Metadata-Version: 2.1
Name: pyabc
Version: 0.12.5
Summary: Distributed, likelihood-free ABC-SMC inference
Home-page: https://github.com/icb-dcm/pyabc
Author: The pyABC developers
Author-email: yannik.schaelte@gmail.com
Maintainer: Yannik Schaelte
Maintainer-email: yannik.schaelte@gmail.com
License: BSD-3-Clause
Download-URL: 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
Description: pyABC
        =====
        
        .. figure:: https://raw.githubusercontent.com/ICB-DCM/pyABC/main/doc/logo/logo.svg
           :alt: pyABC logo
           :width: 30 %
           :align: center
        
        |CI| |docs| |codecov| |pypi| |doi| |black|
        
        Massively parallel, distributed and scalable ABC-SMC
        (Approximate Bayesian Computation - Sequential Monte Carlo)
        for parameter estimation of complex stochastic models.
        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 especially R and Julia.
        
        - **Documentation:** https://pyabc.rtfd.io
        - **Examples:** http://pyabc.rtfd.io/en/latest/examples.html
        - **Contact:** https://pyabc.rtfd.io/en/latest/about.html
        - **Bug reports:** https://github.com/icb-dcm/pyabc/issues
        - **Source code:** https://github.com/icb-dcm/pyabc
        - **Cite:** https://pyabc.rtfd.io/en/latest/cite.html
        
        .. |CI| image:: https://github.com/ICB-DCM/pyABC/workflows/CI/badge.svg
           :target: https://github.com/ICB-DCM/pyABC/actions
           :alt: CI
        
        .. |docs| image:: https://readthedocs.org/projects/pyabc/badge/?version=latest
           :target: http://pyabc.readthedocs.io/en/latest/
           :alt: Docs
        
        .. |codecov| image:: https://codecov.io/gh/ICB-DCM/pyABC/branch/main/graph/badge.svg
           :target: https://codecov.io/gh/ICB-DCM/pyABC
           :alt: Codecov
        
        .. |pypi| image:: https://badge.fury.io/py/pyabc.svg
           :target: https://badge.fury.io/py/pyabc
           :alt: PyPI
        
        .. |doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3257587.svg
           :target: https://doi.org/10.5281/zenodo.3257587
           :alt: DOI
        
        .. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
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           :alt: Code style: Black
        
Keywords: likelihood-free,inference,abc,approximate bayesian computation,sge,distributed
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
Provides-Extra: webserver
Provides-Extra: pyarrow
Provides-Extra: R
Provides-Extra: julia
Provides-Extra: copasi
Provides-Extra: ot
Provides-Extra: petab
Provides-Extra: amici
Provides-Extra: yaml2sbml
Provides-Extra: migrate
Provides-Extra: plotly
Provides-Extra: autograd
Provides-Extra: examples
Provides-Extra: doc
Provides-Extra: test
Provides-Extra: test_petab
