{
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  "Package": "missCompare",
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  "Title": "Intuitive Missing Data Imputation Framework",
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  "Authors@R": "c(person(\"Tibor\", \"V. Varga\", email = \"tirgit@hotmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-2383-699X\")),\nperson(\"David\", \"Westergaard\", email = \"david.westergaard@cpr.ku.dk\", role= c(\"aut\"), comment = c(ORCID = \"0000-0003-0128-8432\")))",
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  "Description": "Offers a convenient pipeline to test and compare various\nmissing data imputation algorithms on simulated and real data.\nThese include simpler methods, such as mean and median\nimputation and random replacement, but also include more\nsophisticated algorithms already implemented in popular R\npackages, such as 'mi', described by Su et al. (2011)\n<doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren\nand Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>;\n'missForest', described by Stekhoven and Buhlmann (2012)\n<doi:10.1093/bioinformatics/btr597>; 'missMDA', described by\nJosse and Husson (2016) <doi:10.18637/jss.v070.i01>; and\n'pcaMethods', described by Stacklies et al. (2007)\n<doi:10.1093/bioinformatics/btm069>. The central assumption\nbehind 'missCompare' is that structurally different datasets\n(e.g. larger datasets with a large number of correlated\nvariables vs. smaller datasets with non correlated variables)\nwill benefit differently from different missing data imputation\nalgorithms. 'missCompare' takes measurements of your dataset\nand sets up a sandbox to try a curated list of standard and\nsophisticated missing data imputation algorithms and compares\nthem assuming custom missingness patterns. 'missCompare' will\nalso impute your real-life dataset for you after the selection\nof the best performing algorithm in the simulations. The\npackage also provides various post-imputation diagnostics and\nvisualizations to help you assess imputation performance.",
  "License": "MIT + file LICENSE",
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  "BugReports": "https://github.com/Tirgit/missCompare/issues",
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  "Repository": "https://tirgit.r-universe.dev",
  "Date/Publication": "2020-11-30 16:32:15 UTC",
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  "Author": "Tibor V. Varga [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-2383-699X>),\nDavid Westergaard [aut] (ORCID:\n<https://orcid.org/0000-0003-0128-8432>)",
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    "test_aregImpute",
    "test_kNN",
    "test_mean_imp",
    "test_median_imp",
    "test_mi",
    "test_mice_mixed",
    "test_missForest",
    "test_missMDA_EM",
    "test_missMDA_reg",
    "test_pcaMethods_BPCA",
    "test_pcaMethods_Nipals",
    "test_pcaMethods_NLPCA",
    "test_pcaMethods_PPCA",
    "test_pcaMethods_svdImpute",
    "test_random_imp"
  ],
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      "title": "Clinical dataset with missingness",
      "object": "clindata_miss",
      "class": [
        "data.frame"
      ],
      "fields": [
        "age",
        "sex",
        "waist",
        "BMI",
        "SBP",
        "DBP",
        "FG",
        "PPG",
        "TC",
        "TG",
        "HDL",
        "education"
      ],
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      "table": true,
      "tojson": true
    }
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    {
      "page": "all_patterns",
      "title": "Missing data spike-in in various missing data patterns",
      "topics": [
        "all_patterns"
      ]
    },
    {
      "page": "clean",
      "title": "Dataframe cleaning for missing data handling",
      "topics": [
        "clean"
      ]
    },
    {
      "page": "clindata_miss",
      "title": "Clinical dataset with missingness",
      "topics": [
        "clindata_miss"
      ]
    },
    {
      "page": "get_data",
      "title": "Extraction of metadata from dataframes",
      "topics": [
        "get_data"
      ]
    },
    {
      "page": "impute_data",
      "title": "Missing data imputation with various methods",
      "topics": [
        "impute_data"
      ]
    },
    {
      "page": "impute_simulated",
      "title": "Imputation algorithm tester on simulated data",
      "topics": [
        "impute_simulated"
      ]
    },
    {
      "page": "MAP",
      "title": "Missing data spike-in in MAP pattern",
      "topics": [
        "MAP"
      ]
    },
    {
      "page": "MAR",
      "title": "Missing data spike-in in MAR pattern",
      "topics": [
        "MAR"
      ]
    },
    {
      "page": "MCAR",
      "title": "Missing data spike-in in MCAR pattern",
      "topics": [
        "MCAR"
      ]
    },
    {
      "page": "missCompare",
      "title": "'missCompare': Missing Data Imputation Comparison Framework",
      "topics": [
        "missCompare"
      ]
    },
    {
      "page": "MNAR",
      "title": "Missing data spike-in in MNAR pattern",
      "topics": [
        "MNAR"
      ]
    },
    {
      "page": "post_imp_diag",
      "title": "Post imputation diagnostics",
      "topics": [
        "post_imp_diag"
      ]
    },
    {
      "page": "simulate",
      "title": "Simulation of matrix with no missingness",
      "topics": [
        "simulate"
      ]
    },
    {
      "page": "test_AmeliaII",
      "title": "Testing the 'Amelia II' missing data imputation algorithm",
      "topics": [
        "test_AmeliaII"
      ]
    },
    {
      "page": "test_aregImpute",
      "title": "Testing the 'Hmisc' aregImpute missing data imputation algorithm",
      "topics": [
        "test_aregImpute"
      ]
    },
    {
      "page": "test_kNN",
      "title": "Testing the 'VIM' kNN missing data imputation algorithm",
      "topics": [
        "test_kNN"
      ]
    },
    {
      "page": "test_mean_imp",
      "title": "Testing the mean imputation algorithm",
      "topics": [
        "test_mean_imp"
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    },
    {
      "page": "test_median_imp",
      "title": "Testing the median imputation algorithm",
      "topics": [
        "test_median_imp"
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    },
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        "Before you start...",
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        "Computation time comparison",
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