  In this paper we describe the implementation of semi-structured deep
distributional regression, a flexible framework to learn conditional
distributions based on the combination of additive regression models and deep
networks. Our implementation encompasses (1) a modular neural network building
system based on the deep learning library \pkg{TensorFlow} for the fusion 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 set up such models. The software package
allows to define models in a user-friendly manner via a formula interface that
is inspired by classical statistical model frameworks such as \pkg{mgcv}. The
packages' modular design and functionality provides a unique resource for both
scalable estimation of complex statistical models and the combination of
approaches from deep learning and statistics. This allows for state-of-the-art
predictive performance while simultaneously retaining the indispensable
interpretability of classical statistical models.
