Learn R Programming

klaR (version 1.7-3)

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

14,713

Version

1.7-3

License

GPL-2 | GPL-3

Maintainer

Last Published

December 13th, 2023

Functions in klaR (1.7-3)

Statlog German Credit

Scale membership values according to a beta scaling

Naive Bayes Classifier

West German Business Cycles 1955-1994

Calculation of transition probabilities

Lines from classborders to the center

Extracts variable cluster IDs

Computation of an Eight Direction Arranged Map

Density of a Multivariate Normal Distribution

Classification scatterplot matrix

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

Estimate density of a given kernel

Stepwise forward variable selection for classification

Plotting the 2-d partitions of classification methods

Socioeconomic data for the most populous countries.

Calculation of Condition Indices for Linear Regression

Function to calculate e- or softmax scaled membership values

meclight.default

Minimal Error Classification

Pairwise variable selection for classification in local models

K-Modes Clustering

Localized Linear Discriminant Analysis (LocLDA)

Internal function to convert a distance structure to a matrix

Nearest Mean Classification

Plotting the 2-d partitions of classification methods

Localized Linear Discriminant Analysis (LocLDA)

Tabulation of prediction errors by classes

Regularized Discriminant Analysis (RDA)

Friedman's classification benchmark data

Simple k Nearest Neighbours Classification

Calculation of HMM Sum of Path

predict method for locpvs objects

Linear transformation of data

Plotting marginal posterior class probabilities

plot.NaiveBayes

Naive Bayes Plot

Plot information values

predict.NaiveBayes

Naive Bayes Classifier

Simple k nearest Neighbours

Plotting Eight Direction Arranged Maps or Self-Organizing Maps

predict method for pvs objects

predict.meclight

Prediction of Minimal Error Classification

Interface to SVMlight

Pairwise variable selection for classification

Transforming of 4 dimensional values in a barycentric coordinate system.

Plotting of 4 dimensional membership representation simplex

predict.svmlight

Interface to SVMlight

Stepwise variable selection for classification

Weights of evidence

Weights of evidence

Regularized Discriminant Analysis (RDA)

Barycentric plots

Barycentric plots

Variable clustering based variable selection

Uschi's classification performance measures

Barycentric plots

Barycentric plots

Barycentric plots

Barycentric plots

Computation of criterion S of a visualization

Benchmarking on B3 data

Calculation of beta scaling parameters