useR! 2024

The success of the R programming language is largely due to its ease of creating and sharing R packages. We propose an opinionated framework called “openstatsguide”, published on openstatsware.org/guide.html, which can guide R package developers towards a minimum set of good practices. As far as we know from our literature search, this is the first attempt at providing a small and concise set of rules for package developers. This applies not just to R, but can also be used for functionally oriented programming languages used in data science, and we give examples for R, Python, and Julia. Rather than a full and detailed how-to guide, we keep “openstatsguide” short and on a high level, thus lowering the entry point for novice and seasoned developers alike. Our hope is that this guide can increase the adoption of software engineering good practices in the statistics community. In this talk we describe the motivation and scope of “openstatsguide”, relationship with existing work, the set of good practices, the maintenance model and ideas for future complementary guides produced by the openstatsware.org working group.