Metadata-Version: 2.1
Name: KDE-diffusion
Version: 0.9.1
Summary: Exposes the library's API and hold its meta information.
Home-page: UNKNOWN
License: UNKNOWN
Keywords: kernel density estimation, statistics
Author: John Hennig
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Requires-Dist: NumPy
Requires-Dist: SciPy
Requires-Dist: Sphinx ; extra == "doc"
Requires-Dist: Sphinx-RTD-theme ; extra == "doc"
Requires-Dist: reCommonmark ; extra == "doc"
Requires-Dist: pyTest ; extra == "test"
Project-URL: Documentation, https://kde-diffusion.readthedocs.io
Project-URL: Source, https://github.com/john-hennig/kde-diffusion
Provides-Extra: doc
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.][1] The reference
implementation for [1d][2] and [2d][3], in Matlab, was provided by the
paper's first author, Zdravko Botev. This is a re-implementation in
Python, with added test coverage.

For more information, refer to the [full documentation at
Read-the-Docs][4].

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

