Package: VIM
Version: 7.3.0
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: grDevices, robustbase, stats, sp, vcd, nnet, e1071, methods,
        Rcpp, utils, graphics, laeken, ranger, MASS, Matrix, xgboost,
        data.table(>= 1.9.4), mlr3, mlr3pipelines, R6, paradox (>=
        1.0.0), mlr3tuning (>= 1.0.0), mlr3learners, future
Suggests: dplyr, tinytest, knitr, mgcv, rmarkdown, reactable, covr,
        withr, pdist, enetLTS, robmixglm, stringr, glmnet, validate,
        ECOSolveR, cellWise, crmReg, mice, car, lgr, rpart, missRanger,
        tidyr
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 random forest
    and gradient boosting (XGBoost) imputation
    (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
BugReports: https://github.com/statistikat/VIM/issues
Repository: CRAN
LinkingTo: Rcpp
Encoding: UTF-8
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
RoxygenNote: 7.3.3
NeedsCompilation: yes
Packaged: 2026-09-01 13:55:48 UTC; matthias
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>
Date/Publication: 2026-09-02 10:20:02 UTC
Built: R 4.6.1; aarch64-apple-darwin23; 2026-09-02 12:35:12 UTC; unix
Archs: VIM.so.dSYM
