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  "Package": "RaCE.NMA",
  "Title": "Rank-Clustered Estimation for Network Meta-Analysis",
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  "Authors@R": "c(\nperson(\"Michael\", \"Pearce\", , \"michaelpearce@reed.edu\", role = c(\"aut\", \"cre\",\"cph\"),\ncomment = c(ORCID = \"0000-0002-9313-271X\")),\nperson(\"Shouhao\", \"Zhou\", , \"szhou1@pennstatehealth.psu.edu\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-8124-5047\")))",
  "Description": "An implementation of the RaCE-NMA (Rank-Clustered\nEstimation for Network Meta-Analysis) model for post-hoc\nclustering of treatments or interventions by rank in network\nmeta-analysis data. Functions for model estimation, assessment,\nand displaying results are provided. For more details, see\nPearce and Zhou (2025) <doi:10.1017/rsm.2025.10049>.",
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    "User": "root"
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  "Author": "Michael Pearce [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-9313-271X>), Shouhao Zhou [aut]\n(ORCID: <https://orcid.org/0000-0002-8124-5047>)",
  "Maintainer": "Michael Pearce <michaelpearce@reed.edu>",
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      "title": "Posterior relative treatment effects for non-baseline treatments based on Wang et. al (2022)",
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    {
      "page": "calculate_Rhat",
      "title": "Calculate Gelman Diagnostics for Fitted RaCE NMA Models",
      "topics": [
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      ]
    },
    {
      "page": "calculate_SUCRA_MNBT",
      "title": "Calculate SUCRA and MNBT based on MCMC draws in NMA or RaCE-NMA models",
      "topics": [
        "calculate_SUCRA_MNBT"
      ]
    },
    {
      "page": "clusterplot_ranks",
      "title": "Create a posterior clustering matrix for the interventions based on RaCE NMA models",
      "topics": [
        "clusterplot_ranks"
      ]
    },
    {
      "page": "cumulativeprobplot_ranks",
      "title": "Create a cumulative ranking plot in RaCE NMA models",
      "topics": [
        "cumulativeprobplot_ranks"
      ]
    },
    {
      "page": "fit_raceNMA",
      "title": "Fit a Bayesian Rank-Clustered Estimation model for Network Meta-Analysis (RaCE-NMA). Recommended for internal use only; use mcmc_RCMVN instead.",
      "topics": [
        "fit_raceNMA"
      ]
    },
    {
      "page": "forestplot_muhat",
      "title": "Create posterior forest plots for relative treatment effect estimates from a standard or RaCE NMA study",
      "topics": [
        "forestplot_muhat"
      ]
    },
    {
      "page": "mcmc_raceNMA",
      "title": "Fit a Bayesian Rank-Clustered Estimation model for Network Meta-Analysis (RaCE-NMA) using multiple MCMC chains",
      "topics": [
        "mcmc_raceNMA"
      ]
    },
    {
      "page": "sample_partition_correlation",
      "title": "Partition sampling in Bayesian RaCE-NMA models (internal use only)",
      "topics": [
        "sample_partition_correlation"
      ]
    },
    {
      "page": "sample_partition_independence",
      "title": "Partition sampling in Bayesian RaCE-NMA models (internal use only)",
      "topics": [
        "sample_partition_independence"
      ]
    },
    {
      "page": "toy_data",
      "title": "Toy data of posterior relative treatment effects",
      "topics": [
        "toy_data"
      ]
    },
    {
      "page": "traceplot_K",
      "title": "Create trace plots for the K parameter in RaCE NMA models",
      "topics": [
        "traceplot_K"
      ]
    },
    {
      "page": "traceplot_mu",
      "title": "Create trace plots for the mu parameter in RaCE NMA models",
      "topics": [
        "traceplot_mu"
      ]
    },
    {
      "page": "wang_posterior",
      "title": "Posterior relative treatment effects for non-baseline treatments based on Wang et. al (2022)",
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      ]
    }
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