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>).

Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'.
   Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).

We included functions to assess the performance of
 risk models. The package contains functions for the various measures that are
 used in empirical studies, including univariate and multivariate odds ratios
 (OR) of the predictors, the c-statistic (or area under the receiver operating
 characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
 reclassification table, net reclassification improvement (NRI) and
 integrated discrimination improvement (IDI). Also included are functions
 to create plots, such as risk distributions, ROC curves, calibration plot,
 discrimination box plot and predictiveness curves. In addition to functions
 to assess the performance of risk models, the package includes functions to
 obtain weighted and unweighted risk scores as well as predicted risks using
 logistic regression analysis. These logistic regression functions are
 specifically written for models that include genetic variables, but they
 can also be applied to models that are based on non-genetic risk factors only.
 Finally, the package includes function to construct a simulated dataset with 
 genotypes, genetic risks, and disease status for a hypothetical population, which 
 is used for the evaluation of genetic risk models.