The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks' predictions with so-called feature attribution methods. Aside from the unified and user-friendly framework, the package stands out in three ways: It is generally the first R package implementing feature attribution methods for neural networks. Secondly, it operates independently of the deep learning library allowing the interpretation of models from any R package, including keras, torch, neuralnet, and even custom models. Despite its flexibility, innsight benefits internally from the torch package's fast and efficient array calculations, which builds on LibTorch $-$ PyTorch's C++ backend $-$ without a Python dependency. Finally, it offers a variety of visualization tools for tabular, signal, image data or a combination of these. Additionally, the plots can be rendered interactively using the plotly package.

翻译：R包innsight提供了一个通用工具箱，通过所谓的特征归因方法揭示深度神经网络预测结果中变量层面的解释。除了统一的友好框架外，该包的三大特色在于：它是首个实现神经网络特征归因方法的R包；其次，它独立于深度学习库运行，可解释来自任何R包的模型，包括keras、torch、neuralnet甚至自定义模型。尽管具有灵活性，innsight内部仍受益于torch包基于LibTorch（PyTorch的C++后端）的高效数组计算，且无需依赖Python环境；最后，它提供了适用于表格、信号、图像数据或其组合的多种可视化工具，且可借助plotly包实现交互式渲染。