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. (2021) <arXiv:2104.02705>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

Version: 0.1
Depends: R (≥ 4.0.0)
Imports: tensorflow (≥ 2.2.0), tfprobability, keras, mgcv, dplyr, purrr, R6, reticulate (≥ 1.14), Matrix, magrittr, Metrics, tfruns, methods, utils
Suggests: testthat, knitr
Published: 2021-10-04
Author: David Ruegamer [aut, cre], Florian Pfisterer [ctb], Philipp Baumann [ctb], Chris Kolb [ctb]
Maintainer: David Ruegamer <david.ruegamer at>
License: GPL-3
NeedsCompilation: no
CRAN checks: deepregression results


Reference manual: deepregression.pdf


Package source: deepregression_0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deepregression_0.1.tgz, r-oldrel (arm64): deepregression_0.1.tgz, r-release (x86_64): deepregression_0.1.tgz, r-oldrel (x86_64): deepregression_0.1.tgz


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