Metadata-Version: 2.4
Name: pycatdap
Version: 0.2.0
Summary: Python implementation of CATDAP (CATegorical Data Analysis Program)
Project-URL: Homepage, https://github.com/nbx-liz/pycatdap
Project-URL: Repository, https://github.com/nbx-liz/pycatdap
Project-URL: Issues, https://github.com/nbx-liz/pycatdap/issues
Author: nbx
License-Expression: MIT
License-File: LICENSE
Keywords: AIC,CATDAP,categorical-data,statistics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Provides-Extra: plot
Requires-Dist: matplotlib>=3.7; extra == 'plot'
Description-Content-Type: text/markdown

# pycatdap

Python implementation of CATDAP (CATegorical Data Analysis Program).

CATDAP applies Akaike's Information Criterion (AIC) to categorical data analysis.
Originally developed by Sakamoto & Katsura (1980) at the Institute of Statistical Mathematics, Japan.

## Features

- **CATDAP-01**: Pairwise AIC evaluation of categorical variable associations
- **CATDAP-02**: Optimal explanatory variable subset search with continuous variable binning

## Installation

```bash
pip install pycatdap
```

## Quick Start

```python
import pycatdap

# CATDAP-01: Pairwise AIC analysis
result1 = pycatdap.catdap1(data, response_names=["Survived"])
print(result1.aic)

# CATDAP-02: Optimal subset search
result2 = pycatdap.catdap2(data, response_name="symptoms")
print(result2.subsets)
```

## Development

```bash
git clone https://github.com/rem/pycatdap.git
cd pycatdap
uv venv && source .venv/bin/activate
uv pip install -e ".[all]"
pytest
```

## License

MIT
