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 |
Reference manual: | deepregression.pdf |
Package source: | deepregression_2.2.0.tar.gz |
Windows binaries: | r-devel: deepregression_2.2.0.zip, r-release: deepregression_2.2.0.zip, r-oldrel: deepregression_2.2.0.zip |
macOS binaries: | r-devel (arm64): deepregression_2.2.0.tgz, r-release (arm64): deepregression_2.2.0.tgz, r-oldrel (arm64): deepregression_2.2.0.tgz, r-devel (x86_64): deepregression_2.2.0.tgz, r-release (x86_64): deepregression_2.2.0.tgz, r-oldrel (x86_64): deepregression_2.2.0.tgz |
Old sources: | deepregression archive |
Reverse depends: | deeptrafo |
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