Analyze small-sample clustered or longitudinal data using modified
    generalized estimating equations with bias-adjusted covariance estimator. 
    The package provides any combination of three modified generalized
    estimating equations and 11 bias-adjusted covariance estimators.

Analyze small-sample clustered or longitudinal data with binary 
    outcome using modified generalized estimating equations (GEE) with 
    bias-adjusted covariance estimator. The package provides any combination of 
    three GEE methods and 12 covariance estimators.

To provide a comprehensive analysis of high dimensional longitudinal
    data,this package provides analysis for any combination of 1) simultaneous
    variable selection and estimation, 2) mean regression or quantile regression
    for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced
    or imbalanced data, 5) moderate, high or even ultra-high dimensional data,
    via computationally efficient implementations of penalized generalized
    estimating equations.

Provides a general, flexible framework for estimating parameters
    and empirical sandwich variance estimator from a set of unbiased estimating
    equations (i.e., M-estimation in the vein of Stefanski & Boos (2002)
    <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002)
    are published in the corresponding Journal of Statistical Software paper
    "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020)
    <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample 
    variance corrections.

Functions to compute generalized eigenvalues and eigenvectors,
             the generalized Schur decomposition and
             the generalized Singular Value Decomposition of a matrix pair,
             using Lapack routines.

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.