Package: VIM
Version: 7.1.0
Title: Visualization and Imputation of Missing Values
Authors@R: c(
  person("Matthias", "Templ", email = "matthias.templ@gmail.com", role = c("aut","cre")),
  person("Alexander", "Kowarik", email = "alexander.kowarik@statistik.gv.at", role = c("aut"), comment=c(ORCID="0000-0001-8598-4130")),
  person("Andreas", "Alfons", role = c("aut")),
  person("Johannes", "Gussenbauer", role = c("aut")),
  person("Nina", "Niederhametner", role = c("aut")),
  person("Eileen", "Vattheuer", role = c("aut")),
  person("Gregor", "de Cillia", email = "gregor.decillia@statistik.gv.at", role = c("aut")),
  person("Bernd", "Prantner", role = c("ctb")),
  person("Wolfgang", "Rannetbauer", role = c("aut"))
  )
Depends: R (>= 4.1.0),colorspace,grid
Imports: car, grDevices, robustbase, stats, sp, vcd, nnet, e1071,
        methods, Rcpp, utils, graphics, laeken, ranger, MASS, xgboost,
        data.table(>= 1.9.4), mlr3, mlr3pipelines, R6, paradox,
        mlr3tuning, mlr3learners, future
Suggests: dplyr, tinytest, knitr, mgcv, rmarkdown, reactable, covr,
        withr, pdist, enetLTS, robmixglm, stringr, glmnet, cellWise,
        crmReg, mice, lgr
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, <doi:10.1007/s11634-011-0102-y>.
        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,
        <doi:10.18637/jss.v074.i07>, iterative robust model-based
        multiple imputation (Templ 2011,
        <doi:10.1016/j.csda.2011.04.012>; Templ 2023,
        <doi:10.3390/math11122729>), and machine learning–based
        approaches such as robust GAM-based multiple imputation (Templ
        2024, <doi:10.1007/s11222-024-10429-1>) as well as gradient
        boosting (XGBoost) and transformer-based methods
        (Niederhametner et al., <doi:10.1177/18747655251339401>).
        General background and practical guidance on imputation are
        provided in the Springer book by Templ (2023)
        <doi:10.1007/978-3-031-30073-8>.
LazyData: TRUE
ByteCompile: TRUE
License: GPL (>= 2)
URL: https://github.com/statistikat/VIM
LinkingTo: Rcpp
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: 2026-07-02 10:05:31 UTC; root
Author: Matthias Templ [aut, cre],
  Alexander Kowarik [aut] (ORCID:
    <https://orcid.org/0000-0001-8598-4130>),
  Andreas Alfons [aut],
  Johannes Gussenbauer [aut],
  Nina Niederhametner [aut],
  Eileen Vattheuer [aut],
  Gregor de Cillia [aut],
  Bernd Prantner [ctb],
  Wolfgang Rannetbauer [aut]
Maintainer: Matthias Templ <matthias.templ@gmail.com>