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  "Title": "Bayesian Multiple-Membership Multilevel Models with\nParameterizable Weight Functions",
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  "Description": "Implements Bayesian multiple-membership multilevel models\nwith parameterizable weight functions via 'JAGS' to model how\nlower-level units jointly shape higher-level outcomes\n(micro-macro link) across a range of outcome types (e.g.,\nlinear, logit, and survival models). Supports estimation and\ncomparison of alternative aggregation mechanisms, allows weight\nmatrices to be endogenized through parameters and covariates,\nand accommodates complex dependence structures that extend\nbeyond traditional multilevel frameworks. For details, see\nRosche (2026) \"A Multilevel Model for Coalition Governments.\nUncovering Party-Level Dependencies Within and Between\nGovernments\" <doi:10.31235/osf.io/4bafr_v2>.",
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  "Repository": "https://benrosche.r-universe.dev",
  "Date/Publication": "2026-08-04 11:50:31 UTC",
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  "Author": "Benjamin Rosche [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-5196-625X>)",
  "Maintainer": "Benjamin Rosche <benrosche@nyu.edu>",
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    "make_jagsdata",
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    "mm",
    "monetPlot",
    "posterior_predict",
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    "prior",
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    "tidy",
    "varDecomp",
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        "investiture",
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        "n",
        "majority",
        "mwc",
        "rile_SD",
        "pseat",
        "prime",
        "rile",
        "cohesion",
        "finance",
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      ],
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      "table": true,
      "tojson": true
    }
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      "title": "Extract posterior draws from a bml model",
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        "as_draws_array.bml",
        "as_draws_df.bml",
        "as_draws_matrix.bml"
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    },
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      "topics": [
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    {
      "page": "bml_delta",
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    {
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      "topics": [
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    {
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      "title": "Membership turnover between two snapshots",
      "topics": [
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      ]
    },
    {
      "page": "bml-families",
      "title": "Model families for bml",
      "topics": [
        "bernoulli",
        "bml-families",
        "cox",
        "weibull"
      ]
    },
    {
      "page": "bmlCompare",
      "title": "Compare fitted bml models in a publication-style table",
      "topics": [
        "bmlCompare"
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    },
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      "title": "Coalition Governments in Western Democracies (1944-2014)",
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    },
    {
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      "title": "Coefficient plot for fitted bml models",
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        "conditional_effects.bml"
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      "title": "Extract fixed coefficients from a bml model",
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      "topics": [
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      "page": "get_prior",
      "title": "List every settable prior of a bml model",
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      "page": "glance.bml",
      "title": "Glance at a fitted bml model",
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    {
      "page": "hm",
      "title": "Define a hierarchical nesting structure",
      "topics": [
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    },
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      "title": "Specify identifier variables for multiple-membership and hierarchical structures",
      "topics": [
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      "title": "Pointwise log-likelihood of a bml model",
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        "log_lik.bml"
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      "title": "Summarize MCMC convergence diagnostics",
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    {
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      "title": "Define a multiple-membership block (the micro-to-macro aggregation)",
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      "title": "Visualize posterior distributions with density and trace plots",
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      "title": "Draws from the posterior predictive distribution",
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      "title": "Random effects: partial pooling",
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        "re"
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      "page": "summary.bml",
      "title": "Summarize a fitted bml model",
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        "summary.bml"
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      "title": "Tidy a fitted bml model",
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      "title": "Variance decomposition for fitted bml models",
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    },
    {
      "page": "vars",
      "title": "Specify member-level attributes for mm() blocks",
      "topics": [
        "vars"
      ]
    },
    {
      "page": "w",
      "title": "Specify the membership weights of an mm() block",
      "topics": [
        "w"
      ]
    },
    {
      "page": "waic.bml",
      "title": "Widely applicable information criterion for bml models",
      "topics": [
        "waic.bml"
      ]
    }
  ],
  "_readme": "https://github.com/benrosche/bml/raw/HEAD/README.md",
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      "filename": "examples.html",
      "title": "Examples",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. All friends, or just your best friend? (network regression)",
        "Model 1: linear-in-means",
        "Model 2: best friend only",
        "Comparing the two models",
        "Cross-validation: which aggregation does the data prefer?",
        "2. Air quality and home values (spatial regression)",
        "Data and spatial weights",
        "Baseline bml: equal weights",
        "Parameterised weights: similarity across covariates",
        "Do the weights actually vary with similarity?",
        "Benchmark: spatial Durbin error model",
        "Model comparison",
        "3. Political parties and the survival of coalition governments (micro-macro regression)",
        "The data",
        "Equal-weight baseline",
        "Letting weights vary across parties",
        "Prime minister's party against equal weights",
        "Prime minister against seat share",
        "Comparing the models",
        "MCMC diagnostics",
        "brms-style accessors",
        "4. Does the average matter, or the spread? (emergent features)",
        "5. What is the aggregation function? (estimating f itself)",
        "6. Does context change the aggregate's effect? (cross-level interaction)",
        "7. Heterogeneous member effects (explained and residual)"
      ],
      "created": "2021-05-02 21:14:34",
      "modified": "2026-07-13 12:03:30",
      "commits": 23
    },
    {
      "source": "faq.Rmd",
      "filename": "faq.html",
      "title": "Frequently Asked Questions",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. What's the difference between a multiple-membership model and a conventional multilevel model?",
        "2. What's the difference between a conventional MMMM and the extended MMMM implemented in bml?",
        "3. When should I use bml instead of other multilevel modeling packages?",
        "4. What outcome types and distributions does bml support?",
        "5. How do I specify the weights, and what are the scale and ar options?",
        "6. How do I fix parameters to known values?",
        "Main equation and hm() blocks: Using fix()",
        "Weights w(): Omitting parameters",
        "7. I get \"Error in node w.1[...]: Invalid parent values\" — what does this mean?",
        "8. I'm coming from brms — what maps onto what?"
      ],
      "created": "2021-05-02 21:14:34",
      "modified": "2026-08-04 10:09:13",
      "commits": 9
    },
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      "filename": "bml.html",
      "title": "Getting Started with bml",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
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        "Model 1: Basic Multiple-Membership Model",
        "Model 2: Parameterizing the Weight Function",
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        "8.1 Intraclass Correlation Coefficients (ICC)",
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        "9.1 Multiple MM Blocks",
        "9.2 Autoregressive Random Effects",
        "9.3 Complementary Membership Groups",
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