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:
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')) |
- exampleAE - A simulated data
- exampleDrugs - A simulated data
- OmopReference - The OMOP reference set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:24f5a76203. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 119 | ||
| source / vignettes | OK | 136 | ||
| linux-release-x86_64 | OK | 130 | ||
| macos-release-arm64 | OK | 104 | ||
| macos-oldrel-arm64 | OK | 106 | ||
| windows-devel | OK | 88 | ||
| windows-release | OK | 86 | ||
| windows-oldrel | OK | 73 | ||
| wasm-release | OK | 98 |
Exports:Analyze_oneAE
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Detection of adverse drug events by analyzing Metropolis-Hastings Markov chain trajectory. | MHTrajectoryR-package MHTrajectoryR |
| Signal detection using via variable selection in logistic regression. The Bayesian Information Criterion maximization is assessed using Metropolis-Hastings algorithm. | Analyze_oneAE |
| A simulated data | exampleAE |
| A simulated data | exampleDrugs |
| The OMOP reference set | OmopReference |
