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Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, Random forest and XGBoost. 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. 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).
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