Metadata-Version: 2.4
Name: pyabc
Version: 0.12.17
Summary: Distributed, likelihood-free ABC-SMC inference
Author-email: The pyABC developers <jan.hasenauer@uni-bonn.de>
Maintainer-email: Jonas Arruda <jonas.arruda@uni-bonn.de>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/icb-dcm/pyabc
Project-URL: Download, https://github.com/icb-dcm/pyabc/releases
Project-URL: Bug Tracker, https://github.com/icb-dcm/pyabc/issues
Project-URL: Documentation, https://pyabc.readthedocs.io
Project-URL: Changelog, https://github.com/ICB-DCM/pyABC/blob/main/CHANGELOG.rst
Project-URL: Repository, https://github.com/icb-dcm/pyabc
Project-URL: Source Code, https://github.com/icb-dcm/pyabc
Keywords: likelihood-free,inference,abc,approximate Bayesian computation,sge,distributed
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# pyABC

<p align="left">
  <img src="https://raw.githubusercontent.com/ICB-DCM/pyABC/main/doc/logo/logo.svg" alt="pyABC logo" width="30%">
</p>

[![CI](https://github.com/ICB-DCM/pyABC/workflows/CI/badge.svg)](https://github.com/ICB-DCM/pyABC/actions)
[![Docs](https://readthedocs.org/projects/pyabc/badge/?version=latest)](https://pyabc.readthedocs.io/en/latest/)
[![Codecov](https://codecov.io/gh/ICB-DCM/pyABC/branch/develop/graph/badge.svg)](https://codecov.io/gh/ICB-DCM/pyABC)
[![PyPI](https://badge.fury.io/py/pyabc.svg)](https://badge.fury.io/py/pyabc)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3257587.svg)](https://doi.org/10.5281/zenodo.3257587)
[![Python](https://img.shields.io/badge/Python-%3E%3D3.10-blue?logo=python&logoColor=green)](https://python.org)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-41B5BE.svg)](https://github.com/astral-sh/ruff)

**pyABC** is a massively parallel, distributed, and scalable ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) framework for parameter estimation of complex stochastic models. It provides numerous state-of-the-art algorithms for efficient, accurate, robust likelihood-free inference, described in the documentation and illustrated in example notebooks. Written in Python, with support for integration with R and Julia.

---

### Resources

- 📖 **Documentation**: [https://pyabc.rtfd.io](https://pyabc.rtfd.io)
- 💡 **Examples**: [https://pyabc.rtfd.io/en/latest/examples.html](https://pyabc.rtfd.io/en/latest/examples.html)
- 💬 **Contact**: [https://pyabc.rtfd.io/en/latest/about.html](https://pyabc.rtfd.io/en/latest/about.html)
- 🐛 **Bug Reports**: [https://github.com/icb-dcm/pyabc/issues](https://github.com/icb-dcm/pyabc/issues)
- 💻 **Source Code**: [https://github.com/icb-dcm/pyabc](https://github.com/icb-dcm/pyabc)
- 📄 **Cite**: [https://pyabc.rtfd.io/en/latest/cite.html](https://pyabc.rtfd.io/en/latest/cite.html)

#### Related Projects
- 🧠 **Neural Posterior Estimation**: [BayesFlow](https://bayesflow.org/main/_examples/From_ABC_to_BayesFlow.html)
