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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.
| Version: | 1.0.1 |
| Depends: | R (≥ 2.10) |
| Imports: | parallel, mgcv |
| Published: | 2016-04-05 |
| Author: | Matthieu Marbac and Mohammed Sedki |
| Maintainer: | Mohammed Sedki <Mohammed.sedki at u-psud.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | MHTrajectoryR results |
| Reference manual: | MHTrajectoryR.pdf |
| Package source: | MHTrajectoryR_1.0.1.tar.gz |
| Windows binaries: | r-devel: MHTrajectoryR_1.0.1.zip, r-release: MHTrajectoryR_1.0.1.zip, r-oldrel: MHTrajectoryR_1.0.1.zip |
| macOS binaries: | r-release (arm64): MHTrajectoryR_1.0.1.tgz, r-oldrel (arm64): MHTrajectoryR_1.0.1.tgz, r-release (x86_64): MHTrajectoryR_1.0.1.tgz, r-oldrel (x86_64): MHTrajectoryR_1.0.1.tgz |
| Old sources: | MHTrajectoryR archive |
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