Package: MHTrajectoryR 1.0.1

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.

Authors:Matthieu Marbac and Mohammed Sedki

MHTrajectoryR_1.0.1.tar.gz
MHTrajectoryR_1.0.1.zip(r-4.7)MHTrajectoryR_1.0.1.zip(r-4.6)MHTrajectoryR_1.0.1.zip(r-4.5)
MHTrajectoryR_1.0.1.tgz(r-4.6-any)MHTrajectoryR_1.0.1.tgz(r-4.5-any)
MHTrajectoryR_1.0.1.tar.gz(r-4.7-any)MHTrajectoryR_1.0.1.tar.gz(r-4.6-any)
MHTrajectoryR_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
MHTrajectoryR/json (API)

# Install 'MHTrajectoryR' in R:
install.packages('MHTrajectoryR', repos = c('https://masedki.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 5 scripts 192 downloads 1 exports 4 dependencies

Last updated from:24f5a76203. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK119
source / vignettesOK136
linux-release-x86_64OK130
macos-release-arm64OK104
macos-oldrel-arm64OK106
windows-develOK88
windows-releaseOK86
windows-oldrelOK73
wasm-releaseOK98

Exports:Analyze_oneAE

Dependencies:latticeMatrixmgcvnlme