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klaR (version 0.6-15)

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

16,884

Version

0.6-15

License

GPL-2 | GPL-3

Maintainer

Uwe Ligges

Last Published

February 19th, 2020

Functions in klaR (0.6-15)

Benchmarking on B3 data

Function to calculate e- or softmax scaled membership values

Friedman's classification benchmark data

Plotting the 2-d partitions of classification methods

Stepwise forward variable selection for classification

Tabulation of prediction errors by classes

Regularized Discriminant Analysis (RDA)

Calculation of HMM Sum of Path

Computation of an Eight Direction Arranged Map

Plotting Eight Direction Arranged Maps or Self-Organizing Maps

Simple k Nearest Neighbours Classification

Linear transformation of data

Simple k nearest Neighbours

West German Business Cycles 1955-1994

Barycentric plots

Nearest Mean Classification

Calculation of Condition Indices for Linear Regression

Calculation of transition probabilities

Classification scatterplot matrix

Plotting the 2-d partitions of classification methods

Lines from classborders to the center

Plotting marginal posterior class probabilities

Stepwise variable selection for classification

Transforming of 4 dimensional values in a barycentric coordinate system.

Localized Linear Discriminant Analysis (LocLDA)

Statlog German Credit

Internal function to convert a distance structure to a matrix

predict method for locpvs objects

Uschi's classification performance measures

Variable clustering based variable selection

Barycentric plots

Weights of evidence

plot.NaiveBayes

Naive Bayes Plot

Regularized Discriminant Analysis (RDA)

Localized Linear Discriminant Analysis (LocLDA)

Socioeconomic data for the most populous countries.

Barycentric plots

Naive Bayes Classifier

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

K-Modes Clustering

Extracts variable cluster IDs

Plot information values

Pairwise variable selection for classification in local models

predict.meclight

Prediction of Minimal Error Classification

Barycentric plots

meclight.default

Minimal Error Classification

predict.svmlight

Interface to SVMlight

Barycentric plots

predict.NaiveBayes

Naive Bayes Classifier

predict method for pvs objects

Pairwise variable selection for classification

Weights of evidence

Barycentric plots

Plotting of 4 dimensional membership representation simplex

Interface to SVMlight

Scale membership values according to a beta scaling

Computation of criterion S of a visualization

Density of a Multivariate Normal Distribution

Estimate density of a given kernel

Calculation of beta scaling parameters