Nowadays, neural networks are considered one of the most effective methods for various tasks such as anomaly detection, computer-aided disease detection, or natural language processing. However, these networks suffer from the “black-box” problem which makes it difficult to understand how they make decisions. In order to solve this issue, an R package called neuralGAM is introduced. This package implements a Neural Network topology based on Generalized Additive Models, allowing users to fit an independent Neural Network to estimate the contribution of each feature to the output variable, yielding a highly accurate and interpretable deep learning model. The neuralGAM package provides a flexible framework for training Generalized Additive Neural Networks, which does not impose any restrictions on the per-term neural network architecture, while the overall model remains additive. We illustrate the use of the neuralGAM package in both synthetic and real data examples.