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的变量级解释。除了统一且用户友好的框架外，该包在三个方面脱颖而出：首先，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，允许解释来自任何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在内部受益于torch包快速高效的数组计算，该计算基于LibTorch——PyTorch的C++后端——且不依赖Python。最后，它为表格、信号、图像数据或其组合提供了多种可视化工具。此外，这些图表可以使用plotly包进行交互式渲染。