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
DOI:	10.32614/CRAN.package.hdbm
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

Documentation:

Reference manual:	hdbm.pdf
Vignettes:	High Dimensional Bayesian Mediation Analysis in R

Downloads:

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

Linking:

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