UNITE Toolbox
Unified diagnostic evaluation of scientific models based on information theory
The UNITE Toolbox is a Python library for incorporating Information Theory into data analysis and modeling workflows. The toolbox collects different methods of estimating information-theoretic quantities in one easy-to-use Python package. Currently, UNITE includes functions to calculate entropy $H(X)$, Kullback-Leibler divergence $D_{KL}(p||q)$, and mutual information $I(X; Y)$, using three methods:
- Kernel density-based estimation (KDE)
- Binning using histograms
- k-nearest neighbor-based estimation (k-NN)
Installation
Although the code is still highly experimental and in very active development,
a release version is available on PyPI and can be installed using pip.
pip install unite_toolbox
Or uv.
uv add unite_toolbox
Check the pyproject.toml for requirements.
How-to
In the documentation please find tutorials on the general usage of the toolbox and some applications.
1 maintainer
Metadata
1 maintainer
Release files for unite-toolbox 0.2.1
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Total release size: 30.9 kB
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