The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Getting Started with buzzMed

Introduction

buzzMed is 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.

Basic Usage

First, load the package:

library(buzzMed)

To run a basic mediation model, use one of the buzzEBM functions depending on your data types (continuous or categorical):

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)

More Information

For detailed theory on the Bayesian mediation selection process, please refer to the package documentation for the specific buzzEBM functions.

References

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.