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 u-psud.fr>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	MHTrajectoryR results

Documentation:

Reference manual:

MHTrajectoryR.pdf

Downloads:

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
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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