Neural networks are important tools for data-intensive analysis and are commonly applied to model non-linear relationships between dependent and independent variables. However, neural networks are usually seen as "black boxes" that offer minimal information about how the input variables are used to predict the response in a fitted model. This article describes the \pkg{NeuralSens} package that can be used to perform sensitivity analysis of neural networks using the partial derivatives method. Functions in the package can be used to obtain the sensitivities of the output with respect to the input variables, evaluate variable importance based on sensitivity measures and characterize relationships between input and output variables. Methods to calculate sensitivities are provided for objects from common neural network packages in \proglang{R}, including \pkg{neuralnet}, \pkg{nnet}, \pkg{RSNNS}, \pkg{h2o}, \pkg{neural}, \pkg{forecast} and \pkg{caret}. The article presents an overview of the techniques for obtaining information from neural network models, a theoretical foundation of how are calculated the partial derivatives of the output with respect to the inputs of a multi-layer perceptron model, a description of the package structure and functions, and applied examples to compare \pkg{NeuralSens} functions with analogous functions from other available \proglang{R} packages.

中文翻译：

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NeuralSens：神经网络的敏感性分析

神经网络是数据密集型分析的重要工具，通常用于对因变量和自变量之间的非线性关系进行建模。然而，神经网络通常被视为“黑匣子”，它提供关于如何使用输入变量来预测拟合模型中的响应的最少信息。本文介绍了 \pkg{NeuralSens} 包，该包可用于使用偏导数方法对神经网络进行灵敏度分析。包中的函数可用于获取输出相对于输入变量的敏感性、基于敏感性度量评估变量重要性以及表征输入和输出变量之间的关系。为\proglang{R} 中常见神经网络包中的对象提供了计算灵敏度的方法，包括\pkg{neuralnet}、\pkg{nnet}、\pkg{RSNNS}、\pkg{h2o}、\pkg{neural} , \pkg{forecast} 和 \pkg{caret}。本文概述了从神经网络模型中获取信息的技术，如何计算输出相对于多层感知器模型输入的偏导数的理论基础，包结构和功能的描述，并应用示例将 \pkg{NeuralSens} 函数与来自其他可用 \proglang{R} 包的类似函数进行比较。