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)

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

Alternatively, the latest updates can be installed directly from this repository

pip install git+https://github.com/manuel-alvarez-chaves/unite_toolbox

Check the pyproject.toml for requirements.

In the documentation please find tutorials on the general usage of the toolbox and some applications.