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  "Title": "Design-Robust Meta-Analysis via Variance-Function Models",
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  "Description": "Implements Design-Robust Meta-Analysis (DR-Meta), a\nvariance-function random-effects framework in which\nbetween-study heterogeneity is modelled as a function of a\nstudy-level design robustness index, allowing heterogeneity to\ndepend systematically on study quality or design strength\nrather than being treated as a single nuisance parameter. The\npackage provides profiled restricted maximum likelihood (REML)\nestimation of the overall effect and variance-function\nparameters, study-specific weights, heterogeneity diagnostics\n(tau-squared, I-squared), influence and leave-one-out analysis,\nand graphical tools including forest plots and influence plots.\nThe DR-Meta framework nests classical fixed-effects and\nstandard random-effects meta-analysis as special cases, making\nit a strict generalisation of existing approaches.",
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  "Date/Publication": "2026-04-01 22:46:53 UTC",
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    {
      "page": "drmeta-package",
      "title": "drmeta: Design-Robust Meta-Analysis",
      "topics": [
        "drmeta-package"
      ]
    },
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      "page": "coef.drmeta",
      "title": "Extract Coefficients from a drmeta Object",
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      "title": "Confidence Interval for a drmeta Object",
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      "title": "Design Robustness from Study Design Type",
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    {
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      "title": "Heterogeneity Diagnostics for DR-Meta",
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      "title": "Leave-One-Out Influence Diagnostics for DR-Meta",
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      "title": "Weight Diagnostic Plot for DR-Meta",
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      "topics": [
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        "Installation",
        "Step 1: Build the Design Robustness Index",
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        "Compare exponential vs linear variance function",
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        "Weight vs DR (Lemma 3)",
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        "Step 5: Influence Diagnostics (LOO)",
        "Step 6: Publication Bias",
        "Connection to the Literature",
        "References"
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