GEE estimation of the parameters in mean structures with possible
    correlation between the outcomes. User-specified mean link and variance
    functions are allowed, along with observation weighting. The 'M' in the name
    'geeM' is meant to emphasize the use of the Matrix package, which allows for an
    implementation based fully in R.

Causal mediation analysis for a single exposure/treatment and a
    single mediator, both allowed to be either continuous or binary. The package
    implements the difference method and provides point and interval estimates as
    well as testing for the natural direct and indirect effects and the mediation
    proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.

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 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.