Provides adaptive association tests for SNP level, gene level and pathway level analyses.

Estimation of generalized linear models with
    correlated/clustered observations by use of generalized estimating
    equations (GEE). See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details. Several types of
    clustering are supported, including exchangeable variance
    structures, AR1 structures, M-dependent, user-specified variance
    structures and more. The model fitting computations are performed
    using modified code from the 'geeM' package, while the interface
    and output objects have been written to resemble the 'geepack'
    package. The package also contains additional tools for working
    with and inspecting results from the 'geepack' package, e.g. a
    'confint' method for 'geeglm' objects from 'geepack'.

Population-averaged models have been increasingly used in the design and analysis of 
             cluster randomized trials (CRTs). To facilitate the applications of population-averaged 
             models in CRTs, the package implements the generalized estimating equations (GEE) and 
             matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal 
             mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the 
             general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by 
             Li et al. (2022) <doi:10.1093/biostatistics/kxaa056>
             specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives 
             a simple and efficient estimating equations approach based on the cluster-period means to 
             estimate the intervention effects as well as correlation parameters. In addition, the package 
             also provides functions for generating correlated binary data with specific mean vector and 
             correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or 
             the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.