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Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. High dimensional Bayesian mediation (HDBM), developed by Song et al (2018) <doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects.
| Version: | 0.9.0 |
| Imports: | Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown |
| Published: | 2019-08-28 |
| Author: | Alexander Rix [aut, cre], Yanyi Song [aut] |
| Maintainer: | Alexander Rix <alexrix at umich.edu> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | hdbm results |
| Reference manual: | hdbm.pdf |
| Vignettes: |
High Dimensional Bayesian Mediation Analysis in R |
| Package source: | hdbm_0.9.0.tar.gz |
| Windows binaries: | r-devel: hdbm_0.9.0.zip, r-release: hdbm_0.9.0.zip, r-oldrel: hdbm_0.9.0.zip |
| macOS binaries: | r-release (arm64): hdbm_0.9.0.tgz, r-oldrel (arm64): hdbm_0.9.0.tgz, r-release (x86_64): hdbm_0.9.0.tgz, r-oldrel (x86_64): hdbm_0.9.0.tgz |
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