论文标题
Juliaconnotor：用于在R中整合Julia的功能取向的界面
The JuliaConnectoR: a functionally oriented interface for integrating Julia in R
论文作者
论文摘要
像许多考虑新的编程语言朱莉娅的小组一样，我们面临着访问R的算法的挑战。因此，我们开发了R Cange Juliaconnector，可从Cran存储库和Github（https://github.com/stefan-m-lenz/stefan-mlenz/juliaconnector）提供高级学习工具。为了维护性和稳定性，我们决定使用优化的二进制格式来交换数据，决定在TCP上进行R和Julia之间的交流。我们的软件包还专门包含可在R中提供方便的交互式用途的功能。这使得与Julia开发R扩展是易于使用R中的Julia packages从R中调用功能。我们用代码示例说明了包装的进一步功能，并讨论了与Juliacall和Xrjulia的两个替代软件包的优势。最后，我们以更广泛的示例来展示包装的用法，用于使用神经普通的微分方程，这是一种最近引起了很多关注的深度学习技术。该示例还提供了更一般的指导，以将朱莉娅的深度学习技术整合到R中。
Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github.com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available. For maintainability and stability, we decided to base communication between R and Julia on TCP, using an optimized binary format for exchanging data. Our package also specifically contains features that allow for a convenient interactive use in R. This makes it easy to develop R extensions with Julia or to simply call functionality from Julia packages in R. Interacting with Julia objects and calling Julia functions becomes user-friendly, as Julia functions and variables are made directly available as objects in the R workspace. We illustrate the further features of our package with code examples, and also discuss advantages over the two alternative packages JuliaCall and XRJulia. Finally, we demonstrate the usage of the package with a more extensive example for employing neural ordinary differential equations, a recent deep learning technique that has received much attention. This example also provides more general guidance for integrating deep learning techniques from Julia into R.
