kulprit 0.2.0

Kullback-Leibler projections for Bayesian model selection.

Kullback-Leibler projections for Bayesian model selection in Python.

Overview

Kulprit (Pronounced: kuːl.prɪt) is a package for variable selection for Bambi models. Kulprit is under active development so use it with care. If you find any bugs or have any feature requests, please open an issue.

Installation

Kulprit requires a working Python interpreter (3.9+). We recommend installing Python and key numerical libraries using the Anaconda Distribution, which has one-click installers available on all major platforms.

Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be installed in one line using pip:

pip install kulprit

Alternatively, if you want the bleeding edge version of the package you can install it from GitHub:

pip install git+https://github.com/bambinos/kulprit.git

Documentation

The Kulprit documentation can be found in the official docs. If you are not familiar with the theory behind Kulprit or need some practical advice on how to use Kulprit or interpret its results, we recommend you read the paper Robust and efficient projection predictive inference. You may also find useful this guide on Cross-Validation and model selection.

Development

Read our development guide in CONTRIBUTING.md.

Contributions

Kulprit is a community project and welcomes contributions. Additional information can be found in the Contributing Readme.

For a list of contributors see the GitHub contributor page

Citation

If you use Bambi and want to cite it please use

@misc{mclatchie2023,
    title={Robust and efficient projection predictive inference}, 
    author={Yann McLatchie and Sölvi Rögnvaldsson and Frank Weber and Aki Vehtari},
    year={2023},
    eprint={2306.15581},
    archivePrefix={arXiv},
    primaryClass={stat.ME}
}

Donations

If you want to support Kulprit financially, you can make a donation to our sister project PyMC.

Code of Conduct

Kulprit wishes to maintain a positive community. Additional details can be found in the Code of Conduct

License

MIT License