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) . Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.


Reference manual

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0.1 by David Ruegamer, 17 days ago

Browse source code at

Authors: David Ruegamer [aut, cre] , Florian Pfisterer [ctb] , Philipp Baumann [ctb] , Chris Kolb [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports tensorflow, tfprobability, keras, mgcv, dplyr, purrr, R6, reticulate, Matrix, magrittr, Metrics, tfruns, methods, utils

Suggests testthat, knitr

See at CRAN