Learn R Programming

⚠️There's a newer version (1.7-3) of this package.Take me there.

klaR (version 1.7-0)

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

10,777

Version

1.7-0

License

GPL-2 | GPL-3

Maintainer

Last Published

March 10th, 2022

Functions in klaR (1.7-0)

West German Business Cycles 1955-1994

Statlog German Credit

Calculation of transition probabilities

Computation of criterion S of a visualization

Computation of an Eight Direction Arranged Map

Lines from classborders to the center

Benchmarking on B3 data

Naive Bayes Classifier

Calculation of beta scaling parameters

Estimate density of a given kernel

Scale membership values according to a beta scaling

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

Pairwise variable selection for classification in local models

Density of a Multivariate Normal Distribution

meclight.default

Minimal Error Classification

Socioeconomic data for the most populous countries.

Localized Linear Discriminant Analysis (LocLDA)

K-Modes Clustering

Internal function to convert a distance structure to a matrix

Friedman's classification benchmark data

Tabulation of prediction errors by classes

Extracts variable cluster IDs

Stepwise forward variable selection for classification

Plotting the 2-d partitions of classification methods

Calculation of HMM Sum of Path

plot.NaiveBayes

Naive Bayes Plot

Plotting marginal posterior class probabilities

predict.meclight

Prediction of Minimal Error Classification

Function to calculate e- or softmax scaled membership values

Nearest Mean Classification

Localized Linear Discriminant Analysis (LocLDA)

predict method for locpvs objects

Calculation of Condition Indices for Linear Regression

Plotting the 2-d partitions of classification methods

predict method for pvs objects

Linear transformation of data

Simple k nearest Neighbours

Classification scatterplot matrix

Regularized Discriminant Analysis (RDA)

Stepwise variable selection for classification

Weights of evidence

Plotting Eight Direction Arranged Maps or Self-Organizing Maps

Variable clustering based variable selection

Plotting of 4 dimensional membership representation simplex

Barycentric plots

Barycentric plots

Pairwise variable selection for classification

Barycentric plots

Transforming of 4 dimensional values in a barycentric coordinate system.

Uschi's classification performance measures

Plot information values

Regularized Discriminant Analysis (RDA)

predict.NaiveBayes

Naive Bayes Classifier

Barycentric plots

Barycentric plots

Interface to SVMlight

predict.svmlight

Interface to SVMlight

Barycentric plots

Simple k Nearest Neighbours Classification

Weights of evidence