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: | 2025-09-06 |
| 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[issues need fixing before 2025-12-19] |