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  "Title": "Multilevel Analysis of Individual Heterogeneity and\nDiscriminatory Accuracy",
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  "Authors@R": "c(\nperson(\"Hamid\", \"Bulut\", email = \"me@hamidbulut.com\", role = c(\"aut\", \"cre\"))\n)",
  "Description": "Tools for Multilevel Analysis of Individual Heterogeneity\nand Discriminatory Accuracy (MAIHDA) for intersectional\ninequality research. Methods are described in Merlo (2018)\n<doi:10.1016/j.socscimed.2017.12.026> and Evans et al. (2018)\n<doi:10.1016/j.socscimed.2017.11.011>. The package creates\nintersectional strata, fits multilevel MAIHDA models, estimates\nvariance partition coefficients, proportional change in\nvariance, stratum effects, and discriminatory-accuracy\nsummaries, and provides diagnostic and presentation plots.",
  "License": "MIT + file LICENSE",
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    "compare_maihda",
    "compare_maihda_groups",
    "fit_maihda",
    "glance",
    "maihda",
    "maihda_auc",
    "maihda_cumulative",
    "maihda_discriminatory_accuracy",
    "maihda_ic",
    "maihda_interactions",
    "maihda_mor",
    "maihda_table",
    "maihda_upset_size",
    "maihda_vpc_response",
    "make_strata",
    "plot_comparison",
    "plot_group_comparison",
    "plot_prediction_deviation_panels",
    "predict_maihda",
    "run_maihda_app",
    "stepwise_pcv",
    "theme_maihda",
    "tidy"
  ],
  "_datasets": [
    {
      "name": "maihda_country_data",
      "title": "Cross-National Educational Achievement Data for MAIHDA",
      "object": "maihda_country_data",
      "class": [
        "data.frame"
      ],
      "fields": [
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        "gender",
        "ses",
        "escs",
        "math",
        "reading",
        "low_math"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "maihda_health_data",
      "title": "NHANES Health Data Subset for MAIHDA Use",
      "object": "maihda_health_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "BMI",
        "Obese",
        "Age",
        "Gender",
        "Race",
        "Education",
        "Poverty"
      ],
      "rows": 3000,
      "table": true,
      "tojson": true
    },
    {
      "name": "maihda_long_data",
      "title": "Simulated Longitudinal Data for MAIHDA",
      "object": "maihda_long_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "wave",
        "gender",
        "ethnicity",
        "education",
        "age",
        "wellbeing",
        "low_wellbeing"
      ],
      "rows": 3000,
      "table": true,
      "tojson": true
    },
    {
      "name": "maihda_sim_data",
      "title": "Simulated Health Data for MAIHDA Use",
      "object": "maihda_sim_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "gender",
        "race",
        "education",
        "age",
        "health_outcome",
        "binary_outcome"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
    },
    {
      "name": "maihda_sparse_data",
      "title": "Sparse Intersectional Data for Bayesian MAIHDA",
      "object": "maihda_sparse_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "gender",
        "ethnicity",
        "education",
        "age_group",
        "y",
        "event"
      ],
      "rows": 240,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "calculate_pvc",
      "title": "Calculate Proportional Change in Between-Stratum Variance (PCV)",
      "topics": [
        "calculate_pvc"
      ]
    },
    {
      "page": "compare_maihda",
      "title": "Compare MAIHDA Models",
      "topics": [
        "compare_maihda"
      ]
    },
    {
      "page": "compare_maihda_groups",
      "title": "Compare MAIHDA Metrics Across Levels of a Grouping Variable",
      "topics": [
        "compare_maihda_groups"
      ]
    },
    {
      "page": "fit_maihda",
      "title": "Fit MAIHDA Model",
      "topics": [
        "fit_maihda"
      ]
    },
    {
      "page": "maihda",
      "title": "Run a Complete MAIHDA Analysis",
      "topics": [
        "maihda"
      ]
    },
    {
      "page": "maihda_auc",
      "title": "Area under the ROC curve (C-statistic), rank-based",
      "topics": [
        "maihda_auc"
      ]
    },
    {
      "page": "maihda_country_data",
      "title": "Cross-National Educational Achievement Data for MAIHDA",
      "topics": [
        "maihda_country_data"
      ]
    },
    {
      "page": "maihda_cumulative",
      "title": "Cumulative (ordinal) family marker for MAIHDA models",
      "topics": [
        "maihda_cumulative"
      ]
    },
    {
      "page": "maihda_discriminatory_accuracy",
      "title": "Discriminatory accuracy of a binary MAIHDA model",
      "topics": [
        "maihda_discriminatory_accuracy"
      ]
    },
    {
      "page": "maihda_glance",
      "title": "Glance at a MAIHDA model or analysis",
      "topics": [
        "glance.maihda_analysis",
        "glance.maihda_model",
        "glance.maihda_summary",
        "maihda_glance"
      ]
    },
    {
      "page": "maihda_health_data",
      "title": "NHANES Health Data Subset for MAIHDA Use",
      "topics": [
        "maihda_health_data"
      ]
    },
    {
      "page": "maihda_ic",
      "title": "Information criteria for MAIHDA models",
      "topics": [
        "maihda_ic"
      ]
    },
    {
      "page": "maihda_interactions",
      "title": "Flag strata with credibly non-zero intersectional interaction",
      "topics": [
        "maihda_interactions"
      ]
    },
    {
      "page": "maihda_long_data",
      "title": "Simulated Longitudinal Data for MAIHDA",
      "topics": [
        "maihda_long_data"
      ]
    },
    {
      "page": "maihda_mor",
      "title": "Median Odds Ratio (MOR) for a logistic MAIHDA model",
      "topics": [
        "maihda_mor"
      ]
    },
    {
      "page": "maihda_sim_data",
      "title": "Simulated Health Data for MAIHDA Use",
      "topics": [
        "maihda_sim_data"
      ]
    },
    {
      "page": "maihda_sparse_data",
      "title": "Sparse Intersectional Data for Bayesian MAIHDA",
      "topics": [
        "maihda_sparse_data"
      ]
    },
    {
      "page": "maihda_table",
      "title": "Canonical MAIHDA results table and ranked-strata table",
      "topics": [
        "maihda_table"
      ]
    },
    {
      "page": "maihda_tidiers",
      "title": "Tidy a MAIHDA summary, model, or analysis",
      "topics": [
        "maihda_tidiers",
        "tidy.maihda_analysis",
        "tidy.maihda_model",
        "tidy.maihda_summary"
      ]
    },
    {
      "page": "maihda_upset_size",
      "title": "Recommended Figure Size for the UpSet Stratum Plot",
      "topics": [
        "maihda_upset_size"
      ]
    },
    {
      "page": "maihda_vpc_response",
      "title": "Response-scale VPC for a binomial MAIHDA model",
      "topics": [
        "maihda_vpc_response"
      ]
    },
    {
      "page": "make_strata",
      "title": "Create Strata from Multiple Variables",
      "topics": [
        "make_strata"
      ]
    },
    {
      "page": "plot_prediction_deviation_panels",
      "title": "Plot Prediction Deviation Panels",
      "topics": [
        "plot_prediction_deviation_panels"
      ]
    },
    {
      "page": "plot.maihda_analysis",
      "title": "Plot a MAIHDA Analysis",
      "topics": [
        "plot.maihda_analysis"
      ]
    },
    {
      "page": "plot.maihda_comparison",
      "title": "Plot a MAIHDA Model Comparison",
      "topics": [
        "plot.maihda_comparison"
      ]
    },
    {
      "page": "plot.maihda_group_comparison",
      "title": "Plot a MAIHDA Group Comparison",
      "topics": [
        "plot.maihda_group_comparison"
      ]
    },
    {
      "page": "plot.maihda_model",
      "title": "Plot MAIHDA Model Results",
      "topics": [
        "plot.maihda_model"
      ]
    },
    {
      "page": "predict_maihda",
      "title": "Predict from MAIHDA Model",
      "topics": [
        "predict_maihda"
      ]
    },
    {
      "page": "print.maihda_analysis",
      "title": "Print a MAIHDA Analysis",
      "topics": [
        "print.maihda_analysis"
      ]
    },
    {
      "page": "print.maihda_group_comparison",
      "title": "Print method for MAIHDA group comparisons",
      "topics": [
        "print.maihda_group_comparison"
      ]
    },
    {
      "page": "print.maihda_ic",
      "title": "Print MAIHDA information criteria",
      "topics": [
        "print.maihda_ic"
      ]
    },
    {
      "page": "print.maihda_interactions",
      "title": "Print a MAIHDA interaction diagnostic",
      "topics": [
        "print.maihda_interactions"
      ]
    },
    {
      "page": "print.maihda_long_pcv",
      "title": "Print a longitudinal MAIHDA PCV",
      "topics": [
        "print.maihda_long_pcv"
      ]
    },
    {
      "page": "print.maihda_model",
      "title": "Print method for maihda_model",
      "topics": [
        "print.maihda_model"
      ]
    },
    {
      "page": "print.maihda_stepwise",
      "title": "Print a stepwise MAIHDA table",
      "topics": [
        "print.maihda_stepwise"
      ]
    },
    {
      "page": "print.maihda_strata",
      "title": "Print method for maihda_strata objects",
      "topics": [
        "print.maihda_strata"
      ]
    },
    {
      "page": "print.maihda_summary",
      "title": "Print method for maihda_summary objects",
      "topics": [
        "print.maihda_summary"
      ]
    },
    {
      "page": "print.maihda_table",
      "title": "Print a MAIHDA results table",
      "topics": [
        "print.maihda_table"
      ]
    },
    {
      "page": "print.pvc_result",
      "title": "Print method for PVC results",
      "topics": [
        "print.pvc_result"
      ]
    },
    {
      "page": "run_maihda_app",
      "title": "Run MAIHDA Shiny Application",
      "topics": [
        "run_maihda_app"
      ]
    },
    {
      "page": "stepwise_pcv",
      "title": "Stepwise Proportional Change in Variance (PCV)",
      "topics": [
        "stepwise_pcv"
      ]
    },
    {
      "page": "summary.maihda_analysis",
      "title": "Summarize a MAIHDA Analysis",
      "topics": [
        "summary.maihda_analysis"
      ]
    },
    {
      "page": "summary.maihda_model",
      "title": "Summarize MAIHDA Model",
      "topics": [
        "summary.maihda_model"
      ]
    },
    {
      "page": "theme_maihda",
      "title": "MAIHDA plot theme",
      "topics": [
        "theme_maihda"
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      "source": "bayesian_sparse_maihda.Rmd",
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      "title": "Bayesian MAIHDA for sparse intersections",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Sparse intersectional cells",
        "A dataset with a known interaction",
        "What lme4 reports",
        "What brms adds: posterior uncertainty",
        "Comparison",
        "Why the interval, not the point, is what changes"
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      "title": "Comparing Intersectional Inequality Across Groups",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Example data: countries, gender, and socioeconomic status",
        "One-call workflow",
        "Visualizing the comparison",
        "Share versus magnitude",
        "Additive share (PCV) by group",
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      "title": "Crossed random effects in MAIHDA: dimensions and contexts",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Two different things called \"cross-classified\"",
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        "Running a crossed-dimensions analysis",
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        "Comparing across a higher-level group",
        "A Bayesian fit",
        "Two important caveats",
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        "context = vs. group =",
        "Notes"
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      "title": "Finding interaction patterns",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Run a standard analysis and choose the multiplicity rule",
        "Highlight flagged strata",
        "Is an interaction negligible? (equivalence / ROPE)",
        "See also",
        "References"
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      "modified": "2026-07-02 08:00:02",
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      "filename": "interactive_app.html",
      "title": "Interactive Data Analysis with MAIHDA",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Launching the Application",
        "Online Version",
        "Local Version",
        "App Features",
        "1. Data Import",
        "2. Variable Selection & Strata Creation",
        "3. Model Fitting & Settings",
        "4. Interactive Visualizations",
        "5. Stepwise Variance Analysis (PCV)",
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      "modified": "2026-06-18 09:49:18",
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      "title": "Interpreting MAIHDA Plots and Diagnostics",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "vpc -- variance partition",
        "predicted -- stratum predictions with intervals",
        "obs_vs_shrunken -- shrinkage made visible",
        "effect_decomp -- additive vs. intersection-specific",
        "prediction_deviation -- the deviation dashboard",
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      "modified": "2026-07-02 08:00:02",
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      "source": "introduction.Rmd",
      "filename": "introduction.html",
      "title": "Introduction to MAIHDA",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
        "The data",
        "The maihda() workflow",
        "Visual diagnostics",
        "A crossed-dimensions alternative",
        "A contextual cross-classified model",
        "Design-weighted MAIHDA (survey data)",
        "Comparing across groups",
        "Under the hood: the building blocks",
        "Fit a single model",
        "A custom adjusted model and the PCV",
        "Stepwise PCV",
        "Discriminatory accuracy and the response-scale VPC",
        "The group comparison directly",
        "Where to next",
        "Interactive Shiny App",
        "References"
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      "modified": "2026-07-02 08:00:02",
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      "filename": "longitudinal.html",
      "title": "Longitudinal MAIHDA: intersectional inequalities over time",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "From a snapshot to a trajectory",
        "The data",
        "Fitting and the time-varying VPC",
        "Decomposing the trajectory: additive vs. multiplicative",
        "Scope and cautions",
        "Reference"
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      "modified": "2026-06-18 09:49:18",
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      "filename": "binary_outcomes.html",
      "title": "MAIHDA for Binary Outcomes (Discriminatory Accuracy)",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Why binary outcomes?",
        "Fitting a logistic MAIHDA model",
        "The VPC is on the latent scale",
        "Adjusted model and PCV",
        "Discriminatory accuracy (AUC and Median Odds Ratio)",
        "Plots adapt to the binomial family",
        "Count outcomes work the same way",
        "References"
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      "modified": "2026-06-18 09:49:18",
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      "filename": "planning_a_study.html",
      "title": "Planning a MAIHDA analysis",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Before you fit",
        "Is MAIHDA the right tool?",
        "The central tradeoff: more dimensions means emptier cells",
        "What sparse cells do: singular fits",
        "Continuous variables and the analytic sample",
        "What the summaries can and cannot tell you",
        "Which engine, which design?",
        "A suggested learning path",
        "References"
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      "modified": "2026-06-18 09:49:18",
      "commits": 1
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    {
      "source": "reporting_results.Rmd",
      "filename": "reporting_results.html",
      "title": "Reporting MAIHDA results: tidy output and publication tables",
      "author": "Hamid Bulut",
      "engine": "knitr::rmarkdown",
      "headings": [
        "From a fitted model to a manuscript",
        "glance() -- the one-row headline",
        "tidy() -- estimates as a tidy tibble",
        "maihda_table() -- the two canonical write-up tables",
        "Choosing a model structure with maihda_ic()",
        "See also",
        "References"
      ],
      "created": "2026-06-18 09:49:18",
      "modified": "2026-06-18 09:49:18",
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