classifly: Explore classification models in high dimensions

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.

Version: 0.3
Depends: rpart, MASS, nnet, class, e1071, reshape
Suggests: randomForest, rggobi
Published: 2011-01-29
Author: Hadley Wickham
Maintainer: Hadley Wickham <h.wickham at>
License: MIT
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: classifly results


Reference manual: classifly.pdf
Package source: classifly_0.3.tar.gz
MacOS X binary: classifly_0.3.tgz
Windows binary:
Old sources: classifly archive

Reverse dependencies:

Reverse depends: upclass