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  "Package": "bipd",
  "Type": "Package",
  "Title": "Bayesian Individual Patient Data Meta-Analysis using 'JAGS'",
  "Version": "0.3",
  "Date": "2022-05-30",
  "Authors@R": "c( \nperson(\"Michael\", \"Seo\", email = \"swj8874@gmail.com\", role = c(\"aut\", \"cre\")))",
  "Description": "We use a Bayesian approach to run individual patient data\nmeta-analysis and network meta-analysis using 'JAGS'. The\nmethods incorporate shrinkage methods and calculate\npatient-specific treatment effects as described in Seo et al.\n(2021) <DOI:10.1002/sim.8859>. This package also includes\nuser-friendly functions that impute missing data in an\nindividual patient data using mice-related packages.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.1.2",
  "VignetteBuilder": "knitr",
  "Config/pak/sysreqs": "jags",
  "Repository": "https://mikejseo.r-universe.dev",
  "Date/Publication": "2022-06-03 18:04:16 UTC",
  "RemoteUrl": "https://github.com/mikejseo/bipd",
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    "User": "root"
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  "Author": "Michael Seo [aut, cre]",
  "Maintainer": "Michael Seo <swj8874@gmail.com>",
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  "_tags": [],
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  "_topics": [
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    "cpp"
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  "_assets": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
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  "_realowner": "mikejseo",
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      "date": "2022-01-26"
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      "date": "2022-03-02"
    },
    {
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      "date": "2022-06-05"
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    "findMissingPattern",
    "generate_ipdma_example",
    "generate_ipdnma_example",
    "generate_sysmiss_ipdma_example",
    "ipd.run",
    "ipd.run.parallel",
    "ipdma.impute",
    "ipdma.model.deft.onestage",
    "ipdma.model.onestage",
    "ipdnma.model.onestage",
    "treatment.effect"
  ],
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    {
      "page": "bipd-package",
      "title": "bipd: A package for individual patient data meta-analysis using 'JAGS'",
      "topics": [
        "bipd-package"
      ]
    },
    {
      "page": "add.mcmc",
      "title": "Convenient function to add results (i.e. combine mcmc.list)",
      "topics": [
        "add.mcmc"
      ]
    },
    {
      "page": "findMissingPattern",
      "title": "Find missing data pattern in a given data",
      "topics": [
        "findMissingPattern"
      ]
    },
    {
      "page": "generate_ipdma_example",
      "title": "Generate a simulated IPD-MA data for demonstration",
      "topics": [
        "generate_ipdma_example"
      ]
    },
    {
      "page": "generate_ipdnma_example",
      "title": "Generate a simualted IPD-NMA data for demonstration",
      "topics": [
        "generate_ipdnma_example"
      ]
    },
    {
      "page": "generate_sysmiss_ipdma_example",
      "title": "Generate a simulated IPD-MA data with systematically missing covariates",
      "topics": [
        "generate_sysmiss_ipdma_example"
      ]
    },
    {
      "page": "ipd.run",
      "title": "Run the model using the ipd object",
      "topics": [
        "ipd.run"
      ]
    },
    {
      "page": "ipd.run.parallel",
      "title": "Run the model using the ipd object with parallel computation",
      "topics": [
        "ipd.run.parallel"
      ]
    },
    {
      "page": "ipdma.impute",
      "title": "Impute missing data in individual participant data with two treatments (i.e. placebo and a treatment).",
      "topics": [
        "ipdma.impute"
      ]
    },
    {
      "page": "ipdma.model.deft.onestage",
      "title": "Make a (deft-approach) one-stage individual patient data meta-analysis object containing data, priors, and a JAGS model code",
      "topics": [
        "ipdma.model.deft.onestage"
      ]
    },
    {
      "page": "ipdma.model.onestage",
      "title": "Make an one-stage individual patient data meta-analysis object containing data, priors, and a JAGS model code",
      "topics": [
        "ipdma.model.onestage"
      ]
    },
    {
      "page": "ipdnma.model.onestage",
      "title": "Make an one-stage individual patient data network meta-analysis object containing data, priors, and a JAGS model code",
      "topics": [
        "ipdnma.model.onestage"
      ]
    },
    {
      "page": "treatment.effect",
      "title": "Calculate patient-specific treatment effect",
      "topics": [
        "treatment.effect"
      ]
    }
  ],
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    "coda",
    "dplyr",
    "generics",
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    "lattice",
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    "withr"
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    },
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      "package": "libstdc++6",
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      "filename": "Imputing-missing-values-in-IPD.html",
      "title": "Imputing missing values in IPD",
      "author": "Michael Seo",
      "engine": "knitr::rmarkdown",
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        "Multiple Imputations",
        "Try your data"
      ],
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