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Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
| Version: | 2.0.1 |
| Depends: | R (≥ 4.1.0) |
| Imports: | shiny, data.table, DT, predhy (≥ 2.1), BGLR, pls, glmnet, xgboost, lightgbm, foreach, doParallel, parallel, htmltools |
| Published: | 2024-06-17 |
| DOI: | 10.32614/CRAN.package.predhy.GUI |
| Author: | Yang Xu [aut], Guangning Yu [aut], Yuxiang Zhang [aut, cre], Yanru Cui [ctb], Shizhong Xu [ctb], Chenwu Xu [ctb] |
| Maintainer: | Yuxiang Zhang <yuxiangzhang_99 at foxmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | predhy.GUI results |
| Reference manual: | predhy.GUI.pdf |
| Package source: | predhy.GUI_2.0.1.tar.gz |
| Windows binaries: | r-devel: predhy.GUI_2.0.1.zip, r-release: predhy.GUI_2.0.1.zip, r-oldrel: predhy.GUI_2.0.1.zip |
| macOS binaries: | r-release (arm64): predhy.GUI_2.0.1.tgz, r-oldrel (arm64): predhy.GUI_2.0.1.tgz, r-release (x86_64): predhy.GUI_2.0.1.tgz, r-oldrel (x86_64): predhy.GUI_2.0.1.tgz |
| Old sources: | predhy.GUI archive |
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