Generalized estimating equations solver for parameters in
    mean, scale, and correlation structures, through mean link,
    scale link, and correlation link. Can also handle clustered
    categorical responses. See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details.

Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.

Analyze small-sample clustered or longitudinal data with binary 
    outcome 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.

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.