haimbar/SEMMS: Variable selection in Generalized Linear Models - an empirical Bayes approach
We fit a three-component mixture model to the coefficients in the linear predictors in a GLM model (normal, Poisson, or bionmial response.) We use an empirical Bayes approach to fit the model parameters. Fitting is done via a Generalized Alternating Maximization algorithm.
Getting started
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Package details |
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| Author | Haim Bar |
| Maintainer | Haim Bar <haim.bar@uconn.edu> |
| License | MIT + file LICENSE |
| Version | 0.2.5 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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