A Python package to estimate the Dirichlet distribution, calculate maximum likelihood, and test for independence from a variable based on fitting nested Dirichlet distribution hypotheses.

Most of this package is a port of Thomas P. Minka's wonderful Fastfit MATLAB code. Much thanks to him for that and his clear paper "Estimating a Dirichlet distribution".

This likelihood ratio test for independence will determine whether two Dirichlet-distributed data sets are likely to be from the same distribution or from two different ones, much like a chi-square or G-test for independence, but with Dirichlet models.

The dirichlet.simplex module creates scatter, contour, and filled contour 2-simplex plots. To use this, be sure to install the simplex package "extra" (e.g. pip install dirichlet[simplex]).

Note that this package at the moment doesn't support sparse data vectors due to the numerical fitting algorithm that uses the gamma function. Possibly some sort of additive smoothing would make this package work in your context, but that will depend on your application.

pip install dirichlet
# or
uv add dirichlet

This has only been tested with Python 3.10+. Other versions may work, but they haven't been tested.

To install dev tooling, run:

uv sync --frozen --locked --all-groups --all-extras

# To format
uv run ruff format

# To test
uv run pytest

# To lint
uv run ruff

A Github workflow will run tests against several Python versions.