rMIDAS: 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) <doi:10.33774/apsa-2020-3tk40-v3>. 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.

Version: 0.1.0
Depends: R (≥ 3.6.0), data.table, mltools, reticulate
Suggests: testthat
Published: 2020-09-29
Author: Thomas Robinson ORCID iD [aut, cre, cph], Ranjit Lall ORCID iD [aut, cph], Alex Stenlake [ctb, cph]
Maintainer: Thomas Robinson <ts.robinson1994 at gmail.com>
BugReports: https://github.com/MIDASverse/rMIDAS/issues
License: Apache License (≥ 2.0)
URL: https://github.com/MIDASverse/rMIDAS
NeedsCompilation: no
Citation: rMIDAS citation info
Materials: README NEWS
CRAN checks: rMIDAS results


Reference manual: rMIDAS.pdf
Package source: rMIDAS_0.1.0.tar.gz
Windows binaries: r-devel: rMIDAS_0.1.0.zip, r-release: rMIDAS_0.1.0.zip, r-oldrel: rMIDAS_0.1.0.zip
macOS binaries: r-release: rMIDAS_0.1.0.tgz, r-oldrel: rMIDAS_0.1.0.tgz


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