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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 |
| Reference manual: | STPGA.pdf |
| 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-oldrel (arm64): STPGA_5.2.1.tgz, r-release (x86_64): STPGA_5.2.1.tgz, r-oldrel (x86_64): STPGA_5.2.1.tgz |
| Old sources: | STPGA archive |
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