Package: MHTrajectoryR
Type: Package
Title: Bayesian Model Selection in Logistic Regression for the
        Detection of Adverse Drug Reactions
Version: 1.0.1
Date: 2016-02-10
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <Mohammed.sedki@u-psud.fr>
Description: Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
License: GPL (>= 2)
Imports: parallel, mgcv
Depends: R (>= 2.10)
Repository: CRAN
NeedsCompilation: no
Packaged: 2016-04-05 12:48:04 UTC; sedki
Date/Publication: 2016-04-05 17:40:22
Built: R 4.6.0; ; 2026-04-27 18:24:14 UTC; unix
