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-4.5)MHTrajectoryR_1.0.1.zip(r-4.4)MHTrajectoryR_1.0.1.zip(r-4.3)
MHTrajectoryR_1.0.1.tgz(r-4.4-any)MHTrajectoryR_1.0.1.tgz(r-4.3-any)
MHTrajectoryR_1.0.1.tar.gz(r-4.5-noble)MHTrajectoryR_1.0.1.tar.gz(r-4.4-noble)
MHTrajectoryR_1.0.1.tgz(r-4.4-emscripten)MHTrajectoryR_1.0.1.tgz(r-4.3-emscripten)
MHTrajectoryR.pdf |MHTrajectoryR.html✨
MHTrajectoryR/json (API)
| # Install 'MHTrajectoryR' in R: |
| install.packages('MHTrajectoryR', repos = c('https://masedki.r-universe.dev', 'https://cloud.r-project.org')) |
- OmopReference - The OMOP reference set
- exampleAE - A simulated data
- exampleDrugs - A simulated data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 9 years agofrom:24f5a76203. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 20 2024 |
| R-4.5-win | OK | Nov 20 2024 |
| R-4.5-linux | OK | Nov 20 2024 |
| R-4.4-win | OK | Nov 20 2024 |
| R-4.4-mac | OK | Nov 20 2024 |
| R-4.3-win | OK | Nov 20 2024 |
| R-4.3-mac | OK | Nov 20 2024 |
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 |
