Fitting Deep Distributional Regression [R package deepregression version 2.3.2]

David Ruegamer

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: 2.3.2

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, coro (≥ 1.0.3), torchvision (≥ 0.5.1), luz (≥ 0.4.0), torch

Suggests: testthat, knitr, covr

Published: 2025年09月06日

DOI: 10.32614/CRAN.package.deepregression

Author: David Ruegamer [aut, cre], Christopher Marquardt [ctb], Laetitia Frost [ctb], Florian Pfisterer [ctb], Philipp Baumann [ctb], Chris Kolb [ctb], Lucas Kook [ctb]

Maintainer: David Ruegamer <david.ruegamer at gmail.com>

License: GPL-3

NeedsCompilation: no

Citation: deepregression citation info

CRAN checks: deepregression results