Metadata-Version: 2.1
Name: pyabc
Version: 0.10.10
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://pyabc.readthedocs.io/en/latest/releasenotes.html
Description: pyABC
        =====
        
        .. figure:: https://raw.githubusercontent.com/ICB-DCM/pyABC/master/doc/logo/logo.svg
           :alt: pyABC logo
           :width: 30 %
           :align: center
        
        |CI| |docs| |codacy| |codecov| |pypi| |doi|
        
        Massively parallel, distributed and scalable ABC-SMC
        (Approximate Bayesian Computation - Sequential Monte Carlo)
        for parameter estimation of complex stochastic models.
        Implemented in Python with support of the R language.
        
        - **Documentation:** `https://pyabc.readthedocs.io <https://pyabc.readthedocs.io>`_
        - **Contact:** `https://pyabc.readthedocs.io/en/latest/about.html <https://pyabc.readthedocs.io/en/latest/about.html>`_
        - **Source:** `https://github.com/icb-dcm/pyabc <https://github.com/icb-dcm/pyabc>`_
        - **Bug reports:** `https://github.com/icb-dcm/pyabc/issues <https://github.com/icb-dcm/pyabc/issues>`_
        
        Examples
        --------
        
        Many examples are available as Jupyter Notebooks in the
        `examples directory <https://github.com/icb-dcm/pyabc/tree/master/doc/examples>`_
        and also for download and for online inspection in the
        `example section of the documentation <http://pyabc.readthedocs.io/en/latest/examples.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
        
        .. |codacy| image:: https://api.codacy.com/project/badge/Grade/923a9ab160e6420b9fc468701be60a98
           :target: https://www.codacy.com/app/yannikschaelte/pyABC?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=ICB-DCM/pyABC&amp;utm_campaign=Badge_Grade
           :alt: codacy
        
        .. |codecov| image:: https://codecov.io/gh/ICB-DCM/pyABC/branch/master/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.3364560.svg
           :target: https://doi.org/10.5281/zenodo.3364560
           :alt: doi
        
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 :: 3.8
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: r
Provides-Extra: petab
Provides-Extra: docs
Provides-Extra: test
Provides-Extra: quality
