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:

References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  2. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016)
  3. Chambers, John M.: Extending R (2016)
  4. Conroy, Bryan; Eshelman, Larry; Potes, Cristhian; Xu-Wilson, Minnan: A dynamic ensemble approach to robust classification in the presence of missing data (2016)
  5. Jorge Tendeiro and Rob Meijer and A. Niessen: PerFit: An R Package for Person-Fit Analysis in IRT (2016)
  6. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016)
  7. Neeraj Bokde, Kishore Kulat, Marcus W Beck, Gualberto Asencio-Cortes: R package imputeTestbench to compare imputations methods for univariate time series (2016) arXiv
  8. Faraway, Julian J.: Linear models with R (2015)
  9. Nguyen, Cattram D.; Lee, Katherine J.; Carlin, John B.: Posterior predictive checking of multiple imputation models (2015)
  10. Xiaoyue Cheng and Dianne Cook and Heike Hofmann: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface (2015)
  11. Daniel Oberski: lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models (2014)
  12. Mortaza Jamshidian; Siavash Jalal; Camden Jansen: MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR) (2014)
  13. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  14. Adebayo, Samson B.; Fahrmeir, Ludwig; Seiler, Christian; Heumann, Christian: Geoadditive latent variable modeling of count data on multiple sexual partnering in Nigeria (2011)
  15. Drechsler, Jörg: Synthetic datasets for statistical disclosure control. Theory and implementation (2011)
  16. James Honaker; Gary King; Matthew Blackwell: Amelia II: A Program for Missing Data (2011)
  17. Recai Yucel: State of the Multiple Imputation Software (2011)
  18. Macaro, Christian: Bayesian non-parametric signal extraction for Gaussian time series (2010)
  19. Schomaker, Michael; Wan, Alan T. K.; Heumann, Christian: Frequentist model averaging with missing observations (2010)
  20. Wollschläger, Daniel: Foundations of data analysis with R. An application oriented introduction. (2010)

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