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: 2.1 Depends: R (≥ 4.1.0) Imports: shiny, data.table, DT, predhy (≥ 2.1.2), BGLR, pls, glmnet, xgboost, lightgbm, foreach, doParallel, parallel, htmltools Published: 2025-04-14 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=predhy.GUI to link to this page.
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