Projection Pursuit for supervised classification

c(
person("Thomas", "Maierhofer", email = "thomasjmaierhofer@gmail.com", role = c("aut", "cre")),
person("Karen", "Fuchs", email = "karenfuchs@gmx.de", role = c("ctb")))
Efficient implementation of k-nearest neighbor estimator and a kernel estimator for functional data classification.
BBmisc (>= 1.11), checkmate (>= 1.8.2), dtw, fda, fda.usc,
fdasrvf, proxy, stats
Thomas Maierhofer [aut, cre],
Karen Fuchs [ctb]

c(
person("Thomas", "Maierhofer", email = "thomasjmaierhofer@gmail.com", role = c("aut", "cre")),
person("Karen", "Fuchs", email = "karenfuchs@gmx.de", role = c("ctb")),
person("Florian", "Pfisterer", email = "pfistererf@googlemail.com", role = c("aut")))
Efficient implementation of k-nearest neighbor estimation and kernel estimation for functional data classification.
BBmisc (>= 1.11), checkmate (>= 1.8.2), dtw, fda, fda.usc,
fdasrvf, proxy, rucrdtw, stats, zoo
Thomas Maierhofer [aut, cre],
Karen Fuchs [ctb],
Florian Pfisterer [aut]

Generates a Visualization of Classifier Performance as a Grid of
Diagnostic Plots
person("Aaron", "Defazio", email = "aaron.defazio@ambiata.com", role = c("aut", "cre"))
Generates a visualization of binary classifier performance as a grid of
diagnostic plots with just one function call. Includes ROC curves,
prediction density, accuracy, precision, recall and calibration plots, all using
ggplot2 for easy modification.
Debug your binary classifiers faster and easier!
grid, ROCR, caret, gridExtra (>= 2.2), stats, utils, png,

Rcpp (>= 0.12), grid, ROCR, caret, gridExtra (>= 2.2), stats,
utils, png,

c(
person("Aaron", "Defazio", email = "aaron.defazio@gmail.com", role = c("aut")),
person("Huw", "Campbell", email = "huw.campbell@ambiata.com", role = c("cre")))

c(
person("Aaron", "Defazio", email = "aaron.defazio@gmail.com", role = c("aut", "cre")),
person("Huw", "Campbell", email = "huw.campbell@ambiata.com", role = c("aut")))
Aaron Defazio [aut, cre],
Huw Campbell [aut]

Explore classification models in high dimensions
rggobi, 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.  See \url{http://had.co.nz/classifly} for more details.