Thursday, January 29, 2026 From rOpenSci (https://ropensci.org/blog/2026/01/29/news-january-2026/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
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!
We are pleased to announce the opening of a new call for applications for the rOpenSci Champions Program in Spanish, which will begin in 2026. We are accepting applications for both the roles of Champion as well as Mentor. The application deadline is February 20, 2026.
As in the previous cohort, the 2026 program will be developed entirely in Spanish and will have a regional focus on Latin America with the objective of further strengthening the research and open science software in this region.
Did you miss last Wednesday’s Community Call: Research Software in Latin America (Spanish)? Watch the video.
Upcoming:
Read more, including application instructions, in our blog post.
Read our reflections on our work last year, and our plans for this year: Closing The Loop with Our 2025 Wrap-up.
At the beginning of each year, the Code of Conduct Committee reviews the past year and prepares two reports for the community: the CoC updates and the transparency report, both available on our blog. This is also the time to review the Committee’s composition and confirm its members. We are especially grateful to Natalia Morandeira for renewing her commitment as a Committee member.
We refactored the R-universe CI workflows to make it possible to run the exact same workflow from your own GitHub repository. This allows you to test or debug the build and check process on your R package, exactly as it will happen on R-universe, but without actually deploying to https://r-universe.dev.
Read more in our tech note and in the R-universe docs.
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!
The following two packages recently became a part of our software suite:
read.abares, developed by Adam H. Sparks: Downloads and imports agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) https://www.agriculture.gov.au/abares and the Australian Bureau of Statistics (ABS) https://www.abs.gov.au. Supports multiple data formats including spreadsheets, comma‑separated value (CSV) files, and geospatial data such as shapefiles and GeoTIFFs. Covers topics such as broadacre crops, livestock, soils, commodities and related agricultural information. The package standardises field names and data formats to improve interoperability and simplify analysis. It also streamlines the import of geospatial data and corrects common issues found in these data sources upon loading. It is available on CRAN. It has been reviewed by Nicholas Potter and María Paula Caldas.
dfms, developed by Sebastian Krantz together with Rytis Bagdziunas: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) https://doi.org/10.1016/j.jeconom.2011.02.012; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) https://doi.org/10.1162/REST_a_00225; or the adapted EM algorithm of Banbura and Modugno (2014) https://doi.org/10.1002/jae.2306, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the Armadillo C++ library and the collapse package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the news content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) https://doi.org/10.1111/1468-0262.00273. It is available on CRAN. It has been reviewed by Santtu Tikka and Eli Holmes.
Discover more packages, read more about Software Peer Review.
The following seventeen packages have had an update since the last newsletter: sits (v1.5.4), cffr (v1.2.1), charlatan (v0.6.2), dataset (0.4.2), dbparser (v2.2.1), dfms (v1.0.0), dittodb (v0.1.10), EDIutils (v2.0.0), gutenbergr (v0.4.1), mantis (v1.0.1), openalexR (v3.0.1), patentsview (v1.0.0), read.abares (v2.0.0), rerddap (v1.2.2), rnaturalearth (v1.2.0), rotl (v3.1.1), and weathercan (v0.7.8).
There are eighteen recently closed and active submissions and 2 submissions on hold. Issues are at different stages:
One at ‘6/approved’:
One at ‘5/awaiting-reviewer(s)-response’:
Five at ‘4/review(s)-in-awaiting-changes’:
suwo, Access Nature Media Repositories Through R. Submitted by Marcelo Araya-Salas.
Athlytics, Academic R Package for Sports Physiology Analysis from Local Strava Data. Submitted by Ang.
cowfootR, Tools to Estimate the Carbon Footprint of Dairy Farms. Submitted by Juan Moreno.
openFDA, openFDA API. Submitted by Simon Parker.
galamm, Generalized Additive Latent and Mixed Models. Submitted by Øystein Sørensen. (Stats).
Four at ‘3/reviewer(s)-assigned’:
ernest, A Toolkit for Nested Sampling. Submitted by Kyle Dewsnap. (Stats).
rcrisp, Automate the Delineation of Urban River Spaces. Submitted by Claudiu Forgaci. (Stats).
reviser, Tools for Studying Revision Properties in Real-Time Time Series Vintages. Submitted by Marc Burri.
dfms, Dynamic Factor Models. Submitted by Sebastian Krantz. (Stats).
Seven at ‘1/editor-checks’:
RAQSAPI, A Simple Interface to the US EPA Air Quality System Data Mart API. Submitted by mccroweyclinton-EPA.
logolink, An Interface for Running NetLogo Simulations. Submitted by Daniel Vartanian.
fcmconfr, Fuzzy Cognitive Map Analysis in R. Submitted by benroston. (Stats).
ActiGlobe, Wearable Recording Processor for Time Shift Adjustment and Data Analysis. Submitted by C. William Yao.
coevolve, Fit Bayesian Generalized Dynamic Phylogenetic Models using Stan. Submitted by Scott Claessens. (Stats).
priorsense, Prior Diagnostics and Sensitivity Analysis. Submitted by Noa Kallioinen. (Stats).
capybara, Fast and Memory Efficient Fitting of Linear Models With High-Dimensional. Submitted by Mauricio “Pachá” Vargas Sepúlveda.
Find out more about Software Peer Review and how to get involved.
Testing the R-universe build workflow from your own GitHub repository by Jeroen Ooms. We refactored the R-universe CI workflows to make it possible to run the exact same workflow from your own GitHub repository.
rOpenSci 2025 Code of Conduct Transparency Report by Yanina Bellini Saibene, Natalia Morandeira, and Mark Padgham. rOpenSci 2025 Code of Conduct Transparency Report. Other languages: Reporte de Transparencia del Código de Conducta de rOpenSci 2025 (es).
rOpenSci Code of Conduct Annual Review by Yanina Bellini Saibene, Natalia Morandeira, and Mark Padgham. Updates for version 2.5 of rOpenSci’s Code of Conduct. Other languages: Revisión anual del código de conducta de rOpenSci (es).
Open call for the rOpenSci Champions Program 2026! by Yanina Bellini Saibene. You can now apply to participate in the 2026 Champions Program. We are looking for people to participate in mentoring roles and to become Champions. Other languages: Convocatoria abierta al Programa de Campeonas y Campeones de rOpenSci 2026 (es).
Closing The Loop with Our 2025 Wrap-up by The rOpenSci Team. a post where we reflect on a year of shared learning and collaboration. Take a look at our wrap-up and our plans for 2026.
Software Libre de Investigación en América Latina, conversaciones con la comunidad de Campeon(a|e)s de rOpenSci by Yanina Bellini Saibene. De cara al llamado 2026 del Programa de Campeon(a|e)s de rOpenSci, conversamos con integrantes de la cohorte en español sobre sus experiencias, aprendizajes y el valor de participar en una comunidad de software libre y ciencia abierta.
How to Recruit a New Maintainer for Your Package by Yanina Bellini Saibene, Noam Ross, and Maëlle Salmon. Finding a new maintainer for your R package can be a overwhelming task. In this post, we share practical tips and strategies to help you find people that can contribute and take over your package. . Other languages: Cómo sumar nuevas personas al mantenimiento de tu paquete (es).
Code Hosting Options Beyond GitHub by Mark Padgham and Steffi LaZerte. How to manage and mirror code repositories across different platforms.
Introducing distionary for Building and Probing Distributions by Vincenzo Coia. The distionary package is on CRAN and brings a new way to build and probe probability distributions in R, forming the building blocks of the probaverse ecosystem.
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?.
hddtools, Tools to discover hydrological data, accessing catalogues and databases from various data providers. Issue for volunteering.
qualtRics, download Qualtrics survey data. Issue for volunteering.
photosearcher, searches Flickr for photographs and metadata. Issue for volunteering.
Refer to our help wanted page – before opening a PR, we recommend asking in the issue whether help is still needed.
Some useful tips for R package developers. 👀
Athanasia Monika Mowinckel has been curating an Advent Calendar about R Package Development. Read the full recap of all the tips on her blog.
Read Heather Turner’s blog post on the funding granted to the project “Enabling the Next Generation of Contributors to R”. The team (Heather Turner, Ella Kaye, Simon Urbanek, Peter Dalgaard, Gabe Becker, Kylie Bemis, Mikael Jagan, and Jeroen Ooms) will focus on addressing “the challenge of sustaining the R Project” through direct contributions among other aspects.
Adam Sparks wrote up his strategy for generating a README badge displaying test coverage, without using a third-party such as codecov.io.
Documentation can have code that is run dynamically either at install, build or render time
An example can be found in dplyr where code lists relevant methods present in loaded package. That code is run when the user loads the help page (render time).
Other examples for render, install and build.
The ghostty project was recently featured on Hacker News for its issue tracker policy. The process is to have users open a discussion using GitHub Discussions, then to convert the discussion to an issue if relevant.
“This approach is based on years of experience maintaining open source projects and observing that 80-90% of what users think are bugs are either misunderstandings, environmental problems, or configuration errors by the users themselves. For what’s left, the majority are often feature requests (unimplemented features) and not bugs (malfunctioning features). Of the features requests, almost all are underspecified and require more guidance by a maintainer to be worked on.”
The tldraw project announced that they no longer accept PRs from external contributors. In this case, the motivation is to not waste precious developers’ time on AI slop.
“Like many other open-source projects on GitHub, we’ve recently seen a significant increase in contributions generated entirely by AI tools. While some of these pull requests are formally correct, most suffer from incomplete or misleading context, misunderstanding of the codebase, and little to no follow-up engagement from their authors.”
Read about the Journal of Open Source Software updated its submission requirements to adapt JOSS for a world with heavy AI use for software development.
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