hdbma-package {hdbma}

Bayesian Mediation Analysis with High-Dimensional Data

Description

Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.

Details

The DESCRIPTION file:

Package:	hdbma
Type:	Package
Title:	Bayesian Mediation Analysis with High-Dimensional Data
Version:	1.0
Date:	2023-12-16
Authors@R:	c(person("Qingzhao Yu", role = c("aut", "cre","cph"), email = "[e-mail]"), person("Bin Li", role = "aut") )
Maintainer:	Qingzhao Yu <[e-mail]>
Depends:	R (>= 2.14.1), R2jags,gplots,MASS,survival,splines
Imports:	lattice, methods
Encoding:	UTF-8
Description:	Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.
License:	GPL (>= 2)
URL:	https://www.r-project.org, https://publichealth.lsuhsc.edu/Faculty_pages/qyu/index.html
RoxygenNote:	7.2.3
Author:	Qingzhao Yu [aut, cre, cph], Bin Li [aut]

Index of help topics:

hdbma                   High-Dimensional Bayesian Mediation Analysis
hdbma-package           Bayesian Mediation Analysis with
                        High-Dimensional Data
print.summary.hdbma     Print
summary.hdbma           Summary for hdbma results
weight_behavior         Weight_Behavior Data Set

The main function is hdbma to perform the Bayesian mediation anlysis with adaptive Laplace priors.

Author(s)

NA

Maintainer: Qingzhao Yu <[e-mail]>

References

Yu, Q., Hagan, J., Wu, X., Richmond-Bryant, J., and Li, B., 2023, High-Dimensional Bayesian Mediation Analysis with Adaptive Laplace Priors. Submitted.

Examples

#See examples at summary.hdbma.