Package: knnTree
Version: 1.2.4
Date: 2008-05-06
Title: k-nn classification with variable selection inside leaves of a tree
Author: Sam Buttrey <buttrey@nps.edu>
Maintainer: Sam Buttrey <buttrey@nps.edu>
Description: Construct or predict with k-nearest-neighbor classifiers,
using cross-validation to select k, choose variables (by forward
or backwards selection), and choose scaling (from among no scaling,
scaling each column by its SD, or scaling each column by its MAD).
The finished classifier will consist of a classification tree with
one such k-nn classifier in each leaf.
License: GPL version 2
Packaged: Tue May  6 16:58:49 2008; sebuttre

Title: Nearest-neighbor classification with categorical variables
Description: This program scales categorical variables in such a way as to
make NN classification as accurate as possible. It also handles continuous
variables and prior probabilities, and does intelligent variable selection
and estimation of error rates and the right number of NN's.

Package: knncat
Version: 1.1.10
Date: 2006-05-24
Title: Nearest-neighbor classification with categorical variables
Author: Sam Buttrey
Maintainer: Sam Buttrey <buttrey@nps.edu>
Description: This program scales categorical variables in such a way as to
make NN classification as accurate as possible. It also handles continuous
variables and prior probabilities, and does intelligent variable selection
and estimation of error rates and the right number of NN's.
License: GPL version 2
Packaged: Wed May 24 09:20:57 2006; buttrey

Description: This program scales categorical variables in such a way as
to make NN classification as accurate as possible. It also
handles continuous variables and prior probabilities, and does
intelligent variable selection and estimation of error rates
Packaged: Tue May 6 15:10:17 2008; sebuttre

Package: knncat
Version: 1.1.4
Date: 2005-10-06
Title: Nearest-neighbor classification with categorical variables
Author: Sam Buttrey
Maintainer: Sam Buttrey <buttrey@nps.edu>
Description: This program scales categorical variables in such a way as to
make NN classification as accurate as possible. It also handles continuous
variables and prior probabilities, and does intelligent variable selection
and estimation of error rates and the right number of NN's.
License: GPL version 2
Packaged: Thu Oct  6 13:01:55 2005; buttrey