Features the marginal parametric and semi-parametric proportional hazards mixture cure models for analyzing clustered survival data with a possible cure fraction. A reference is Yi Niu and Yingwei Peng (2014) <doi:10.1016/j.jmva.2013.09.003>.

Simplifies the creation, management, and updating of local databases using data extracted from 'Google Earth Engine' ('GEE'). It integrates with 'GEE' to store, aggregate, and process spatio-temporal data, leveraging 'SQLite' for efficient, serverless storage. The 'geeLite' package provides utilities for data transformation and supports real-time monitoring and analysis of geospatial features, making it suitable for researchers and practitioners in geospatial science. For details, see Kurbucz and Andrée (2025) "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package" <https://hdl.handle.net/10986/43165>.

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.

Fits marginal regression models for repeated or clustered
    responses using generalized estimating equations (GEE). Provides ordinary
    GEE estimators, bias-reduced and bias-corrected GEE estimators, and
    Jeffreys-type penalized GEE estimators for binary, count and continuous
    responses. Methods are described in Touloumis (2026a)
    <doi:10.48550/arXiv.2606.16043> and Touloumis (2026b)
    <doi:10.48550/arXiv.2606.16058>.