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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rMIDAS to link to this page.
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