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. Software Depends: rpart, MASS, nnet, class, e1071, reshape Suggests: randomForest, rggobi Hadley Wickham <h.wickham@gmail.com> Hadley Wickham <h.wickham@gmail.com> MIT 2011-01-29 application/tgz http://CRAN.R-project.org/package=classifly