spvcm
was archived on 2024-07-09 as per #18
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
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.
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.
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.