Bayesian Model Selection in Logistic Regression for the Detection of Adverse Drug Reactions R package MHTrajectoryR version 1.0.1 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. Software Depends: R (>= 2.10) Imports: parallel, mgcv Mohammed Sedki <Mohammed.sedki@u-psud.fr> Comprehensive R Archive Network (CRAN) Matthieu Marbac and Mohammed Sedki GPL (>= 2) 2016-04-05 application/tgz https://CRAN.R-project.org/package=MHTrajectoryR doi:10.32614/CRAN.package.MHTrajectoryR