  This paper describes the implementation of semi-structured deep
distributional regression, a flexible framework to learn distributions based on
a combination of additive regression models and deep neural networks.
deepregression is implemented in both R and Python, using the deep learning
libraries TensorFlow and PyTorch, respectively. The implementation consists of
(1) a modular neural network building system for the combination of various
statistical and deep learning approaches, (2) an orthogonalization cell to
allow for an interpretable combination of different subnetworks as well as (3)
pre-processing steps necessary to initialize such models. The software package
allows to define models in a user-friendly manner using distribution
definitions via a formula environment that is inspired by classical statistical
model frameworks such as mgcv. The packages' modular design and functionality
provides a unique resource for rapid and reproducible prototyping of complex
statistical and deep learning models while simultaneously retaining the
indispensable interpretability of classical statistical models.
