Contributed R packages are developed by anyone and may differ in popularity and accuracy.
Assessing the accuracy of a contributed open-source R package should be done via risk assessment. The term risk refers to the risk of an error in the code that, when used appropriately, could lead to an incorrect calculation. This incorrect calculation may lead to an incorrect decision during data analysis. The relative impact of an error should be determined by the individual organisation. Impact is therefore not considered as part of the risk assessment.
RValidationHub developed an R package titled
riskmetric (under development). The goal of
riskmetric is to assess the risk of contributed R packages.
Riskmetric has four groups of metric criteria:
- Unit testing metrics - includes unit test coverage and composite coverage of dependencies
- Documentation metrics - availability of vignettes, news tracking, example(s) and return object description for exported functions
- Community engagement - number of downloads, availability of the code in a public repository, formal bug tracking and user interaction
- Maintainability and reuse - number of active contributors, author / maintainer contacts, and type of license
For a comprehensive list of metrics, see Metric Development - Github.