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.