Deep neural networks (DNN) have become a central method in ecology. To build and train DNNs in deep learning (DL) applications, most users rely on one of the major deep learning frameworks, in particular PyTorch or TensorFlow. Using these frameworks, however, requires substantial experience and time. Here, we present ‘cito', a user-friendly R package for DL that allows specifying DNNs in the familiar formula syntax used by many R packages. To fit the models, ‘cito' takes advantage of the numerically optimized ‘torch' library, including the ability to switch between training models on the CPU or the graphics processing unit (GPU) which allows the efficient training of large DNNs. Moreover, ‘cito' includes many user-friendly functions for model plotting and analysis, including explainable AI (xAI) metrics for effect sizes and variable importance. All xAI metrics as well as predictions can optionally be bootstrapped to generate confidence intervals, including p-values. To showcase a typical analysis pipeline using ‘cito', with its built-in xAI features, we built a species distribution model of the African elephant. We hope that by providing a user-friendly R framework to specify, deploy and interpret DNNs, ‘cito' will make this interesting class of models more accessible to ecological data analysis. A stable version of ‘cito' can be installed from the comprehensive R archive network (CRAN).

中文翻译：

---

“cito”：使用“torch”训练神经网络的 R 包


深度神经网络（DNN）已成为生态学的核心方法。为了在深度学习 (DL) 应用程序中构建和训练 DNN，大多数用户依赖于主要的深度学习框架之一，特别是 PyTorch 或 TensorFlow。然而，使用这些框架需要大量的经验和时间。在这里，我们推出了‘cito’，这是一个用户友好的 DL R 包，允许使用许多 R 包使用的熟悉的公式语法来指定 DNN。为了适应模型，“cito”利用了数值优化的“torch”库，包括在 CPU 或图形处理单元 (GPU) 上的训练模型之间切换的能力，从而可以高效训练大型 DNN。此外，“cito”包含许多用于模型绘制和分析的用户友好功能，包括用于影响大小和变量重要性的可解释的 AI (xAI) 指标。所有 xAI 指标以及预测都可以选择引导以生成置信区间，包括 p 值。为了展示使用“cito”及其内置 xAI 功能的典型分析流程，我们构建了非洲象的物种分布模型。我们希望通过提供一个用户友好的 R 框架来指定、部署和解释 DNN，“cito”将使这类有趣的模型更容易用于生态数据分析。可以从综合 R 存档网络 (CRAN) 安装稳定版本的“cito”。