Package: VIM 7.5.1

VIM: Visualization and Imputation of Missing Values

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>.

Authors: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]