Software Application Profile: BKMR-CMA: a novel R command for Bayesian mediation analysis in environmental mixture studies
Abstract
Motivation: Machine learning is becoming increasingly used in high-dimensional mediation settings. In this work, we present software that implements the Bayesian Kernel Machine Regression—Causal Mediation Analysis (BKMR-CMA) algorithm, which extends the popular BKMR methodology to causal mediation analysis in the presence of multiple correlated exposures. Its functions are implemented in the R package causalmixtures.
Implementation: The mediation.bkmr command conducts a mediation analysis evaluating the potential intermediate role of a variable on the association between a mixture of exposures and an outcome. The CDE.bkmr command computes controlled direct effects when fixing the mediator to different levels.
General features: The BKMR-CMA method, implemented in the mediation.bkmr and the CDE.bkmr commands from the causalmixtures R package, is able to decompose the total effect into the natural direct and the natural indirect effect, while evaluating interventions on the mediator, incorporating nonlinear associations between exposure, mediator, and outcome, accounting for correlations between exposures, testing for interactions, and providing tools for detailed visualization of results.
Availability: The causalmixtures R package is freely available for download on Github: https://arcedr.github.io/causalmixtures/. Details of implementation can be found in the quick start guide: https://arcedr.github.io/causalmixtures/articles/BKMRCMA_QuickStart.html.
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BibTeX
@article{chai2026software,
title = {{Software Application Profile: BKMR-CMA: a novel R command for Bayesian mediation analysis in environmental mixture studies}},
author = {Zilan Chai and Arce Domingo-Relloso and Katrina L Devick and Maitreyi Mazumndar and Ana Navas-Acien and Brent A Coull and Linda Valeri},
journal = {International journal of epidemiology},
year = {2026},
month = oct,
doi = {10.1093/ije/dyag235},
url = {https://europepmc.org/article/PMC/PMC13723334},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Software Application Profile: BKMR-CMA: a novel R command for Bayesian mediation analysis in environmental mixture studies
AU - Zilan Chai
AU - Arce Domingo-Relloso
AU - Katrina L Devick
AU - Maitreyi Mazumndar
AU - Ana Navas-Acien
AU - Brent A Coull
AU - Linda Valeri
PY - 2026
DA - 2026/10/31
JO - International journal of epidemiology
DO - 10.1093/ije/dyag235
UR - https://europepmc.org/article/PMC/PMC13723334
AB - Motivation: Machine learning is becoming increasingly used in high-dimensional mediation settings. In this work, we present software that implements the Bayesian Kernel Machine Regression—Causal Mediation Analysis (BKMR-CMA) algorithm, which extends the popular BKMR methodology to causal mediation analysis in the presence of multiple correlated exposures. Its functions are implemented in the R package causalmixtures. Implementation: The mediation.bkmr command conducts a mediation analysis evaluating the potential intermediate role of a variable on the association between a mixture of exposures and an outcome. The CDE.bkmr command computes controlled direct effects when fixing the mediator to different levels. General features: The BKMR-CMA method, implemented in the mediation.bkmr and the CDE.bkmr commands from the causalmixtures R package, is able to decompose the total effect into the natural direct and the natural indirect effect, while evaluating interventions on the mediator, incorporating nonlinear associations between exposure, mediator, and outcome, accounting for correlations between exposures, testing for interactions, and providing tools for detailed visualization of results. Availability: The causalmixtures R package is freely available for download on Github: https://arcedr.github.io/causalmixtures/. Details of implementation can be found in the quick start guide: https://arcedr.github.io/causalmixtures/articles/BKMRCMA_QuickStart.html.
SN - 0300-5771
SN - 1464-3685
ER -