Metadata-Version: 2.4
Name: kdetools
Version: 0.2.3
Summary: Kernel Density Estimation tools and functions
Project-URL: Homepage, https://github.com/MutaharChalmers/kdetools
Author-email: Mutahar Chalmers <mutahar.chalmers@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Mutahar Chalmers
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Requires-Dist: numpy>=1.18
Requires-Dist: pyarrow>=10.0
Requires-Dist: scipy>=1.10
Description-Content-Type: text/markdown

# kdetools
Some useful tools for working with Kernel Density Estimates (KDEs) built on top of the `scipy` Gaussian KDE code. Currently includes a subclass of `scipy.stats.gaussian_kde` with an additional method (`conditional_resample`) for conditional random sampling from a multivariate KDE and cross-validation based bandwidth estimators, and a new class (`kdecdf`) for vectorised 1D KDE fitting for multiple variables and conversion to smoothed empirical CDFs.
