Metadata-Version: 2.2
Name: mlogitviz
Version: 0.1.1
Summary: A library to compute and visualize marginal effects for multinomial logistic regression models
Home-page: https://github.com/payamv3/mlogitviz
Author: Payam Saeedi and Eric Williams
Author-email: ps4019@rit.edu
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: statsmodels
Requires-Dist: matplotlib
Requires-Dist: seaborn
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# mlogitviz

**mlogitviz** is a Python library for fitting multinomial logistic regression models and visualizing marginal effects as heatmaps. It is designed for ease of use, including clear options for evaluating marginal effects at different points (e.g., overall vs. mean) and treating predictors as count or continuous variables.

## Features

- **Model Fitting:**  
  Fit a multinomial logistic regression using `statsmodels` and compute marginal effects.
  
- **Visualization:**  
  Create heatmaps of probability changes relative to a baseline outcome, with options to annotate significance.

## Installation

You can install the library via pip once it’s published on PyPI:

```bash
pip install mlogitviz
