MICE

R package mice: Multivariate Imputation by Chained Equations. Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.


References in zbMATH (referenced in 84 articles , 1 standard article )

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  1. Hadrien Lorenzo, Jérôme Saracco, Rodolphe Thiébaut: Supervised Learning for Multi-Block Incomplete Data (2019) arXiv
  2. Uiwon Hwang, Dahuin Jung, Sungroh Yoon: HexaGAN: Generative Adversarial Nets for Real World Classification (2019) arXiv
  3. Ardakani, Omid M.; Kishor, N. Kundan; Song, Suyong: Re-evaluating the effectiveness of inflation targeting (2018)
  4. Audigier, Vincent; White, Ian R.; Jolani, Shahab; Debray, Thomas P. A.; Quartagno, Matteo; Carpenter, James; van Buuren, Stef; Resche-Rigon, Matthieu: Multiple imputation for multilevel data with continuous and binary variables (2018)
  5. Bertsimas, Dimitris; Pawlowski, Colin; Zhuo, Ying Daisy: From predictive methods to missing data imputation: an optimization approach (2018)
  6. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  7. Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
  8. Kuo, Kun-Lin; Wang, Yuchung J.: Simulating conditionally specified models (2018)
  9. Mair, Patrick: Modern psychometrics with R (2018)
  10. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  11. Murray, Jared S.: Multiple imputation: a review of practical and theoretical findings (2018)
  12. Sun, BaoLuo; Tchetgen Tchetgen, Eric J.: On inverse probability weighting for nonmonotone missing at random data (2018)
  13. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  14. Chaojie Wang, Linghao Shen, Han Li, Xiaodan Fan: Efficient Bayesian Nonparametric Inference for Categorical Data with General High Missingness (2017) arXiv
  15. Geronimi, J.; Saporta, G.: Variable selection for multiply-imputed data with penalized generalized estimating equations (2017)
  16. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  17. Hayes, Timothy; McArdle, John J.: Should we impute or should we weight? examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables (2017)
  18. Johan Steen and Tom Loeys and Beatrijs Moerkerke and Stijn Vansteelandt: medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models (2017) not zbMATH
  19. Nienkemper-Swanepoel, Johané; von Maltitz, Michael J.: Investigating the performance of a variation of multiple correspondence analysis for multiple imputation in categorical data sets (2017)
  20. Ordóñez Galán, Celestino; Sánchez Lasheras, Fernando; de Cos Juez, Francisco Javier; Bernardo Sánchez, Antonio: Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions (2017)

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