Validation and qualification are two important topics in regulatory interactions. For R package validation, Roche, GSK and Novartis have an ongoing collaboration to extend the ideas in the R Validation Hub. For example, by using new tools like covtracer, to automate via CICD tools validation (and re-validation) of packages once we feel the quality of their documentation and unit testing is sufficient. thevalidatoR represents a simplified version of this approach.

When we talk about package validation, the focus here is on the generalised functions we put in a package as a developer. There is also the user perspective of how to downstream quality check the usage of such packages and functions in creating clinical reporting deliverables. For this reason, we categorise the packages separately below, dependent on relevance.

Package Validation


Automated tracing of code coverage via a network of test execution, linking tests to code & code to documentation. This package is used to provide the tracebility matrix for CICD based validation.


A GitHub action on the GitHub Marketplace to provide standardised build reports for validation. An alternative approach to valtools that relies on the normal R framework for validation.


Validation framework for R packages that allows you to bolt in additional validation documentation.


Framework to quantify R package risk by assessing a number of meaningful metrics.


Shiny utility for riskmetric.

Quality Checking


The diffdf package is designed to enable detailed comparison of two data.frames.