hdbm: High Dimensional Bayesian Mediation Analysis

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: hdbm_0.9.0.tgz, r-oldrel: hdbm_0.9.0.tgz

Reverse dependencies:

Reverse imports: autohd


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