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SBGLM: Sparse Bayes Generalized Linear Models

Installation

if (!require(devtools)) {
    install.packages('devtools')
}
devtools::install_github('miguelbiron/SBGLM')

Description

The purpose of this package is mainly a way to store a lot of code that I have rotting in my laptop, which I think is relevant and useful. The models will follow the Bayesian tradition of the “spike-and-slab” prior for sparsity (Mitchell and Beauchamp 1988), so do not expect to see the so called “Bayesian Lasso” here because it doesn’t work. The idea is that, as time permits, I will be adding more models to the package.

Implemented models

  • A Bayesian sparse linear regression model (sblm)
    • Similar in spirit to the spike-and-slab, but with a slightly different approach. I describe this model in this blog entry.
  • Non-parametric Sparse Factor Analysis (Knowles and Ghahramani 2011).
    • Check out this blog entry where you can find more information on this model.

TODO

  • SBLM
    • Should we treat an intercept differently?
  • NSFA
    • Use pre-allocated matrices instead of current naive solution of varying size dynamically.
  • Add more models.

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A collection of functions for performing Bayesian inference on selected sparse (generalized) linear models.

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