Learn R Programming

klaR (version 1.7-4)

Classification and Visualization

Description

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.

Copy Link

Version

Install

install.packages('klaR')

Monthly Downloads

16,182

Version

1.7-4

License

GPL-2 | GPL-3

Maintainer

Uwe Ligges

Last Published

February 23rd, 2026

Functions in klaR (1.7-4)

Estimate density of a given kernel

Density of a Multivariate Normal Distribution

Nearest Mean Classification

Stepwise forward variable selection for classification

Calculation of HMM Sum of Path

Localized Linear Discriminant Analysis (LocLDA)

meclight.default

Minimal Error Classification

Pairwise variable selection for classification in local models

Plotting the 2-d partitions of classification methods

Friedman's classification benchmark data

Tabulation of prediction errors by classes

K-Modes Clustering

Plot information values

predict method for pvs objects

predict.meclight

Prediction of Minimal Error Classification

Localized Linear Discriminant Analysis (LocLDA)

predict method for locpvs objects

Regularized Discriminant Analysis (RDA)

Simple k Nearest Neighbours Classification

Plotting marginal posterior class probabilities

plot.NaiveBayes

Naive Bayes Plot

predict.NaiveBayes

Naive Bayes Classifier

Weights of evidence

Simple k nearest Neighbours

Regularized Discriminant Analysis (RDA)

Linear transformation of data

Stepwise variable selection for classification

predict.svmlight

Interface to SVMlight

Plotting Eight Direction Arranged Maps or Self-Organizing Maps

Transforming of 4 dimensional values in a barycentric coordinate system.

Pairwise variable selection for classification

Plotting of 4 dimensional membership representation simplex

Barycentric plots

Uschi's classification performance measures

Barycentric plots

Barycentric plots

Barycentric plots

Interface to SVMlight

Barycentric plots

Barycentric plots

Weights of evidence

Variable clustering based variable selection

Statlog German Credit

Scale membership values according to a beta scaling

Function to calculate e- or softmax scaled membership values

Internal function to convert a distance structure to a matrix

Computation of criterion S of a visualization

Calculation of Condition Indices for Linear Regression

Benchmarking on B3 data

West German Business Cycles 1955-1994

Calculation of transition probabilities

Calculation of beta scaling parameters

Plotting the 2-d partitions of classification methods

Lines from classborders to the center

Naive Bayes Classifier

Classification scatterplot matrix

Extracts variable cluster IDs

Computation of an Eight Direction Arranged Map

Socioeconomic data for the most populous countries.

Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.