Metadata-Version: 2.1
Name: pyabc
Version: 0.9.8
Summary: Distributed, likelihood-free ABC-SMC inference
Home-page: https://github.com/icb-dcm/pyabc
Author: Emmanuel Klinger, Yannik Schälte, Elba Raimundez
Author-email: yannik.schaelte@gmail.com
License: UNKNOWN
Keywords: likelihood-free inference,abc,approximate bayesian computation,sge,distributed
Platform: all
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: cloudpickle
Requires-Dist: flask-bootstrap
Requires-Dist: flask
Requires-Dist: bokeh
Requires-Dist: redis
Requires-Dist: dill
Requires-Dist: gitpython
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: sqlalchemy
Requires-Dist: click
Requires-Dist: feather-format
Requires-Dist: bkcharts
Requires-Dist: distributed
Requires-Dist: pygments
Requires-Dist: IPython

# pyABC

![pyABC](https://raw.githubusercontent.com/ICB-DCM/pyABC/master/doc/logo.png)

[![Build Status](https://travis-ci.org/ICB-DCM/pyABC.svg?branch=master)](https://travis-ci.org/ICB-DCM/pyABC)
[![docs](https://readthedocs.org/projects/pyabc/badge/?version=latest)](http://pyabc.readthedocs.io/en/latest/)

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).


