Metadata-Version: 2.1
Name: pyjags_arviz
Version: 0.0.2
Summary: Makes MCMC samples from PyJAGS usable in ArviZ
Home-page: https://github.com/michaelnowotny/pyjags_arviz
Author: Michael Christoph Nowotny
Author-email: nowotnym@gmail.com
License: MIT
Description: 
        # pyjags_arviz
        Makes MCMC samples from PyJAGS usable in ArviZ
        
        ## Table of Contents
        
        1.  [Installation](#installation)
        2.  [Getting Started](#getting-started)
        
        ## Installation
        
        1.  Install via PIP: 
            <pre>
            pip install pyjags_arviz 
            </pre>
            or 
            <pre>
            pip3 install pyjags_arviz 
            </pre>
            if not using Anaconda.
            
            To get the latest version, clone the repository from github, 
            open a terminal/command prompt, navigate to the root folder and install via
            <pre>
            pip install .
            </pre>
            or 
            <pre>
            pip3 install . 
            </pre>
            if not using Anaconda.
        
        ## Usage
        Import the function convert_pyjags_samples_dict_to_arviz_inference_data via
        <pre>
        from pyjags_arviz import convert_pyjags_samples_dict_to_arviz_inference_data
        </pre>
        
        Having sampled the from the posterior distribution using PyJAGS via
        <pre>
        samples \
            = jags_model.sample(...)
        </pre>
        one can write 
        <pre>
        idata = convert_pyjags_samples_dict_to_arviz_inference_data(samples)
        </pre>
        to convert the dictionary returned from PyJAGS to an ArviZ InferenceData object.
        
        This object can be used in ArviZ to generate trace plots and compute diagnostics.  
        Trace plot:
        <pre>
        az.plot_trace(idata)
        </pre>
        
        Effective sample size:
        <pre>
        az.ess(idata)
        </pre>
        
        Gelman and Rubin statistic:
        <pre>
        az.rhat(idata)
        </pre>
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
