deepImp: Imputation with Deep Learning Methods
Imputation of mixed-type and compositional data with neural networks.
Each network head is a generalised linear model plus a neural-network correction.
Missing values are imputed by point predictions or, for multiple imputation, by
predictive draws on bootstrap fits, with pooling through 'mice'. The architecture
(number and size of hidden layers, dropout, activation, optimiser) and the
training settings are user-configurable. See Templ (2021)
<doi:10.1007/978-3-030-71175-7>.
| Version: |
1.2.0 |
| Depends: |
R (≥ 4.1) |
| Imports: |
torch, nnet, VIM, stats, utils, graphics |
| Suggests: |
future, future.apply, keras3, knitr, mice, rmarkdown, robCompositions, testthat (≥ 3.0.0) |
| Published: |
2026-11-14 |
| DOI: |
10.32614/CRAN.package.deepImp |
| Author: |
Matthias Templ 
[aut, cre] |
| Maintainer: |
Matthias Templ <matthias.templ at gmail.com> |
| BugReports: |
https://github.com/matthias-da/deepImp/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/matthias-da/deepImp |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
deepImp results |
Documentation:
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