This repository provides TensorFlow/Keras and PyTorch implementations of evidential layers and loss functions based on a NeurIPS 2020 paper. The library enables neural networks to learn uncertainty estimates through evidential prior distributions (Normal Inverse-Gamma), adding evidential layers such as DenseNormalGamma to existing models. It supports both regression and classification tasks, providing calibrated confidence measures alongside predictions.