The JuliaConnectoR: A Functionally-Oriented Interface for Integrating Julia in R
Dublin Core
Title
The JuliaConnectoR: A Functionally-Oriented Interface for Integrating Julia in R
Subject
: language bridge, R, Julia, deep learning, neural ordinary differential equations.
Description
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 Comprehensive R Archive
Network (CRAN), the official R package repository, and from 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 the transmission control protocol, 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.
challenge of accessing the algorithms that we develop in Julia from R. Therefore, we
developed the R package JuliaConnectoR, available from the Comprehensive R Archive
Network (CRAN), the official R package repository, and from 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 the transmission control protocol, 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.
Creator
Stefan Lenz
Source
https://www.jstatsoft.org/article/view/v101i06
Publisher
University of Freiburg
Date
January 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Stefan Lenz, “The JuliaConnectoR: A Functionally-Oriented Interface for Integrating Julia in R,” Repository Horizon University Indonesia, accessed April 21, 2025, https://repository.horizon.ac.id/items/show/8240.