STPGA
5.2.1Selection of Training Populations by Genetic Algorithm
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Overview
About
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.
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- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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- Examples that run
- 17%
- Documented parameters
- 97%
- Return-value docs
- 100%
- References docs
- 18%
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Dependencies
Declared dependencies
10 external dependencies (excludes base and recommended)
Depends (6)
R >= 2.10AlgDesignscalesscatterplot3demoagrDevices
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none
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none
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none
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Listed in earlier versions (1)
no longer listed · 1.0 to 5.2.1
Package Timeline
8 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 5.2.1Latest
- 5.22018-09-21 · diff ↗
- 5.12018-08-16 · diff ↗
- 5.02018-07-22 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 4.02017-03-02 · diff ↗
- 3.02016-08-09 · diff ↗
Show 5 earlier events
- 2.02016-05-05 · diff ↗
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- 1.02014-05-31
- RR 3.1.0 released · 2014-04-10
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Package metadata
- First published
- 2014-05-31
- Total releases
- 8 / 12 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 390 KB / 1 file
- Download size
- 409 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet