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  "Package": "BLOQ",
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  "Version": "0.1-2",
  "Date": "2025-05-05",
  "Title": "Methods to Impute and Analyze Data with BLOQ Observations",
  "Authors@R": "c(\nperson(\"Vahid\",\"Nassiri\", email = \"vahid.nassiri@openanalytics.eu\", role = \"cre\"),\nperson(\"Helen\", \"Barnett\", email= \"h.barnett@lancaster.ac.uk\", role = \"aut\"),\nperson(\"Helena\", \"Geys\", email= \"HGEYS@its.jnj.com\", role = \"aut\"),\nperson(\"Tom\", \"Jacobs\", email= \"TJACOBS1@its.jnj.com\", role = \"aut\"),\nperson(\"Thomas\", \"Jaki\", email= \"t.jaki@lancaster.ac.uk\", role = \"aut\"))",
  "Maintainer": "Vahid Nassiri <vahid.nassiri@openanalytics.eu>",
  "Description": "Provides methods for estimating the area under the\nconcentration versus time curve (AUC) and its standard error in\nthe presence of Below the Limit of Quantification (BLOQ)\nobservations. Two approaches are implemented: direct estimation\nusing censored maximum likelihood, and a two-step approach that\nfirst imputes BLOQ values using various methods and then\ncomputes the AUC using the imputed data. Technical details are\ndescribed in Barnett et al. (2020), \"Methods for\nNon-Compartmental Pharmacokinetic Analysis With Observations\nBelow the Limit of Quantification,\" Statistics in\nBiopharmaceutical Research.\n<doi:10.1080/19466315.2019.1701546>.",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.2",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-01 08:29:36 UTC",
    "User": "root"
  },
  "Author": "Vahid Nassiri [cre], Helen Barnett [aut], Helena Geys [aut],\nTom Jacobs [aut], Thomas Jaki [aut]",
  "Repository": "https://vahidnassiri.r-universe.dev",
  "Date/Publication": "2025-05-05 16:43:09 UTC",
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  "_assets": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
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      "date": "2018-08-13"
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      "date": "2020-06-07"
    },
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    "estimateAUCandStdErr",
    "estimateAUCwithCMLperTimePoint",
    "estimateAUCwithFullCML",
    "estimateAUCwithMVNCML",
    "estimateAUCwithPairwiseCML",
    "imputeBLOQ",
    "imputeCML",
    "imputeConstant",
    "imputeKernelDensityEstimation",
    "imputeROS",
    "simulateBealModelFixedEffects",
    "simulateBealModelMixedEffects"
  ],
  "_help": [
    {
      "page": "estimateAUCandStdErr",
      "title": "Estimate AUC and its standard error",
      "topics": [
        "estimateAUCandStdErr"
      ]
    },
    {
      "page": "estimateAUCwithCMLperTimePoint",
      "title": "estimate AUC with censored maximum likelihood per time point",
      "topics": [
        "estimateAUCwithCMLperTimePoint"
      ]
    },
    {
      "page": "estimateAUCwithFullCML",
      "title": "estimate AUC with Full censored maximum likelihood",
      "topics": [
        "estimateAUCwithFullCML"
      ]
    },
    {
      "page": "estimateAUCwithMVNCML",
      "title": "estimate AUC with multivariate normal censored maximum likelihood",
      "topics": [
        "estimateAUCwithMVNCML"
      ]
    },
    {
      "page": "estimateAUCwithPairwiseCML",
      "title": "estimate AUCwith pairwise censored maximum likelihood",
      "topics": [
        "estimateAUCwithPairwiseCML"
      ]
    },
    {
      "page": "imputeBLOQ",
      "title": "impute BLOQ's with various methods",
      "topics": [
        "imputeBLOQ"
      ]
    },
    {
      "page": "imputeCML",
      "title": "imputing BLOQ's using censored maximum likelihood",
      "topics": [
        "imputeCML"
      ]
    },
    {
      "page": "imputeConstant",
      "title": "imputing BLOQ's with a constant value",
      "topics": [
        "imputeConstant"
      ]
    },
    {
      "page": "imputeKernelDensityEstimation",
      "title": "imputing BLOQ's using kernel density estimation",
      "topics": [
        "imputeKernelDensityEstimation"
      ]
    },
    {
      "page": "imputeROS",
      "title": "imputing BLOQ's using regression on order statistics",
      "topics": [
        "imputeROS"
      ]
    },
    {
      "page": "simulateBealModelFixedEffects",
      "title": "simulate data from Beal model with fixed effects",
      "topics": [
        "simulateBealModelFixedEffects"
      ]
    },
    {
      "page": "simulateBealModelMixedEffects",
      "title": "simulate data from Beal model with fixed and random effects",
      "topics": [
        "simulateBealModelMixedEffects"
      ]
    }
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    "generics",
    "lattice",
    "maxLik",
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    "sandwich",
    "zoo"
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