VIM

R package VIM: Visualization and Imputation of Missing Values. This package introduces new tools for the visualization of missing and/or imputed values, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missings and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface allows an easy handling of the implemented plot methods


References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Jaeger, Byron C.; Long, D. Leann; Long, Dustin M.; Sims, Mario; Szychowski, Jeff M.; Min, Yuan-I; McClure, Leslie A.; Howard, George; Simon, Noah: Oblique random survival forests (2019)
  2. Kocbek, Primoz; Fijacko, Nino; Soguero-Ruiz, Cristina; Mikalsen, Karl Øyvind; Maver, Uros; Povalej Brzan, Petra; Stozer, Andraz; Jenssen, Robert; Skrøvseth, Stein Olav; Stiglic, Gregor: Maximizing interpretability and cost-effectiveness of surgical site infection (SSI) predictive models using feature-specific regularized logistic regression on preoperative temporal data (2019)
  3. Parrella, Maria Lucia; Albano, Giuseppina; La Rocca, Michele; Perna, Cira: Reconstructing missing data sequences in multivariate time series: an application to environmental data (2019)
  4. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  5. Faisal, Shahla; Tutz, Gerhard: Missing value imputation for gene expression data by tailored nearest neighbors (2017)
  6. Matthias Templ and Bernhard Meindl and Alexander Kowarik and Olivier Dupriez: Simulation of Synthetic Complex Data: The R Package simPop (2017) not zbMATH
  7. Templ, Matthias: Statistical disclosure control for microdata. Methods and applications in R (2017)
  8. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  9. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
  10. Aste, Marco; Boninsegna, Massimo; Freno, Antonino; Trentin, Edmondo: Techniques for dealing with incomplete data: a tutorial and survey (2015)
  11. Tutz, Gerhard; Ramzan, Shahla: Improved methods for the imputation of missing data by nearest neighbor methods (2015)
  12. Xiaoyue Cheng and Dianne Cook and Heike Hofmann: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface (2015) not zbMATH
  13. Andreas Alfons; Matthias Templ: Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken (2013) not zbMATH
  14. Stef van Buuren; Karin Groothuis-Oudshoorn: mice: Multivariate Imputation by Chained Equations in R (2011) not zbMATH
  15. Todorov, Valentin; Templ, Matthias; Filzmoser, Peter: Detection of multivariate outliers in business survey data with incomplete information (2011) ioport