Implements fast, safe, and customizable assertions routines, which 
    can be used in place of base::stopifnot().

Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.

Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. 
  This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package
  is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.

Performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) <doi:10.1534/g3.116.038059>; Xu Y et al (2021) <doi: 10.1111/pbi.13458>).

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>).
			 A complete manual for this package is provided in the manual folder of the package installation directory. 
			 You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").

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).