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) , 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.


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install.packages("hdbm")

0.9.0 by Alexander Rix, 3 months ago


Browse source code at https://github.com/cran/hdbm


Authors: Alexander Rix [aut, cre] , Yanyi Song [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp

Suggests knitr, rmarkdown

Linking to Rcpp, RcppArmadillo


See at CRAN