Metadata-Version: 2.1
Name: pymcmcstat
Version: 1.5.0rc2
Summary: A library to perform MCMC simulations using DRAM
Home-page: https://github.com/prmiles/pymcmcstat
Author: Paul Miles
Author-email: prmiles@ncsu.edu
License: MIT
Download-URL: https://github.com/prmiles/pymcmcstat
Description: `pymcmcstat`
        ============
        
        |docs| |build| |coverage| |license| |codacy| |zenodo|
        
        The `pymcmcstat` package is a Python program for running Markov Chain Monte Carlo (MCMC) simulations.
        Included in this package is the ability to use different Metropolis based sampling techniques:
        
        * Metropolis-Hastings (MH): Primary sampling method.
        * Adaptive-Metropolis (AM): Adapts covariance matrix at specified intervals.
        * Delayed-Rejection (DR): Delays rejection by sampling from a narrower distribution.  Capable of `n`-stage delayed rejection.
        * Delayed Rejection Adaptive Metropolis (DRAM): DR + AM
        
        The `pymcmcstat homepage <https://prmiles.wordpress.ncsu.edu/codes/python-packages/pymcmcstat/>`_ contains tutorials for users as well as installation instructions.
        
        Python implementation of MATLAB toolbox "mcmcstat".  This code is designed to replicate the functionality of the MATLAB routines developed and posted here: http://helios.fmi.fi/~lainema/mcmc/
        
        The user interface is designed to be as similar to the MATLAB version as possible, but this implementation has taken advantage of certain data structure concepts more amenable to Python.  
        
        Installation
        ============
        
        This code can be found on the `Github project page <https://github.com/prmiles/pymcmcstat>`_.  It is open source and provided under the MIT license.
        To install directly from Github,
        
        ::
        
            pip install git+https://github.com/prmiles/pymcmcstat.git
        
        You can also clone the repository and run ``python  setup.py install``.
        
        Package is also available on the PyPI distribution site.
        
        ::
        
            pip install pymcmcstat
        
        Getting Started
        ===============
        
        - Tutorial `notebooks <https://nbviewer.jupyter.org/github/prmiles/notebooks/tree/master/pymcmcstat/index.ipynb>`_
        - `Documentation <http://pymcmcstat.readthedocs.io/>`_
        
        License
        =======
        
        `MIT <https://github.com/prmiles/pymcmcstat/blob/master/LICENSE.txt>`_
        
        Contributors
        ============
        
        See the `GitHub contributor
        page <https://github.com/prmiles/pymcmcstat/graphs/contributors>`_
           
        .. |docs| image:: https://readthedocs.org/projects/pymcmcstat/badge/?version=latest
            :target: https://pymcmcstat.readthedocs.io/en/latest/?badge=latest
            :scale: 100%
            
        .. |build| image:: https://travis-ci.org/prmiles/pymcmcstat.svg?branch=master
            :target: https://travis-ci.org/prmiles/pymcmcstat
            :scale: 100%
            
        .. |license| image:: https://img.shields.io/badge/License-MIT-yellow.svg
            :target: https://github.com/prmiles/pymcmcstat/blob/master/LICENSE.txt
        
        .. |coverage| image:: https://coveralls.io/repos/github/prmiles/pymcmcstat/badge.svg
            :target: https://coveralls.io/github/prmiles/pymcmcstat
        
        .. |codacy| image:: https://api.codacy.com/project/badge/Grade/b1a33340c57a47648f993e124c75e93a    
            :target: https://www.codacy.com/app/prmiles/pymcmcstat?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=prmiles/pymcmcstat&amp;utm_campaign=Badge_Grade
        
        .. |zenodo| image:: https://zenodo.org/badge/107596954.svg
            :target: https://zenodo.org/badge/latestdoi/107596954
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Classifier: Framework :: IPython
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Provides-Extra: docs
Provides-Extra: plotting
