predhy.GUI: Genomic Prediction of Hybrid Performance with Graphical User
Interface
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 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
shiny, data.table, DT, predhy (≥ 2.1), BGLR, pls, glmnet, xgboost, lightgbm, foreach, doParallel, parallel, htmltools |
| Published: |
2024-05-27 |
| 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:
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