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.