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: 2.2.0 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, coro (≥ 1.0.3), torchvision (≥ 0.5.1), luz (≥ 0.4.0), torch Suggests: testthat, knitr, covr Published: 2024-12-02 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 Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=deepregression to link to this page.
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