Most teams are validating far more than they need to.
Take an R package with 500 functions. The average team uses about 5.
Most validation tools require you to check all 500. Every time.
Litmus changes that. Validate only what you use.
jumpingrivers.com/litmus/
Open source R is powerful. Governing it at scale is hard.
Most regulated teams have 500+ packages and no consistent way to answer the question auditors always ask: how do you know these are safe to use?
Litmus automates that — assessing packages across code, docs, maintenance and vulnerability signals, then generating reports you can show.
jumpingrivers.com/litmus
New blog post: "Litmus: Maintainer Criteria"
How often do bugs get fixed? Does the package use source control? Is it a solo or group effort? These critical questions help assess the long-term viability and risk profile of R packages in your workflow.
Read more: https://www.jumpingrivers.com/blog/r-validation-maintainers-litmus/
#RStats #DataScience #PackageValidation
https://www.jumpingrivers.com/blog/r-validation-maintainers-litmus/
How often do bugs get fixed? Does the package use source control? Is the package a solo or a group effort? These questions aid our understanding about the long-term viability of a package, and how "risky" it is.
Not all R packages are clearly “good” or “risky”, most fall somewhere in between. This post introduces our scoring framework around package documentation. We investigate the different measures, then look at a few well known packages.
#rstats #packagevalidation #softwaredevelopment
https://www.jumpingrivers.com/blog/r-validation-documentation-litmus/
Not all R packages are clearly “good” or “risky”, most fall somewhere in between. This post introduces our scoring framework around package documentation. We investigate the different measures, then look at a few well known packages.
Jumping Rivers