In jmleach-bst/ssnet: Fit Spike-and-Slab Elastic Net GLM With Spatial Priors on Parameters

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ssnet

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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.

Installation

The R package ssnet is in development, which version can be installed from GitHub with:

# install.packages("devtools")
devtools::install_github("jmleach-bst/ssnet")

Example

Coming soon - examples and vignette. However, note that the documentation is thorough (in my opinion), and the most useful functions' documentation contain examples.

jmleach-bst/ssnet documentation built on March 4, 2024, 5:04 p.m.