rpart, MASS, nnet, class, e1071, reshape
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.

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.

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.

Classification Accuracy and Consistency under IRT models.
IRT classification uses the probability that candidates of
a given ability, will answer correctly questions of a specified
difficulty to calculate the probability of their achieving
every possible score in a test. Due to the IRT assumption of
conditional independence (that is every answer given is assumed
to depend only on the latent trait being measured) the
probability of candidates achieving these potential scores can
be expressed by multiplication of probabilities for item
responses for a given ability. Once the true score and the
probabilities of achieving all other scores have been
determined for a candidate the probability of their score lying
in the same category as that of their true score
(classification accuracy), or the probability of consistent
classification in a category over administrations
(classification consistency), can be calculated.
Rcpp (>= 0.9.10), plyr, ggplot2, R2WinBUGS, lattice, reshape2,
methods, R2jags