deepregression: Fitting Deep Distributional Regression (original) (raw)
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 |
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