Metadata-Version: 2.1
Name: xicorpy
Version: 0.2
Summary: Python implementation of Chatterjee's Rank Correlation, its modifications, and other offshoots
Home-page: https://swarnakumar.github.io/xicorpy
License: MIT
Author: Swarna Vallabhaneni
Author-email: swarnakumar@gmail.com
Maintainer: Swarna Vallabhaneni
Maintainer-email: swarnakumar@gmail.com
Requires-Python: >=3.7.1,<3.11
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides-Extra: docs
Provides-Extra: typings
Requires-Dist: mkdocs-material (>=8.1,<8.2) ; extra == "docs"
Requires-Dist: mkdocstrings (>=0.17,<0.18) ; extra == "docs"
Requires-Dist: mypy (==0.931) ; extra == "typings"
Requires-Dist: numpy (>=1.17,<2.0)
Requires-Dist: pandas (>=1.2,<2.0)
Requires-Dist: scikit-learn (>=1.0.2,<2.0.0)
Requires-Dist: scipy (>=1.7,<2.0)
Project-URL: Repository, https://github.com/swarnakumar/xicorpy
Description-Content-Type: text/markdown

# Chatterjee's Xi, its Applications, and Offshoots

XicorPy is a Python package implementing **Chatterjee's Xi**, and its various offshoots. You can use the package with raw python objects, NumPy arrays, or Pandas DataFrames.

Please see the [Documentation][docs] for an introductory tutorial and a full
user guide.

## Features

The package currently implements:   

1. Chatterjee's Xi from [1]
2. Modified Xi from [2]
3. Codependence Coefficient from [3]
4. Feature Ordering by Conditional Independence (FOCI) for Feature Selection from [3]


## Usage

The package is available on PyPI. You can install using pip: `pip install xicorpy`.


```python
import xicorpy

x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]
y = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]
xi = xicorpy.compute_xi_correlation(x, y)

xi, p_value = xicorpy.compute_xi_correlation(x, y, get_p_values=True)

```

Refer to the [Docs][docs] for more details.

## Contributing to XiCorPy

Any help with the package is greatly appreciated! Pull requests and bug reports are greatly welcome!


## Citations:

1. [Chatterjee (2020). "A new coefficient of correlation"](https://arxiv.org/abs/1909.10140)
2. [Lin and Han (2021). "On boosting the power of Chatterjee's rank correlation"](https://arxiv.org/abs/2108.06828)
3. [Azadkia and Chatterjee (2021). "A simple measure of conditional dependence"](https://arxiv.org/abs/1910.12327)

<!-- Links -->
[docs]: https://swarnakumar.github.io/xicorpy/

