Romeb: An R Package for Robust Median-Based Bayesian Linear Growth Curve Modeling with Missing Data

Growth curve modeling (GCM) has been widely used in social and behavioral sciences to analyze longitudinal data. However, it remains a significant challenge for GCM to handle missing data in longitudinal research, especially when data are nonnormally distributed. Although the robust median-based Bayesian approach for GCM developed by Tong effectively addresses both ignorable and nonignorable missing data in various data scenarios, particularly for nonnormally distributed data, no dedicated software was previously available to implement this advanced method, posing a barrier for researchers without extensive statistical or programming backgrounds. This article introduces a newly developed R package, Romeb, which streamlines the application of the robust median-based Bayesian linear GCM approach. An empirical example is provided to demonstrate the functionality of Romeb, accompanied by multiple figures and diagnostic tests.