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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | MHTrajectoryR_1.0.1.tar.gz |
33.9 KiB |
1.0.1 |
rolling linux/noble R-4.5 | MHTrajectoryR_1.0.1.tar.gz |
33.8 KiB |
1.0.1 |
rolling source/ R- | MHTrajectoryR_1.0.1.tar.gz |
9.8 KiB |
1.0.1 |
latest linux/jammy R-4.5 | MHTrajectoryR_1.0.1.tar.gz |
33.9 KiB |
1.0.1 |
latest linux/noble R-4.5 | MHTrajectoryR_1.0.1.tar.gz |
33.8 KiB |
1.0.1 |
latest source/ R- | MHTrajectoryR_1.0.1.tar.gz |
9.8 KiB |
1.0.1 |
2026-04-26 source/ R- | MHTrajectoryR_1.0.1.tar.gz |
9.8 KiB |
1.0.1 |
2026-04-23 source/ R- | MHTrajectoryR_1.0.1.tar.gz |
9.8 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | MHTrajectoryR_1.0.1.zip |
37.8 KiB |
1.0.1 |
2025-04-20 source/ R- | MHTrajectoryR_1.0.1.tar.gz |
9.8 KiB |