High Dimensional Bayesian Mediation Analysis

Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) and Song et al (2020) , 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.


Reference manual

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1.2 by Mike Kleinsasser, a year ago


Report a bug at https://github.com/umich-cphds/bama/issues

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

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

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, parallel

Suggests knitr, rmarkdown

Linking to Rcpp, RcppArmadillo, RcppDist, BH

See at CRAN