Conditional KDE¶

Conditional Kernel Density Estimation¶

A Python package for conditional kernel density estimation. This library provides efficient implementations for estimating conditional probability densities using kernel methods.

Free software: MIT license
Documentation: https://conditional-kde.readthedocs.io
PyPI: https://pypi.org/project/conditional_kde/
Source Code: https://github.com/dprelogo/conditional_kde

Installation¶

Install from PyPI:

pip install conditional_kde

For development installation:

git clone https://github.com/dprelogo/conditional_kde.git
cd conditional_kde
pip install -e .[dev]

Quick Start¶

from conditional_kde import ConditionalKDE

# Example usage
ckde = ConditionalKDE()
# Add your code example here

Features¶

Gaussian and interpolated kernel density estimation
Support for conditional density estimation
Efficient implementation using NumPy and SciPy
Comprehensive test coverage
Type hints for better IDE support

Credits¶

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.