<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Selection of Training Populations by Genetic Algorithm</dc:title>
  <dc:title>R package STPGA version 5.2.1</dc:title>
  <dc: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. </dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 2.10), AlgDesign, scales, scatterplot3d, emoa, grDevices</dc:relation>
  <dc:relation>Suggests: R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix</dc:relation>
  <dc:creator>Deniz Akdemir &lt;deniz.akdemir.work@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Deniz Akdemir</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2018-11-24</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=STPGA</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.STPGA</dc:identifier>
</oai_dc:dc>