Methods for fitting macroevolutionary models to phylogenetic trees
	Pennell (2014) <doi:10.1093/bioinformatics/btu181>.

Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions.

Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).

Represents generalized geometric ellipsoids with the "(U,D)" representation. It allows degenerate
	and/or unbounded ellipsoids, together with methods for linear and duality transformations, and for plotting.
	Thus ellipsoids are naturally extended to include lines, hyperplanes, points, cylinders, etc.
	This permits exploration of a variety to statistical issues that can be visualized using ellipsoids
	as discussed by Friendly, Fox & Monette (2013), Elliptical Insights: Understanding Statistical Methods 
	Through Elliptical Geometry <doi:10.1214/12-STS402>.