Package index • wsMed

Main functions

Core functions for within-subject mediation analysis

wsMed(): Analyze two-condition within-subject mediation

Staged workflow

Specify, fit, infer, and extract without repeating earlier stages

wsmed_model(): Specify a two-condition mediation model
wsmed_fit(): Fit a specified mediation model without simulation
wsmed_infer(): Apply uncertainty quantification to an existing fit
wsmed_inspect(): Inspect a modular workflow without recomputation
wsmed_reproducibility(): Export the settings and provenance of an analysis

Reports, summaries, and effect comparisons

Use print() and summary() to read reports and extract structured tables, and plot() to visualize results. The functions below support focused effect queries and comparisons.

wsmed_effects(): Extract effects from fitted models or stored inference
wsmed_contrasts(): Contrast effects using their joint stored draws

Model generation

Functions for generating SEM syntax for different mediation structures

printGM(): Print formatted SEM model syntax
GenerateModelP(): Generate a parallel mediation model
GenerateModelCN(): Generate a serial mediation model
GenerateModelCP(): Generate a serial-parallel mediation model
GenerateModelPC(): Generate a parallel-serial mediation model
GenerateModelCustom(): Generate a user-defined mediation model

Missing data and imputation

Functions to handle missing data with multiple imputation

ImputeData(): Impute missing data using multiple imputation
PrepareMissingData(): Prepare data with missing values for mediation analysis
MCMI2(): Monte Carlo confidence intervals for multiple imputation SEM models
PrepareData(): Prepare data for two-condition mediation

Standardization and conditional effects

Standardize conditional results and visualize effect curves

standardize_moderation(): Standardize conditional mediation results using existing joint draws
plot_moderation_curve(): Plot conditional effects across a continuous moderator
plot_effects(): Forest plot of mediation effects
plot_conditional_effects(): Plot conditional effects at moderator levels
plot_contrasts(): Plot differences between mediation effects

Data and variable types

Packaged datasets and supported categorical predictors

wsmed_examples: Data for the manuscript examples
example_data: Simulated data for mediation tutorials
wsmed_categorical: Categorical predictors and supported variable types