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
DOI: 10.32614/CRAN.package.MHTrajectoryR
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <Mohammed.sedki at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: MHTrajectoryR results


Reference manual: MHTrajectoryR.pdf


Package source: MHTrajectoryR_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MHTrajectoryR_1.0.1.tgz, r-oldrel (arm64): MHTrajectoryR_1.0.1.tgz, r-release (x86_64): MHTrajectoryR_1.0.1.tgz, r-oldrel (x86_64): MHTrajectoryR_1.0.1.tgz
Old sources: MHTrajectoryR archive


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