Metadata-Version: 2.1
Name: KDE-diffusion
Version: 1.0.4
Summary: Kernel density estimation via diffusion in 1d and 2d
Keywords: kernel density estimation,statistics
Author: John Hennig
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Requires-Dist: NumPy
Requires-Dist: SciPy
Requires-Dist: Sphinx ; extra == "docs"
Requires-Dist: Furo ; extra == "docs"
Requires-Dist: MyST-parser ; extra == "docs"
Requires-Dist: Commonmark ; extra == "docs"
Requires-Dist: pyTest ; extra == "test"
Requires-Dist: pyTest-cov ; extra == "test"
Requires-Dist: coverage-badge ; extra == "test"
Requires-Dist: Flake8 ; extra == "test"
Requires-Dist: pyproject-Flake8 ; extra == "test"
Project-URL: Documentation, https://kde-diffusion.readthedocs.io
Project-URL: Source, https://github.com/john-hen/kde-diffusion
Provides-Extra: docs
Provides-Extra: test

﻿*Kernel density estimation via diffusion in 1d and 2d*

Provides the fast, adaptive kernel density estimator based on linear
diffusion processes for one-dimensional and two-dimensional input data
as outlined in the [2010 paper by Botev et al.][paper] The reference
implementation for [1d] and [2d], in Matlab, was provided by the paper's
first author, Zdravko Botev. This is a re-implementation in Python,
with added test coverage.

Find the full [documentation on Read-the-Docs][docs].

[paper]: https://dx.doi.org/10.1214/10-AOS799
[1d]:    https://mathworks.com/matlabcentral/fileexchange/14034
[2d]:    https://mathworks.com/matlabcentral/fileexchange/17204
[docs]:  https://kde-diffusion.readthedocs.io

