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klaR (version 0.6-14)

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

15,111

Version

0.6-14

License

GPL-2

Maintainer

Uwe Ligges

Last Published

March 19th, 2018

Functions in klaR (0.6-14)

Benchmarking on B3 data

Scale membership values according to a beta scaling

Computation of criterion S of a visualization

West German Business Cycles 1955-1994

Calculation of beta scaling parameters

Calculation of transition probabilities

Computation of an Eight Direction Arranged Map

Statlog German Credit

Function to calculate e- or softmax scaled membership values

Lines from classborders to the center

Naive Bayes Classifier

Classification scatterplot matrix

Socioeconomic data for the most populous countries.

Calculation of Condition Indices for Linear Regression

Plotting the 2-d partitions of classification methods

Estimate density of a given kernel

Density of a Multivariate Normal Distribution

Extracts variable cluster IDs

Calculation of HMM Sum of Path

Localized Linear Discriminant Analysis (LocLDA)

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

Friedman's classification benchmark data

meclight.default

Minimal Error Classification

Internal function to convert a distance structure to a matrix

K-Modes Clustering

Stepwise forward variable selection for classification

Pairwise variable selection for classification in local models

Simple k Nearest Neighbours Classification

Tabulation of prediction errors by classes

Plot information values

Plotting the 2-d partitions of classification methods

Nearest Mean Classification

predict method for pvs objects

Localized Linear Discriminant Analysis (LocLDA)

predict.NaiveBayes

Naive Bayes Classifier

predict.meclight

Prediction of Minimal Error Classification

predict method for locpvs objects

Regularized Discriminant Analysis (RDA)

Plotting marginal posterior class probabilities

Plotting of 4 dimensional membership representation simplex

plot.NaiveBayes

Naive Bayes Plot

predict.svmlight

Interface to SVMlight

Linear transformation of data

Weights of evidence

Transforming of 4 dimensional values in a barycentric coordinate system.

Plotting Eight Direction Arranged Maps or Self-Organizing Maps

Barycentric plots

Barycentric plots

Simple k nearest Neighbours

Pairwise variable selection for classification

Barycentric plots

Stepwise variable selection for classification

Barycentric plots

Regularized Discriminant Analysis (RDA)

Weights of evidence

Barycentric plots

Barycentric plots

Uschi's classification performance measures

Variable clustering based variable selection

Interface to SVMlight