deepImp: Imputation with Deep Learning Methods

Imputation of mixed-type and compositional data with neural networks. The architecture (number and size of hidden layers, dropout, activation, optimiser) is user-configurable. See Templ (2021) <doi:10.1007/978-3-030-71175-7>.

Version: 1.1.0
Depends: R (≥ 4.1)
Imports: torch, luz, VIM, robCompositions, stats, utils, graphics
Suggests: keras3, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-06-10
DOI: 10.32614/CRAN.package.deepImp (may not be active yet)
Author: Matthias Templ ORCID iD [aut, cre]
Maintainer: Matthias Templ <matthias.templ at gmail.com>
License: GPL-2
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: deepImp results

Documentation:

Reference manual: deepImp.html , deepImp.pdf
Vignettes: Neural-network imputation with deepImp (source)

Downloads:

Package source: deepImp_1.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): deepImp_1.1.0.tgz, r-oldrel (arm64): deepImp_1.1.0.tgz, r-release (x86_64): deepImp_1.1.0.tgz, r-oldrel (x86_64): deepImp_1.1.0.tgz

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