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Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, LightGBM and XGBoost. 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: | 2.1.0 |
| Depends: | R (≥ 4.1.0) |
| Imports: | BGLR, pls, glmnet, xgboost, lightgbm, foreach, doParallel, parallel |
| Published: | 2024-03-29 |
| Author: | Yang Xu, Guangning Yu, Yanru Cui, Shizhong Xu, Chenwu Xu |
| Maintainer: | Yang Xu <xuyang_89 at 126.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | predhy results |
| Reference manual: | predhy.pdf |
| Package source: | predhy_2.1.0.tar.gz |
| Windows binaries: | r-devel: predhy_2.1.0.zip, r-release: predhy_2.1.0.zip, r-oldrel: predhy_2.1.0.zip |
| macOS binaries: | r-release (arm64): predhy_2.1.0.tgz, r-oldrel (arm64): predhy_2.1.0.tgz, r-release (x86_64): predhy_2.1.0.tgz, r-oldrel (x86_64): predhy_2.1.0.tgz |
| Old sources: | predhy archive |
| Reverse imports: | predhy.GUI |
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These binaries (installable software) and packages are in development.
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