deepregression: Fitting Deep Distributional Regression
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2021) <arXiv:2104.02705>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
| Version: |
0.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
tensorflow (≥ 2.2.0), tfprobability, keras, mgcv, dplyr, purrr, R6, reticulate (≥ 1.14), Matrix, magrittr, Metrics, tfruns, methods, utils |
| Suggests: |
testthat, knitr |
| Published: |
2021-10-04 |
| Author: |
David Ruegamer [aut, cre],
Florian Pfisterer [ctb],
Philipp Baumann [ctb],
Chris Kolb [ctb] |
| Maintainer: |
David Ruegamer <david.ruegamer at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
deepregression results |
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