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 DOI: 10.32614/CRAN.package.STPGA Author: Deniz Akdemir Maintainer: Deniz Akdemir <deniz.akdemir.work at gmail.com> License: GPL-3 NeedsCompilation: no CRAN checks: STPGA results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=STPGA to link to this page.
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