Start with the defaults | Paired variables | Choose the mediator structure | Predefined models | User-defined models | Add optional model components | Covariates | Moderators | Missing data and confidence intervals | Choose a missing-data strategy | Choose the inference method | Standardized effects | Inspect and present the results | Manuscript data
This package is considered a duplicate. The official version of this package is found at:https://yangzhen1999.r-universe.dev/wsMed
Package: wsMed 1.2.0
Wendie Yang
wsMed: Within-Subject Mediation Analysis Using Structural Equation Modeling
Within-subject mediation analysis using structural equation modeling. Examine how changes in an outcome variable between two conditions are mediated through one or more variables. Supports within-subject mediation analysis using the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, and extends Monte Carlo confidence interval estimation to missing data scenarios using the 'semmcci' package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.
Authors:
wsMed_1.2.0.tar.gz
wsMed_1.2.0.zip(r-4.7-any)wsMed_1.2.0.zip(r-4.6-any)
wsMed_1.2.0.tar.gz(r-4.7-any)wsMed_1.2.0.tar.gz(r-4.6-any)
wsMed_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
wsMed/json (API)
| # Install 'wsMed' in R: |
| install.packages('wsMed', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yangzhen1999/wsmed/issues
Pkgdown/docs site:https://yangzhen1999.github.io
Datasets:
- example_data - Simulated data for mediation tutorials
- wsmed_examples - Data for the manuscript examples
Last updated from:9645d50d87. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 335 | ||
| source / vignettes | OK | 240 | ||
| linux-release | OK | 325 | ||
| windows-devel | OK | 195 | ||
| windows-release | OK | 287 | ||
| wasm-release | OK | 147 |
Exports:GenerateModelCNGenerateModelCPGenerateModelCustomGenerateModelPGenerateModelPCImputeDataMCMI2plot_conditional_effectsplot_contrastsplot_effectsplot_moderation_curvePrepareDataPrepareMissingDataprintGMstandardize_moderationwsMedwsmed_contrastswsmed_effectswsmed_fitwsmed_inferwsmed_inspectwsmed_modelwsmed_reproducibility
Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrevaluatefarverforcatsforeachgenericsGGallyggplot2ggstatsglmnetglueGPArotationgtablehavenhighrhmsisobanditeratorsjomoknitrlabelinglatticelavaanlavaan.printerlifecyclelme4magrittrMASSMatrixmiceminqamitmlmnormtnlmenloptrnnetnumDerivordinalpanpatchworkpbivnormpillarpkgconfigprettyunitsprogresspsychpurrrquadprogR6rbibutilsRColorBrewerRcppRcppEigenRdpackreadrreformulasrlangrpartS7scalessemboottoolssemmccishapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsviridisLitevroomwithrxfunyaml
Analysis options: a complete walkthrough
Last update: 2026-10-28
Started: 2025-12-11
Choose an analysis
Prepare your data | Choose your model | Add moderation | Handle missing data | Read, compare and present results | Go further when you need it
Last update: 2026-10-28
Started: 2026-10-28
Comparing wsMed and MEMORE
What is compared? | Parallel mediation | Serial mediation | Reading the comparison
Last update: 2026-10-28
Started: 2025-12-11
Diagnostics, reproducibility, and migration
Fit once and keep the inference settings | Inspect fitted datasets and inference separately | Interpret the effect being reported | Saved-object compatibility | Export a compact analysis manifest | Move from the previous one-call workflow | Statistical scope of validation
Last update: 2026-10-28
Started: 2026-10-28
Get started: two mediation examples
Install wsMed | Match your columns to the two conditions | Example 1: parallel mediation | Run the analysis | Read the results | Plot the indirect effects | Example 2: add a continuous moderator | Read the conditional effects | Plot how the indirect effects change | Specify more only when you need it | Continue with your own analysis
Last update: 2026-10-28
Started: 2026-10-28
Inspecting generated SEM syntax
Demonstration of printGM() | Using printGM() with wsMed() | Using printGM() with GenerateModel*()
Last update: 2026-10-28
Started: 2025-12-11
Model equations: parallel mediation
Difference scores and centered averages | Regression equations | Relation to the two condition equations | Indirect pathways | Total effects and contrasts | Condition-specific slopes | Standardization and moderation
Last update: 2026-10-28
Started: 2025-12-11
Model equations: parallel-serial mediation
Difference scores and centered averages | Regression equations | Indirect pathways | Total effects and contrasts | Condition-specific slopes | Standardization and moderation
Last update: 2026-10-28
Started: 2025-12-11
Model equations: serial mediation
Difference scores and centered averages | Regression equations | Indirect pathways | Total effects and contrasts | Condition-specific slopes | Standardization and moderation
Last update: 2026-10-28
Started: 2025-12-11
Model equations: serial-parallel mediation
Difference scores and centered averages | Regression equations | Indirect pathways | Total effects and contrasts | Condition-specific slopes | Standardization and moderation
Last update: 2026-10-28
Started: 2025-12-11
Model-compatible multiple imputation
Match imputation to the analysis | Generate the illustrative incomplete data | Impute using the outcome interaction | Use either wsMed interface | What the validation can establish
Last update: 2026-10-28
Started: 2026-10-28
Plotting mediation effects and contrasts
Choose a figure | Reproducible two-condition data | A consistent plot method | Interval conventions | Optional named plotting helpers | Forest plots | Conditional effects by group or selected level | Differences need their own intervals | Conditional curves and their highlighted ranges | Inspect and export
Last update: 2026-10-28
Started: 2026-09-18
Read and export results
Read the default report | Get a statistical summary | Plot the results | Choose more specific output | Request standardized effects | Select an effect family | Change the confidence level | Choose moderator values | Compare two pathways | Extract or export results when needed
Last update: 2026-10-28
Started: 2026-10-28
Reuse a fit: the staged workflow
The workflow at a glance | 1. Specify the model | What can you inspect now? | 2. Fit the model | What can you extract from the fit? | 3. Perform inference | What can you extract from inference? | 4. Read and extract effect results | What can you extract from the summary? | Decide where to restart after a change | Add a custom structure and moderator | Handle missing data at the fitting stage | What changes in extraction after MI? | Relate the stages to a one-call analysis | Save a stage and continue later
Last update: 2026-10-28
Started: 2026-10-28
Specifying user-defined mediation models
Mediator numbering | Specifying a custom model | Fitting the complete model with wsMed | Input requirements | Indirect effects | Parameter labels | Add a moderator | Choose the moderated paths | Fit and read the results | Choose moderator values | Use a categorical moderator | Standardized moderated custom models | Optional: inspect the generated equations | [M_
Last update: 2026-10-28
Started: 2026-09-18
Standardized effects
Request standardized results | What is standardized? | Continuous moderators: two different quantities | Fit a reproducible example | Categorical moderators | Confidence intervals, bootstrap and both engines | Missing data and reporting | More plots for reporting | Imputation compatibility | Optional implementation checks | Check the stored point estimates | Legacy moderation tables
Last update: 2026-10-28
Started: 2026-09-18
Working with categorical predictors
Supported variable roles | Choose the reference category | Fit categorical moderated mediation | Conditional effects and group differences | Optional: fit the same model in stages | Categorical covariates | Missing categorical predictors
Last update: 2026-10-28
Started: 2026-10-28
