{ "_id": "6a4792778b9f0d9747cf2264", "Package": "VIM", "Version": "7.1.0", "Title": "Visualization and Imputation of Missing Values", "Authors@R": "c(\nperson(\"Matthias\", \"Templ\", email = \"matthias.templ@gmail.com\", role = c(\"aut\",\"cre\")),\nperson(\"Alexander\", \"Kowarik\", email = \"alexander.kowarik@statistik.gv.at\", role = c(\"aut\"), comment=c(ORCID=\"0000-0001-8598-4130\")),\nperson(\"Andreas\", \"Alfons\", role = c(\"aut\")),\nperson(\"Johannes\", \"Gussenbauer\", role = c(\"aut\")),\nperson(\"Nina\", \"Niederhametner\", role = c(\"aut\")),\nperson(\"Eileen\", \"Vattheuer\", role = c(\"aut\")),\nperson(\"Gregor\", \"de Cillia\", email = \"gregor.decillia@statistik.gv.at\", role = c(\"aut\")),\nperson(\"Bernd\", \"Prantner\", role = c(\"ctb\")),\nperson(\"Wolfgang\", \"Rannetbauer\", role = c(\"aut\"))\n)", "Description": "Provides methods for imputation and visualization of\nmissing values. It includes graphical tools to explore the\namount, structure and patterns of missing and/or imputed\nvalues, supporting exploratory data analysis and helping to\ninvestigate potential missingness mechanisms (details in\nAlfons, Templ and Filzmoser, .\nThe quality of imputations can be assessed visually using a\nwide range of univariate, bivariate and multivariate plots. The\npackage further provides several imputation methods, including\nefficient implementations of k-nearest neighbour and hot-deck\nimputation (Kowarik and Templ 2013,\n, iterative robust model-based\nmultiple imputation (Templ 2011,\n; Templ 2023,\n), and machine learning–based\napproaches such as robust GAM-based multiple imputation (Templ\n2024, ) as well as gradient\nboosting (XGBoost) and transformer-based methods\n(Niederhametner et al., ).\nGeneral background and practical guidance on imputation are\nprovided in the Springer book by Templ (2023)\n.", "LazyData": "TRUE", "ByteCompile": "TRUE", "License": "GPL (>= 2)", "URL": "https://github.com/statistikat/VIM", "RoxygenNote": "7.3.3", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake make libicu-dev", "Repository": "https://statistikat.r-universe.dev", "Date/Publication": "2026-07-02 07:47:17 UTC", "RemoteUrl": "https://github.com/statistikat/vim", "RemoteRef": "HEAD", "RemoteSha": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-02 10:05:31 UTC", "User": "root" }, "Author": "Matthias Templ [aut, cre],\nAlexander Kowarik [aut] (ORCID:\n),\nAndreas Alfons [aut],\nJohannes Gussenbauer [aut],\nNina Niederhametner [aut],\nEileen Vattheuer [aut],\nGregor de Cillia [aut],\nBernd Prantner [ctb],\nWolfgang Rannetbauer [aut]", "Maintainer": "Matthias Templ ", "_user": "statistikat", "_type": "src", "_file": "VIM_7.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/4d88babe994766700827cf6f05d093bd42c78384c6eb8a22b2e6995bf1814a0f", "_filesize": 6176714, "_sha256": "4d88babe994766700827cf6f05d093bd42c78384c6eb8a22b2e6995bf1814a0f", "_expires": "2026-10-11T10:44:06.000Z", "_created": "2026-07-02T10:05:31.000Z", "_published": "2026-07-03T10:44:07.619Z", "_jobs": [ { "job": 84983168412, "time": 438, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "8035559969" }, { "job": 84983168543, "time": 438, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8035558819" }, { "job": 84983168347, "time": 388, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "8035540336" }, { "job": 84983168743, "time": 424, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8035553437" }, { "job": 84983168454, "time": 450, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "8035602966" }, { "job": 84983168491, "time": 720, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "8035690594" }, { "job": 84983168400, "time": 314, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "8035687656" }, { "job": 84983168445, "time": 502, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8035643871" }, { "job": 84983168373, "time": 411, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8035378380" }, { "job": 84983168500, "time": 208, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8035466518" }, { "job": 84983168380, "time": 394, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "8035541320" }, { "job": 84983168417, "time": 414, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "8035550413" }, { "job": 84983168494, "time": 426, "config": "windows-release", "r": "4.6.1", "check": "OK", "artifact": "8035553940" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515", "_status": "success", "_upstream": "https://github.com/statistikat/vim", "_commit": { "id": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "author": "evatt ", "committer": "evatt ", "message": "Refactor feature handling in register_gam_learners function and improve warnings for dropped predictors.\n", "time": 1782978437 }, "_maintainer": { "name": "Matthias Templ", "email": "matthias.templ@gmail.com", "login": "matthias-da", "description": "Matthias Templ is a Professor at the University of Applied Sciences Northwestern Switzerland. \nInterest: imputation, anonymization", "uuid": 5263685 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "colorspace", "role": "Depends" }, { "package": "grid", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "car", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "robustbase", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "sp", "role": "Imports" }, { "package": "vcd", "role": "Imports" }, { "package": "nnet", "role": "Imports" }, { "package": "e1071", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "laeken", "role": "Imports" }, { "package": "ranger", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "xgboost", "role": "Imports" }, { "package": "data.table", "version": ">= 1.9.4", "role": "Imports" }, { "package": "mlr3", "role": "Imports" }, { "package": "mlr3pipelines", "role": "Imports" }, { "package": "R6", "role": "Imports" }, { "package": "paradox", "role": "Imports" }, { "package": "mlr3tuning", "role": "Imports" }, { "package": "mlr3learners", "role": "Imports" }, { "package": "future", "role": "Imports" }, { "package": "dplyr", "role": "Suggests" }, { "package": "tinytest", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "mgcv", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "reactable", "role": "Suggests" }, { "package": "covr", "role": "Suggests" }, { "package": "withr", "role": "Suggests" }, { "package": "pdist", "role": "Suggests" }, { "package": "enetLTS", "role": "Suggests" }, { "package": "robmixglm", "role": "Suggests" }, { "package": "stringr", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "cellWise", "role": "Suggests" }, { "package": "crmReg", "role": "Suggests" }, { "package": "mice", "role": "Suggests" }, { "package": "lgr", "role": "Suggests" } ], "_owner": "statistikat", "_selfowned": true, "_usedby": 22, "_updates": [ { "week": "2025-50", "n": 6 }, { "week": "2026-02", "n": 2 }, { "week": "2026-09", "n": 3 }, { "week": "2026-10", "n": 4 }, { "week": "2026-11", "n": 5 }, { "week": "2026-27", "n": 11 } ], "_tags": [], "_topics": [ "hotdeck", "imputation-methods", "model-predictions", "visualization", "cpp", "openmp" ], "_stars": 91, "_contributors": [ { "user": "alexkowa", "count": 345, "uuid": 5180786 }, { "user": "eileenva", "count": 107, "uuid": 197917636 }, { "user": "gregordecillia", "count": 56, "uuid": 9311130 }, { "user": "bernhard-da", "count": 49, "uuid": 5228437 }, { "user": "matthias-da", "count": 27, "uuid": 5263685 }, { "user": "birgitkarlhuber", "count": 7, "uuid": 117508202 }, { "user": "torockel", "count": 4, "uuid": 60826877 }, { "user": "michaelchirico", "count": 3, "uuid": 7606389 }, { "user": "dascho", "count": 1, "uuid": 5443282 }, { "user": "ninanhm", "count": 1, "uuid": 76611719 }, { "user": "paulponcet", "count": 1, "uuid": 7842619 } ], "_userbio": { "uuid": 18658295, "type": "organization", "name": "Unofficial Organization for Open Source Projects of Statistics Austria", "followers": 33 }, "_downloads": { "count": 14166, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/VIM" }, "_mentions": 64, "_devurl": "https://github.com/statistikat/vim", "_searchresults": 3720, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/VIM.html", "manual.pdf" ], "_homeurl": "https://github.com/statistikat/vim", "_realowner": "statistikat", "_cranurl": true, "_releases": [ { "version": "3.0.2", "date": "2012-10-11" }, { "version": "3.0.3", "date": "2013-01-09" }, { "version": "3.0.3.1", "date": "2013-04-11" }, { "version": "4.0.0", "date": "2013-10-10" }, { "version": "4.1.0", "date": "2014-10-25" }, { "version": "4.3.0", "date": "2015-07-07" }, { "version": "4.4.1", "date": "2015-09-15" }, { "version": "4.5.0", "date": "2016-06-30" }, { "version": "4.6.0", "date": "2016-10-17" }, { "version": "4.7.0", "date": "2017-04-11" }, { "version": "4.8.0", "date": "2019-02-11" }, { "version": "5.1.0", "date": "2020-02-05" }, { "version": "5.1.1", "date": "2020-03-10" }, { "version": "6.0.0", "date": "2020-05-08" }, { "version": "6.1.0", "date": "2021-01-19" }, { "version": "6.1.1", "date": "2021-07-22" }, { "version": "6.2.2", "date": "2022-08-25" }, { "version": "6.2.6", "date": "2025-09-18" }, { "version": "7.0.0", "date": "2026-01-10" } ], "_exports": [ "aggr", "alphablend", "as.mids.vimmi", "barMiss", "bgmap", "cellWeightsMCD", "colormapMiss", "colormapMissLegend", "colSequence", "colSequenceHCL", "colSequenceRGB", "complete", "countInf", "countNA", "evaluation", "gapMiss", "gowerD", "growdotMiss", "histMiss", "hotdeck", "impPCA", "imputeCellEM", "imputeCellIRMI", "imputeCellM", "imputeCellMCD", "imputeCellMM", "imputeCellReg", "imputeCellwise", "imputeRobust", "imputeRobustChain", "initialise", "irmi", "kNN", "mapMiss", "marginmatrix", "marginplot", "matchImpute", "matrixplot", "maxCat", "medianSamp", "mosaicMiss", "msecor", "msecov", "nrmse", "pairsVIM", "parcoordMiss", "pbox", "pfc", "prepare", "rangerImpute", "regressionImp", "rugNA", "sampleCat", "scattJitt", "scattmatrixMiss", "scattMiss", "spineMiss", "tableMiss", "vimpute", "xgboostImpute" ], "_datasets": [ { "name": "Animals_na", "title": "Animals_na", "object": "Animals_na", "class": [ "data.frame" ], "fields": [ "lbody", "lbrain" ], "rows": 28, "table": true, "tojson": true }, { "name": "bcancer", "title": "Breast cancer Wisconsin data set", "object": "bcancer", "class": [ "data.frame" ], "fields": [ "ID", "clump_thickness", "uniformity_cellsize", "uniformity_cellshape", "adhesion", "epithelial_cellsize", "bare_nuclei", "chromatin", "normal_nucleoli", "mitoses", "class" ], "rows": 699, "table": true, "tojson": true }, { "name": "brittleness", "title": "Brittleness index data set", "object": "brittleness", "class": [ "data.frame" ], "fields": [ "TK104", "TK105", "TK107" ], "rows": 23, "table": true, "tojson": true }, { "name": "chorizonDL", "title": "C-horizon of the Kola data with missing values", "object": "chorizonDL", "class": [ "data.frame" ], "fields": [ "*ID", "XCOO", "YCOO", "Ag", "Ag_INAA", "Al", "Al2O3", "As", "As_INAA", "Au_INAA", "B", "Ba", "Ba_INAA", "Be", "Bi", "Br_IC", "Br_INAA", "Ca", "Ca_INAA", "CaO", "Cd", "Ce_INAA", "Cl_IC", "Co", "Co_INAA", "EC", "Cr", "Cr_INAA", "Cs_INAA", "Cu", "Eu_INAA", "F_IC", "Fe", "Fe_INAA", "Fe2O3", "Hf_INAA", "Hg", "Hg_INAA", "Ir_INAA", "K", "K2O", "La", "La_INAA", "Li", "LOI", "Lu_INAA", "wt_INAA", "Mg", "MgO", "Mn", "MnO", "Mo", "Mo_INAA", "Na", "Na_INAA", "Na2O", "Nd_INAA", "Ni", "Ni_INAA", "NO3_IC", "P", "P2O5", "Pb", "pH", "PO4_IC", "Rb", "S", "Sb", "Sb_INAA", "Sc", "Sc_INAA", "Se", "Se_INAA", "Si", "SiO2", "Sm_INAA", "Sn_INAA", "SO4_IC", "Sr", "Sr_INAA", "SUM_XRF", "Ta_INAA", "Tb_INAA", "Te", "Th", "Th_INAA", "Ti", "TiO2", "U_INAA", "V", "W_INAA", "Y", "Yb_INAA", "Zn", "Zn_INAA", "ELEV", "*COUN", "*ASP", "TOPC", "LITO", "Al_XRF", "Ca_XRF", "Fe_XRF", "K_XRF", "Mg_XRF", "Mn_XRF", "Na_XRF", "P_XRF", "Si_XRF", "Ti_XRF" ], "rows": 606, "table": true, "tojson": true }, { "name": "colic", "title": "Colic horse data set", "object": "colic", "class": [ "data.frame" ], "fields": [ "surgery", "age", "hospitalID", "temp_rectal", "pulse", "respiratory_rate", "temp_extreme", "pulse_peripheral", "capillayr_refill_time", "pain", "peristalsis", "abdominal_distension", "nasogastric_tube", "nasogastric_reflux", "nasogastric_reflux_PH", "rectal_examination", "abdomen", "cell_volume", "protein", "abdominocentesis_appearance", "abdomcentesis_protein", "outcome", "surgical_lesion", "lesion_type1", "lesion_type2", "lesion_type3", "cp_data", "temp_extreme_ordered", "temp_extreme_num", "mucous_membranes_col", "mucous_membranes_group" ], "rows": 300, "table": true, "tojson": true }, { "name": "collisions", "title": "Subset of the collision data", "object": "collisions", "class": [ "data.frame" ], "fields": [ "date", "borough", "zip.code", "latitude", "longitude", "on.street.name", "cross.street.name", "off.street.name", "number.of.persons.injured", "number.of.persons.killed", "number.of.pedestrians.injured", "number.of.pedestrians.killed", "number.of.cyclist.injured", "number.of.cyclist.killed", "number.of.motorist.injured", "number.of.motorist.killed", "contributing.factor.vehicle.1", "contributing.factor.vehicle.2", "contributing.factor.vehicle.3", "contributing.factor.vehicle.4", "contributing.factor.vehicle.5", "unique.key", "vehicle.type.code.1", "vehicle.type.code.2", "vehicle.type.code.3", "vehicle.type.code.4", "vehicle.type.code.5" ], "rows": 6867, "table": true, "tojson": true }, { "name": "diabetes", "title": "Indian Prime Diabetes Data", "object": "diabetes", "class": [ "data.frame" ], "fields": [ "Pregnancies", "Glucose", "BloodPressure", "SkinThickness", "Insulin", "BMI", "DiabetesPedigreeFunction", "Age", "Outcome" ], "rows": 768, "table": true, "tojson": true }, { "name": "food", "title": "Food consumption", "object": "food", "class": [ "data.frame" ], "fields": [ "Country", "Real.coffee", "Instant.coffee", "Tea", "Sweetener", "Biscuits", "Powder.soup", "Tin.soup", "Potatoes", "Frozen.fish", "Frozen.veggies", "Apples", "Oranges", "Tinned.fruit", "Jam", "Garlic", "Butter", "Margarine", "Olive.oil", "Yoghurt", "Crisp.bread" ], "rows": 16, "table": true, "tojson": true }, { "name": "kola.background", "title": "Background map for the Kola project data", "object": "kola.background", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "pulplignin", "title": "Pulp lignin content", "object": "pulplignin", "class": [ "data.frame" ], "fields": [ "Observation", "Y.Kappa", "ChipRate", "BF.CMratio", "BlowFlow", "ChipLevel4", "T.upperExt.2", "T.lowerExt.2", "UCZAA", "WhiteFlow.4", "AAWhiteSt.4", "AA.Wood.4", "ChipMoisture.4", "SteamFlow.4", "Lower.HeatT.3", "Upper.HeatT.3", "ChipMass.4", "WeakLiquorF", "BlackFlow.2", "WeakWashF", "SteamHeatF.3", "T.Top.Chips.4", "SulphidityL.4" ], "rows": 301, "table": true, "tojson": true }, { "name": "SBS5242", "title": "Synthetic subset of the Austrian structural business statistics data", "object": "SBS5242", "class": [ "matrix", "array" ], "fields": [ "PW", "BWS_F", "Umsatz", "PERSA", "BEZWD", "BEZWDVK", "BESCH", "USB", "ISACH" ], "rows": 262, "table": true, "tojson": true }, { "name": "sleep", "title": "Mammal sleep data", "object": "sleep", "class": [ "data.frame" ], "fields": [ "BodyWgt", "BrainWgt", "NonD", "Dream", "Sleep", "Span", "Gest", "Pred", "Exp", "Danger" ], "rows": 62, "table": true, "tojson": true }, { "name": "tao", "title": "Tropical Atmosphere Ocean (TAO) project data", "object": "tao", "class": [ "data.frame" ], "fields": [ "Year", "Latitude", "Longitude", "Sea.Surface.Temp", "Air.Temp", "Humidity", "UWind", "VWind" ], "rows": 736, "table": true, "tojson": true }, { "name": "testdata", "title": "Simulated data set for testing purpose", "object": "testdata", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "toydataMiss", "title": "Simulated toy data set for examples", "object": "toydataMiss", "class": [ "data.frame" ], "fields": [ "xfull", "yfull", "ymissMNAR", "ymissMAR", "ymissMCAR", "ymissMCAR_ind", "ymissMAR_ind", "ymissMNAR_ind", "MCAR", "MAR", "MNAR", "group" ], "rows": 100, "table": true, "tojson": true }, { "name": "wine", "title": "Wine tasting and price", "object": "wine", "class": [ "spec_tbl_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "country", "points", "price", "province", "taster_name", "taster_twitter_handle", "variety", "winery", "variety_main" ], "rows": 9627, "table": true, "tojson": true } ], "_help": [ { "page": "aggr", "title": "Aggregations for missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "aggr", "plot.aggr", "print.aggr", "print.summary.aggr", "summary.aggr" ] }, { "page": "alphablend", "title": "Alphablending for colors", "topics": [ "alphablend" ] }, { "page": "Animals_na", "title": "Animals_na", "topics": [ "Animals_na" ] }, { "page": "as.mids.vimmi", "title": "Convert a vimmi object to a mice mids object", "topics": [ "as.mids.vimmi" ] }, { "page": "barMiss", "title": "Barplot with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "barMiss" ] }, { "page": "bcancer", "title": "Breast cancer Wisconsin data set", "topics": [ "bcancer" ] }, { "page": "bgmap", "title": "Backgound map", "topics": [ "bgmap" ] }, { "page": "brittleness", "title": "Brittleness index data set", "topics": [ "brittleness" ] }, { "page": "cellWeightsMCD", "title": "Compute per-cell weights using MCD-based conditional residuals", "topics": [ "cellWeightsMCD" ] }, { "page": "chorizonDL", "title": "C-horizon of the Kola data with missing values", "topics": [ "chorizonDL" ] }, { "page": "colic", "title": "Colic horse data set", "topics": [ "colic" ] }, { "page": "collisions", "title": "Subset of the collision data", "topics": [ "collisions" ] }, { "page": "colormapMiss", "title": "Colored map with information about missing/imputed values", "topics": [ "colormapMiss", "colormapMissLegend" ] }, { "page": "colSequence", "title": "HCL and RGB color sequences", "topics": [ "colSequence", "colSequenceHCL", "colSequenceRGB" ] }, { "page": "complete", "title": "Extract completed datasets", "topics": [ "complete" ] }, { "page": "complete.vimmi", "title": "Extract completed datasets from a vimmi object", "topics": [ "complete.vimmi" ] }, { "page": "countInf", "title": "Count number of infinite or missing values", "topics": [ "countInf", "countNA" ] }, { "page": "diabetes", "title": "Indian Prime Diabetes Data", "topics": [ "diabetes" ] }, { "page": "evaluation", "title": "Error performance measures", "topics": [ "evaluation", "msecor", "msecov", "nrmse", "pfc" ] }, { "page": "food", "title": "Food consumption", "topics": [ "food" ] }, { "page": "gapMiss", "title": "Missing value gap statistics", "topics": [ "gapMiss" ] }, { "page": "gowerD", "title": "Computes the extended Gower distance of two data sets", "topics": [ "gowerD" ] }, { "page": "growdotMiss", "title": "Growing dot map with information about missing/imputed values", "topics": [ "bubbleMiss", "growdotMiss" ] }, { "page": "histMiss", "title": "Histogram with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "histMiss" ] }, { "page": "hotdeck", "title": "Hot-Deck Imputation", "concept": [ "imputation methods" ], "topics": [ "hotdeck" ] }, { "page": "impPCA", "title": "Iterative EM PCA imputation", "concept": [ "imputation methods" ], "topics": [ "impPCA" ] }, { "page": "imputeCellEM", "title": "Cellwise-robust EM imputation for mixed data", "concept": [ "imputation methods" ], "topics": [ "imputeCellEM" ] }, { "page": "imputeCellIRMI", "title": "Cellwise-robust iterative regression imputation for mixed data", "concept": [ "imputation methods" ], "topics": [ "imputeCellIRMI" ] }, { "page": "imputeCellM", "title": "Cellwise M-estimation imputation", "concept": [ "imputation methods" ], "topics": [ "imputeCellM" ] }, { "page": "imputeCellMCD", "title": "Cellwise MCD-based imputation for mixed data", "concept": [ "imputation methods" ], "topics": [ "imputeCellMCD" ] }, { "page": "imputeCellMM", "title": "Cell-weighted MM imputation for mixed data (Path A)", "topics": [ "imputeCellMM" ] }, { "page": "imputeCellReg", "title": "Cellwise-robust regression imputation for mixed data", "topics": [ "imputeCellReg" ] }, { "page": "imputeCellwise", "title": "Unified cellwise-robust imputation dispatcher", "concept": [ "imputation methods" ], "topics": [ "imputeCellwise" ] }, { "page": "imputeRobust", "title": "Robust imputation", "concept": [ "imputation methods" ], "topics": [ "imputeRobust" ] }, { "page": "imputeRobustChain", "title": "FUNCTION_TITLE", "concept": [ "imputation methods" ], "topics": [ "imputeRobustChain" ] }, { "page": "initialise", "title": "Initialization of missing values", "topics": [ "initialise" ] }, { "page": "irmi", "title": "Iterative robust model-based imputation (IRMI)", "concept": [ "imputation methods" ], "topics": [ "irmi" ] }, { "page": "kNN", "title": "k-Nearest Neighbour Imputation", "concept": [ "imputation methods" ], "topics": [ "kNN" ] }, { "page": "kola.background", "title": "Background map for the Kola project data", "topics": [ "kola.background" ] }, { "page": "mapMiss", "title": "Map with information about missing/imputed values", "topics": [ "mapMiss" ] }, { "page": "marginmatrix", "title": "Marginplot Matrix", "concept": [ "plotting functions" ], "topics": [ "marginmatrix" ] }, { "page": "marginplot", "title": "Scatterplot with additional information in the margins", "concept": [ "plotting functions" ], "topics": [ "marginplot" ] }, { "page": "matchImpute", "title": "Fast matching/imputation based on categorical variable", "concept": [ "imputation methods" ], "topics": [ "matchImpute" ] }, { "page": "matrixplot", "title": "Matrix plot", "concept": [ "plotting functions" ], "topics": [ "iimagMiss", "matrixplot", "TKRmatrixplot" ] }, { "page": "maxCat", "title": "Aggregation function for a factor variable", "topics": [ "maxCat" ] }, { "page": "medianSamp", "title": "Aggregation function for a ordinal variable", "concept": [ "imputation methods" ], "topics": [ "medianSamp" ] }, { "page": "mosaicMiss", "title": "Mosaic plot with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "mosaicMiss" ] }, { "page": "pairsVIM", "title": "Scatterplot Matrices", "concept": [ "plotting functions" ], "topics": [ "pairsVIM" ] }, { "page": "parcoordMiss", "title": "Parallel coordinate plot with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "parcoordMiss" ] }, { "page": "pbox", "title": "Parallel boxplots with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "pbox" ] }, { "page": "prepare", "title": "Transformation and standardization", "topics": [ "prepare" ] }, { "page": "pulplignin", "title": "Pulp lignin content", "topics": [ "pulplignin" ] }, { "page": "rangerImpute", "title": "Random Forest Imputation", "concept": [ "imputation methods" ], "topics": [ "rangerImpute" ] }, { "page": "regressionImp", "title": "Regression Imputation (via vimpute)", "concept": [ "imputation methods" ], "topics": [ "regressionImp" ] }, { "page": "rugNA", "title": "Rug representation of missing/imputed values", "topics": [ "rugNA" ] }, { "page": "sampleCat", "title": "Random aggregation function for a factor variable", "concept": [ "imputation methods" ], "topics": [ "sampleCat" ] }, { "page": "SBS5242", "title": "Synthetic subset of the Austrian structural business statistics data", "topics": [ "SBS5242" ] }, { "page": "scattJitt", "title": "Bivariate jitter plot", "concept": [ "plotting functions" ], "topics": [ "scattJitt" ] }, { "page": "scattmatrixMiss", "title": "Scatterplot matrix with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "scattmatrixMiss" ] }, { "page": "scattMiss", "title": "Scatterplot with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "scattMiss" ] }, { "page": "sleep", "title": "Mammal sleep data", "topics": [ "sleep" ] }, { "page": "spineMiss", "title": "Spineplot with information about missing/imputed values", "concept": [ "plotting functions" ], "topics": [ "spineMiss" ] }, { "page": "tableMiss", "title": "create table with highlighted missings/imputations", "topics": [ "tableMiss" ] }, { "page": "tao", "title": "Tropical Atmosphere Ocean (TAO) project data", "topics": [ "tao" ] }, { "page": "testdata", "title": "Simulated data set for testing purpose", "topics": [ "testdata" ] }, { "page": "toydataMiss", "title": "Simulated toy data set for examples", "topics": [ "toydataMiss" ] }, { "page": "vimmi", "title": "VIM Multiple Imputations (vimmi)", "concept": [ "imputation methods" ], "topics": [ "print.vimmi", "summary.vimmi", "vimmi" ] }, { "page": "vimpute", "title": "Impute missing values with prefered model, sequentially, with hyperparametertuning and with PMM (if wanted)", "concept": [ "imputation methods" ], "topics": [ "vimpute" ] }, { "page": "wine", "title": "Wine tasting and price", "topics": [ "wine" ] }, { "page": "with.vimmi", "title": "Evaluate an expression across all imputations", "topics": [ "with.vimmi" ] }, { "page": "xgboostImpute", "title": "Xgboost Imputation", "concept": [ "imputation methods" ], "topics": [ "xgboostImpute" ] } ], "_readme": "https://github.com/statistikat/vim/raw/HEAD/README.md", "_rundeps": [ "abind", "backports", "bbotk", "boot", "broom", "car", "carData", "checkmate", "class", "cli", "codetools", "colorspace", "cowplot", "cpp11", "data.table", "DEoptimR", "Deriv", "digest", "doBy", "dplyr", "e1071", "evaluate", "farver", "forecast", "Formula", "fracdiff", "future", "future.apply", "generics", "ggplot2", "globals", "glue", "gtable", "isoband", "jsonlite", "labeling", "laeken", "lattice", "lgr", "lifecycle", "listenv", "lme4", "lmtest", "magrittr", "MASS", "Matrix", "MatrixModels", "matrixStats", "mgcv", "microbenchmark", "minqa", "mirai", "mlbench", "mlr3", "mlr3learners", "mlr3measures", "mlr3misc", "mlr3pipelines", "mlr3tuning", "modelr", "moocore", "nanonext", "nlme", "nloptr", "nnet", "numDeriv", "palmerpenguins", "paradox", "parallelly", "pbkrtest", "pillar", "pkgconfig", "proxy", "PRROC", "purrr", "quantreg", "R6", "ranger", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "Rdpack", "reformulas", "rlang", "robustbase", "S7", "scales", "sp", "SparseM", "stringi", "stringr", "survival", "tibble", "tidyr", "tidyselect", "timeDate", "urca", "utf8", "uuid", "vcd", "vctrs", "viridisLite", "withr", "xgboost", "zoo" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" }, { "shlib": "libgomp", "package": "libgomp1", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "openmp", "homepage": "http://gcc.gnu.org/", "description": "GCC OpenMP (GOMP) support library" } ], "_vignettes": [ { "source": "donorImp.Rmd", "filename": "donorImp.html", "title": "Donor based Imputation Methods", "author": "Wolfgang Rannetbauer", "engine": "knitr::rmarkdown", "headings": [ "Overview", "Data", "Imputation", "Diagnosing the results", "Performance of method" ], "created": "2020-07-15 09:27:02", "modified": "2026-03-07 22:38:25", "commits": 9 }, { "source": "impPCA.Rmd", "filename": "impPCA.html", "title": "Imputation Method based on Iterative EM PCA", "author": "Birgit Karlhuber", "engine": "knitr::rmarkdown", "headings": [ "Data", "Imputation", "Performance of method" ], "created": "2026-01-08 09:07:10", "modified": "2026-03-07 22:38:25", "commits": 2 }, { "source": "xgboostImpute.Rmd", "filename": "xgboostImpute.html", "title": "Imputation Method based on xgboost", "author": "Birgit Karlhuber", "engine": "knitr::rmarkdown", "headings": [ "Data", "Imputation", "Diagnosing the result", "Imputing multiple variables", "Performance of method" ], "created": "2026-01-08 09:07:10", "modified": "2026-03-07 22:38:25", "commits": 3 }, { "source": "irmi.Rmd", "filename": "irmi.html", "title": "Imputation Method IRMI", "author": "Wolfgang Rannetbauer", "engine": "knitr::rmarkdown", "headings": [ "Overview", "Data", "Imputing multiple variables", "Diagnosing the results", "Performance of method" ], "created": "2020-07-14 13:46:47", "modified": "2026-03-07 22:38:25", "commits": 16 }, { "source": "vimpute.Rmd", "filename": "vimpute.html", "title": "Imputation Method vimpute", "author": "Eileen Vattheuer", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Function Arguments", "Data", "Basic Usage", "Default Imputation", "Advanced Options", "Parameter method", "Parameter pmm", "Parameter pmm_k", "Parameter pmm_k_method", "Parameter learner_params", "Parameter formula", "Parameter makeNA", "Parameter donorcond", "Parameters boot, robustboot and uncert", "Parameter m", "Parameter tune", "Parameters nseq and eps", "Parameter imp_var", "Parameter pred_history", "Performance" ], "created": "2026-01-08 09:07:10", "modified": "2026-07-01 12:28:24", "commits": 6 }, { "source": "modelImp.Rmd", "filename": "modelImp.html", "title": "Model based Imputation Methods", "author": "Gregor de Cillia", "engine": "knitr::rmarkdown", "headings": [ "Data", "Imputation", "Diagnosing the results", "Imputing multiple variables", "Performance of method" ], "created": "2020-04-17 12:23:03", "modified": "2026-03-07 22:38:25", "commits": 6 }, { "source": "VisualImp.Rmd", "filename": "VisualImp.html", "title": "Supportive Graphic Methods", "author": "Wolfgang Rannetbauer, Birgit Karlhuber", "engine": "knitr::rmarkdown", "headings": [ "Overview", "Data", "Function aggr()", "Function barMiss()", "Function scattMiss()", "Function histMiss()", "Function matrixplot()", "Function marginplot()", "Function marginmatrix()", "Function pbox()", "Function parcoordMiss", "Function spineMiss", "Function mosaicMiss", "Function scattmatrixMiss", "Function scattJitt", "Additional functions", "Function pairsVIM", "Function colSequence", "Function rugNA", "Function alphablend" ], "created": "2020-07-15 15:08:24", "modified": "2026-03-07 22:38:25", "commits": 19 }, { "source": "VIM.Rmd", "filename": "VIM.html", "title": "VIM", "engine": "knitr::rmarkdown", "headings": [ "Visualize missing values", "Impute missing values", "Visualize imputed values" ], "created": "2020-04-14 17:52:29", "modified": "2026-03-07 22:38:25", "commits": 5 } ], "_score": 14.880586746918837, "_indexed": true, "_nocasepkg": "vim", "_universes": [ "statistikat", "matthias-da" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "7.1.0", "date": "2026-07-02T10:09:50.000Z", "distro": "resolute", "arch": "aarch64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/f2d9192c6ad9837249e4d630cbba5cc9b03f137cc43bad129d78c870fa5c771c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.7.0", "os": "linux", "version": "7.1.0", "date": "2026-07-02T10:09:06.000Z", "distro": "resolute", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/a4d1c65be41f5daf7bc2363623902d3fab8ffbe7beaa08e283b1ca849946d922", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.6.1", "os": "linux", "version": "7.1.0", "date": "2026-07-02T10:08:50.000Z", "distro": "resolute", "arch": "aarch64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/8e0fbd8eab48fd521c337655cbeb2fce8f016a785618563172823f9890421206", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.6.1", "os": "linux", "version": "7.1.0", "date": "2026-07-02T10:09:05.000Z", "distro": "resolute", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/08bdb6b6f926d300b8e2299b10f83c269ac81bcfa92147e760f3a2439da800a0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.5.3", "os": "mac", "version": "7.1.0", "date": "2026-07-02T10:09:27.000Z", "arch": "aarch64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/c56bf4a4aa3197e0a3e5df3b8dab89e157b896fff2596ee7354705123cba35d1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.5.3", "os": "mac", "version": "7.1.0", "date": "2026-07-02T10:10:13.000Z", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/20ea8838b05b85cb35769098da8b61db020ef6eb16fe0f1fd31769f707e3b937", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.6.1", "os": "mac", "version": "7.1.0", "date": "2026-07-02T10:15:15.000Z", "arch": "aarch64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/b2b0b4bffaa05e6e75559f7a23749eef5e3488053b985ef4568d7db776614c93", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.6.1", "os": "mac", "version": "7.1.0", "date": "2026-07-02T10:11:44.000Z", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/b5afec37d325cfb40f51d38f8bde854379602dc4fcf033df04dbcedbc60604db", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.6.0", "os": "wasm", "version": "7.1.0", "date": "2026-07-02T10:09:33.000Z", "arch": "emscripten", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/f0b73af66171485a91939cca56b4a9b97f54b11912cfcf762cbd944c735bc26a", "status": "success", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.7.0", "os": "win", "version": "7.1.0", "date": "2026-07-02T10:07:35.000Z", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/f9e92ae35ac28f6148b9058e76b7d2bd3f8ad17876a75ec2686ebbc105271ad4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.5.3", "os": "win", "version": "7.1.0", "date": "2026-07-02T10:07:57.000Z", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/7b2bb52b2faefbb372e00ba96954b82c428621e3976d804914a1f02f26bb5430", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" }, { "r": "4.6.1", "os": "win", "version": "7.1.0", "date": "2026-07-02T10:07:59.000Z", "arch": "x86_64", "commit": "1d12d043fdbb877e4eadf8987a0bdbcf64781ad1", "fileid": "https://r2.ropensci.org/76bc25ad8e99543d31c6faf1a9505d0a28e4c80d85e9e440a7a136da058aa693", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/statistikat/actions/runs/28581587515" } ] }