deepregression: Fitting Deep Distributional Regression

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

Version: 1.0.0
Depends: R (≥ 4.0.0), tensorflow (≥ 2.2.0), tfprobability, keras (≥ 2.2.0)
Imports: mgcv, dplyr, R6, reticulate (≥ 1.14), Matrix, magrittr, tfruns, methods
Suggests: testthat, knitr, covr
Published: 2023-01-17
DOI: 10.32614/CRAN.package.deepregression
Author: David Ruegamer [aut, cre], Florian Pfisterer [ctb], Philipp Baumann [ctb], Chris Kolb [ctb], Lucas Kook [ctb]
Maintainer: David Ruegamer <david.ruegamer at>
License: GPL-3
NeedsCompilation: no
Citation: deepregression citation info
CRAN checks: deepregression results


Reference manual: deepregression.pdf


Package source: deepregression_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deepregression_1.0.0.tgz, r-oldrel (arm64): deepregression_1.0.0.tgz, r-release (x86_64): deepregression_1.0.0.tgz, r-oldrel (x86_64): deepregression_1.0.0.tgz
Old sources: deepregression archive

Reverse dependencies:

Reverse depends: deeptrafo


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