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wsMed

Mediation and moderated mediation for two-condition within-subject designs.

Install

These tutorials describe wsMed 1.2.0. Install the released package from CRAN:

install.packages("wsMed")

Or install the version on the GitHub master branch:

# install.packages("pak")
pak::pak("Yangzhen1999/wsMed@master")

Restart R after updating. packageVersion("wsMed") shows the installed version.

First analysis

Each row is one participant. The paired mediator columns are A1 / A2 and B1 / B2; the paired outcome columns are D1 / D2. The contrast is condition 2 minus condition 1.

library(wsMed)
data(example_data)

result <- wsMed(
  data = example_data,
  M_C1 = c("A1", "B1"),
  M_C2 = c("A2", "B2"),
  Y_C1 = "D1",
  Y_C2 = "D2"
)

print(result)
summary(result)
plot(result)

This uses the defaults: parallel mediation, listwise deletion, Monte Carlo inference with 20,000 draws, 95% CIs and unstandardized output. The dataset is complete. The Get started guide explains the report and adds a moderator in a second example.

Learn more

• Choose an analysis
• Read and export results
• Plot gallery
• Analysis in separate steps

Use unordered factors for categorical between-subject covariates and moderators, with the first level as the reference. The categorical predictor guide explains coding and missing-data restrictions.

The empirical manuscript data are included as wsmed_examples. For source editing and tutorial rebuilding, see the maintainer guide.

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.
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