nlmixr2 inter occasion varibility

Inter-occasion variability Happy new year! With the holidays over we had the opportunity to celebrate many special occasions. I invite you to celebrate a new occasion for nlmixr2 – inter-occasion variability. We now support inter-occasion variability estimation in nlmixr2! Implementing inter-occasion variability in models To implement inter-occasion variability in nlmixr2 models you simply have to specify that the variable is an occasion variable, say occ: iov.cl ~ 0.1 | occ The ~ syntax is similar to how between subject variability is specified, the addition of | tells that this a between occ variability.

nlmixr2 5.0

nlmixr2 5.0 As time progresses, dependencies update, dependencies come and go, and sometimes these dependencies change what happens to rxode2/nlmixr2 models. One dependency change (qs being dropped) means that models from before nlmixr2/rxode2 5.0 have the UI/fit models saved to disk will may not be able to load completely in nlmixr2 5.0. This is because internally nlmixr2/rxode2 encoded its models in binary qs format. Why is qs going away? While the maintainer wanted to keep qs and its file format around, R is changing and what is acceptable to access within C/C++ from R has changed.

nlmixr2 is becoming a R Consortium Working Group

nlmixr2 Working Group We are very excited to announce we are joining the R consortium as a Working Group (WG). Our vision will remain the same: 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. What is a R Consortium Working Group? A R Consortium ISC working group is a group of volunteers for a project that “requires the skills not possessed by a single individual, or the amount of work required is more than can be accomplished by a single person in a reasonable amount of time.

laplace and agq estimation methods

nlmixr2 log-likelihood In 2022 we announced the focei log-likelihood. However, in our last advisory committee meeting Mats Karlsson pointed out that focei log-likelihood may not be the best approach. He believed that Stuart L. Beal and Lewis B. Sheiner did not include this method since they may have been trying to protect users from methods that may not make sense (for example, maybe focei log-likelihood is not an accurately enough approximation of the likelihood).

mceta – Monte-Carlo focei?

nlmixr2 4.0 mceta Two of popular algorithms for fitting nonlinear fixed effects models in pharmacometrics are first order conditional estimation (with interaction), sometimes called focei. This is often considered a classical estimation method As time progressed, Monte-Carlo methods started being used, such as stochastic approximation estimation-maximization (saem). This uses Mote-Carlo Methods to try to find the maximum expectation of the nonlinear-mixed effects model. The new feature I am discussing is mceta for focei, which combines the Monte-Carlo sampling with focei.