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 thrilled to launch our Champions program!
rOpenSci Champions Program is part of a series of activities and projects we are carrying out to ensure our research software serves everyone in our communities. That is why this program focuses on people who belong to groups that are historically and systematically excluded from the open software and research software communities and who are interested in contributing to rOpenSci and the broader open source and research software communities.
If you are selected as a Champion, over the course of a year you will develop a project, get training, and be mentored. You will also receive a small stipend.
First three packages to pass Statistical Software Review!
We are overjoyed to have the first three packages pass through our Statistical Software Review process. The canaper package by Joel Nitta was approved on the 14th of September 2022, followed shortly after by the melt package by Eunseop Kim on the 20th of September, and then the aorsf package by Byron Jaeger on the 22nd of September. Among many exciting aspects of the expansion of software peer review to statistical software is the introduction of new statistics-specific “Peer Reviewed” badges. Until now, every rOpenSci package has used the same single badge. Our new badges for statistical packages include a “grade” of bronze, silver, or gold, and the version number of the Statistical Software Standards with which a package complies. You can see the first statistical software badges on the front pages of the canaper, melt and aorsf repositories, the first two with a “silver” badge for compliance with version 0.1 of our statistical standards, and aorsf with a “gold” badge. There are already a number of other statistical packages under review, and we’ll be excited to see many more of these new badges appearing on rOpenSci packages soon.
Check out our Calls for new maintainers!
A few packages are looking for new maintainers. Do you want to maintain software that will help fellow R users extract tables out of PDFs, or access data from the IUCN Red list of endangered species, or perhaps something else completely? See if one of these packages appeals to you! You could volunteer on your own or as a team of maintainers (the more the merrier!). The rOpenSci team will be happy to help you with specifics, and will provide resources, tips and PR reviews as needed. Thank you!
Coworking sessions continue!
Join us for social coworking & office hours monthly on first Tuesdays! Hosted by Steffi LaZerte and various community hosts. Everyone welcome. No RSVP needed. Consult our Events page to find your local time and how to join.
Tuesday, 04 October 2022 9 AM Australian Western / 1:00 UTC “Start writing that package!” Hosted by community host Nick Tierney and Steffi LaZerte
- Cowork on a project of your choice;
- Take time to look up how to write a package;
- Start putting together that package you’ve always meant to;
- Or talk to Nick Tierney and others about how to get started.
Tuesday, 01 November 2022 9 AM North American Pacific / 16:00 UTC “Champions Program Application Clinic” Hosted by rOpenSci community manager Yanina Bellini Saibene and Steffi LaZerte
Our pkgcheck checks now have an additional category beyond just pass (✔️) or fail (✖️). Some checks indicate aspects which are worth considering, but which might not necessarily be considered check failures. We have introduced the symbol 👀 to denote aspects of packages which authors might consider modifying or tweaking at their discretion, but which do not indicate failing checks. The only check in this category at present is the check for duplicated function names, but other 👀 checks are likely to be included as our automated check system expands its scope in the future.
The following three packages recently became a part of our software suite:
aorsf, developed by Byron Jaeger: Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but aorsf runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2022) arXiv:2208.01129. It is available on CRAN. It has been reviewed by Christopher Jackson, Marvin Wright, and Lukas Burk.
melt, developed by Eunseop Kim: Performs multiple empirical likelihood tests for linear and generalized linear models. The package offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the testing procedures are given in Kim, MacEachern, and Peruggia (2021) arxiv:2112.09206. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. It is available on CRAN. It has been reviewed by Pierre Chausse, and Alex Stringer.
The following twenty packages have had an update since the last newsletter: frictionless (
v1.0.1), gert (
v1.9.0), binman (
v0.1.3), canaper (
v0.0.3), datefixR (
v1.2.0), EDIutils (
v1.0.2), geojsonio (
v0.9.5), gittargets (
0.0.5), ijtiff (
v2.2.8), MODIStsp (
v2.0.9), osmextract (
v0.4.1), restez (
v2.1.2), rgbif (
v3.7.3), spiro (
v0.1.1), tarchetypes (
0.7.1), targets (
0.13.4), taxlist (
v0.2.3), tic (
v0.13.1), wdman (
v0.2.6), and webmockr (
Software Peer Review
There are thirteen recently closed and active submissions and 3 submissions on hold. Issues are at different stages:
Three at ‘6/approved’:
Four at ‘4/review(s)-in-awaiting-changes’:
One at ‘3/reviewer(s)-assigned’:
Two at ‘2/seeking-reviewer(s)’:
Two at ‘1/editor-checks’:
Find out more about Software Peer Review and how to get involved.
On the blog
How to Participate with rOpenSci by Yanina Bellini Saibene. Are you curious about rOpenSci? Wondering how to start participating? 🤔 Glad you asked! Here’s a summary on how to get involved with us.
Cómo participar en rOpenSci by Yanina Bellini Saibene. ¿Tienes curiosidad por rOpenSci? Te preguntas cómo empezar a participar? 🤔 ¡Nos alegramos! Aquí tienes un resumen de cómo participar con nosotros.
Announcing the rOpenSci Champions Program! by Yanina Bellini Saibene. We are thrilled to launch our Champions Program pilot.
¡Anunciamos el programa de Campeones y Campeonas de rOpenSci! by Yanina Bellini Saibene. ¡Estamos muy felices de realizar el lanzamiento nuestro programa de Campeones y Campeonas.
Two use cases of our packages and resources have been reported since we sent the last newsletter.
Using taxize and highcharter in R to extract and visualize taxonomic data. Reported by Stepminer.
Using handlr to convert bibtex citation to cff. Reported by Athanasia Monika Mowinckel.
Call for maintainers
We’re looking for a new maintainer, or a new maintainer team, for each of the following packages:
If you’re interested, please comment in the issues or email
For more info, see
Package development corner
Some useful tips for R package developers. 👀
Improve your code with lintr’s new linters!
The most recent lintr release includes a bunch of new linters originally authored by Google developers. Have a look at the list, and run it on one of your codebases! Thanks to Hugo Gruson for this tip.
To run all linters there are you could run
lintr::lint_package(linters = lintr::linters_with_tags(tags = NULL)) (or first filter a bit the list returned by
lintr::linters_with_tags(tags = NULL) in case you see too many false positives for one of the linters).
Minimum R version dependency in R packages
Curious about why and how to make your R package depend on a minimal R version? See this R-hub blog post by Hugo Gruson and Maëlle Salmon.
Tweak the commit message obtained by merging a GitHub pull request
You might be interested in New options for controlling the default commit message when merging a pull request to make the merge/squash commit messages more informative.
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.