Metadata-Version: 2.1
Name: exhbma
Version: 0.1.12
Summary: Exhaustive Search with Bayesian Model Averaging
Home-page: https://github.com/okada-lab/exhbma
License: MIT
Author: Koki Obinata
Author-email: koki.obi.321@gmail.com
Requires-Python: >=3.8,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: matplotlib (>=3.5.1,<4.0.0)
Requires-Dist: numpy (>=1.22.1,<2.0.0)
Requires-Dist: pydantic (>=1.9.0,<2.0.0)
Requires-Dist: scikit-learn (>=1.0.2,<2.0.0)
Requires-Dist: scipy (>=1.7.3,<2.0.0)
Requires-Dist: tqdm (>=4.62.3,<5.0.0)
Project-URL: Repository, https://github.com/okada-lab/exhbma
Description-Content-Type: text/markdown

# Exhaustive Search with Bayesian Model Averaging (ExhBMA)

# Installation
```
pip install exhbma
```

# Documentation
User documentation is available [here](https://exhbma.readthedocs.io).
You can try sample notebooks in the [tutorials](/tutorials) directory.

# Reference paper
If you use this package in your research, please cite the following paper where the package was originally introduced: ["Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference"](https://doi.org/10.1080/27660400.2022.2108353).

BibTeX entry:
```
@article{obinata2022data,
  title={Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference},
  author={Obinata, Koki and Nakayama, Tomofumi and Ishikawa, Atsushi and Sodeyama, Keitaro and Nagata, Kenji and Igarashi, Yasuhiko and Okada, Masato},
  journal={Science and Technology of Advanced Materials: Methods},
  volume={2},
  number={1},
  pages={355--364},
  year={2022},
  publisher={Taylor \& Francis}
}
```

