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>.

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>.