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 137 articles , 1 standard article )

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  1. 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)
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  3. 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
  4. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  5. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  6. Bishoyi, Abhishek; Wang, Xiaojing; Dey, Dipak K.: Learning semiparametric regression with missing covariates using Gaussian process models (2020)
  7. 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)
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  9. Kamgar, Saeideh; Meinfelder, Florian; Münnich, Ralf; Navvabpour, Hamidreza: Estimation within the new integrated system of household surveys in Germany (2020)
  10. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
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  12. Noghrehchi, Firouzeh; Stoklosa, Jakub; Penev, Spiridon: Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables (2020)
  13. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)
  14. Robin, Geneviève; Klopp, Olga; Josse, Julie; Moulines, Éric; Tibshirani, Robert: Main effects and interactions in mixed and incomplete data frames (2020)
  15. Rodrigues, G. S.; Nott, David J.; Sisson, S. A.: Likelihood-free approximate Gibbs sampling (2020)
  16. Struski, Lukasz; Śmieja, Marek; Tabor, Jacek: Pointed subspace approach to incomplete data (2020)
  17. Tianhui Zhou, Guangyu Tong, Fan Li, Laine E. Thomas, Fan Li: PSweight: An R Package for Propensity Score Weighting Analysis (2020) arXiv
  18. van Ginkel, Joost R.: Standardized regression coefficients and newly proposed estimators for (R^2) in multiply imputed data (2020)
  19. Wu, Peng; Zeng, Donglin; Wang, Yuanjia: Matched learning for optimizing individualized treatment strategies using electronic health records (2020)
  20. Zahid, Faisal Maqbool; Faisal, Shahla; Heumann, Christian: Variable selection techniques after multiple imputation in high-dimensional data (2020)

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