# MMER: Python Package for Multivariate Mixed Effects Regression

[![Python](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![PyPI](https://img.shields.io/pypi/v/mmer.svg)](https://pypi.org/project/mmer)
[![Documentation Status](https://readthedocs.org/projects/mmer/badge/?version=latest)](https://mmer.readthedocs.io/en/latest/?badge=latest)  

## Overview

**MMER** is a Python package for multivariate mixed-effects regression featuring a modular fixed-effects component. It supports parametric and non-parametric machine learning regressors (neural networks, random forests, XGBoost), handles multiple responses and grouping factors, and provides direct access to the covariance matrices arising from its multivariate formulation [[1]](#references).

## Table of Contents
- [Features and Installation](#features-and-installation)
- [User Guide](#user-guide)
- [License](#license)
- [Contact](#contact)
- [References](#references)

## Features and Installation

See the [Documentation](https://mmer.readthedocs.io/en/latest/?badge=latest).

## User Guide

The full documentation, including examples and the complete API reference, is available at [mmer.readthedocs.io](https://mmer.readthedocs.io/en/latest/?badge=latest).

## License

MMER is released under the [MIT License](https://opensource.org/licenses/MIT).  
See the [LICENSE](LICENSE) file for the full text.

## Contact

For questions or assistance, please feelfree to contact:

**S.M. Sajad Hussaini**  
📧 [hussaini.smsajad@gmail.com](mailto:hussaini.smsajad@gmail.com)

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

## Support the Project

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

[![PayPal](https://img.shields.io/badge/PayPal-Donate-blue.svg)](https://www.paypal.com/paypalme/sajadhussaini)

## 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: [To be added]*  
(Expected publication in the Journal of Earthquake Engineering and Structural Dynamics)

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