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https://github.com/bambinos/kulprit
Kullback-Leibler projections for Bayesian model selection in Python
https://github.com/bambinos/kulprit
bayesian-inference kullback-leibler-divergence model-selection
Last synced: 5 days ago
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Kullback-Leibler projections for Bayesian model selection in Python
- Host: GitHub
- URL: https://github.com/bambinos/kulprit
- Owner: bambinos
- License: mit
- Created: 2022-02-17T14:03:16.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-20T00:28:18.000Z (4 months ago)
- Last Synced: 2024-11-08T03:09:07.790Z (12 days ago)
- Topics: bayesian-inference, kullback-leibler-divergence, model-selection
- Language: Python
- Homepage: https://kulprit.readthedocs.io
- Size: 3.91 MB
- Stars: 30
- Watchers: 6
- Forks: 4
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
Kullback-Leibler projections for Bayesian model selection in Python.
[](https://badge.fury.io/py/kulprit)
[](https://github.com/bambinos/kulprit/actions/workflows/test.yml)
[](https://codecov.io/gh/bambinos/kulprit)
[](https://github.com/ambv/black)
## Overview
Kulprit _(Pronounced: kuːl.prɪt)_ is a package for variable selection for [Bambi](https://github.com/bambinos/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](https://www.anaconda.com/products/individual#Downloads), 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](https://kulprit.readthedocs.io/en/latest/). 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](https://arxiv.org/abs/2306.15581). You may also find useful this [guide](https://avehtari.github.io/modelselection/CV-FAQ.html) on Cross-Validation and model selection.
## Development
Read our development guide in [CONTRIBUTING.md](https://github.com/bambinos/kulprit/blob/main/CONTRIBUTING.md).
## Contributions
Kulprit is a community project and welcomes contributions. Additional information can be found in the [Contributing](https://github.com/bambinos/kulprit/blob/main/docs/CONTRIBUTING.md) Readme.
For a list of contributors see the [GitHub contributor](https://github.com/bambinos/kulprit/graphs/contributors) 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](https://numfocus.org/donate-to-pymc) 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](https://github.com/bambinos/kulprit/blob/main/docs/CODE_OF_CONDUCT.md)
## License
[MIT License](https://github.com/bambinos/kulprit/blob/main/LICENSE)