klaR v0.6-7

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

Date 2012-08-28
SystemRequirements SVMlight
License GPL-2
URL http://www.statistik.tu-dortmund.de
Packaged 2012-08-28 17:03:40 UTC; ligges
Repository CRAN
Date/Publication 2012-08-28 17:08:44

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