klaR v0.6-15

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Classification and Visualization

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.

Functions in klaR

Name Description
benchB3 Benchmarking on B3 data
e.scal Function to calculate e- or softmax scaled membership values
friedman.data Friedman's classification benchmark data
drawparti Plotting the 2-d partitions of classification methods
greedy.wilks Stepwise forward variable selection for classification
errormatrix Tabulation of prediction errors by classes
predict.rda Regularized Discriminant Analysis (RDA)
hmm.sop Calculation of HMM Sum of Path
EDAM Computation of an Eight Direction Arranged Map
shardsplot Plotting Eight Direction Arranged Maps or Self-Organizing Maps
predict.sknn Simple k Nearest Neighbours Classification
rerange Linear transformation of data
sknn Simple k nearest Neighbours
B3 West German Business Cycles 1955-1994
tritrafo Barycentric plots
nm Nearest Mean Classification
cond.index Calculation of Condition Indices for Linear Regression
calc.trans Calculation of transition probabilities
classscatter Classification scatterplot matrix
partimat Plotting the 2-d partitions of classification methods
centerlines Lines from classborders to the center
plineplot Plotting marginal posterior class probabilities
stepclass Stepwise variable selection for classification
quadtrafo Transforming of 4 dimensional values in a barycentric coordinate system.
predict.loclda Localized Linear Discriminant Analysis (LocLDA)
GermanCredit Statlog German Credit
distmirr Internal function to convert a distance structure to a matrix
predict.locpvs predict method for locpvs objects
ucpm Uschi's classification performance measures
xtractvars Variable clustering based variable selection
tripoints Barycentric plots
woe Weights of evidence
plot.NaiveBayes Naive Bayes Plot
rda Regularized Discriminant Analysis (RDA)
loclda Localized Linear Discriminant Analysis (LocLDA)
countries Socioeconomic data for the most populous countries.
triplot Barycentric plots
NaiveBayes Naive Bayes Classifier
corclust Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.
kmodes K-Modes Clustering
cvtree Extracts variable cluster IDs
plot.woe Plot information values
locpvs Pairwise variable selection for classification in local models
predict.meclight Prediction of Minimal Error Classification
triperplines Barycentric plots
meclight.default Minimal Error Classification
predict.svmlight Interface to SVMlight
trigrid Barycentric plots
predict.NaiveBayes Naive Bayes Classifier
predict.pvs predict method for pvs objects
pvs Pairwise variable selection for classification
predict.woe Weights of evidence
triframe Barycentric plots
quadplot Plotting of 4 dimensional membership representation simplex
svmlight Interface to SVMlight
betascale Scale membership values according to a beta scaling
TopoS Computation of criterion S of a visualization
.dmvnorm Density of a Multivariate Normal Distribution
dkernel Estimate density of a given kernel
b.scal Calculation of beta scaling parameters
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Details

Date 2020-02-18
SystemRequirements SVMlight
License GPL-2 | GPL-3
URL http://www.statistik.tu-dortmund.de
NeedsCompilation no
Packaged 2020-02-18 22:04:05 UTC; ligges
Repository CRAN
Date/Publication 2020-02-19 06:00:15 UTC

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