Title: Mass Transportation Distance Rank Histogram
Author: Didem Sari<dsari@iastate.edu>, Sarah M. Ryan <smryan@iastate.edu>
Description: The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.

Title: Maximum Tangent Likelihood and Other Robust Estimation for
Description: Provides several robust estimators for linear regression and variable selection. They are Maximum tangent likelihood estimator (Qin, et al. (2017) <arXiv:1708.05439>), least absolute deviance estimator, and Huber loss. The penalized version of each of these estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to produce consistent estimates for both fixed and high-dimensional settings.

Title: Maximum Tangent Likelihood Estimation for Linear Regression
c(person("Shaobo", "Li", email="shaobo.li@ku.edu", role=c("aut", "cre")),
Description: Several robust estimators for linear regression and variable selection are provided.
Included are Maximum tangent likelihood estimator (Qin, et al., 2017),
least absolute deviance estimator and Huber regression. The penalized version of each of these
estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to
produce consistent estimates for both fixed and high-dimensional settings.

Title: Genomic Selection using Multiple Traits
Author: Neeraj Budhlakoti, D C Mishra, Anil Rai
Description: Genomic selection (GS) is recent development in animal and plant breeding. In GS whole genome markers information is used to predict genetic merit of an individual. This package is basically developed for genomic predictions by estimating marker effects. These marker effects are then further used for calculation of genotypic merit of individual i.e. genome estimated breeding values (GEBVs). However, as genetic correlations between quantitative traits under breeding studies are obvious. These correlations indicate that one trait carry information over other traits. Current, single-trait genomic selection (STGS) methods could not able to utilize this information. Genomic selection based on multiple traits (MTGS) could be a better alternative to STGS. This package performs genomic selection using multi traits information hence named as MTGS i.e. multi trait genomic selection. MTGS is a comprehensive package which gives single step solution for genomic selection using various MTGS based methods.