The R package geepack provides a flexible approach for fitting generalized estimating equations (GEE) to analyze clustered data. GEE is a method for analyzing data where observations within clusters may be correlated. It focuses on modeling the mean of the observations without fully specifying the joint distribution. The geepack package implements the GEE approach and offers several features, including an interface function geeglm that is similar to glm, a jackknife variance estimator, and the ability to incorporate covariates into scale and correlation parameters. The paper describes the application of the GEE approach using the geepack package with an example of clustered binary data. It outlines the theory of GEE, including the estimation method and the working correlation structures. The geeglm function is discussed in detail, including its arguments and the anova method for model comparison. The package also allows for user-defined correlation structures and provides several variance estimation methods, including the sandwich estimator and jackknife estimators. The paper presents an analysis of respiratory data using the GEE approach, demonstrating the application of the geepack package. The results show that the estimates of the parameters are consistent across different working correlation structures, and the standard errors are similar across all methods. The analysis also highlights the importance of the age effect in the model. The geepack package is described as a valuable tool for analyzing clustered data, offering a flexible and efficient approach for estimating mean, scale, and correlation parameters. It provides a range of features that make it suitable for a wide variety of applications in statistical analysis.The R package geepack provides a flexible approach for fitting generalized estimating equations (GEE) to analyze clustered data. GEE is a method for analyzing data where observations within clusters may be correlated. It focuses on modeling the mean of the observations without fully specifying the joint distribution. The geepack package implements the GEE approach and offers several features, including an interface function geeglm that is similar to glm, a jackknife variance estimator, and the ability to incorporate covariates into scale and correlation parameters. The paper describes the application of the GEE approach using the geepack package with an example of clustered binary data. It outlines the theory of GEE, including the estimation method and the working correlation structures. The geeglm function is discussed in detail, including its arguments and the anova method for model comparison. The package also allows for user-defined correlation structures and provides several variance estimation methods, including the sandwich estimator and jackknife estimators. The paper presents an analysis of respiratory data using the GEE approach, demonstrating the application of the geepack package. The results show that the estimates of the parameters are consistent across different working correlation structures, and the standard errors are similar across all methods. The analysis also highlights the importance of the age effect in the model. The geepack package is described as a valuable tool for analyzing clustered data, offering a flexible and efficient approach for estimating mean, scale, and correlation parameters. It provides a range of features that make it suitable for a wide variety of applications in statistical analysis.