===========================================================================
``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/
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"description": "===========================================================================\n``spvcm``: Gibbs sampling for spatially-correlated variance-components\n===========================================================================\n\n.. image:: https://travis-ci.org/pysal/spvcm.svg?branch=master\n :target: https://travis-ci.org/pysal/spvcm\n.. image:: https://zenodo.org/badge/79168765.svg\n :target: https://zenodo.org/badge/latestdoi/79168765\n\nThis 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_.\n\n*author*: Levi John Wolf\n\n*email*: ``levi.john.wolf@gmail.com``\n\n*institution*: University of Bristol & University of Chicago Center for Spatial Data Science\n\n*preprint*: on the `Open Science Framework`_\n\n--------------------\nInstallation\n--------------------\n\nThis package works best in Python 3.5, but unittests pass in Python 2.7 as well. \nOnly Python 3.5+ is officially supported. \n\nTo 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. \n\nOnce Anaconda is installed, ``spvcm`` can be installed using ``pip``, the Python Package Manager. \n\n``pip install spvcm``\n\nTo install this from source, one can also navigate to the source directory and use:\n\n``pip install ./``\n\nwhich will install the package from the target source directory. \n\n-------------------\nUsage\n-------------------\n\nTo use the package, start up a Python interpreter and run:\n``import spvcm.api as spvcm``\n\nThen, many differnet variance components model specificaions are available in:\n\n``spvcm.both``\n``spvcm.upper``\n``spvcm.lower``\n\nFor more thorough directions, consult the Jupyter Notebook, ``using the sampler.ipynb``, which is provided in the ``spvcm/examples`` directory. \n\n-------------------\nCitation\n-------------------\n\nLevi John Wolf. (2016). `Gibbs Sampling for a class of spatially-correlated variance components models`. University of Chicago Center for Spatial Data Science Technical Report. \n\n.. _Distribution: https://https://www.continuum.io/downloads\n.. _Analytics: https://continuum.io\n.. _walkthrough: https://github.com/ljwolf/spvcm/blob/master/spvcm/examples/using_the_sampler.ipynb\n.. _`Open Science Framework`: https://osf.io/ks6t3/",
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