Predictive Inference on Totals and Averages of Finite
Populations Segmented in Planned and Unplanned Domains.
Juan Carlos Martinez-Ovando, Sergio I. Olivares-Guzman, Adriana Roldan-Rodriguez.
This package computes Bayesian predictive inference on totals and averages of finite populations segmented in planned and unplanned domains. Inference is based on Bayesian nonparametric methods using species-sampling models (specifically the Dirichlet process).
The authors appreciate advice from Pedro
Gonzalez-Alegria and Claudia Velazquez-Villegas of Banco de
Mexico.

Genomic Prediction of Hybrid Performance
Yang Xu, Yanru Cui, Shizhong Xu, Chenwu Xu
Performs genomic prediction of hybrid performance with genomic best linear unbiased prediction (Xu S et al (2014) <doi:10.1073/pnas.1413750111>). The package also provides fast cross-validation and mating design for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).

Yang Xu, Guangning Yu, Yanru Cui, Shizhong Xu, Chenwu Xu
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, Random forest and XGBoost. 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>).
BGLR, pls, glmnet, randomForest, xgboost, foreach, doParallel,
parallel

Draw Three Dimensional Predict Plot Using Package 'rgl'
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).
ggplot2(>= 3.1.0), rgl(>= 0.99.16), dplyr, ggiraphExtra,
modelr, prediction, purrr, rlang, stringr, magrittr, stats,
TH.data, reshape2, plyr, tidyr

ggplot2(>= 3.1.0), rgl(>= 0.99.16), dplyr, ggiraphExtra,
modelr, prediction, purrr, rlang, stringr, magrittr, moonBook,
stats, TH.data, reshape2, plyr, tidyr

Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. Visual results are instantly comprehensible.