TSDFGS: Training Set Determination for Genomic Selection

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 (arm64): TSDFGS_1.0.tgz, r-release (x86_64): TSDFGS_1.0.tgz, r-oldrel: TSDFGS_1.0.tgz


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