STPGA: Selection of Training Populations by Genetic Algorithm

Combining Predictive Analytics and Experimental Design to Optimize Results. 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: 5.2.1
Depends: R (≥ 2.10), AlgDesign, scales, scatterplot3d, emoa, grDevices
Suggests: R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix
Published: 2018-11-24
Author: Deniz Akdemir
Maintainer: Deniz Akdemir <deniz.akdemir.work at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: STPGA results

Documentation:

Reference manual: STPGA.pdf

Downloads:

Package source: STPGA_5.2.1.tar.gz
Windows binaries: r-devel: STPGA_5.2.1.zip, r-release: STPGA_5.2.1.zip, r-oldrel: STPGA_5.2.1.zip
macOS binaries: r-release (arm64): STPGA_5.2.1.tgz, r-release (x86_64): STPGA_5.2.1.tgz, r-oldrel: STPGA_5.2.1.tgz
Old sources: STPGA archive

Linking:

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