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      "version": "3.19.0",
      "date": "2025-12-11"
    }
  ],
  "_exports": [
    ".norm.draw",
    ".pmm.match",
    "ampute",
    "ampute.default.freq",
    "ampute.default.odds",
    "ampute.default.type",
    "ampute.default.weights",
    "appendbreak",
    "as.mids",
    "as.mira",
    "as.mitml.result",
    "bwplot",
    "cbind",
    "cc",
    "cci",
    "complete",
    "construct.blocks",
    "convergence",
    "D1",
    "D2",
    "D3",
    "densityplot",
    "estimice",
    "extractBS",
    "fico",
    "filter",
    "fix.coef",
    "flux",
    "fluxplot",
    "futuremice",
    "getfit",
    "getqbar",
    "glance",
    "glm.mids",
    "ibind",
    "ic",
    "ici",
    "is.mads",
    "is.mids",
    "is.mipo",
    "is.mira",
    "is.mitml.result",
    "lm.mids",
    "mads",
    "make.blocks",
    "make.blots",
    "make.calltype",
    "make.formulas",
    "make.method",
    "make.post",
    "make.predictorMatrix",
    "make.visitSequence",
    "make.where",
    "matchindex",
    "mcar",
    "md.pairs",
    "md.pattern",
    "mdc",
    "mice",
    "mice.impute.2l.bin",
    "mice.impute.2l.lmer",
    "mice.impute.2l.norm",
    "mice.impute.2l.pan",
    "mice.impute.2lonly.mean",
    "mice.impute.2lonly.norm",
    "mice.impute.2lonly.pmm",
    "mice.impute.cart",
    "mice.impute.jomoImpute",
    "mice.impute.lasso.logreg",
    "mice.impute.lasso.norm",
    "mice.impute.lasso.select.logreg",
    "mice.impute.lasso.select.norm",
    "mice.impute.lda",
    "mice.impute.logreg",
    "mice.impute.logreg.boot",
    "mice.impute.mean",
    "mice.impute.midastouch",
    "mice.impute.mnar.logreg",
    "mice.impute.mnar.norm",
    "mice.impute.mpmm",
    "mice.impute.norm",
    "mice.impute.norm.boot",
    "mice.impute.norm.nob",
    "mice.impute.norm.predict",
    "mice.impute.panImpute",
    "mice.impute.passive",
    "mice.impute.pmm",
    "mice.impute.polr",
    "mice.impute.polyreg",
    "mice.impute.quadratic",
    "mice.impute.rf",
    "mice.impute.ri",
    "mice.impute.sample",
    "mice.mids",
    "mice.theme",
    "mids",
    "mids2mplus",
    "mids2spss",
    "mipo",
    "mira",
    "name.blocks",
    "name.formulas",
    "ncc",
    "nelsonaalen",
    "nic",
    "nimp",
    "norm.draw",
    "parlmice",
    "pool",
    "pool.compare",
    "pool.r.squared",
    "pool.scalar",
    "pool.scalar.syn",
    "pool.syn",
    "pool.table",
    "predict_mi",
    "quickpred",
    "rbind",
    "squeeze",
    "stripplot",
    "supports.transparent",
    "tidy",
    "version",
    "xyplot"
  ],
  "_datasets": [
    {
      "name": "boys",
      "title": "Growth of Dutch boys",
      "object": "boys",
      "class": [
        "data.frame"
      ],
      "fields": [
        "age",
        "hgt",
        "wgt",
        "bmi",
        "hc",
        "gen",
        "phb",
        "tv",
        "reg"
      ],
      "rows": 748,
      "table": true,
      "tojson": true
    },
    {
      "name": "brandsma",
      "title": "Brandsma school data used Snijders and Bosker (2012)",
      "object": "brandsma",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sch",
        "pup",
        "iqv",
        "iqp",
        "sex",
        "ses",
        "min",
        "rpg",
        "lpr",
        "lpo",
        "apr",
        "apo",
        "den",
        "ssi"
      ],
      "rows": 4106,
      "table": true,
      "tojson": true
    },
    {
      "name": "employee",
      "title": "Employee selection data",
      "object": "employee",
      "class": [
        "data.frame"
      ],
      "fields": [
        "IQ",
        "wbeing",
        "jobperf"
      ],
      "rows": 20,
      "table": true,
      "tojson": true
    },
    {
      "name": "fdd",
      "title": "SE Fireworks disaster data",
      "object": "fdd",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "trt",
        "pp",
        "trtp",
        "sex",
        "etn",
        "age",
        "trauma",
        "prop1",
        "prop2",
        "prop3",
        "crop1",
        "crop2",
        "crop3",
        "masc1",
        "masc2",
        "masc3",
        "cbcl1",
        "cbcl3",
        "prs1",
        "prs2",
        "prs3",
        "ypa1",
        "ypb1",
        "ypc1",
        "yp1",
        "ypa2",
        "ypb2",
        "ypc2",
        "yp2",
        "ypa3",
        "ypb3",
        "ypc3",
        "yp3",
        "yca1",
        "ycb1",
        "ycc1",
        "yc1",
        "yca2",
        "ycb2",
        "ycc2",
        "yc2",
        "yca3",
        "ycb3",
        "ycc3",
        "yc3",
        "ypf1",
        "ypf2",
        "ypf3",
        "ypp1",
        "ypp2",
        "ypp3",
        "ycf1",
        "ycf2",
        "ycf3",
        "ycp1",
        "ycp2",
        "ycp3",
        "cbin1",
        "cbin3",
        "cbex1",
        "cbex3",
        "bir1",
        "bir2",
        "bir3"
      ],
      "rows": 52,
      "table": true,
      "tojson": true
    },
    {
      "name": "fdd.pred",
      "title": "SE Fireworks disaster data",
      "object": "fdd.pred",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "id",
        "trt",
        "pp",
        "trtp",
        "sex",
        "etn",
        "age",
        "trauma",
        "prop1",
        "prop2",
        "prop3",
        "crop1",
        "crop2",
        "crop3",
        "masc1",
        "masc2",
        "masc3",
        "cbcl1",
        "cbcl3",
        "prs1",
        "prs2",
        "prs3",
        "ypa1",
        "ypb1",
        "ypc1",
        "yp1",
        "ypa2",
        "ypb2",
        "ypc2",
        "yp2",
        "ypa3",
        "ypb3",
        "ypc3",
        "yp3",
        "yca1",
        "ycb1",
        "ycc1",
        "yc1",
        "yca2",
        "ycb2",
        "ycc2",
        "yc2",
        "yca3",
        "ycb3",
        "ycc3",
        "yc3",
        "ypf1",
        "ypf2",
        "ypf3",
        "ypp1",
        "ypp2",
        "ypp3",
        "ycf1",
        "ycf2",
        "ycf3",
        "ycp1",
        "ycp2",
        "ycp3",
        "cbin1",
        "cbin3",
        "cbex1",
        "cbex3",
        "bir1",
        "bir2",
        "bir3"
      ],
      "rows": 65,
      "table": true,
      "tojson": true
    },
    {
      "name": "fdgs",
      "title": "Fifth Dutch growth study 2009",
      "object": "fdgs",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "reg",
        "age",
        "sex",
        "hgt",
        "wgt",
        "hgt.z",
        "wgt.z"
      ],
      "rows": 10030,
      "table": true,
      "tojson": true
    },
    {
      "name": "leiden85",
      "title": "Leiden 85+ study",
      "object": "leiden85",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "mammalsleep",
      "title": "Mammal sleep data",
      "object": "mammalsleep",
      "class": [
        "data.frame"
      ],
      "fields": [
        "species",
        "bw",
        "brw",
        "sws",
        "ps",
        "ts",
        "mls",
        "gt",
        "pi",
        "sei",
        "odi"
      ],
      "rows": 62,
      "table": true,
      "tojson": true
    },
    {
      "name": "mnar_demo_data",
      "title": "MNAR demo data",
      "object": "mnar_demo_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "X",
        "Y",
        "Z"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
    },
    {
      "name": "nhanes",
      "title": "NHANES example - all variables numerical",
      "object": "nhanes",
      "class": [
        "data.frame"
      ],
      "fields": [
        "age",
        "bmi",
        "hyp",
        "chl"
      ],
      "rows": 25,
      "table": true,
      "tojson": true
    },
    {
      "name": "nhanes2",
      "title": "NHANES example - mixed numerical and discrete variables",
      "object": "nhanes2",
      "class": [
        "data.frame"
      ],
      "fields": [
        "age",
        "bmi",
        "hyp",
        "chl"
      ],
      "rows": 25,
      "table": true,
      "tojson": true
    },
    {
      "name": "pattern1",
      "title": "Datasets with various missing data patterns",
      "object": "pattern1",
      "class": [
        "data.frame"
      ],
      "fields": [
        "A",
        "B",
        "C"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "pattern2",
      "title": "Datasets with various missing data patterns",
      "object": "pattern2",
      "class": [
        "data.frame"
      ],
      "fields": [
        "A",
        "B",
        "C"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "pattern3",
      "title": "Datasets with various missing data patterns",
      "object": "pattern3",
      "class": [
        "data.frame"
      ],
      "fields": [
        "A",
        "B",
        "C"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "pattern4",
      "title": "Datasets with various missing data patterns",
      "object": "pattern4",
      "class": [
        "data.frame"
      ],
      "fields": [
        "A",
        "B",
        "C"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "popmis",
      "title": "Hox pupil popularity data with missing popularity scores",
      "object": "popmis",
      "class": [
        "data.frame"
      ],
      "fields": [
        "pupil",
        "school",
        "popular",
        "sex",
        "texp",
        "const",
        "teachpop"
      ],
      "rows": 2000,
      "table": true,
      "tojson": true
    },
    {
      "name": "pops",
      "title": "Project on preterm and small for gestational age infants (POPS)",
      "object": "pops",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "pops.pred",
      "title": "Project on preterm and small for gestational age infants (POPS)",
      "object": "pops.pred",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "patid",
        "geslacht",
        "ras",
        "opltot",
        "sch14jr",
        "dish5r",
        "mag",
        "zwd",
        "gebgew",
        "sga",
        "grad_t",
        "grad_u",
        "so5",
        "p12",
        "p13",
        "dos",
        "oq",
        "spra14",
        "cogn14",
        "grad_a",
        "p8",
        "hoor14",
        "grad_v",
        "p5",
        "p6",
        "visus14",
        "neuro5j",
        "grad_n",
        "p10",
        "p11",
        "mobi14",
        "hand14",
        "cp_ipr",
        "bpd",
        "grad_r",
        "attentpr",
        "kalverbr",
        "mbd",
        "lengtemo",
        "lengteva",
        "l5_sd",
        "l10_sd",
        "l14_sd",
        "b5_sd",
        "b10_sd",
        "b14_sd",
        "emo14",
        "pijn14",
        "totsas",
        "totprob2",
        "ha_sv",
        "ha_sa",
        "ha_sp",
        "ha_fy",
        "ha_ro",
        "ha_ged",
        "ha_vr",
        "ha_gz",
        "var1663",
        "sch910r",
        "iq",
        "min_avlr",
        "a10u",
        "e_tot",
        "a10b",
        "adhd",
        "l19_sd",
        "b19_sd",
        "m3kpvisi",
        "m3kphear",
        "m3kpspra",
        "m3kpemot",
        "m3kppijn",
        "m3kpambu",
        "m3kpdext",
        "m3kpcogn",
        "vr1",
        "vr2",
        "vr3",
        "vr4",
        "vr5",
        "vr6",
        "coping",
        "seffz",
        "opl19rec",
        "occrec"
      ],
      "rows": 86,
      "table": true,
      "tojson": true
    },
    {
      "name": "potthoffroy",
      "title": "Potthoff-Roy data",
      "object": "potthoffroy",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "sex",
        "d8",
        "d10",
        "d12",
        "d14"
      ],
      "rows": 27,
      "table": true,
      "tojson": true
    },
    {
      "name": "selfreport",
      "title": "Self-reported and measured BMI",
      "object": "selfreport",
      "class": [
        "data.frame"
      ],
      "fields": [
        "src",
        "id",
        "pop",
        "age",
        "sex",
        "hm",
        "wm",
        "hr",
        "wr",
        "prg",
        "edu",
        "etn",
        "web",
        "bm",
        "br"
      ],
      "rows": 2060,
      "table": true,
      "tojson": true
    },
    {
      "name": "tbc",
      "title": "Terneuzen birth cohort",
      "object": "tbc",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "occ",
        "nocc",
        "first",
        "typ",
        "age",
        "sex",
        "hgt.z",
        "wgt.z",
        "bmi.z",
        "ao"
      ],
      "rows": 3951,
      "table": true,
      "tojson": true
    },
    {
      "name": "tbc.target",
      "title": "Terneuzen birth cohort",
      "object": "tbc.target",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "ao",
        "bmi.z.jv"
      ],
      "rows": 2612,
      "table": true,
      "tojson": true
    },
    {
      "name": "toenail",
      "title": "Toenail data",
      "object": "toenail",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "outcome",
        "treatment",
        "month",
        "visit"
      ],
      "rows": 1908,
      "table": true,
      "tojson": true
    },
    {
      "name": "toenail2",
      "title": "Toenail data",
      "object": "toenail2",
      "class": [
        "data.frame"
      ],
      "fields": [
        "patientID",
        "outcome",
        "treatment",
        "time",
        "visit"
      ],
      "rows": 1908,
      "table": true,
      "tojson": true
    },
    {
      "name": "walking",
      "title": "Walking disability data",
      "object": "walking",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sex",
        "age",
        "YA",
        "YB",
        "src"
      ],
      "rows": 890,
      "table": true,
      "tojson": true
    },
    {
      "name": "windspeed",
      "title": "Subset of Irish wind speed data",
      "object": "windspeed",
      "class": [
        "data.frame"
      ],
      "fields": [
        "RochePt",
        "Rosslare",
        "Shannon",
        "Dublin",
        "Clones",
        "MalinHead"
      ],
      "rows": 433,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "pmm.match",
      "title": "Finds an imputed value from matches in the predictive metric (deprecated)",
      "topics": [
        ".pmm.match"
      ]
    },
    {
      "page": "ampute",
      "title": "Generate missing data for simulation purposes",
      "topics": [
        "ampute"
      ]
    },
    {
      "page": "anova",
      "title": "Compare several nested models",
      "topics": [
        "anova.mira"
      ]
    },
    {
      "page": "appendbreak",
      "title": "Appends specified break to the data",
      "topics": [
        "appendbreak"
      ]
    },
    {
      "page": "as.mids",
      "title": "Converts an imputed dataset (long format) into a 'mids' object",
      "topics": [
        "as.mids"
      ]
    },
    {
      "page": "as.mira",
      "title": "Create a 'mira' object from repeated analyses",
      "topics": [
        "as.mira"
      ]
    },
    {
      "page": "as.mitml.result",
      "title": "Converts into a 'mitml.result' object",
      "topics": [
        "as.mitml.result"
      ]
    },
    {
      "page": "boys",
      "title": "Growth of Dutch boys",
      "topics": [
        "boys"
      ]
    },
    {
      "page": "brandsma",
      "title": "Brandsma school data used Snijders and Bosker (2012)",
      "topics": [
        "brandsma"
      ]
    },
    {
      "page": "bwplot.mads",
      "title": "Box-and-whisker plot of amputed and non-amputed data",
      "topics": [
        "bwplot.mads"
      ]
    },
    {
      "page": "bwplot.mids",
      "title": "Box-and-whisker plot of observed and imputed data",
      "topics": [
        "bwplot",
        "bwplot.mids"
      ]
    },
    {
      "page": "cbind",
      "title": "Combine R objects by rows and columns",
      "topics": [
        "cbind",
        "rbind"
      ]
    },
    {
      "page": "cc",
      "title": "Select complete cases",
      "topics": [
        "cc"
      ]
    },
    {
      "page": "cci",
      "title": "Complete case indicator",
      "topics": [
        "cci"
      ]
    },
    {
      "page": "complete.mids",
      "title": "Extracts the completed data from a 'mids' object",
      "topics": [
        "complete",
        "complete.mids"
      ]
    },
    {
      "page": "construct.blocks",
      "title": "Construct blocks from 'formulas' and 'predictorMatrix'",
      "topics": [
        "construct.blocks"
      ]
    },
    {
      "page": "convergence",
      "title": "Computes convergence diagnostics for a 'mids' object",
      "topics": [
        "convergence"
      ]
    },
    {
      "page": "D1",
      "title": "Compare two nested models using D1-statistic",
      "topics": [
        "D1"
      ]
    },
    {
      "page": "D2",
      "title": "Compare two nested models using D2-statistic",
      "topics": [
        "D2"
      ]
    },
    {
      "page": "D3",
      "title": "Compare two nested models using D3-statistic",
      "topics": [
        "D3"
      ]
    },
    {
      "page": "densityplot.mids",
      "title": "Density plot of observed and imputed data",
      "topics": [
        "densityplot",
        "densityplot.mids"
      ]
    },
    {
      "page": "employee",
      "title": "Employee selection data",
      "topics": [
        "employee"
      ]
    },
    {
      "page": "estimice",
      "title": "Computes least squares parameters",
      "topics": [
        "estimice"
      ]
    },
    {
      "page": "extractBS",
      "title": "Extract broken stick estimates from a 'lmer' object",
      "topics": [
        "extractBS"
      ]
    },
    {
      "page": "fdd",
      "title": "SE Fireworks disaster data",
      "topics": [
        "fdd",
        "fdd.pred"
      ]
    },
    {
      "page": "fdgs",
      "title": "Fifth Dutch growth study 2009",
      "topics": [
        "fdgs"
      ]
    },
    {
      "page": "fico",
      "title": "Fraction of incomplete cases among cases with observed",
      "topics": [
        "fico"
      ]
    },
    {
      "page": "filter.mids",
      "title": "Subset rows of a 'mids' object",
      "topics": [
        "filter.mids"
      ]
    },
    {
      "page": "fix.coef",
      "title": "Fix coefficients and update model",
      "topics": [
        "fix.coef"
      ]
    },
    {
      "page": "flux",
      "title": "Influx and outflux of multivariate missing data patterns",
      "topics": [
        "flux"
      ]
    },
    {
      "page": "fluxplot",
      "title": "Fluxplot of the missing data pattern",
      "topics": [
        "fluxplot"
      ]
    },
    {
      "page": "futuremice",
      "title": "Wrapper function that runs MICE in parallel",
      "topics": [
        "futuremice"
      ]
    },
    {
      "page": "getfit",
      "title": "Extract list of fitted models",
      "topics": [
        "getfit"
      ]
    },
    {
      "page": "getqbar",
      "title": "Extract estimate from 'mipo' object",
      "topics": [
        "getqbar"
      ]
    },
    {
      "page": "glm.mids",
      "title": "Generalized linear model for 'mids' object",
      "topics": [
        "glm.mids"
      ]
    },
    {
      "page": "ibind",
      "title": "Enlarge number of imputations by combining 'mids' objects",
      "topics": [
        "ibind"
      ]
    },
    {
      "page": "ic",
      "title": "Select incomplete cases",
      "topics": [
        "ic"
      ]
    },
    {
      "page": "ici",
      "title": "Incomplete case indicator",
      "topics": [
        "ici",
        "ici,data.frame-method",
        "ici,matrix-method",
        "ici,mids-method"
      ]
    },
    {
      "page": "is.mads",
      "title": "Check for 'mads' object",
      "topics": [
        "is.mads"
      ]
    },
    {
      "page": "is.mids",
      "title": "Check for 'mids' object",
      "topics": [
        "is.mids"
      ]
    },
    {
      "page": "is.mipo",
      "title": "Check for 'mipo' object",
      "topics": [
        "is.mipo"
      ]
    },
    {
      "page": "is.mira",
      "title": "Check for 'mira' object",
      "topics": [
        "is.mira"
      ]
    },
    {
      "page": "is.mitml.result",
      "title": "Check for 'mitml.result' object",
      "topics": [
        "is.mitml.result"
      ]
    },
    {
      "page": "leiden85",
      "title": "Leiden 85+ study",
      "topics": [
        "leiden85"
      ]
    },
    {
      "page": "lm.mids",
      "title": "Linear regression for 'mids' object",
      "topics": [
        "lm.mids"
      ]
    },
    {
      "page": "mads",
      "title": "Multivariate amputed data set ('mads')",
      "topics": [
        "mads",
        "print.mads",
        "summary.mads"
      ]
    },
    {
      "page": "make.blocks",
      "title": "Creates a 'blocks' argument",
      "topics": [
        "make.blocks"
      ]
    },
    {
      "page": "make.blots",
      "title": "Creates a 'blots' argument",
      "topics": [
        "make.blots"
      ]
    },
    {
      "page": "make.calltype",
      "title": "Create calltype of the imputation model",
      "topics": [
        "make.calltype"
      ]
    },
    {
      "page": "make.formulas",
      "title": "Creates a 'formulas' argument",
      "topics": [
        "make.formulas"
      ]
    },
    {
      "page": "make.method",
      "title": "Creates a 'method' argument",
      "topics": [
        "make.method"
      ]
    },
    {
      "page": "make.post",
      "title": "Creates a 'post' argument",
      "topics": [
        "make.post"
      ]
    },
    {
      "page": "make.predictorMatrix",
      "title": "Creates a 'predictorMatrix' argument",
      "topics": [
        "make.predictorMatrix"
      ]
    },
    {
      "page": "make.visitSequence",
      "title": "Creates a 'visitSequence' argument",
      "topics": [
        "make.visitSequence"
      ]
    },
    {
      "page": "make.where",
      "title": "Creates a 'where' argument",
      "topics": [
        "make.where"
      ]
    },
    {
      "page": "mammalsleep",
      "title": "Mammal sleep data",
      "topics": [
        "mammalsleep",
        "sleep"
      ]
    },
    {
      "page": "matchindex",
      "title": "Find index of matched donor units",
      "topics": [
        "matchindex"
      ]
    },
    {
      "page": "md.pairs",
      "title": "Missing data pattern by variable pairs",
      "topics": [
        "md.pairs"
      ]
    },
    {
      "page": "md.pattern",
      "title": "Missing data pattern",
      "topics": [
        "md.pattern"
      ]
    },
    {
      "page": "mdc",
      "title": "Graphical parameter for missing data plots",
      "topics": [
        "mdc"
      ]
    },
    {
      "page": "mice.impute.2l.bin",
      "title": "Imputation by a two-level logistic model using 'glmer'",
      "concept": [
        "univariate-2l"
      ],
      "topics": [
        "mice.impute.2l.bin"
      ]
    },
    {
      "page": "mice.impute.2l.lmer",
      "title": "Imputation by a two-level normal model using 'lmer'",
      "concept": [
        "univariate-2l"
      ],
      "topics": [
        "mice.impute.2l.lmer"
      ]
    },
    {
      "page": "mice.impute.2l.norm",
      "title": "Imputation by a two-level normal model",
      "concept": [
        "univariate-2l"
      ],
      "topics": [
        "mice.impute.2l.norm"
      ]
    },
    {
      "page": "mice.impute.2l.pan",
      "title": "Imputation by a two-level normal model using 'pan'",
      "concept": [
        "univariate-2l"
      ],
      "topics": [
        "2l.pan",
        "mice.impute.2l.pan"
      ]
    },
    {
      "page": "mice.impute.2lonly.mean",
      "title": "Imputation of most likely value within the class",
      "concept": [
        "univariate-2lonly"
      ],
      "topics": [
        "2lonly.mean",
        "mice.impute.2lonly.mean"
      ]
    },
    {
      "page": "mice.impute.2lonly.norm",
      "title": "Imputation at level 2 by Bayesian linear regression",
      "concept": [
        "univariate-2lonly"
      ],
      "topics": [
        "2lonly.norm",
        "mice.impute.2lonly.norm"
      ]
    },
    {
      "page": "mice.impute.2lonly.pmm",
      "title": "Imputation at level 2 by predictive mean matching",
      "concept": [
        "univariate-2lonly"
      ],
      "topics": [
        "2lonly.pmm",
        "mice.impute.2lonly.pmm"
      ]
    },
    {
      "page": "mice.impute.cart",
      "title": "Imputation by classification and regression trees",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "cart",
        "mice.impute.cart"
      ]
    },
    {
      "page": "mice.impute.jomoImpute",
      "title": "Multivariate multilevel imputation using 'jomo'",
      "concept": [
        "multivariate-2l"
      ],
      "topics": [
        "mice.impute.jomoImpute"
      ]
    },
    {
      "page": "mice.impute.lasso.logreg",
      "title": "Imputation by direct use of lasso logistic regression",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "lasso.logreg",
        "mice.impute.lasso.logreg"
      ]
    },
    {
      "page": "mice.impute.lasso.norm",
      "title": "Imputation by direct use of lasso linear regression",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "lasso.norm",
        "mice.impute.lasso.norm"
      ]
    },
    {
      "page": "mice.impute.lasso.select.logreg",
      "title": "Imputation by indirect use of lasso logistic regression",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "lasso.select.logreg",
        "mice.impute.lasso.select.logreg"
      ]
    },
    {
      "page": "mice.impute.lasso.select.norm",
      "title": "Imputation by indirect use of lasso linear regression",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "lasso.select.norm",
        "mice.impute.lasso.select.norm"
      ]
    },
    {
      "page": "mice.impute.lda",
      "title": "Imputation by linear discriminant analysis",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "mice.impute.lda"
      ]
    },
    {
      "page": "mice.impute.logreg",
      "title": "Imputation by logistic regression",
      "concept": [
        "univariate imputation functions"
      ],
      "topics": [
        "mice.impute.logreg"
      ]
    },
    {
      "page": "mice.impute.logreg.boot",
      "title": "Imputation by logistic regression using the bootstrap",
      "concept": [
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      ],
      "topics": [
        "mice.impute.logreg.boot"
      ]
    },
    {
      "page": "mice.impute.mean",
      "title": "Imputation by the mean",
      "concept": [
        "univariate imputation functions"
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      "topics": [
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      ]
    },
    {
      "page": "mice.impute.midastouch",
      "title": "Imputation by predictive mean matching with distance aided donor selection",
      "concept": [
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      ],
      "topics": [
        "mice.impute.midastouch"
      ]
    },
    {
      "page": "mice.impute.mnar",
      "title": "Imputation under MNAR mechanism by NARFCS",
      "concept": [
        "univariate imputation functions"
      ],
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        "mice.impute.mnar.logreg",
        "mice.impute.mnar.norm",
        "mnar.logreg",
        "mnar.norm"
      ]
    },
    {
      "page": "mice.impute.mpmm",
      "title": "Imputation by multivariate predictive mean matching",
      "concept": [
        "univariate imputation functions"
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        "mpmm"
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    {
      "page": "mice.impute.norm",
      "title": "Imputation by Bayesian linear regression",
      "concept": [
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      "page": "mice.impute.norm.boot",
      "title": "Imputation by linear regression, bootstrap method",
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      "page": "mice.impute.norm.nob",
      "title": "Imputation by linear regression without parameter uncertainty",
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    {
      "page": "mice.impute.norm.predict",
      "title": "Imputation by linear regression through prediction",
      "concept": [
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      ],
      "topics": [
        "mice.impute.norm.predict",
        "norm.predict"
      ]
    },
    {
      "page": "mice.impute.panImpute",
      "title": "Impute multilevel missing data using 'pan'",
      "concept": [
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      "topics": [
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    },
    {
      "page": "mice.impute.passive",
      "title": "Passive imputation",
      "topics": [
        "mice.impute.passive"
      ]
    },
    {
      "page": "mice.impute.pmm",
      "title": "Imputation by predictive mean matching",
      "concept": [
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        "pmm"
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    },
    {
      "page": "mice.impute.polr",
      "title": "Imputation of ordered data by polytomous regression",
      "concept": [
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    },
    {
      "page": "mice.impute.polyreg",
      "title": "Imputation of unordered data by polytomous regression",
      "concept": [
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    },
    {
      "page": "mice.impute.quadratic",
      "title": "Imputation of quadratic terms",
      "concept": [
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      "topics": [
        "mice.impute.quadratic",
        "quadratic"
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    },
    {
      "page": "mice.impute.rf",
      "title": "Imputation by random forests",
      "concept": [
        "univariate imputation functions"
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    },
    {
      "page": "mice.impute.ri",
      "title": "Imputation by the random indicator method for nonignorable data",
      "concept": [
        "univariate imputation functions"
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        "mice.impute.ri",
        "ri"
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    },
    {
      "page": "mice.impute.sample",
      "title": "Imputation by simple random sampling",
      "topics": [
        "mice.impute.sample"
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    },
    {
      "page": "mice.mids",
      "title": "Multivariate Imputation by Chained Equations (Iteration Step)",
      "topics": [
        "mice.mids"
      ]
    },
    {
      "page": "mice.theme",
      "title": "Set the theme for the plotting Trellis functions",
      "topics": [
        "mice.theme"
      ]
    },
    {
      "page": "mids",
      "title": "Multiply imputed data set ('mids')",
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        "mids",
        "mids-class",
        "plot.mids",
        "print.mids",
        "summary.mids"
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    },
    {
      "page": "mids2mplus",
      "title": "Export 'mids' object to Mplus",
      "topics": [
        "mids2mplus"
      ]
    },
    {
      "page": "mids2spss",
      "title": "Export 'mids' object to SPSS",
      "topics": [
        "mids2spss"
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    {
      "page": "mira",
      "title": "Create an object of class \"mira\"",
      "topics": [
        "mira",
        "mira-class"
      ]
    },
    {
      "page": "mnar_demo_data",
      "title": "MNAR demo data",
      "topics": [
        "mnar_demo_data"
      ]
    },
    {
      "page": "name.blocks",
      "title": "Name imputation blocks",
      "topics": [
        "name.blocks"
      ]
    },
    {
      "page": "name.formulas",
      "title": "Name formula list elements",
      "topics": [
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    {
      "page": "ncc",
      "title": "Number of complete cases",
      "topics": [
        "ncc"
      ]
    },
    {
      "page": "nelsonaalen",
      "title": "Cumulative hazard rate or Nelson-Aalen estimator",
      "topics": [
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        "nelsonaalen"
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      "page": "nhanes",
      "title": "NHANES example - all variables numerical",
      "topics": [
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    {
      "page": "nhanes2",
      "title": "NHANES example - mixed numerical and discrete variables",
      "topics": [
        "nhanes2"
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    {
      "page": "nic",
      "title": "Number of incomplete cases",
      "topics": [
        "nic"
      ]
    },
    {
      "page": "nimp",
      "title": "Number of imputations per block",
      "topics": [
        "nimp"
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    },
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      "page": "norm.draw",
      "title": "Draws values of beta and sigma by Bayesian linear regression",
      "topics": [
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        "norm.draw"
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    {
      "page": "parlmice",
      "title": "Wrapper function that runs MICE in parallel",
      "topics": [
        "parlmice"
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    {
      "page": "pattern",
      "title": "Datasets with various missing data patterns",
      "topics": [
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        "pattern1",
        "pattern2",
        "pattern3",
        "pattern4"
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    },
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      "page": "pool",
      "title": "Combine estimates by pooling rules",
      "topics": [
        "pool",
        "pool.syn"
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      "page": "pool.compare",
      "title": "Compare two nested models fitted to imputed data",
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      "page": "pool.r.squared",
      "title": "Pools R^2 of m models fitted to multiply-imputed data",
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      "title": "Combines estimates from a tidy table",
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    {
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      "title": "Potthoff-Roy data",
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      "page": "predict_mi",
      "title": "Predict method for linear models with multiply imputed data",
      "topics": [
        "predict_mi"
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      "page": "print",
      "title": "Print a 'mira' object",
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    },
    {
      "page": "quickpred",
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    {
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      "topics": [
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        "selfreport"
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      "page": "squeeze",
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      "title": "Terneuzen birth cohort",
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      "page": "toenail",
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      "title": "Echoes the package version number",
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      "title": "Walking disability data",
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    {
      "page": "windspeed",
      "title": "Subset of Irish wind speed data",
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      "title": "Evaluate an expression in multiple imputed datasets",
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