Repository
CRAN
Date/Publication
2018-08-28 12:30:03 UTC

install.packages('mlr')

2.13

3 months ago

https://github.com/mlr-org/mlr

Bernd Bischl

BSD_2_clause + file LICENSE

Depends on

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)

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