doi:10.1111/biom.13189> and Song et al (2020) <doi:10.48550/arXiv.2009.11409>, 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.">

bama: High Dimensional Bayesian Mediation Analysis (original) (raw)

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) <doi:10.1111/biom.13189> and Song et al (2020) <doi:10.48550/arXiv.2009.11409>, 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: 1.3.1
Depends: R (≥ 3.5)
Imports: Rcpp, parallel
LinkingTo: Rcpp, RcppArmadillo, RcppDist, BH
Suggests: knitr, rmarkdown
Published: 2025-09-20
DOI: 10.32614/CRAN.package.bama
Author: Alexander Rix [aut], Mike Kleinsasser [aut, cre], Yanyi Song [aut]
Maintainer: Mike Kleinsasser
BugReports: https://github.com/umich-cphds/bama/issues
License: GPL-3
URL: https://github.com/umich-cphds/bama
NeedsCompilation: yes
Materials: README
In views: Bayesian
CRAN checks: bama results

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