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 143 articles , 1 standard article )

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  1. Bertsimas, Dimitris; Orfanoudaki, Agni; Pawlowski, Colin: Imputation of clinical covariates in time series (2021)
  2. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  3. Gazzola, Gianluca; Jeong, Myong K.: Support vector regression for polyhedral and missing data (2021)
  4. Javadi, Sara; Bahrampour, Abbas; Saber, Mohammad Mehdi; Garrusi, Behshid; Baneshi, Mohammad Reza: Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable (2021)
  5. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  6. Nascimben, Mauro; Venturin, Manolo; Rimondini, Lia: Double-stage discretization approaches for biomarker-based bladder cancer survival modeling (2021)
  7. Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky: dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference (2021) arXiv
  8. Qian, Zhaozhi; Alaa, Ahmed M.; van der Schaar, Mihaela: CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19 (2021)
  9. Thaweethai, Tanayott; Arterburn, David E.; Coleman, Karen J.; Haneuse, Sebastien: Robust inference when combining inverse-probability weighting and multiple imputation to address missing data with application to an electronic health records-based study of bariatric surgery (2021)
  10. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  11. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  12. Bishoyi, Abhishek; Wang, Xiaojing; Dey, Dipak K.: Learning semiparametric regression with missing covariates using Gaussian process models (2020)
  13. Frank, Anna-Simone J.; Matteson, David S.; Solvang, Hiroko K.; Lupattelli, Angela; Nordeng, Hedvig: Extending balance assessment for the generalized propensity score under multiple imputation (2020)
  14. Jiang, Wei; Josse, Julie; Lavielle, Marc; TraumaBase Group: Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework (2020)
  15. Kamgar, Saeideh; Meinfelder, Florian; Münnich, Ralf; Navvabpour, Hamidreza: Estimation within the new integrated system of household surveys in Germany (2020)
  16. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  17. Mayer, Imke; Sverdrup, Erik; Gauss, Tobias; Moyer, Jean-Denis; Wager, Stefan; Josse, Julie: Doubly robust treatment effect estimation with missing attributes (2020)
  18. Noghrehchi, Firouzeh; Stoklosa, Jakub; Penev, Spiridon: Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables (2020)
  19. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)
  20. Robin, Geneviève; Klopp, Olga; Josse, Julie; Moulines, Éric; Tibshirani, Robert: Main effects and interactions in mixed and incomplete data frames (2020)

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