rMIDAS: Multiple Imputation with Denoising Autoencoders

A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.

Version:	1.0.0
Depends:	R (≥ 3.6.0), data.table, mltools, reticulate
Imports:	rappdirs, Rdpack
Suggests:	testthat, knitr, rmarkdown
Published:	2023-10-11
DOI:	10.32614/CRAN.package.rMIDAS
Author:	Thomas Robinson [aut, cre, cph], Ranjit Lall [aut, cph], Alex Stenlake [ctb, cph], Elviss Dvinskis [ctb]
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
SystemRequirements:	Python (>= 3.6.0)
Citation:	rMIDAS citation info
Materials:	README NEWS
In views:	MissingData
CRAN checks:	rMIDAS results

Documentation:

Reference manual:	rMIDAS.pdf
Vignettes:	Using custom Python versions Imputing missing data using rMIDAS Running rMIDAS on a server instance

Downloads:

Package source:	rMIDAS_1.0.0.tar.gz
Windows binaries:	r-devel: rMIDAS_1.0.0.zip, r-release: rMIDAS_1.0.0.zip, r-oldrel: rMIDAS_1.0.0.zip
macOS binaries:	r-release (arm64): rMIDAS_1.0.0.tgz, r-oldrel (arm64): rMIDAS_1.0.0.tgz, r-release (x86_64): rMIDAS_1.0.0.tgz, r-oldrel (x86_64): rMIDAS_1.0.0.tgz
Old sources:	rMIDAS archive

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