Metadata-Version: 2.1
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
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: libpysal
Requires-Dist: spreg
Requires-Dist: pandas
Requires-Dist: seaborn

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``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/
