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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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.