NA Data Imputation Algorithms

Creates a uniform interface for several advanced imputations missing data methods. Every available method can be used as a part of 'mlr3' pipelines which allows easy tuning and performance evaluation. Most of the used functions work separately on the training and test sets (imputation is trained on the training set and impute training data. After that imputation is again trained on the test set and impute test data).


Reference manual

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0.4.1 by Jan Borowski, a year ago

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Authors: Jan Borowski , Piotr Fic

Documentation:   PDF Manual  

Task views: Missing Data

GPL license

Imports mlr3learners, missForest, missMDA, doParallel, testthat, Amelia, VIM, softImpute, missRanger, methods, mice, data.table, foreach, glmnet

Depends on mlr3, mlr3pipelines, paradox

Suggests knitr, rmarkdown, kableExtra, magrittr

See at CRAN