Metadata-Version: 2.1
Name: noisy-bnsp
Version: 0.1.0
Summary: A Python library to simulate and inference on the noisy bivariate Neyman-Scott process.
Author-email: Takaaki Shiotani <shtm.1058@gmail.com>
Project-URL: Homepage, https://github.com/shtmjp/noisy-bnsp
Project-URL: Issues, https://github.com/shtmjp/noisy-bnsp/issues
Keywords: point process,simulation,inference
Classifier: Programming Language :: C
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.12
Description-Content-Type: text/markdown
Requires-Dist: cython>=3.0.11
Requires-Dist: numpy>=2.2.1
Requires-Dist: scipy>=1.14.1

# noisy-bnsp

A Python package to simulate and estimate the noisy-BNSP model.

## How to install

1. install `gsl` (also known as: gnu-scientific-library)
2. `pip install noisy-bnsp`

## Reference

- Shiotani, T., & Yoshida, N. (2024). Statistical inference for highly correlated stationary point processes and noisy bivariate Neyman-Scott processes. arXiv preprint arXiv:2410.05732.

## TODO

- Add docs by sphinx
- Add real-data example
