MHTrajectoryR: Bayesian Model Selection in Logistic Regression for the Detection of Adverse Drug Reactions

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.

Version: 1.0.1
Depends: R (≥ 2.10)
Imports: parallel, mgcv
Published: 2016-04-05
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <Mohammed.sedki at u-psud.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: MHTrajectoryR results

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Reference manual: MHTrajectoryR.pdf
Package source: MHTrajectoryR_1.0.1.tar.gz
Windows binaries: r-devel: MHTrajectoryR_1.0.1.zip, r-release: MHTrajectoryR_1.0.1.zip, r-oldrel: MHTrajectoryR_1.0.1.zip
OS X El Capitan binaries: r-release: MHTrajectoryR_1.0.1.tgz
OS X Mavericks binaries: r-oldrel: MHTrajectoryR_1.0.1.tgz
Old sources: MHTrajectoryR archive

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