missForest

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 40 articles )

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  1. Roy, Arkaprava; Ghosal, Subhashis: Optimal Bayesian smoothing of functional observations over a large graph (2022)
  2. Yuxuan Zhao, Madeleine Udell: gcimpute: A Package for Missing Data Imputation (2022) arXiv
  3. Benjamin Christoffersen, Mark Clements, Keith Humphreys, Hedvig Kjellström: Asymptotically Exact and Fast Gaussian Copula Models for Imputation of Mixed Data Types (2021) arXiv
  4. Fernandes, Sofia; Antunes, Mário; Gomes, Diogo; Aguiar, Rui L.: Misalignment problem in matrix decomposition with missing values (2021)
  5. Gerard, David; Stephens, Matthew: Unifying and generalizing methods for removing unwanted variation based on negative controls (2021)
  6. Londschien, Malte; Kovács, Solt; Bühlmann, Peter: Change-point detection for graphical models in the presence of missing values (2021)
  7. 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)
  8. Winn, Emily T.; Vazquez, Marilyn; Loliencar, Prachi; Taipale, Kaisa; Wang, Xu; Heo, Giseon: A survey of statistical learning techniques as applied to inexpensive pediatric obstructive sleep apnea data (2021)
  9. Hasler, Caren; Craiu, Radu V.: Nonparametric imputation method for nonresponse in surveys (2020)
  10. 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)
  11. Kato, Ryo; Hoshino, Takahiro: Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous-discrete covariates (2020)
  12. Kolb, Samuel; Teso, Stefano; Dries, Anton; De Raedt, Luc: Predictive spreadsheet autocompletion with constraints (2020)
  13. Mozharovskyi, Pavlo; Josse, Julie; Husson, François: Nonparametric imputation by data depth (2020)
  14. Paolanti, Marina; Frontoni, Emanuele: Multidisciplinary pattern recognition applications: a review (2020)
  15. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)
  16. 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)
  17. Biessmann, Felix; Rukat, Tammo; Schmidt, Phillipp; Naidu, Prathik; Schelter, Sebastian; Taptunov, Andrey; Lange, Dustin; Salinas, David: DataWig: missing value imputation for tables (2019)
  18. Husson, François; Josse, Julie; Narasimhan, Balasubramanian; Robin, Geneviève: Imputation of mixed data with multilevel singular value decomposition (2019)
  19. 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)
  20. Ramosaj, Burim; Pauly, Markus: Predicting missing values: a comparative study on non-parametric approaches for imputation (2019)

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