Title: Selection of Training Populations by Genetic Algorithm
Description: Can 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.
Packaged: 2014-05-31 10:46:30 UTC; denizakdemir

Packaged: 2016-05-05 01:54:06 UTC; denizakdemir

Description: Can be utilized to select a (test data) calibrated training population in high dimensional prediction problems.
Packaged: 2016-08-09 02:11:35 UTC; denizakdemir

Description: 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.
Suggests: R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix
Packaged: 2017-03-02 01:43:36 UTC; denizakdemir

Packaged: 2018-07-22 11:59:46 UTC; denizakdemir

Depends: R (>= 3.4.0), AlgDesign, scales, scatterplot3d, emoa,

Description: 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.
Depends: R (>= 2.10), AlgDesign, scales, scatterplot3d, emoa, grDevices
Packaged: 2018-11-24 16:30:53 UTC; denizakdemir