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.
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 git+https://github.com/ericsuh/dirichlet.git
This has only been tested with Python 3.6+. Other versions may work, but they haven't been tested.
Note: These instructions have only been tested on Ubuntu/Debian.
Dev dependencies are listed in requirements-dev.txt. You can install them
with:
pip install -r requirements-dev.txt
Please use black to format your code when contributing
This project uses tox and
pytest for testing. To run tests,
generally you can just run:
tox
To test a particular version of Python, you will need to have it
installed and in your $PATH ahead of time.