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  "Package": "wsMed",
  "Title": "Within-Subject Mediation Analysis Using Structural Equation\nModeling",
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  "Description": "Within-subject mediation analysis using structural\nequation modeling. Examine how changes in an outcome variable\nbetween two conditions are mediated through one or more\nvariables. Supports within-subject mediation analysis using the\n'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>,\nand extends Monte Carlo confidence interval estimation to\nmissing data scenarios using the 'semmcci' package by Pesigan\nand Cheung (2023) <doi:10.3758/s13428-023-02114-4>.",
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  "NeedsCompilation": "no",
  "Author": "Wendie Yang [aut, cre] (ORCID:\n<https://orcid.org/0009-0000-8388-6481>), Shu Fai Cheung [aut]\n(ORCID: <https://orcid.org/0000-0002-9871-9448>)",
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  "Date/Publication": "2026-09-18 03:14:31 UTC",
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  "_published": "2026-09-18T08:33:11.446Z",
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    "message": "Merge pull request #8 from Yangzhen1999/feature/custom-model\n\nRelease wsMed 1.1.0: custom models, standardization fixes, and new plots",
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  "_topics": [
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    "missing-data",
    "sem",
    "standardization",
    "within-subject"
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      "date": "2025-12-11"
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    "GenerateModelCN",
    "GenerateModelCP",
    "GenerateModelCustom",
    "GenerateModelP",
    "GenerateModelPC",
    "ImputeData",
    "MCMI2",
    "plot_conditional_effects",
    "plot_contrasts",
    "plot_effects",
    "plot_moderation_curve",
    "PrepareData",
    "PrepareMissingData",
    "printGM",
    "standardize_moderation",
    "wsMed"
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    {
      "name": "example_data",
      "title": "Example Data for within subject mediation",
      "object": "example_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "A1",
        "A2",
        "A3",
        "B1",
        "B2",
        "B3",
        "C1",
        "C2",
        "C3",
        "D1",
        "D2",
        "D3",
        "Group",
        "W_Group"
      ],
      "rows": 100,
      "table": true,
      "tojson": true
    }
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    {
      "page": "GenerateModelCN",
      "title": "Generate Chained Mediation Model",
      "topics": [
        "GenerateModelCN"
      ]
    },
    {
      "page": "GenerateModelCP",
      "title": "Generate Combined Parallel and Chained Mediation Model",
      "topics": [
        "GenerateModelCP"
      ]
    },
    {
      "page": "GenerateModelCustom",
      "title": "Generate a User-Defined Within-Subject Mediation Model",
      "topics": [
        "GenerateModelCustom"
      ]
    },
    {
      "page": "GenerateModelP",
      "title": "Generate Parallel Mediation Model",
      "topics": [
        "GenerateModelP"
      ]
    },
    {
      "page": "GenerateModelPC",
      "title": "Generate Parallel and Chained Mediation Model",
      "topics": [
        "GenerateModelPC"
      ]
    },
    {
      "page": "ImputeData",
      "title": "Impute Missing Data Using Multiple Imputation",
      "topics": [
        "ImputeData"
      ]
    },
    {
      "page": "MCMI2",
      "title": "Monte Carlo Confidence Intervals for Multiple Imputation SEM Models",
      "topics": [
        "MCMI2"
      ]
    },
    {
      "page": "plot_conditional_effects",
      "title": "Plot conditional effects at moderator levels",
      "topics": [
        "plot_conditional_effects"
      ]
    },
    {
      "page": "plot_contrasts",
      "title": "Plot differences between mediation effects",
      "topics": [
        "plot_contrasts"
      ]
    },
    {
      "page": "plot_effects",
      "title": "Forest plot of mediation effects",
      "topics": [
        "plot_effects"
      ]
    },
    {
      "page": "plot_moderation_curve",
      "title": "Plot conditional effects across a continuous moderator",
      "topics": [
        "plot_moderation_curve"
      ]
    },
    {
      "page": "PrepareData",
      "title": "Prepare Data for Two-Condition Within-Subject Mediation (WsMed)",
      "topics": [
        "PrepareData"
      ]
    },
    {
      "page": "PrepareMissingData",
      "title": "Prepare Data with Missing Values for Mediation Analysis",
      "topics": [
        "PrepareMissingData"
      ]
    },
    {
      "page": "print.wsMed",
      "title": "Print Method for wsMed Objects",
      "topics": [
        "print.wsMed"
      ]
    },
    {
      "page": "printGM",
      "title": "Print Formatted SEM Model Syntax",
      "topics": [
        "printGM"
      ]
    },
    {
      "page": "standardize_moderation",
      "title": "Standardize conditional mediation results using existing joint draws",
      "topics": [
        "standardize_moderation"
      ]
    },
    {
      "page": "wsMed",
      "title": "Within-Subject Mediation Analysis (Two-Condition)",
      "topics": [
        "wsMed"
      ]
    }
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      "filename": "printGM.html",
      "title": "Demonstration of printGM()",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Demonstration of printGM()",
        "Using printGM() with WsMed()",
        "Using printGM() with GenerateModel*()"
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      "modified": "2026-09-18 03:14:31",
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      "filename": "GenerateModelCN.html",
      "title": "GenerateModelCN",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "1. Difference Model Description",
        "1.1 Regression for ( Y_{\\text{diff}} ) and ( M_{\\text{diff}} )",
        "2. Indirect Effects",
        "2.1 Examples of Indirect Effects",
        "3. Total Effect",
        "4. Comparison of Indirect Effects",
        "4.1 Comparing Indirect Effects",
        "Interpretation:",
        "4.2 Example: Three Mediators ( M_1, M_2, M_3 )",
        "Indirect Effects",
        "Comparisons",
        "The indirect effects are compared as follows:[CI_{1\\text{vs}2} = \\text{indirect}1 - \\text{indirect}2][CI{1\\text{vs}3} = \\text{indirect}1 - \\text{indirect}3][CI{2\\text{vs}3} = \\text{indirect}2 - \\text{indirect}3][CI{1\\text{vs}12} = \\text{indirect}1 - \\text{indirect}{12}][CI{1\\text{vs}23} = \\text{indirect}1 - \\text{indirect}{23}][CI{1\\text{vs}123} = \\text{indirect}1 - \\text{indirect}{123}][CI{2\\text{vs}12} = \\text{indirect}2 - \\text{indirect}{12}][CI_{2\\text{vs}23} = \\text{indirect}2 - \\text{indirect}{23}][CI_{2\\text{vs}123} = \\text{indirect}2 - \\text{indirect}{123}][CI_{3\\text{vs}12} = \\text{indirect}3 - \\text{indirect}{12}][CI_{3\\text{vs}23} = \\text{indirect}3 - \\text{indirect}{23}][CI_{3\\text{vs}123} = \\text{indirect}3 - \\text{indirect}{123}][CI_{12\\text{vs}23} = \\text{indirect}{12} - \\text{indirect}{23}][CI_{12\\text{vs}123} = \\text{indirect}{12} - \\text{indirect}{123}][CI_{23\\text{vs}123} = \\text{indirect}{23} - \\text{indirect}{123}]",
        "5. C1 and C2 Coefficients",
        "6. Summary of Regression Equations"
      ],
      "created": "2024-12-09 13:07:25",
      "modified": "2025-11-07 03:47:46",
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        "1.1 Regression for ( Y_{\\text{diff}} ) and ( M_{\\text{diff}} )",
        "2. Indirect Effects",
        "3. Total Effect",
        "4. Comparison of Indirect Effects",
        "4.1 Example: Three Mediators (( M_1, M_2, M_3 ))",
        "5. C1 and C2 Coefficients",
        "Definitions",
        "5.1 Example: Three Mediators (( M_1, M_2, M_3 ))",
        "6. Summary of Regression Equations"
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      "filename": "GenerateModelP.html",
      "title": "GenerateModelP",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "1. Difference Model Description",
        "1.1 Regression for ( Y_1 ) and ( Y_2 )",
        "1.2 Difference Model for ( Y_{\\text{diff}} )",
        "1.3 Regression for ( M_{\\text{diff}} )",
        "2. Indirect Effects",
        "3. Total Effect",
        "4. Comparison of Indirect Effects",
        "4.1 Indirect Effect Definition",
        "4.2 Comparing Indirect Effects",
        "Interpretation",
        "5. C1- and C2-Measurement Coefficients",
        "5.1 Difference Model with Two Mediators",
        "5.2 C2-Measurement Coefficients",
        "5.3 C1-Measurement Coefficients",
        "6. Summary of Regression Equations",
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        "6.2 Defined parameters",
        "Summary"
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      "headings": [
        "Introduction",
        "1. Model Description",
        "1.1 Regression for ( Y_{\\text{diff}} ) and ( M_{\\text{diff}} )",
        "2. Indirect Effects",
        "3. Total Effect",
        "4. Comparison of Indirect Effects",
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        "5. C1- and C2-Measurement Coefficients",
        "Definitions",
        "5.1 Example: Three Mediators (( M_1, M_2, M_3 ))",
        "6. Summary of Regression Equations"
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      "filename": "MEMORE_vs_wsMed.html",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "introduction",
        "parallel mediation",
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        "chained/serial mediation"
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      "filename": "GenerateModelCustom.html",
      "title": "User-defined mediation models with GenerateModelCustom",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Mediator numbering",
        "Preparing example data",
        "[M_",
        "Specifying a custom model",
        "Indirect effects",
        "GenerateModelCustom() identifies every directed path from a mediator to Y.The model above contains three indirect effects:[\\mathtt",
        "[\\mathtt",
        "[\\mathtt",
        "Parameter labels",
        "Fitting the complete model with wsMed",
        "Input requirements",
        "Summary"
      ],
      "created": "2026-09-18 03:14:31",
      "modified": "2026-09-18 03:14:31",
      "commits": 1
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    {
      "source": "WsMed.Rmd",
      "filename": "WsMed.html",
      "title": "Using wsMed: A step-by-step tutorial",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Quick start",
        "Step 1: Specify the repeated-measures variables",
        "Step 2: Choose the mediation structure",
        "Predefined models",
        "User-defined models",
        "Step 3: Choose the estimation options",
        "Missing data and confidence intervals",
        "Standardized effects",
        "Step 4: Add optional model components",
        "Moderators",
        "Covariates",
        "Step 5: Inspect the results",
        "Step 6: Interpret the printed results",
        "Example 1: A model without an external moderator",
        "Variables and analysis sample",
        "Model fit",
        "Total, direct, and indirect effects",
        "[\\text",
        "Level-component paths and condition-specific coefficients",
        "Contrasts between indirect effects",
        "Regression paths, intercepts, and variances",
        "Standardized results",
        "Inspecting and extracting Example 1",
        "Example 2: A model with a continuous moderator",
        "Moderated coefficients",
        "Conditional effects",
        "Significance markers and confidence intervals",
        "Visualizing conditional effects",
        "What to report",
        "Further model details",
        "Summary"
      ],
      "created": "2024-12-27 08:19:18",
      "modified": "2026-09-18 03:14:31",
      "commits": 10
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