The goal of ssnet is to fit spike-and-slab elastic net GLM’s with or
without spatially structured priors. An expectation maximization (EM)
algorithm is used to fit the models, and spatial structure is
incorporated by using Intrinsic Autoregressions (IAR) as priors for
inclusion probabilities. This allows for variable selection to
incorporate assumptions about spatial clustering of variables that
should (not) be included in the model. Outcome distributions supported
are Binomial, Normal, Poisson, and Multinomial.
The R package ssnet is in development, which version can be
installed from GitHub with:
# install.packages("devtools")
devtools::install_github("jmleach-bst/ssnet")Coming soon - examples and vignette. However, note that the documentation is thorough (in my opinion), and the most useful functions’ documentation contain examples.