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 22 articles , 1 standard article )

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  1. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  2. Lun, Zhixin; Khattree, Ravindra: Imputation for skewed data: multivariate Lomax case (2021)
  3. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  4. Wang, Lu; Zhou, Xiao-Hua: Estimation of shape constrained additive models with missing response at random (2021)
  5. Zha, Ruochen; Harel, Ofer: Power calculation in multiply imputed data (2021)
  6. Templ, M.; Gussenbauer, J.; Filzmoser, P.: Evaluation of robust outlier detection methods for zero-inflated complex data (2020)
  7. 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)
  8. 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)
  9. Nengsih, Titin Agustin; Bertrand, Frédéric; Maumy-Bertrand, Myriam; Meyer, Nicolas: Determining the number of components in PLS regression on incomplete data set (2019)
  10. Parrella, Maria Lucia; Albano, Giuseppina; La Rocca, Michele; Perna, Cira: Reconstructing missing data sequences in multivariate time series: an application to environmental data (2019)
  11. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  12. Faisal, Shahla; Tutz, Gerhard: Missing value imputation for gene expression data by tailored nearest neighbors (2017)
  13. Matthias Templ and Bernhard Meindl and Alexander Kowarik and Olivier Dupriez: Simulation of Synthetic Complex Data: The R Package simPop (2017) not zbMATH
  14. Templ, Matthias: Statistical disclosure control for microdata. Methods and applications in R (2017)
  15. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  16. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
  17. Aste, Marco; Boninsegna, Massimo; Freno, Antonino; Trentin, Edmondo: Techniques for dealing with incomplete data: a tutorial and survey (2015)
  18. Tutz, Gerhard; Ramzan, Shahla: Improved methods for the imputation of missing data by nearest neighbor methods (2015)
  19. Xiaoyue Cheng and Dianne Cook and Heike Hofmann: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface (2015) not zbMATH
  20. Andreas Alfons; Matthias Templ: Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken (2013) not zbMATH

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