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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Visualization and Imputation of Missing Values</dc:title>
  <dc:title>R package VIM version 7.0.0</dc:title>
  <dc:subject>CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData)</dc:subject>
  <dc:subject>CRAN Task View: OfficialStatistics (https://CRAN.R-project.org/view=OfficialStatistics)</dc:subject>
  <dc:description>Provides methods for imputation and visualization of 
    missing values. It includes graphical tools to explore the amount, structure 
    and patterns of missing and/or imputed values, supporting exploratory 
    data analysis and helping to investigate potential missingness mechanisms
    (details in Alfons, Templ and Filzmoser, &lt;doi:10.1007/s11634-011-0102-y&gt;. 
    The quality of imputations can be assessed visually using a wide range of 
    univariate, bivariate and multivariate plots. 
    The package further provides several imputation methods, 
    including efficient implementations of k-nearest neighbour and hot-deck 
    imputation (Kowarik and Templ 2013, &lt;doi:10.18637/jss.v074.i07&gt;, 
    iterative robust model-based multiple 
    imputation (Templ 2011, &lt;doi:10.1016/j.csda.2011.04.012&gt;; 
    Templ 2023, &lt;doi:10.3390/math11122729&gt;), and machine learning–based 
    approaches such as robust GAM-based multiple imputation 
    (Templ 2024, &lt;doi:10.1007/s11222-024-10429-1&gt;) as well as gradient boosting 
    (XGBoost) and transformer-based methods 
    (Niederhametner et al., &lt;doi:10.1177/18747655251339401&gt;). 
    General background and practical guidance on imputation are provided in the 
    Springer book by 
    Templ (2023) &lt;doi:10.1007/978-3-031-30073-8&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 4.1.0),colorspace,grid</dc:relation>
  <dc:relation>Imports: car, grDevices, robustbase, stats, sp, vcd, nnet, e1071,
methods, Rcpp, utils, graphics, laeken, ranger, MASS, xgboost,
data.table(&gt;= 1.9.4), mlr3, mlr3pipelines, R6, paradox,
mlr3tuning, mlr3learners, future</dc:relation>
  <dc:relation>LinkingTo: Rcpp</dc:relation>
  <dc:relation>Suggests: dplyr, tinytest, knitr, mgcv, rmarkdown, reactable, covr,
withr, pdist, enetLTS, robmixglm, stringr, glmnet</dc:relation>
  <dc:creator>Matthias Templ &lt;matthias.templ@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Matthias Templ [aut, cre],
  Alexander Kowarik [aut] (ORCID:
    &lt;https://orcid.org/0000-0001-8598-4130&gt;),
  Andreas Alfons [aut],
  Johannes Gussenbauer [aut],
  Nina Niederhametner [aut],
  Eileen Vattheuer [aut],
  Gregor de Cillia [aut],
  Bernd Prantner [ctb],
  Wolfgang Rannetbauer [aut]</dc:contributor>
  <dc:rights>GPL (&gt;= 2)</dc:rights>
  <dc:date>2026-01-10</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=VIM</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.VIM</dc:identifier>
</oai_dc:dc>