The python software catede is a collection of different estimation methods for the entropy and divergence in the context of categorical distribution with finite number of categories. Provided a data sample in the shape of a list or other comprehensive supported formats, the software provides methods to estimate the Shannon entropy, the Simpson index, the Kullback-Leibler divergence and the Hellinger distance. In particular it provides the code for the Dirichlet prior mixture estimators for the divergence, published in Camaglia et al. (2024), Phys. Rev. E.
The package catede can be installed from github by running from terminal the following command line :
pip install git+https://github.com/statbiophys/catede.git.
Cite this as:
Francesco Camaglia, Ilya Nemenman, Thierry Mora, Aleksandra Walczak (2024). Bayesian estimation of the Kullback-Leibler divergence for categorical sytems using mixtures of Dirichlet priors. Phys. Rev. E
A brief tutorial can be found here.
Any issues or questions should be addressed to us.
Free use of catede is granted under the terms of the GNU General Public License version 3 (GPLv3).