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| Package: SEMMS | |
| Type: Package | |
| Title: Variable selection in Generalized Linear Models - an empirical Bayes approach | |
| Version: 0.2.0 | |
| Author: Haim Bar | |
| Maintainer: Haim Bar <haim.bar@uconn.edu> | |
| Imports: Rcpp (>= 1.0.5), RcppArmadillo | |
| LinkingTo: Rcpp, RcppArmadillo | |
| Depends: R (>= 3.6.0), car, MASS, edgefinder | |
| Suggests: knitr | |
| VignetteBuilder: knitr | |
| Description: 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. | |
| License: MIT + file LICENSE | |
| Encoding: UTF-8 | |
| LazyData: true | |
| RoxygenNote: 7.1.1 |