(Robust) Mediation Analysis
Draw bootstrap samples
Dot plot with confidence intervals
Coefficients in (robust) mediation analysis
Confidence intervals from (robust) mediation analysis
Tuning parameters for Huber M-estimation of location and scatter
Huber M-estimator of location and scatter
Maximum likelihood estimator of mean vector and covariance matrix
Density plot of the indirect effect(s)
Diagnostic plot with a tolerance ellipse
(Robustly) fit a mediation model
Create an object of hypothesized mediators or control variables
p-Values from (robust) mediation analysis
Plot (robust) mediation analysis results
Tuning parameters for robust regression
Retest for mediation
tools:::Rd_package_title("robmed")
Set up information for a dot plot with confidence intervals
Set up information for a density plot of the indirect effect(s)
Set up a diagnostic plot with a tolerance ellipse
Set up a diagnostic plot of robust regression weights
Generate data from a fitted mediation model
Summary of results from (robust) mediation analysis
(Robust) mediation analysis
Diagnostic plot of robust regression weights
Robustness weights of Huber M-estimation of location and scatter
Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) <doi:10.31234/osf.io/2hqdy>.