pyjags-jw 0.3.1

Python bindings for JAGS (modernized build so that everything 'just works' (JW)).

PyJAGS: The Python Interface to JAGS

PyJAGS provides a Python interface to JAGS, a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation.

PyJAGS adds the following features on top of JAGS:

• Multicore support for parallel simulation of multiple Markov chains (See Jupyter Notebook Advanced Functionality
• Saving sample MCMC chains to and restoring from HDF5 files
• Functionality to merge samples along iterations or across chains so that sampling can be resumed in consecutive chunks until convergence criteria are satisfied
• Connectivity to the Bayesian analysis and visualization package Arviz

License: GPLv2

Supported Platforms

• Linux: prebuilt wheels for CPython 3.11–3.13 on x86_64 and aarch64 with JAGS + toolchain runtimes fully bundled. pip install pyjags-jw should “just work.”
• macOS: wheels targeted for CPython 3.11–3.13 (x86_64) with bundled JAGS; arm64 coming next. Source builds still require a system JAGS if no wheel is available.
• Windows: planned; source builds currently require a system JAGS.

Installation

pip install pyjags-jw

No system JAGS needed on supported Linux wheels.

    pip install pyjags

Useful Links

• Package on the Python Package Index https://pypi.python.org/pypi/pyjags
• Project page on github https://github.com/michaelnowotny/pyjags
• JAGS manual and examples http://sourceforge.net/projects/mcmc-jags/files/

Acknowledgements

• JAGS was created by Martyn Plummer
• PyJAGS was originally created by Tomasz Miasko
• As of May 2020, PyJAGS is developed by Michael Nowotny