The<i>R</i>Package<b>geepack</b>for Generalized Estimating Equations

TLDR

Clustered data arise in many applications such as longitudinal data and repeated measures, and the GEE approach has been widely used in statistical practice. This paper describes the core features of the R package geepack, which implements the GEE approach for fitting marginal generalized linear models to clustered data. The GEE approach models the mean of correlated observations within clusters without fully specifying their joint distribution. The paper demonstrates the GEE approach with geepack using an example of clustered binary data.

Abstract

This paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data.