klaR v0.6-14

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

Date 2018-03-19
SystemRequirements SVMlight
License GPL-2
URL http://www.statistik.tu-dortmund.de
NeedsCompilation no
Packaged 2018-03-19 10:52:49 UTC; ligges
Repository CRAN
Date/Publication 2018-03-19 11:10:32 UTC

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