{
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  "Package": "BayesCACE",
  "Type": "Package",
  "Title": "Bayesian Model for CACE Analysis",
  "Version": "1.2.3",
  "Date": "2022-10-1",
  "Authors@R": "c(person(given = \"Jinhui\",\nfamily = \"Yang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"james.yangjinhui@gmail.com\",\ncomment = c(ORCID = \"0000-0001-8322-1121\")),\nperson(given = \"Jincheng\",\nfamily = \"Zhou\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0003-2641-2495\")),\nperson(given = \"James\",\nfamily = \"Hodges\",\nrole = \"ctb\"),\nperson(given = \"Haitao\",\nfamily = \"Chu\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0003-0932-598X\"))\n)",
  "SystemRequirements": "JAGS 4.x.y (http://mcmc-jags.sourceforge.net)",
  "Description": "Performs CACE (Complier Average Causal Effect analysis) on\neither a single study or meta-analysis of datasets with binary\noutcomes, using either complete or incomplete noncompliance\ninformation. Our package implements the Bayesian methods\nproposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which\nintroduces a Bayesian hierarchical model for estimating CACE in\nmeta-analysis of clinical trials with noncompliance, and Zhou\net al. (2021) <doi:10.1080/01621459.2021.1900859>, with an\napplication example on Epidural Analgesia.",
  "License": "GPL (>= 2)",
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    "User": "root"
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  "Author": "Jinhui Yang [aut, cre]\n(<https://orcid.org/0000-0001-8322-1121>), Jincheng Zhou [aut]\n(<https://orcid.org/0000-0003-2641-2495>), James Hodges [ctb],\nHaitao Chu [ctb] (<https://orcid.org/0000-0003-0932-598X>)",
  "Maintainer": "Jinhui Yang <james.yangjinhui@gmail.com>",
  "Config/pak/sysreqs": "jags",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2022-10-02 14:00:02 UTC",
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    "message": "version 1.2.3\n",
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    "linkedin": "in/james-yang-56182686",
    "description": "",
    "uuid": 31892941,
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    "extra/citation.html",
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    "extra/contents.json",
    "manual.pdf"
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      "date": "2021-11-08"
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      "date": "2022-01-06"
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      "date": "2022-06-14"
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      "date": "2022-10-02"
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    "cace.meta.ic",
    "cace.study",
    "coda.names",
    "coda.samples.dic",
    "model.meta.c",
    "model.meta.ic",
    "model.study",
    "parse.varname",
    "plt.acf",
    "plt.density",
    "plt.forest",
    "plt.noncomp",
    "plt.trace",
    "prior.meta",
    "prior.study"
  ],
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    {
      "name": "epidural_c",
      "title": "Meta-analysis data with full compliance information",
      "object": "epidural_c",
      "class": [
        "data.frame"
      ],
      "fields": [
        "study.id",
        "study.name",
        "n000",
        "n001",
        "n010",
        "n011",
        "n100",
        "n101",
        "n110",
        "n111"
      ],
      "rows": 10,
      "table": true,
      "tojson": true
    },
    {
      "name": "epidural_ic",
      "title": "Meta-analysis data without full compliance information",
      "object": "epidural_ic",
      "class": [
        "data.frame"
      ],
      "fields": [
        "study.id",
        "study.name",
        "n000",
        "n001",
        "n010",
        "n011",
        "n0s0",
        "n0s1",
        "n100",
        "n101",
        "n110",
        "n111",
        "n1s0",
        "n1s1"
      ],
      "rows": 27,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "cace.meta.c",
      "title": "Bayesian hierarchical models for CACE meta-analysis with complete compliance data",
      "topics": [
        "cace.meta.c"
      ]
    },
    {
      "page": "cace.meta.ic",
      "title": "Bayesian hierarchical models for CACE meta-analysis with incomplete compliance information",
      "topics": [
        "cace.meta.ic"
      ]
    },
    {
      "page": "cace.study",
      "title": "CACE analysis for a single study, or a two-step approach for meta-analysis with complete complice information",
      "topics": [
        "cace.study"
      ]
    },
    {
      "page": "coda.names",
      "title": "Get names of node array",
      "topics": [
        "coda.names"
      ]
    },
    {
      "page": "coda.samples.dic",
      "title": "Generate posterior samples in mcmc.list format",
      "topics": [
        "coda.samples.dic"
      ]
    },
    {
      "page": "epidural_c",
      "title": "Meta-analysis data with full compliance information",
      "topics": [
        "epidural_c"
      ]
    },
    {
      "page": "epidural_ic",
      "title": "Meta-analysis data without full compliance information",
      "topics": [
        "epidural_ic"
      ]
    },
    {
      "page": "model.meta.c",
      "title": "Bayesian hierarchical model code for CACE meta-analysis with complete compliance data",
      "topics": [
        "model.meta.c"
      ]
    },
    {
      "page": "model.meta.ic",
      "title": "Bayesian hierarchical model code for CACE meta-analysis with complete compliance data",
      "topics": [
        "model.meta.ic"
      ]
    },
    {
      "page": "model.study",
      "title": "Model code of CACE analysis for a single study, or a two-step approach for meta-analysis with complete complice information",
      "topics": [
        "model.study"
      ]
    },
    {
      "page": "parse.varname",
      "title": "Parse strings of specific form",
      "topics": [
        "parse.varname"
      ]
    },
    {
      "page": "plt.acf",
      "title": "this plot function creates an acf plot",
      "topics": [
        "plt.acf"
      ]
    },
    {
      "page": "plt.density",
      "title": "this plot function creates a density plot",
      "topics": [
        "plt.density"
      ]
    },
    {
      "page": "plt.forest",
      "title": "this plot function makes a forest plot.",
      "topics": [
        "plt.forest"
      ]
    },
    {
      "page": "plt.noncomp",
      "title": "Plotting noncompliance rates for a given dataset",
      "topics": [
        "plt.noncomp"
      ]
    },
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      "page": "plt.trace",
      "title": "this plot function creates a traceplot",
      "topics": [
        "plt.trace"
      ]
    },
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      "page": "prior.meta",
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      "topics": [
        "prior.meta"
      ]
    },
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      "page": "prior.study",
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        "prior.study"
      ]
    }
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      "title": "BayesCACE paper",
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      "created": "2022-01-05 21:40:02",
      "modified": "2022-01-05 21:40:02",
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