semvec/pypi-packages huggingface dataset:
rbnet 0.0.1
Python implementation of Recursive Bayesian Networks
Requires Python: >=3.9

# Recursive Bayesian Networks

[![tests](https://github.com/robert-lieck/RBN/actions/workflows/tests.yml/badge.svg)](https://github.com/robert-lieck/RBN/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/robert-lieck/RBN/graph/badge.svg?token=6VZ2LIFSL9)](https://codecov.io/gh/robert-lieck/RBN)

![build](https://github.com/robert-lieck/RBN/workflows/build/badge.svg)
[![PyPI version](https://badge.fury.io/py/rbnet.svg)](https://badge.fury.io/py/rbnet)

[![doc](https://github.com/robert-lieck/RBN/actions/workflows/doc.yml/badge.svg)](https://robert-lieck.github.io/RBN/)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

The Python `rbnet` package providing implementations of Recursive Bayesian Networks.

Lieck R, Rohrmeier M (2021) **Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks**. In: *Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)*

```
@inproceedings{lieck2021RBN,
  title = {Recursive {{Bayesian Networks}}: Generalising and {{Unifying Probabilistic Context}}-{{Free Grammars}} and {{Dynamic Bayesian Networks}}},
  booktitle = {Proceedings of the 35th {{Conference}} on {{Neural Information Processing Systems}} ({{NeurIPS}} 2021)},
  author = {Lieck, Robert and Rohrmeier, Martin},
  year = {2021},
}
```

If you are looking for the code to reproduce the results from the NeurIPS 2021 paper, have a look at the NeurIPS 2021 branch [with](https://github.com/robert-lieck/RBN/tree/NeurIPS_2021_with_data) or [without](https://github.com/robert-lieck/RBN/tree/NeurIPS_2021_without_data) data.
ssa-gov 1.0.1
A library enabling users to fetch data from ssa.gov.

# ssa-gov

A tool to fetch data from ssa.gov, particularly name data.

[![Python Version](https://img.shields.io/badge/python-3-blue.svg)](https://www.python.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Build Status](https://git.octr.is/open-source/data/ssa-gov/badges/master/pipeline.svg)](https://git.octr.is/open-source/data/ssa-gov/-/pipelines/latest)

## Installation

You can install the package via pip:

```
pip install ssa-gov
```

## Usage

1. **Fetch Popular Names Data**:

```python
from ssa_gov import get_popular_baby_names

male_data, female_data = get_popular_baby_names(year=2020, n=100)
```

2. **Get Name Popularity Data:**

```python
from ssa_gov import get_baby_name_popularity_data

data = get_baby_name_popularity_data(name="Meryl", since_year=1880, sex='F')
```

## Author

Roberto Banić @ [Octris LLC](https://octr.is)

For inquiries, merge requests, improvement suggestions, and anything else, reach out
to [roberto@octr.is](mailto:roberto@octr.is).

## License

This project is licensed under the MIT License. See the LICENSE file for details.

## Our Open-Source Philosophy

At Octris LLC, we deeply believe in the transformative power of open-source software. Open-source, to us, is more than
just a methodology—it's a philosophy that heralds transparency, community-driven progress, and an ethos of shared
innovation.

Our support particularly leans towards permissive licenses like MIT, which we feel best embody our vision of unhindered
collaboration and freedom in software distribution and modification. While we respect the intentions behind copyleft
licenses like GPLv3, our preference lies with licenses that minimize restrictions, fostering a more inclusive and
adaptive tech ecosystem. By aligning ourselves with such licenses, Octris LLC reaffirms its commitment to creating and
supporting platforms where technology is not just accessible and open, but also freely adaptable to the diverse needs of
users across the globe.