Contact us at

From our blog

Recent blog posts

{riskassessment} App voted best Shiny app at shinyConf 2023! 🎉

By Juliane Manitz on June 2, 2023

The {riskassessment} app, presented by Aaron Clark from the R Validation Hub Executive Committee, was voted best Shiny app at shinyConf 2023. The 2nd Annual Shiny Conference was held in March 2023. It was all virtual with over 4k global registrants. Congratulations!! The app provides a shiny front-end to augment the utility of the riskmetric package, thus user-friendly and interactive access to risk assessment of R packages. The apps functionalities include:

Continue reading

Summary of 2022 Case Studies

By Juliane Manitz on March 15, 2023

Last year, the R validation hub recently initiated a three-part presentation series on case studies in which eight pharmaceutical companies shared their experiences on building a GxP framework with R. These case studies highlighted both easy and challenging aspects of implementing risk assessment for R packages in a GxP environment. In this blog post we attempt to summarize common themes, difference in approaches, and challenges. All implementations followed the risk validation process for R packages outlined in the white paper.

Continue reading

ASA Biopharmaceutical report, Fall 2022

By Andy Nicholls on December 5, 2022

I’m pleased to share that the R Validation Hub’s efforts have been recognised in the ASA Biopharmaceutical report, Fall 2022. Within the edition you can find our paper, Risk Assessment of R Packages: Learnings and Reflections. This paper reflects on our white paper; provides an overview of our {riskmetric} package and Risk Assessment application; and summarises our 2022 case studies (which you can now find on our Case Studies page).

Continue reading

Status Update May 2022

By Juliane Manitz on May 20, 2022

It’s time to bring another update to you on the current status of the R validation hub: The riskmetric R package has been stable on CRAN. Recent work has focused on structuring “cohort metrics” – metrics that are conditioned on the package library or execution environment available to R. In R, package behaviors are often dependent on the rest of the R installation, and this new feature will help to make metrics more inspectible and reproducible, as well as allowing us to ask new questions like, “What would be the effect of installing a new package into an R environment?

Continue reading

Participating Organisations

Members of the following organisations are participating in this project