{
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  "Title": "Estimate Bayesian Multilevel Models for Compositional Data",
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  "Date": "2025-10-25",
  "Authors@R": "c(person(given = \"Flora\",\nfamily = \"Le\",\nrole = c(\"aut\", \"cre\"),\nemail = \"floralebui@gmail.com\",\ncomment = c(ORCID = \"0000-0003-0089-8167\")),\nperson(given = \"Joshua F.\",\nfamily = \"Wiley\",\nrole = c(\"aut\"),\nemail = \"jwiley.psych@gmail.com\",\ncomment = c(ORCID = \"0000-0002-0271-6702\")))",
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  "BugReports": "https://github.com/florale/multilevelcoda/issues",
  "Description": "Implement Bayesian multilevel modelling for compositional\ndata. Compute multilevel compositional data and perform\nlog-ratio transforms at between and within-person levels, fit\nBayesian multilevel models for compositional predictors and\noutcomes, and run post-hoc analyses such as isotemporal\nsubstitution models. References: Le, Stanford, Dumuid, and\nWiley (2025) <doi:10.1037/met0000750>, Le, Dumuid, Stanford,\nand Wiley (2025) <doi:10.1080/00273171.2025.2565598>.",
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  "Date/Publication": "2026-06-18 03:51:04 UTC",
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    "is.diagnostics",
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    "prep_sim_analysis",
    "ranef",
    "simulate_data",
    "sub",
    "submargin",
    "substitution",
    "VarCorr",
    "wsub",
    "wsubmargin"
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        "data.frame"
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        "Time",
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        "Age",
        "Female"
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        "data.frame"
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        "WAKE",
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        "SB"
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      "table": true,
      "tojson": true
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        "array"
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        "WAKE",
        "MVPA",
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        "SB"
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      "table": true,
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      "fields": [],
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      "page": "as.complr",
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      "topics": [
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    },
    {
      "page": "as.data.frame.complr",
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        "as.matrix.complr"
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      "page": "bsub",
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        "gen_beta",
        "gen_gamma"
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      "topics": [
        "diagnostics"
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      "page": "diagnostics.complr",
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        "diagnostics.complr"
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      "page": "draws-index-brmcoda",
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        "nchains",
        "nchains.brmcoda",
        "ndraws",
        "ndraws.brmcoda",
        "niterations",
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        "nvariables",
        "nvariables.brmcoda",
        "variables",
        "variables.brmcoda"
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      "title": "Population-Level Estimates",
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        "fixef.brmcoda"
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    {
      "page": "gen_categorical",
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      "concept": [
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    },
    {
      "page": "gen_custom",
      "title": "Generate Variables with a User-Supplied Function",
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    },
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      "page": "gen_mvn",
      "title": "Generate Normal, Multivariate Normal, and Compositional Variables",
      "concept": [
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      "topics": [
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    },
    {
      "page": "gen_outcome",
      "title": "Generate Dynamic Gaussian and Compositional Outcomes",
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      "topics": [
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    },
    {
      "page": "gen_template",
      "title": "Create a Parameter Template for 'gen_outcome()'",
      "concept": [
        "predictor generators"
      ],
      "topics": [
        "gen_template"
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    },
    {
      "page": "get_sbp",
      "title": "Extract Sequential Binary Partition from a 'complr' object.",
      "topics": [
        "get_sbp"
      ]
    },
    {
      "page": "get_variables",
      "title": "Extract variable names from an object",
      "topics": [
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        "get_variables.brmcoda",
        "get_variables.complr"
      ]
    },
    {
      "page": "get-substitution",
      "title": "Substitution analysis helper functions",
      "topics": [
        "get-substitution"
      ]
    },
    {
      "page": "is.brmcoda",
      "title": "Checks if argument is a 'brmcoda' object",
      "topics": [
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      "page": "is.complr",
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      "page": "is.diagnostics",
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    {
      "page": "is.substitution",
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    {
      "page": "launch_shinystan.brmcoda",
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        "launch_shinystan.brmcoda"
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      "page": "loo.brmcoda",
      "title": "Efficient approximate leave-one-out cross-validation (LOO)",
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        "loo.brmcoda"
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      "title": "Multilevel Compositional Data",
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    {
      "page": "mean.complr",
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