PyMC-BART extends PyMC probabilistic programming framework to be able to define and solve models including a BART random variable. PyMC-BART also includes a few helpers function to aid with the interpretation of those models and perform variable selection.
PyMC-BART is available on Conda-Forge. To set up a suitable Conda environment, run
conda create --name=pymc-bart --channel=conda-forge pymc-bart
conda activate pymc-bartAlternatively, it can be installed with
pip install pymc-bartIn case you want to upgrade to the bleeding edge version of the package you can install from GitHub:
pip install git+https://github.com/pymc-devs/pymc-bart.gitPyMC-BART is a community project and welcomes contributions. Additional information can be found in the Contributing Readme
PyMC-BART wishes to maintain a positive community. Additional details can be found in the Code of Conduct
If you use PyMC-BART and want to cite it please use
Here is the citation in BibTeX format
@misc{quiroga2022bart,
title={Bayesian additive regression trees for probabilistic programming},
author={Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
year={2022},
doi={10.48550/ARXIV.2206.03619},
archivePrefix={arXiv},
primaryClass={stat.CO}
}
PyMC-BART , as other pymc-devs projects, is a non-profit project under the NumFOCUS umbrella. If you want to support PyMC-BART financially, you can donate here.
