Metadata-Version: 1.0
Name: spvcm
Version: 0.3.0
Summary: Fit spatial multilevel models and diagnose convergence
Home-page: https://github.com/ljwolf/spvcm
Author: Levi John Wolf
Author-email: levi.john.wolf@gmail.com
License: 3-Clause BSD
Description: ===========================================================================
        ``spvcm``: Gibbs sampling for spatially-correlated variance-components
        ===========================================================================
        
        .. image:: https://travis-ci.org/pysal/spvcm.svg?branch=master
            :target: https://travis-ci.org/pysal/spvcm
        .. image:: https://zenodo.org/badge/79168765.svg
            :target: https://zenodo.org/badge/latestdoi/79168765
        
        This is a package to estimate spatially-correlated variance components models/varying intercept models. In addition to a general toolkit to conduct Gibbs sampling in Python, the package also provides an interface to PyMC3 and CODA. For a complete overview, consult the walkthrough_.
        
        *author*: Levi John Wolf
        
        *email*: ``levi.john.wolf@gmail.com``
        
        *institution*: University of Bristol & University of Chicago Center for Spatial Data Science
        
        *preprint*: on the `Open Science Framework`_
        
        --------------------
        Installation
        --------------------
        
        This package works best in Python 3.5, but unittests pass in Python 2.7 as well. 
        Only Python 3.5+ is officially supported. 
        
        To install, first install the Anaconda Python Distribution_ from Continuum Analytics_. Installation of the package has been tested in Windows (10, 8, 7) Mac OSX (10.8+) and Linux using Anaconda 4.2.0, with Python version 3.5. 
        
        Once Anaconda is installed, ``spvcm`` can be installed using ``pip``, the Python Package Manager. 
        
        ``pip install spvcm``
        
        To install this from source, one can also navigate to the source directory and use:
        
        ``pip install ./``
        
        which will install the package from the target source directory. 
        
        -------------------
        Usage
        -------------------
        
        To use the package, start up a Python interpreter and run:
        ``import spvcm.api as spvcm``
        
        Then, many differnet variance components model specificaions are available in:
        
        ``spvcm.both``
        ``spvcm.upper``
        ``spvcm.lower``
        
        For more thorough directions, consult the Jupyter Notebook, ``using the sampler.ipynb``, which is provided in the ``spvcm/examples`` directory.  
        
        -------------------
        Citation
        -------------------
        
        Levi John Wolf. (2016). `Gibbs Sampling for a class of  spatially-correlated variance components models`. University of Chicago Center for Spatial Data Science Technical Report. 
        
        .. _Distribution: https://https://www.continuum.io/downloads
        .. _Analytics: https://continuum.io
        .. _walkthrough: https://github.com/ljwolf/spvcm/blob/master/spvcm/examples/using_the_sampler.ipynb
        .. _`Open Science Framework`: https://osf.io/ks6t3/
        
Platform: UNKNOWN
