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, ).
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, ),
iterative robust model-based multiple
imputation (Templ 2011, ;
Templ 2023, ), and machine learning–based
approaches such as robust GAM-based multiple imputation
(Templ 2024, ) as well as random forest
and gradient boosting (XGBoost) imputation
(Niederhametner et al., ).
General background and practical guidance on imputation are provided in the
Springer book by
Templ (2023) .