  Deep neural networks (DNNs) have proven to be highly effective in a variety
of tasks, making them the go-to method for problems requiring high-level
predictive power. Despite this success, the inner workings of DNNs are often
not transparent, making them difficult to interpret or understand. This lack of
interpretability has led to increased research on inherently interpretable
neural networks in recent years. Models such as Neural Additive Models (NAMs)
achieve visual interpretability through the combination of classical
statistical methods with DNNs. However, these approaches only concentrate on
mean response predictions, leaving out other properties of the response
distribution of the underlying data. We propose Neural Additive Models for
Location Scale and Shape (NAMLSS), a modelling framework that combines the
predictive power of classical deep learning models with the inherent advantages
of distributional regression while maintaining the interpretability of additive
models. The code is available at the following link:
https://github.com/AnFreTh/NAMpy
