STPGA: Selection of Training Populations by Genetic Algorithm

Can be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set.

Version: 1.0
Published: 2014-05-31
Author: Deniz Akdemir
Maintainer: Deniz Akdemir <deniz.akdemir.work at gmail.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: STPGA results

Downloads:

Reference manual: STPGA.pdf
Package source: STPGA_1.0.tar.gz
Windows binaries: r-devel: STPGA_1.0.zip, r-release: STPGA_1.0.zip, r-oldrel: STPGA_1.0.zip
OS X Snow Leopard binaries: r-release: STPGA_1.0.tgz, r-oldrel: STPGA_1.0.tgz
OS X Mavericks binaries: r-release: STPGA_1.0.tgz