kulprit 0.5.1

Kullback-Leibler projections for Bayesian model selection in Python.

Overview

Kulprit (Pronounced: kuːl.prɪt) is a package for variable selection for Bambi models. If you find any bugs or have any feature requests, please open an issue.

Installation

Kulprit requires a working Python interpreter (3.11+). 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

By default, Kulprit performs a forward search. If you want to use Lasso (L1 search), you need to install scikit-learn package. You can install it using pip:

pip install kulprit[lasso]

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. The examples provide a quick overview of variable selection and how this problem is tackled by Kulprit. For a more detailed discussion of the theory, but also practical advice, we recommend the paper Advances in Projection Predictive Inference.

Contributions

Kulprit is a community project and welcomes contributions. Additional information can be found in the CONTRIBUTING.md page.

For a list of contributors, see the GitHub contributor page

Citation

If you use Kulprit and want to cite it, please use

@article{mclatchie2024,
    author = {Yann McLatchie and S{\"o}lvi R{\"o}gnvaldsson and Frank Weber and Aki Vehtari},
    title = {{Advances in Projection Predictive Inference}},
    volume = {40},
    journal = {Statistical Science},
    number = {1},
    publisher = {Institute of Mathematical Statistics},
    pages = {128 -- 147},
    keywords = {Bayesian model selection, cross-validation, projection predictive inference},
    year = {2025},
    doi = {10.1214/24-STS949},
    URL = {https://doi.org/10.1214/24-STS949}
}

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