Bayesian spatial regression of communities
sprcom stands for Spatial Regression of Communities and is a statistical package designed to streamline the interpretation and modeling of very high dimensional binary and count-valued data. The underlying model assumes a low-dimensional latent structure via communities or clusters that leads to a parsimonious model.
sprcom can also account for the dependence of these communities on covariates! A number of utility and plotting functions are included to help visualize your results.
sprcom is a wrapper for a PyMC3 model and you can use any PyMC3 estimation method with it including Hamiltonian Monte Carlo and ADVI.
covariates, response, adjacency = load_data(...) n_communities = 5 model = spatial_community_regression(covariates, response, adjacency,n_communities) with model: trace = pm.sample() ...
We've included documentation to help you get up and running. Check out the
florabank1-tutorial notebook for more details!
For questions or comments please contact Christopher Krapu at
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size sprcom-0.2.tar.gz (1.8 kB)||File type Source||Python version None||Upload date||Hashes View|