buzzMed v0.1.2

Exploratory Bayesian Mediation Analysis with Variable Selection

License: GPL v3

Overview

A collection of quantitative tools for selecting mediating effects within exploratory Bayesian mediation models. The package accommodates both continuous and dichotomous outcomes, including the dependent variables and the mediators for identifying and analyzing mediation pathways.

Requirements

This package requires JAGS (Just Another Gibbs Sampler) to be installed on your system. Download from: https://mcmc-jags.sourceforge.io/

Installation

You can install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("olfactorybulb/buzzMed")
library(buzzMed)

Main Functions

The package provides a primary automated interface and four specialized model-fitting functions based on variable types: - buzzEBMcontMcontY(): Continuous mediators, continuous outcome. - buzzEBMcontMcatY(): Continuous mediators, binary outcome. - buzzEBMcatMcontY(): Binary mediators, continuous outcome. - buzzEBMcatMcatY(): Binary mediators, binary outcome.

Example Usage

library(buzzMed)

# Create some toy data to play with
my_data <- data.frame(
    MyPredictor = rnorm(30),
    MyMediator1 = rnorm(30),
    MyMediator2 = rnorm(30),
    MyOutcome = rnorm(30)
)

# Specify your mediation model using syntax 'Y ~ M1 + M2 | X'
model_string <- "MyOutcome ~ MyMediator1 + MyMediator2 | MyPredictor"

# Run the model with continuous mediator and continuous outcome
fit <- buzzEBMcontMcontY(model = model_string, dataset = my_data)

Citation

If you use buzzMed in your research, please cite:

Shi, D., Dexin Shi, & Amanda J. Fairchild (2023). Variable Selection for Mediators under a Bayesian Mediation Model. Structural Equation Modeling: A Multidisciplinary Journal, 30(6), 887-900. DOI: 10.1080/10705511.2022.2164285

License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.