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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 gmail.com> |
| 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: deepregression_1.0.0.zip, r-release: deepregression_1.0.0.zip, r-oldrel: deepregression_1.0.0.zip |
| 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 depends: | deeptrafo |
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