STPGA (5.2.1)

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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.

Maintainer: Deniz Akdemir
Author(s): Deniz Akdemir

License: GPL-3

Uses: AlgDesign, emoa, scales, scatterplot3d, Matrix, R.rsp, UsingR, leaps, quadprog, glmnet, EMMREML

Released about 1 year ago.

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