Bayesian Cognitive Modeling with pyjgags

This is a project to port the code examples in Lee and Wagenmakers' 2013 textbook Bayesian Cognitive Modeling: A Practical Course from MATLAB into Python, using the pyjags package for interfacing Python code with the JAGS Gibbs sampling application.

The main contribution is the module pjbcmassistant, which contains convenience classes ModelHandler and SampleHandler for easily interfacing with pyjags, and for performing basic analysis on the model samples it produces.

Quick Start:

The notebook PyJAGS-BCM Usage Guide provides a demonstration and overview of how to use the module for building and analyzing models.

See the full documentation for details on all methods provided by the module.

NOTE:
In addition to dependencies listed in requirements.txt, this module requires that JAGS (which is not a Python package) be successfully installed on your system prior to configuring pyjags. See the JAGS website and the pyjags installation instructions for details.