mmer 1.0.4

Multivariate Mixed Effects Regression.

MMER: Multivariate Mixed Effects Regression

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 outcomes and grouping factors, and provides direct access to the covariance matrices arising from its multivariate formulation [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

The full documentation, including examples and the complete API reference, is 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
📧 [e-mail]

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