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klaR (version 1.7-2)

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.

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Version

Install

install.packages('klaR')

Monthly Downloads

18,869

Version

1.7-2

License

GPL-2 | GPL-3

Maintainer

Uwe Ligges

Last Published

March 17th, 2023

Functions in klaR (1.7-2)

Calculation of transition probabilities

West German Business Cycles 1955-1994

Calculation of beta scaling parameters

Statlog German Credit

Scale membership values according to a beta scaling

Computation of criterion S of a visualization

Computation of an Eight Direction Arranged Map

Benchmarking on B3 data

Lines from classborders to the center

Naive Bayes Classifier

Internal function to convert a distance structure to a matrix

Density of a Multivariate Normal Distribution

Estimate density of a given kernel

Function to calculate e- or softmax scaled membership values

Calculation of Condition Indices for Linear Regression

Socioeconomic data for the most populous countries.

Classification scatterplot matrix

Plotting the 2-d partitions of classification methods

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

Extracts variable cluster IDs

Stepwise forward variable selection for classification

K-Modes Clustering

Transforming of 4 dimensional values in a barycentric coordinate system.

Tabulation of prediction errors by classes

Pairwise variable selection for classification in local models

Nearest Mean Classification

Localized Linear Discriminant Analysis (LocLDA)

plot.NaiveBayes

Naive Bayes Plot

Plotting marginal posterior class probabilities

Plotting the 2-d partitions of classification methods

meclight.default

Minimal Error Classification

Simple k Nearest Neighbours Classification

Simple k nearest Neighbours

Stepwise variable selection for classification

Friedman's classification benchmark data

Regularized Discriminant Analysis (RDA)

Regularized Discriminant Analysis (RDA)

predict.meclight

Prediction of Minimal Error Classification

Weights of evidence

Localized Linear Discriminant Analysis (LocLDA)

Calculation of HMM Sum of Path

Barycentric plots

Interface to SVMlight

predict.svmlight

Interface to SVMlight

predict method for pvs objects

Uschi's classification performance measures

Weights of evidence

predict method for locpvs objects

Barycentric plots

Pairwise variable selection for classification

Plotting of 4 dimensional membership representation simplex

Barycentric plots

Linear transformation of data

predict.NaiveBayes

Naive Bayes Classifier

Plot information values

Variable clustering based variable selection

Barycentric plots

Barycentric plots

Plotting Eight Direction Arranged Maps or Self-Organizing Maps

Barycentric plots