{
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  "Type": "Package",
  "Title": "Multiple Imputation by Chained Equations with Multilevel Data",
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  "Date": "2026-09-28",
  "Authors@R": "c(person(\"Vincent\",\"Audigier\",email=\"vincent.audigier@cnam.fr\",role=c(\"aut\",\"cre\"),comment=\"CNAM MSDMA team\"),person(\"Matthieu\",\"Resche-Rigon\",email=\"matthieu.resche-rigon@u-paris.fr\",role=c(\"aut\"),comment=\"INSERM ECSTRA team\"),person(\"Johanna\",\"Munoz Avila\",email=\"J.MunozAvila@umcutrecht.nl\", role=\"ctb\",comment=\"Julius Center Methods Group UMC, 2022\"))",
  "Description": "Addons for the 'mice' package to perform multiple\nimputation using chained equations with two-level data.\nIncludes imputation methods dedicated to sporadically and\nsystematically missing values. Imputation of continuous, binary\nor count variables are available. Following the recommendations\nof Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the\nchoice of the imputation method for each variable can be\nfacilitated by a default choice tuned according to the\nstructure of the incomplete dataset. Allows parallel\ncalculation and overimputation for 'mice'.",
  "License": "GPL-2 | GPL-3",
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  "Author": "Vincent Audigier [aut, cre] (CNAM MSDMA team), Matthieu\nResche-Rigon [aut] (INSERM ECSTRA team), Johanna Munoz Avila\n[ctb] (Julius Center Methods Group UMC, 2022)",
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    "extra/citation.json",
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    "overimpute"
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      "object": "Obesity",
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      "class": [
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        "data.frame"
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        "Time",
        "Gender",
        "Height",
        "FamOb",
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        "Weight",
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      ],
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      "table": true,
      "tojson": true
    }
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      "title": "Multiple Imputation by Chained Equations with Multilevel Data",
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        "micemd-package",
        "micemd"
      ]
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      "page": "CHEM97Na",
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      "topics": [
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