klaR v0.6-11

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by Uwe Ligges

Classification and visualization

Miscellaneous functions for classification and visualization developed at the Fakultaet Statistik, Technische Universitaet Dortmund

Functions in klaR

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

Date 2014-06-10
SystemRequirements SVMlight
License GPL-2
URL http://www.statistik.tu-dortmund.de
Packaged 2014-06-10 13:53:44 UTC; ligges
NeedsCompilation no
Repository CRAN
Date/Publication 2014-06-10 18:22:38

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