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>.
| Version: |
7.0.0 |
| 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 |
| LinkingTo: |
Rcpp |
| Suggests: |
dplyr, tinytest, knitr, mgcv, rmarkdown, reactable, covr, withr, pdist, enetLTS, robmixglm, stringr, glmnet |
| Published: |
2026-01-10 |
| DOI: |
10.32614/CRAN.package.VIM |
| Author: |
Matthias Templ [aut, cre],
Alexander Kowarik 
[aut],
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 at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/statistikat/VIM |
| NeedsCompilation: |
yes |
| Citation: |
VIM citation info |
| Materials: |
NEWS |
| In views: |
MissingData, OfficialStatistics |
| CRAN checks: |
VIM results |