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 |