Package: klaR 1.7-3

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.

Authors:Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf, David Meyer

klaR_1.7-3.tar.gz
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klaR.pdf |klaR.html✨
klaR/json (API)
NEWS

# Install 'klaR' in R:
install.packages('klaR', repos = c('https://uligges.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • B3 - West German Business Cycles 1955-1994
  • GermanCredit - Statlog German Credit
  • countries - Socioeconomic data for the most populous countries.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

47 exports 5 stars 4.59 score 74 dependencies 11 dependents 33 mentions 1.2k scripts 8.8k downloads

Last updated 9 months agofrom:6538e009d3. Checks:OK: 6 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 09 2024
R-4.5-winOKSep 09 2024
R-4.5-linuxNOTESep 09 2024
R-4.4-winOKSep 09 2024
R-4.4-macOKSep 09 2024
R-4.3-winOKSep 09 2024
R-4.3-macOKSep 09 2024

Exports:b.scalbenchB3betascalecalc.transcenterlinesclassscattercond.indexcorclustcvtreedkerneldrawpartie.scalEDAMerrormatrixfriedman.datagreedy.wilkshmm.sopkmodeslevel_shardsplotlocldalocpvsmeclightNaiveBayesnmpartimatplineplotpvsquadlinesquadplotquadpointsquadtrafordashardsplotsknnstepclasssvmlightTopoStriframetrigridtrilinestriperplinestriplottripointstritrafoucpmwoextractvars

Dependencies:base64encbitbit64bslibcachemclassclassIntclicliprcombinatcommonmarkcpp11crayondigestdplyre1071fansifastmapfontawesomeforcatsfsgenericsgluehavenhighrhmshtmltoolshttpuvjquerylibjsonliteKernSmoothlabelledlaterlifecyclemagrittrMASSmemoisemimeminiUIpillarpkgconfigprettyunitsprogresspromisesproxypurrrquestionrR.cacheR.methodsS3R.ooR.utilsR6rappdirsRcppreadrrlangrprojrootrstudioapisassshinysourcetoolsstringistringrstylertibbletidyrtidyselecttzdbutf8vctrsvroomwithrxfunxtable

Readme and manuals

Help Manual

Help pageTopics
Calculation of beta scaling parametersb.scal
West German Business Cycles 1955-1994B3
Benchmarking on B3 databenchB3
Scale membership values according to a beta scalingbetascale
Calculation of transition probabilitiescalc.trans
Lines from classborders to the centercenterlines
Classification scatterplot matrixclassscatter
Calculation of Condition Indices for Linear Regressioncond.index
Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.corclust plot.corclust
Socioeconomic data for the most populous countries.countries
Extracts variable cluster IDscvtree
Estimate density of a given kerneldkernel
Plotting the 2-d partitions of classification methodsdrawparti
Function to calculate e- or softmax scaled membership valuese.scal
Computation of an Eight Direction Arranged MapEDAM
Tabulation of prediction errors by classeserrormatrix
Friedman's classification benchmark datafriedman.data
Statlog German CreditGermanCredit
Stepwise forward variable selection for classificationgreedy.wilks greedy.wilks.default greedy.wilks.formula print.greedy.wilks
Calculation of HMM Sum of Pathhmm.sop
K-Modes Clusteringkmodes print.kmodes
Localized Linear Discriminant Analysis (LocLDA)loclda loclda.data.frame loclda.default loclda.formula loclda.matrix print.loclda
Pairwise variable selection for classification in local modelslocpvs
Minimal Error Classificationmeclight meclight.data.frame meclight.default meclight.formula meclight.matrix print.meclight
Naive Bayes ClassifierNaiveBayes NaiveBayes.default NaiveBayes.formula
Nearest Mean Classificationnm nm.data.frame nm.default nm.formula nm.matrix
Plotting the 2-d partitions of classification methodspartimat partimat.data.frame partimat.default partimat.formula partimat.matrix
Plotting marginal posterior class probabilitiesplineplot
Naive Bayes Plotplot.NaiveBayes
Plot information valuesplot.woe
Localized Linear Discriminant Analysis (LocLDA)predict.loclda
predict method for locpvs objectspredict.locpvs
Prediction of Minimal Error Classificationpredict.meclight
Naive Bayes Classifierpredict.NaiveBayes
predict method for pvs objectspredict.pvs
Regularized Discriminant Analysis (RDA)predict.rda
Simple k Nearest Neighbours Classificationpredict.sknn
Interface to SVMlightpredict.svmlight
Weights of evidencepredict.woe
Pairwise variable selection for classificationprint.pvs pvs pvs.default pvs.formula
Plotting of 4 dimensional membership representation simplexquadplot
Regularized Discriminant Analysis (RDA)plot.rda print.rda rda rda.default rda.formula
Plotting Eight Direction Arranged Maps or Self-Organizing Mapslevel_shardsplot plot.EDAM shardsplot
Simple k nearest Neighbourssknn sknn.data.frame sknn.default sknn.formula sknn.matrix
Stepwise variable selection for classificationplot.stepclass print.stepclass stepclass stepclass.default stepclass.formula
Interface to SVMlightsvmlight svmlight.data.frame svmlight.default svmlight.formula svmlight.matrix
Barycentric plotstriframe
Barycentric plotstrigrid
Barycentric plotstriperplines
Barycentric plotstriplot
Barycentric plotstrilines tripoints
Barycentric plotstritrafo
Uschi's classification performance measuresucpm
Weights of evidenceprint.woe woe woe.default woe.formula
Variable clustering based variable selectionxtractvars