R package missForest: Nonparametric Missing Value Imputation using Random Forest. The function ’missForest’ in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

References in zbMATH (referenced in 32 articles )

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  1. Gerard, David; Stephens, Matthew: Unifying and generalizing methods for removing unwanted variation based on negative controls (2021)
  2. Hasler, Caren; Craiu, Radu V.: Nonparametric imputation method for nonresponse in surveys (2020)
  3. 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)
  4. Kato, Ryo; Hoshino, Takahiro: Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous-discrete covariates (2020)
  5. Kolb, Samuel; Teso, Stefano; Dries, Anton; De Raedt, Luc: Predictive spreadsheet autocompletion with constraints (2020)
  6. Mozharovskyi, Pavlo; Josse, Julie; Husson, François: Nonparametric imputation by data depth (2020)
  7. Paolanti, Marina; Frontoni, Emanuele: Multidisciplinary pattern recognition applications: a review (2020)
  8. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)
  9. Storlie, Curtis B.; Therneau, Terry M.; Carter, Rickey E.; Chia, Nicholas; Bergquist, John R.; Huddleston, Jeanne M.; Romero-Brufau, Santiago: Prediction and inference with missing data in patient alert systems (2020)
  10. Biessmann, Felix; Rukat, Tammo; Schmidt, Phillipp; Naidu, Prathik; Schelter, Sebastian; Taptunov, Andrey; Lange, Dustin; Salinas, David: DataWig: missing value imputation for tables (2019)
  11. Marino, Simeone; Zhou, Nina; Zhao, Yi; Wang, Lu; Wu, Qiucheng; Dinov, Ivo D.: HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets (2019)
  12. Ramosaj, Burim; Pauly, Markus: Predicting missing values: a comparative study on non-parametric approaches for imputation (2019)
  13. Bertsimas, Dimitris; Pawlowski, Colin; Zhuo, Ying Daisy: From predictive methods to missing data imputation: an optimization approach (2018)
  14. Fan, Fengfeng; Li, Zhanhuai; Chen, Qun; Chen, Lei: Relational data imputation with quality guarantee (2018)
  15. Scanagatta, Mauro; Corani, Giorgio; Zaffalon, Marco; Yoo, Jaemin; Kang, U.: Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets (2018)
  16. Solaro, N.; Barbiero, A.; Manzi, G.; Ferrari, P. A.: A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns (2018)
  17. Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem: A comparative study of pairwise learning methods based on kernel ridge regression (2018)
  18. Veretennikova, Maria A.; Sikorskii, Alla; Boivin, Michael J.: Parameters of stochastic models for electroencephalogram data as biomarkers for child’s neurodevelopment after cerebral malaria (2018)
  19. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  20. Faisal, Shahla; Tutz, Gerhard: Missing value imputation for gene expression data by tailored nearest neighbors (2017)

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