pyABC
=====
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: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
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