RMediation

Below is the revised README.md file for the RMediation package, incorporating additional features such as MBCO, parametric and semi-parametric bootstrap methods, distribution of the product, and Monte Carlo method for confidence intervals:

RMediation

RMediation is an R package for conducting mediation analysis in observational studies and experimental designs. Mediation analysis allows researchers to assess the mechanisms through which an independent variable affects a dependent variable by examining the role of intermediate variables, known as mediators.

Features

• Implements various mediation analysis methods, including:
  • Distribution of the product method CI
  • Sobel test (asymptotic normal)
  • Monte Carlo method for confidence intervals
  • Likelihood ratio test of mediation using Model-based Constraint Optimization, LRT-MBCO
  • Parametric and semi-parametric bootstrap methods for LRT MBCO
• Provides functions for estimating direct and indirect effects and confidence intervals.
• Includes visualization functions for displaying distribution of indirect estimates.

Installation

You can install the latest version of RMediation from CRAN using the following command:

install.packages("RMediation")

Alternatively, you can install the development version directly from GitHub using the remotes package:

remotes::install_github("quantpsych/RMediation")

Usage

To conduct a basic mediation analysis using RMediation, follow these steps:

1. Load the RMediation package:

library(RMediation)

There are several scenarios for mediation analysis using the RMediation:

1. You have already performed a mediation analysis using a structural equation model (SEM) software and you have coeffiecient and indirect effect estimates along with their standard errors and covariance matrix of the coefficients. For a single mediator model, you can use the medci function to calculate confidence intervals for the indirect effects. For multiple mediator models, you can use the ci function to calculate confidence intervals for the indirect effects. For a general mediation model, you can use the ci function to calculate confidence intervals for the indirect effects. These two functions offer a variety of methods for calculating confidence intervals, such as distribution of the product and Monte Carlo methods.

2. You have not yet performed a mediation analysis in lavaan or OpenMx and you have a fitted model object. In this case, you can use the ci function to estimate indirect effect confidence interval with multiple mediators and the medci function to calculate confidence intervals for the indirect effects with a single mediator. The first argument for these functions are the fitted model object-- you do not need to specify the coefficnet estimates and their standard errors (covariance matrix of the coefficients) as the functions will extract these from the fitted model object.

Contributing

Contributions to RMediation are welcome! If you encounter any issues, have suggestions for improvements, or would like to contribute new features, please open an issue or submit a pull request on GitHub.

Citation

If you use RMediation in your research, please cite it using the following citation:

Tofighi, D. and MacKinnon, D. P. (2011). 'RMediation' An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692--700. <doi:10.3758/s13428-011-0076-x>.

Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.

License

RMediation is licensed under the GPL-3.0. See the LICENSE file for details.