Metadata-Version: 2.4
Name: mbnpy
Version: 0.1.10
Summary: MBNpy is a Python package for Bayesian network applications for large-scale system events (i.e. high-dimensional data).
Home-page: https://github.com/jieunbyun/MBNpy
Author: The DUCO team
Author-email: ji-eun.byun@glasgow.ac.uk
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: geopandas>=1.0
Requires-Dist: matplotlib>=3.10
Requires-Dist: networkx>=3.4
Requires-Dist: numpy>=2.2
Requires-Dist: pandas>=2.2
Requires-Dist: polyline>=2.0
Requires-Dist: pyarrow>=19.0
Requires-Dist: pytest>=8.3
Requires-Dist: scipy>=1.15
Requires-Dist: Shapely>=2.0
Requires-Dist: typer>=0.15
Requires-Dist: typing_extensions>=4.12
Requires-Dist: plotly>=6.2
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# MBNpy

## Overview
**MBNpy** is a Python toolkit for **matrix-based Bayesian network (MBN)**--an alternative data structure to conventional BN. MBN is designed to handle **problems with a large number of parent nodes**, where conventional BN tools often fall short. 
Example applications include [transport networks](https://doi.org/10.1016/j.ress.2019.01.007) and [pipeline networks](https://doi.org/10.1016/j.ress.2021.107468).

## Contact
If you have discussion points, refer to the [discussions tab](https://github.com/jieunbyun/MBNpy/discussions).  
If you have need support, refer to the [issues tab](https://github.com/jieunbyun/MBNpy/issues).

## Installation
### Install using pip
MBNpy requires **Python 3.12+**. To install using pip, run:
```bash
pip install mbnpy
```
### Downloading files from GitHub (development version)
```bash
git clone git@github.com:jieunbyun/MBNpy.git
cd MBNpy
```

## Documentation
For documentation, refer to the [MBNpy docs](https://jieunbyun.github.io/MBNpy-docs/).
For research news and blog articles, refer to the [MBNpy blog](https://jieunbyun.github.io/MBNpy/).

## License
MBNpy is distributed under the MIT License

Copyright (c) <2025> <Ji-Eun Byun>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

## Referencing MBNPy
If you use this software for publication, please cite:

Byun, J. E. & Song, J. (2021). Generalized matrix-based Bayesian network for multi-state systems. *Reliability Engineering & System Safety,* 211, 107468.
