Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <doi:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <doi:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <doi:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <doi:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.
| Version: | 1.0 |
| Imports: | Rcpp (≥ 1.0.0) |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2019-03-07 |
| Author: | Jen-Hsiang Ou and Chen-Tuo Liao |
| Maintainer: | Jen-Hsiang Ou <oumark.me at outlook.com> |
| BugReports: | https://gitlab.com/oumark/TSDFGS/issues |
| License: | GPL (≥ 3) |
| URL: | https://tsdfgs.oumark.me |
| NeedsCompilation: | yes |
| CRAN checks: | TSDFGS results |
| Reference manual: | TSDFGS.pdf |
| Package source: | TSDFGS_1.0.tar.gz |
| Windows binaries: | r-devel: TSDFGS_1.0.zip, r-release: TSDFGS_1.0.zip, r-oldrel: TSDFGS_1.0.zip |
| macOS binaries: | r-release: TSDFGS_1.0.tgz, r-oldrel: TSDFGS_1.0.tgz |
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