MMER: Multivariate Mixed Effects Regression

MMER is a flexible Python framework for multivariate mixed-effects regression. Its defining feature is a plug-and-play architecture that allows you to seamlessly integrate any generic regressor to model the fixed effects, from standard parametric algorithms to advanced machine learning models like Neural Networks, Random Forests, and etc. It natively handles multiple correlated outcomes across various grouping structures, providing direct access to the full random effect and residual covariance matrices [1].

Table of Contents

• Installation
• Documentation & License
• Contact & Support
• References

Installation

Stable release (recommended): Install the latest stable version from PyPI:

pip install mmer

Development version: To use the latest development version (may include experimental or untested changes), install directly from the GitHub repository:

pip install git+https://github.com/Sajad-Hussaini/mmer.git

Documentation & License

📖 Explore the Full Documentation, Tutorials, and API Reference available at mmer.readthedocs.io.

MMER is released under the MIT License. See the LICENSE file for the full text.

Contact & Support

For any questions, assistance, suggestions, or requests to modify API, please feel free to contact:

S. M. Sajad Hussaini
📧 hussaini.smsajad@gmail.com

Please include "MMER" in the subject line for a quicker response.

If you find this package useful, contributions to help maintain and improve it, are always appreciated.

References

Please cite the following references for any formal study:

[1] Primary Reference
A Multivariate Mixed-Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches
DOI: https://doi.org/10.1002/eqe.70168
(Journal of Earthquake Engineering and Structural Dynamics)

[2] MMER Package
Multivariate Mixed Effects Regression
DOI: https://doi.org/10.5281/zenodo.18068839