Original post on the nlmixr2 project blog is here.
The nlmixr2 Working Group continues to expand what open-source R tooling can support in pharmacometrics. In a follow-up to the nlmixr2 7.0 release post, Matthew Fidler focuses on a frequently requested feature: a full covariance step, plus other refinements to how covariance is computed after a fit.
Two related changes stand out. Nearly any covariance method can now be requested from nearly any estimation method, and you can switch a finished fit to a different covariance method without refitting. The default covariance step also now covers every estimated parameter - not just the structural ones - so residual-error terms can return standard errors, %RSE, and confidence intervals in $parFixed the way users expect.
Read the full post from the nlmixr2 blog: nlmixr2 7.0’s covariance step, all grown up
Why nlmixr2?
The vision of nlmixr2 is to develop a R-based open-source nonlinear mixed-effects modeling software package that can compete with commercial pharmacometric tools and is suitable for regulatory submissions.
In short, the goal of nlmixr2 is to support easy and robust nonlinear mixed effects models in R.
Get involved in the nlmixr2 working group
The nlmixr2 working group is open to anyone interested in contributing to the project. You can get involved by:
Main repo: https://github.com/nlmixr2/nlmixr2
Issues: https://github.com/nlmixr2/nlmixr2/issues
Discussions: https://github.com/nlmixr2/nlmixr2/discussions