Mixture Modeling with MplusAutomation

Welcome! This will be a collection of resources that will teach you how to apply mixture modeling using Mplus¹ and MplusAutomation!² These resources will serve as a comprehensive guide to understanding and applying mixture models using Mplus and its automation capabilities with MplusAutomation. Here, you will learn from start to finish how to apply a range of mixture modeling using Mplus with the MplusAutomation package.

Note: This book is a continuous work in progress. The code presented may be updated and/or expanded as research progresses. Please treat the material as a living document rather than a final product.

Development of these resources was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305B220021.

Acknowledgements

The Institute of Mixture Modeling for Equity-Oriented Researchers, Scholars, and Educators (IMMERSE) is an IES funded training grant (R305B220021) to support education scholars in integrating mixture modeling into their research.

Learn more about IMMERSE

- Please visit our website to learn more about the IMMERSE fellowship.

• For all code and materials found in this Bookdown, see here.

• Visit our GitHub account to access all the IMMERSE training materials.

• Follow us on BlueSky and X to stay-up-to date on our fellowship!

How to reference this resource

Nylund-Gibson, K. & Arch, D. (2026). Mixture modeling with MplusAutomation: A Web Resource. University of California, Santa Barbara. Retrieved [Month Day, Year], from https://mixture-modeling.netlify.app/

Authors & Contributors

Resource Conceptualization: Karen Nylund-Gibson, PhD and Dina Arch, PhD

Lead Code Developer: Dina Arch, PhD

Primary Author: Karen Nylund-Gibson, PhD

Additional code contributors:

• Adam Garber, PhD

• Delwin Carter, PhD

• Yidi Zhang, MA

• Travis Candieas

• Minghui Wang

We also thank all the IMMERSE fellows who provided feedback during the development of these materials.