Package: knncat
Version: 1.1.7
Date: 2005-10-12
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 Oct 12 08:59:07 2005; buttrey

Package: knncat
Version: 1.1.8
Date: 2005-10-14
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: Fri Oct 14 08:22:58 2005; buttrey

Package: knncat
Version: 1.1.9
Date: 2005-11-01
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: Tue Nov  1 16:24:28 2005; buttrey

Title: Nearest-neighbor Classification with Categorical Variables
Description: Scale categorical variables in such a way as
to make NN classification as accurate as possible. The code also
intelligent variable selection and estimation of both error rates

Maintainer: Atina Dunlap Brooks <atina.brooks@alumni.ncsu.edu>
Description: A KNN implementaion which allows the specification of the distance used to calculate nearest neighbors (euclidean, binary, etc.), the aggregation method used to summarize repsonse (majority class, mean, etc.) and the method of handling ties (all, random selection, etc.).  Additionally, continuous responses are handled.
Packaged: Thu Feb  1 16:14:41 2007; atbroo