Multiple Imputation using Denoising Autoencoders

A tool that allows users to impute missing data with 'MIDAS', a multiple imputation method using denoising autoencoders as documented in Lall and Robinson (2020) . This method has significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when run on large datasets with many columns or categories. Alongside interfacing with 'Python' to run the core algorithm, this package contains tools to process the data before and after model training, run imputation model diagnostics, generate multiple completed datasets, and estimate multiply-imputed regression models.


Reference manual

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0.1.0 by Thomas Robinson, a month ago

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Authors: Thomas Robinson [aut, cre, cph] , Ranjit Lall [aut, cph] , Alex Stenlake [ctb, cph]

Documentation:   PDF Manual  

Apache License (>= 2.0) license

Depends on data.table, mltools, reticulate

Suggests testthat

See at CRAN