rOpenSci | rOpenSci News Digest, September 2025

rOpenSci News Digest, September 2025

Dear rOpenSci friends, it’s time for our monthly news roundup! You can read this post on our blog. Now let’s dive into the activity at and around rOpenSci!

🔗 rOpenSci HQ

🔗 10 quick tips for making your software outlive your job

Our community manager Yanina Bellini Saibene participated in the paper “10 quick tips for making your software outlive your job”.

“Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job – whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes.”

🔗 From Ideas to Action: Champions start their training

The training phase is off to a strong start!

So far, Champions have taken part in five workshops, including two on Git and GitHub, one on code style – which we opened up not only to Champions and mentors but also to everyone who applied – and two on R package development. Each session has been a chance to learn, share, and grow together as a community. And we’re just getting started: the next workshops will dive into software peer review and community building – key skills for every Champion’s journey!

As usual you can find the materials on our training page.

🔗 Request for feedback

We recently published a blog post requesting feedback for a prototype of an organization-level dashboard for tracking the health and maintenance of an organization’s R packages. We’re still looking for feedback and suggestions, so please read the blog post if you haven’t already, and help us with your ideas.

In her latest contribution to the Science Ouverte blog, María Gutiérrez Sánchez explores how the rOpenSci Champions Program is helping to foster more open, inclusive, and multilingual science:

The program strengthens scientific communities in Latin America through training, mentorship, and networking around open-source software development. More than just broadening diversity in the R community, the initiative aims to redistribute power in the global open science ecosystem, recognizing that sustainable solutions must emerge from within the communities themselves.

Read the full article in French (original) or Spanish.

🔗 Coworking

Read all about coworking!

And remember, you can always cowork independently on work related to R, work on packages that tend to be neglected, or work on what ever you need to get done!

🔗 Software 📦

🔗 New packages

The following four packages recently became a part of our software suite:

  • ssarp, developed by Kristen Martinet: Create Species- and Speciation-Area Relationships using occurrence records or presence-absence matrices. It has been reviewed by Tom Matthews and Joel Nitta.

  • rixpress, developed by Bruno Rodrigues: Streamlines the creation of reproducible analytical pipelines using default.nix expressions generated via rix for reproducibility. Define derivations in R, Python or Julia, chain them into a composition of pure functions and build the resulting pipeline using Nix as the underlying end-to-end build tool. Functions to plot a DAG representation of the pipeline are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the targets package. It has been reviewed by William Michael Landau and Anthony Martinez.

  • hdcuremodels, developed by Kellie J. Archer together with Han Fu: Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022) and Archer et al (2024). False discovery rate controlled variable selection is provided using model-X knock-offs. It is available on CRAN. It has been reviewed by Tung Lam Nguyen and Panagiotis Papastamoulis.

  • dataset, developed by Daniel Antal: The dataset package helps create semantically rich, machine-readable, and interoperable datasets in R. It extends tidy data frames with metadata that preserves meaning, improves interoperability, and makes datasets easier to publish, exchange, and reuse in line with ISO and W3C standards. It is available on CRAN. It has been reviewed by Marcelo Perlin, Anna Márta Mester, and Mauro Lepore.

Discover more packages, read more about Software Peer Review.

🔗 New versions

The following fourteen packages have had an update since the last newsletter: sits (v1.5.3-1), c14bazAAR (5.2.0), comtradr (v1.0.4), dataspice (v1.1.1), ghql (v0.1.2), magick (v2.9.0), paleobioDB (v1.0.1), rgbif (v3.8.3), rinat (v0.1.10), rredlist (v1.1.1), rsvg (v2.7.0), spatsoc (v0.2.10), tarchetypes (0.13.2), and targets (1.11.4).

🔗 Software Peer Review

There are sixteen recently closed and active submissions and 4 submissions on hold. Issues are at different stages:

Find out more about Software Peer Review and how to get involved.

🔗 On the blog

🔗 Tech Notes

🔗 Calls for contributions

🔗 Calls for maintainers

If you’re interested in maintaining any of the R packages below, you might enjoy reading our blog post What Does It Mean to Maintain a Package?.

🔗 Calls for contributions

Refer to our help wanted page – before opening a PR, we recommend asking in the issue whether help is still needed.

🔗 Package development corner

Some useful tips for R package developers. 👀

🔗 Are your function names unique?

Thanks to an idea from Egor Kotov, via https://github.com/ropensci-review-tools/pkgcheck/issues/142, the pkgcheck package now contains a stand-alone function to use during package development, to quickly check whether your function names are unique:

pkgcheck::fn_names_on_cran (c ("min", "max"))
#>            package version fn_name
#> 161627    matlab2r   1.1.0     max
#> 161628    matlab2r   1.1.0     min
#> 178817      mosaic   1.8.3     max
#> 178821      mosaic   1.8.3     min
#> 234203 rapportools     1.1     max
#> 234207 rapportools     1.1     min

🔗 R-universe badge through usethis

The latest version of usethis includes a handy function use_r_universe_badge() that indicates what version of your package is available on R-universe.

🔗 New testthat vignettes

The development version of the testthat package features new vignettes including an useful overview of “Testing challenging functions”.

🔗 AI newsletter by Posit

Posit’s Sara Altman and Simon Couch started a newsletter about AI developments both within and outside of their company.

🔗 Last words

Thanks for reading! If you want to get involved with rOpenSci, check out our Contributing Guide that can help direct you to the right place, whether you want to make code contributions, non-code contributions, or contribute in other ways like sharing use cases. You can also support our work through donations.

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