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: 5.1
Depends: R (≥ 3.4.0), AlgDesign, scales, scatterplot3d, emoa, grDevices
Suggests: R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix
Published: 2018-08-16
Author: Deniz Akdemir
Maintainer: Deniz Akdemir <deniz.akdemir.work at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: STPGA results

Downloads:

Reference manual: STPGA.pdf
Package source: STPGA_5.1.tar.gz
Windows binaries: r-devel: STPGA_5.1.zip, r-release: STPGA_5.1.zip, r-oldrel: STPGA_4.0.zip
OS X binaries: r-release: STPGA_5.0.tgz, r-oldrel: STPGA_4.0.tgz
Old sources: STPGA archive

Linking:

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