[Bioc-devel] Bioconductor package based on Julia?

Hi both,

Many thanks for your detailed responses. The reason we want to build the
package on Julia is that we use approximate Bayesian Computation, which
runs significantly faster in Julia than in R. You?re probably right that
compiling on C++ would be more straightforward and I see the points you
make. My senses was it would be nice to have an R interface as this is more
accessible to many users but reading your responses, I think that?s
probably not the way to take things forward.

Many thanks again for your help and all the best,

Verena

On 30 Jan 2026, at 20:06, Nanda, Pariksheet <PAN79 at pitt.edu> wrote:

Hi Verena,

Polyglot code can be tricky as Herv? points out. Another option, if

you're open to using Rcpp in your Bioconductor package, is the mlpack C++
header-only library that interfaces with Julia, R, Python, and Go (
mlpack.org) and there is a spack package that may simplify the
installation for you (packages.spack.io/package.html?name=mlpack) if
you're not keen on containers and/or prefer native architecture compilation.

That way, you're specifying an external dependency on mlpack only for

which you can also create an R package (CRAN has plenty of C++ header-only
packages you can refer to) and to Herv?'s point you could limit certain
unit tests that are julia-specific to CI systems that provide that
functionality (perhaps your own).

I don't use mlpack and don't know how well it would suit your purpose;

you don't describe what Julia packages or functionality you're using the
Julia code for. mlpack is the only relevant result that showed up from
searching for polyglot r packages in the spack repository with julia [1].

As Herv? mentions, you would have to think carefully about your user

setup experience and how maintainable it would be to have robust packaging
with Julia code compared to rewriting the Julia code into existing R
packages or setup that requires fewer additional steps and moving parts. I
don't meant to discourage you from what you're doing: polyglot code is more
common and in large scientific codes but can need a bit of work to maintain
within language-specific package managers or require a language-agnostic
package manager.

Pariksheet

[1] Namely:
git clone https://github.com/spack/spack.git spack
cd spack
source share/spack/setup-env.sh
spack repo update
spack cd -P
grep -lw r packages/*/*.py | xargs grep -F julia
less packages/mlpack/package.py

Pag?s <hpages.on.github at gmail.com>

Sent: Friday, January 30, 2026 1:26 PM
To: Verena Korber; bioc-devel at r-project.org
Subject: Re: [Bioc-devel] Bioconductor package based on Julia?

Hi Verena,

I personally don't have any experience with using Julia within an R
package, and I don't think we have any packages in Bioconductor that do
this at the moment. As you mentioned, the implication is that users will
need a Julia installation for the package to run. Ask yourself how many
users are going to be comfortable doing this. I don't know how
mainstream Julia has become at this point, and how easy it is to
install. Is it available on the 4 major platforms that we officially
support, i.e. on Linux, Windows, Intel Mac, and Mac Silicon?

It also means that we will need to install Julia on our build machines.
And also that, in the near future, the builds on r-universe will be able
to accommodate this requirement (I'm mentioning this in the context of
an ongoing effort by the core team to move the Bioconductor daily builds
to the r-universe build facilities).

At the very least, you'll need to:

1. List Julia in the SystemRequirements field of the package.

2. Provide an INSTALL file that explains how to install Julia on the 4
major platforms that we support. See
https://contributions.bioconductor.org/sysdep.html?q=install#sysdep

They have JuliaCall and JuliaConnectoR on CRAN for interfacing R with
Julia code, which I'm not familiar with either, but it seems to me that
you'd want to leverage that.

Sorry for the very generic answer. Hopefully someone on this mailing
list has some experience with Julia in R and can give more useful
recommendations.

Best,

H.


On 15/01/2026 08:47, Verena Korber via Bioc-devel wrote:

Hello,

We are currently developing an R package that sources Julia code. Is it

possible to submit sth. like this to Bioconductor and if so, what would the
requirements be? I guess one challenge is that users would need a Julia
installation for the package to run. If anyone has any experience in that
direction, it would be super helpful.

Many thanks and kind regards,

Verena

--
Herv? Pag?s

Bioconductor Core Team
hpages.on.github at gmail.com

       [[alternative HTML version deleted]]

On 30 Jan 2026, at 20:06, Nanda, Pariksheet <PAN79 at pitt.edu<mailto:PAN79 at pitt.edu>> wrote:

Hi Verena,

Polyglot code can be tricky as Herv? points out. Another option, if you're open to using Rcpp in your Bioconductor package, is the mlpack C++ header-only library that interfaces with Julia, R, Python, and Go (mlpack.org<http://mlpack.org/>) and there is a spack package that may simplify the installation for you (packages.spack.io/package.html?name=mlpack<http://packages.spack.io/package.html?name=mlpack>) if you're not keen on containers and/or prefer native architecture compilation.

That way, you're specifying an external dependency on mlpack only for which you can also create an R package (CRAN has plenty of C++ header-only packages you can refer to) and to Herv?'s point you could limit certain unit tests that are julia-specific to CI systems that provide that functionality (perhaps your own).

I don't use mlpack and don't know how well it would suit your purpose; you don't describe what Julia packages or functionality you're using the Julia code for. mlpack is the only relevant result that showed up from searching for polyglot r packages in the spack repository with julia [1].

As Herv? mentions, you would have to think carefully about your user setup experience and how maintainable it would be to have robust packaging with Julia code compared to rewriting the Julia code into existing R packages or setup that requires fewer additional steps and moving parts. I don't meant to discourage you from what you're doing: polyglot code is more common and in large scientific codes but can need a bit of work to maintain within language-specific package managers or require a language-agnostic package manager.

Pariksheet

[1] Namely:
git clone https://github.com/spack/spack.git spack
cd spack
source share/spack/setup-env.sh
spack repo update
spack cd -P
grep -lw r packages/*/*.py | xargs grep -F julia
less packages/mlpack/package.py