Amelia

Amelia: Amelia II: A Program for Missing Data , Amelia II ”multiply imputes” missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 37 articles , 1 standard article )

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  1. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  2. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  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. Zoe Meers, Robert Hickman, Thomas J. Leeper: ggparliament: A ggplot2 extension for parliament plotsin R (2019) not zbMATH
  5. Bertsimas, Dimitris; Pawlowski, Colin; Zhuo, Ying Daisy: From predictive methods to missing data imputation: an optimization approach (2018)
  6. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  7. Lithio, Andrew; Maitra, Ranjan: An efficient (k)-means-type algorithm for clustering datasets with incomplete records (2018)
  8. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  9. Mcneish, Daniel: Missing data methods for arbitrary missingness with small samples (2017)
  10. Vélez, Jorge I.; Marmolejo-Ramos, Fernando: Extension of a graphical diagnostic test for contingency tables (2017)
  11. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  12. Audigier, Vincent; Husson, François; Josse, Julie: Multiple imputation for continuous variables using a Bayesian principal component analysis (2016)
  13. Chambers, John M.: Extending R (2016)
  14. Conroy, Bryan; Eshelman, Larry; Potes, Cristhian; Xu-Wilson, Minnan: A dynamic ensemble approach to robust classification in the presence of missing data (2016)
  15. Jorge Tendeiro and Rob Meijer and A. Niessen: PerFit: An R Package for Person-Fit Analysis in IRT (2016) not zbMATH
  16. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
  17. Neeraj Bokde, Kishore Kulat, Marcus W Beck, Gualberto Asencio-Cortes: R package imputeTestbench to compare imputations methods for univariate time series (2016) arXiv
  18. Faraway, Julian J.: Linear models with R (2015)
  19. Nguyen, Cattram D.; Lee, Katherine J.; Carlin, John B.: Posterior predictive checking of multiple imputation models (2015)
  20. Xiaoyue Cheng and Dianne Cook and Heike Hofmann: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface (2015) not zbMATH

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