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)
NeedsCompilation: no
Packaged: 2026-06-19 10:49:41 UTC; root
Repository: https://masedki.r-universe.dev
Date/Publication: 2016-04-05 17:40:22 UTC
RemoteUrl: https://github.com/cran/MHTrajectoryR
RemoteRef: HEAD
RemoteSha: 24f5a7620396359c455dd93eaf94828e9427d925