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Romeb: An R Package for Robust Median-Based Bayesian Linear Growth Curve Modeling with Missing Data
1Department of Psychology, University of Virginia.
Multivariate Behavioral Research
|August 10, 2026
Summary
Growth curve modeling (GCM) now handles missing, non-normal data with the new Romeb R package. This robust median-based Bayesian approach simplifies complex longitudinal data analysis for researchers.
Area of Science:
- Social and behavioral sciences
- Statistics
- Longitudinal data analysis
Background:
- Growth curve modeling (GCM) is vital for longitudinal data analysis in social and behavioral sciences.
- Handling missing and non-normally distributed data in GCM remains a significant challenge.
- Existing methods lack accessible software for advanced GCM techniques.
Purpose of the Study:
- Introduce the Romeb R package for robust median-based Bayesian linear GCM.
- Provide a user-friendly tool for complex longitudinal data analysis.
- Address limitations in handling missing and non-normal data within GCM.
Main Methods:
- Development of the Romeb R package implementing Tong's robust median-based Bayesian approach.
- Application of the median-based Bayesian method for GCM with missing and non-normal data.
- Utilizing an empirical example to demonstrate package functionality.
Main Results:
- The Romeb package effectively streamlines the application of the robust median-based Bayesian linear GCM approach.
- Demonstrated functionality through an empirical example with figures and diagnostic tests.
- The package facilitates advanced GCM for non-normal and missing data scenarios.
Conclusions:
- The Romeb R package offers a practical solution for researchers analyzing longitudinal data with missing and non-normal distributions.
- This tool lowers the barrier for applying advanced statistical methods in GCM.
- Enhances the capability of social and behavioral scientists in longitudinal data analysis.
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