A lot of what I do as rOpenSci’s community assistant is related to social media.I check for posts about rOpenSci packages, invite people to share usecases,advertise upcoming events, as well as promotingnew packages which completed software peer review.
I’m thrilled to share that CRediTas has passed peer review and been accepted to rOpenSci as well as to CRAN. I am glad to acknowledge the editor Emily Riedered and the two reviewers Marcelo S. Perlin and João Martins. Their comments and support were really insightful. CRediTas is a tiny package to facilitate the tedious job of creating CRediT authors statements for scientific publications.
I’m thrilled to share that waywiser, my R package focused on providing framework-agnostic (but tidymodels-friendly) methods for assessing models fit to spatial data 1 , has passed peer review and been accepted to rOpenSci.
How packages appear in r-universe Last month we explained how r-universe makes it easy to search and browse through the countless R packages, articles, and datasets to let you discover and learn new things. We are continuously growing this database by adding more R projects, to guide you through everything the R ecosystem has to offer. Currently r-universe is tracking and indexing of over 18.000 R packages.
Encontrar la herramienta adecuada para el trabajo Lo más difícil de usar R con eficacia es encontrar los mejores paquetes para el problema que intentas resolver. Creo que esto es incluso, más importante que dominar el lenguaje en sí. Cosa que irás adquiriendo a medida que empieces a utilizar R con más frecuencia.
Finding the right tool for the job The hardest part about effectively using R is finding the best packages for the problem you are trying to solve. I think this is even more important than being fluent in the language itself, which you will pick up along the way as you start using R more frequently. However, building your code on reliable foundations is essential for good results, and difficult to fix later on in a project.
Running your own package registry Tiny update for r-universe users whos run their own package registry: we have changed the preferred git repo name where you host your packages.json registry file. Previously the git repository was simply called universe, i.e. the registry for tiledb-inc.r-universe.dev was in the GitHub repository tiledb-inc/universe.
Introduction Panel data contains measurements from multiple subjects measured over multiple time points.Such data can be encountered in many social science applications such as when analysing register data or cohort studies (for example).Often the aim is to perform causal inference based on such observational data (instead of randomized control trials). A new rOpensci-reviewed R package dynamite available on CRAN implements a new class of
Pretty permanent URLs! We have improved the URL schema for the r-universe.dev front-end to make it even easier to find and share a link to an R package, article, API, or other resource. The old URLs will now automatically be redirected to the new locations, so this should be a non-breaking change. But of course we recommend updating your references to link to the new permanent URLs.
curl 5.0.0 is on CRAN A new major version of the curl package has been released to CRAN. This release both brings internal improvements as well as new user-facing functionality, in particular with respect to concurrent downloads. From the NEWS file: curl 5.0.0 - New function multi_download() which supports concurrent downloads and resuming download for large files, while giving detailed progress information.