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  "Title": "Analysis of Longitudinal Data with Irregular Observation Times",
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  "Date": "2026-01-23",
  "Authors@R": "person(given = \"Eleanor\",\nfamily = \"Pullenayegum\",\nrole = c(\"aut\", \"cre\"),\nemail = \"eleanor.pullenayegum@sickkids.ca\")",
  "Description": "Functions to help with analysis of longitudinal data\nfeaturing irregular observation times, where the observation\ntimes may be associated with the outcome process. There are\nfunctions to quantify the degree of irregularity, fit\ninverse-intensity weighted Generalized Estimating Equations\n(Lin H, Scharfstein DO, Rosenheck RA (2004)\n<doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple\noutputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and\nfit semi-parametric joint models (Liang Y (2009) <doi:\n10.1111/j.1541-0420.2008.01104.x>).",
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  "Language": "en-US",
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  "Repository": "https://epullenayegum.r-universe.dev",
  "Date/Publication": "2026-01-28 21:39:31 UTC",
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  "Author": "Eleanor Pullenayegum [aut, cre]",
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      "date": "2019-02-11"
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    "addcensoredrows",
    "extent.of.irregularity",
    "iiw",
    "iiw.weights",
    "iiwgee",
    "lagfn",
    "Liang",
    "Liangint",
    "mo",
    "outputation"
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    {
      "page": "abacus.plot",
      "title": "Create an abacus plot Creates an abacus plot, depicting visits per subject over time",
      "topics": [
        "abacus.plot"
      ]
    },
    {
      "page": "addcensoredrows",
      "title": "Add rows corresponding to censoring times to a longitudinal dataset",
      "topics": [
        "addcensoredrows"
      ]
    },
    {
      "page": "create.bootstrapped.dataset",
      "title": "Create a single bootstrap sample for clustered data For clustered data, create a bootstrapped sample by sampling, with replacement, the same number of clusters as in the original dataset.",
      "topics": [
        "create.bootstrapped.dataset"
      ]
    },
    {
      "page": "extent.of.irregularity",
      "title": "Measures of extent of visit irregularity Provides visual and numeric measures of the extent of irregularity in observation times in a longitudinal dataset",
      "topics": [
        "extent.of.irregularity"
      ]
    },
    {
      "page": "iiw",
      "title": "Given a proportional hazards model for visit intensities, compute inverse-intensity weights.",
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      ],
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        "iiw"
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      "topics": [
        "Liang"
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      "page": "Liangint",
      "title": "Fit a semi-parametric joint model, incorporating intercept estimation",
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        "Liangint"
      ]
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      "title": "Multiple outputation for longitudinal data subject to irregular observation.",
      "concept": [
        "mo"
      ],
      "topics": [
        "mo"
      ]
    },
    {
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      "title": "Create an outputted dataset for use with multiple outputation.",
      "concept": [
        "mo"
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