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>).

Gradient-Enhanced Kriging as an emulator for computer experiments based on Maximum-Likelihood estimation.

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>.

Implements several extensions of the elastic net regularization
    scheme. These extensions include individual feature penalties for the L1 term,
    feature-feature penalties for the L2 term, as well as translation coefficients
    for the latter.