klaR: Classification and Visualization

Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing.

Version: 1.7-3
Depends: R (≥ 2.10.0), MASS
Imports: combinat, questionr, grDevices, stats, utils, graphics
Suggests: scatterplot3d (≥ 0.3-22), som, mlbench, rpart, e1071
Enhances: clustMixType, randomForest, ClustVarLV
Published: 2023-12-13
DOI: 10.32614/CRAN.package.klaR
Author: Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf, David Meyer
Maintainer: Uwe Ligges <ligges at statistik.tu-dortmund.de>
License: GPL-2 | GPL-3
URL: https://statistik.tu-dortmund.de
NeedsCompilation: no
SystemRequirements: SVMlight
Citation: klaR citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: klaR results


Reference manual: klaR.pdf


Package source: klaR_1.7-3.tar.gz
Windows binaries: r-devel: klaR_1.7-3.zip, r-release: klaR_1.7-3.zip, r-oldrel: klaR_1.7-3.zip
macOS binaries: r-release (arm64): klaR_1.7-3.tgz, r-oldrel (arm64): klaR_1.7-3.tgz, r-release (x86_64): klaR_1.7-3.tgz, r-oldrel (x86_64): klaR_1.7-3.tgz
Old sources: klaR archive

Reverse dependencies:

Reverse imports: CLUSTShiny, DA, diceR, fingerPro, FPDclustering, multilevLCA, pheble, SVMDO
Reverse suggests: assignPOP, butcher, caret, caretEnsemble, discrim, finetune, flowml, fscaret, hda, MLInterfaces, mlr, telescope, tidyAML, tidyclust, TunePareto


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