STPGA: Selection of Training Populations by Genetic Algorithm

To 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. The algorithms in the package can be tweaked to solve some other subset selection problems.

Version: 4.0
Depends: R (≥ 2.10)
Suggests: R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix
Published: 2017-03-02
Author: Deniz Akdemir
Maintainer: Deniz Akdemir < at>
License: GPL-3
NeedsCompilation: no
CRAN checks: STPGA results


Reference manual: STPGA.pdf
Vignettes: STPGA-extdoc
Package source: STPGA_4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: STPGA_4.0.tgz
OS X Mavericks binaries: r-oldrel: STPGA_4.0.tgz
Old sources: STPGA archive


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