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)

DaRUS This is the DaRUS archive for the UNITE toolbox. This archive is currently in version 0.1.9 which is the latest version as of 13.05.2024. This archive will be updated semi-regularly. Check the Github repository for the latest version. 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 Check the pyproject.toml for requirements. Note: pip will install the latest version which might not exactly match this archive. How-to In the documentation please find tutorials on the general usage of the toolbox and some applications.