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  "Title": "Bayesian Multivariate Meta-Analysis",
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  "Author": "Olha Bodnar [aut] (<https://orcid.org/0000-0003-1359-3311>),\nTaras Bodnar [aut] (<https://orcid.org/0000-0001-7855-8221>),\nErik Thorsén [aut, cre]\n(<https://orcid.org/0000-0001-5992-1216>)",
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  "Description": "Objective Bayesian inference procedures for the parameters\nof the multivariate random effects model with application to\nmultivariate meta-analysis. The posterior for the model\nparameters, namely the overall mean vector and the\nbetween-study covariance matrix, are assessed by constructing\nMarkov chains based on the Metropolis-Hastings algorithms as\ndeveloped in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The\nMetropolis-Hastings algorithm is designed under the assumption\nof the normal distribution and the t-distribution when the\nBerger and Bernardo reference prior and the Jeffreys prior are\nassigned to the model parameters. Convergence properties of the\ngenerated Markov chains are investigated by the rank plots and\nthe split hat-R estimate based on the rank normalization, which\nare proposed in Vehtari et al. (2021)\n(<DOI:10.1214/20-BA1221>).",
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  "Date/Publication": "2022-06-09 07:10:27 UTC",
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    "sample_post_nor_jef_marg_Psi",
    "sample_post_nor_ref_marg_mu",
    "sample_post_nor_ref_marg_Psi",
    "sample_post_t_jef_marg_mu",
    "sample_post_t_jef_marg_Psi",
    "sample_post_t_ref_marg_mu",
    "sample_post_t_ref_marg_Psi",
    "split_rank_hatR",
    "summary.BayesMultMeta"
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    {
      "page": "bayes_inference",
      "title": "Summary statistics from a posterior distribution",
      "topics": [
        "bayes_inference"
      ]
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    {
      "page": "BayesMultMeta",
      "title": "Interface for the BayesMultMeta class",
      "topics": [
        "BayesMultMeta"
      ]
    },
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      "title": "Duplication matrix",
      "topics": [
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      ]
    },
    {
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      "title": "Computes the ranks within the pooled draws of Markov chains",
      "topics": [
        "MC_ranks"
      ]
    },
    {
      "page": "plot.BayesMultMeta",
      "title": "Plot a BayesMultMeta object",
      "topics": [
        "plot.BayesMultMeta"
      ]
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    {
      "page": "sample_post_nor_jef_marg_mu",
      "title": "Metropolis-Hastings algorithm for the normal distribution and the Jeffreys prior, where \\mathbf{mu} is generated from the marginal posterior.",
      "topics": [
        "sample_post_nor_jef_marg_mu"
      ]
    },
    {
      "page": "sample_post_nor_jef_marg_Psi",
      "title": "Metropolis-Hastings algorithm for the normal distribution and the Jeffreys prior, where \\mathbf{Psi} is generated from the marginal posterior.",
      "topics": [
        "sample_post_nor_jef_marg_Psi"
      ]
    },
    {
      "page": "sample_post_nor_ref_marg_mu",
      "title": "Metropolis-Hastings algorithm for the normal distribution and the Berger and Bernardo reference prior, where \\mathbf{mu} is generated from the marginal posterior.",
      "topics": [
        "sample_post_nor_ref_marg_mu"
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    },
    {
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      "title": "Metropolis-Hastings algorithm for the normal distribution and the Berger and Bernardo reference prior, where \\mathbf{Psi} is generated from the marginal posterior.",
      "topics": [
        "sample_post_nor_ref_marg_Psi"
      ]
    },
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      "title": "Metropolis-Hastings algorithm for the t-distribution and the Jeffreys prior, where \\mathbf{mu} is generated from the marginal posterior.",
      "topics": [
        "sample_post_t_jef_marg_mu"
      ]
    },
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      "title": "Metropolis-Hastings algorithm for the t-distribution and the Jeffreys prior, where \\mathbf{Psi} is generated from the marginal posterior.",
      "topics": [
        "sample_post_t_jef_marg_Psi"
      ]
    },
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      "title": "Metropolis-Hastings algorithm for the t-distribution and Berger and Bernardo reference prior, where \\mathbf{mu} is generated from the marginal posterior.",
      "topics": [
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    },
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      "title": "Metropolis-Hastings algorithm for the t-distribution and Berger and Bernardo reference prior, where \\mathbf{Psi} is generated from the marginal posterior.",
      "topics": [
        "sample_post_t_ref_marg_Psi"
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    },
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    },
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