{ "info": { "author": "Levi John Wolf", "author_email": "levi.john.wolf@gmail.com", "bugtrack_url": null, "classifiers": [], "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. 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