STPGA
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
5.2.1 |
rolling linux/jammy R-4.5 | STPGA_5.2.1.tar.gz |
522.9 KiB |
5.2.1 |
rolling linux/noble R-4.5 | STPGA_5.2.1.tar.gz |
522.7 KiB |
5.2.1 |
rolling source/ R- | STPGA_5.2.1.tar.gz |
409.7 KiB |
5.2.1 |
latest linux/jammy R-4.5 | STPGA_5.2.1.tar.gz |
522.9 KiB |
5.2.1 |
latest linux/noble R-4.5 | STPGA_5.2.1.tar.gz |
522.7 KiB |
5.2.1 |
latest source/ R- | STPGA_5.2.1.tar.gz |
409.7 KiB |
5.2.1 |
2026-04-26 source/ R- | STPGA_5.2.1.tar.gz |
409.7 KiB |
5.2.1 |
2026-04-23 source/ R- | STPGA_5.2.1.tar.gz |
409.7 KiB |
5.2.1 |
2026-04-09 windows/windows R-4.5 | STPGA_5.2.1.zip |
525.3 KiB |
5.2.1 |
2025-04-20 source/ R- | STPGA_5.2.1.tar.gz |
409.7 KiB |