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 26 articles , 1 standard article )

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  1. Bertsimas, Dimitris; Pawlowski, Colin; Zhuo, Ying Daisy: From predictive methods to missing data imputation: an optimization approach (2018)
  2. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  3. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  4. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016) not zbMATH
  5. Chambers, John M.: Extending R (2016)
  6. Conroy, Bryan; Eshelman, Larry; Potes, Cristhian; Xu-Wilson, Minnan: A dynamic ensemble approach to robust classification in the presence of missing data (2016)
  7. Jorge Tendeiro and Rob Meijer and A. Niessen: PerFit: An R Package for Person-Fit Analysis in IRT (2016) not zbMATH
  8. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
  9. Neeraj Bokde, Kishore Kulat, Marcus W Beck, Gualberto Asencio-Cortes: R package imputeTestbench to compare imputations methods for univariate time series (2016) arXiv
  10. Faraway, Julian J.: Linear models with R (2015)
  11. Nguyen, Cattram D.; Lee, Katherine J.; Carlin, John B.: Posterior predictive checking of multiple imputation models (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. Daniel Oberski: lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models (2014) not zbMATH
  14. Mortaza Jamshidian; Siavash Jalal; Camden Jansen: MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR) (2014) not zbMATH
  15. Schomaker, Michael; Heumann, Christian: Model selection and model averaging after multiple imputation (2014)
  16. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  17. Adebayo, Samson B.; Fahrmeir, Ludwig; Seiler, Christian; Heumann, Christian: Geoadditive latent variable modeling of count data on multiple sexual partnering in Nigeria (2011)
  18. Drechsler, Jörg: Multiple imputation in practice -- a case study using a complex German establishment survey (2011)
  19. Drechsler, Jörg: Synthetic datasets for statistical disclosure control. Theory and implementation (2011)
  20. James Honaker; Gary King; Matthew Blackwell: Amelia II: A Program for Missing Data (2011) not zbMATH

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