nonmem2rx and babelmixr2

nonmem2rx/babelmixr2 I am really excited to announce that the nlmixr2 team has released a new version of babelmixr2 and a new package nonmem2rx that allows you to convert NONMEM to rxode2 or even a nlmixr2 object. To install, simply upgrade babelmixr2 with: install.packages(c("nonmem2rx", "babelmixr2")) What you can do with nonmem2rx/babelmixr2 You can do many useful tasks directly converting between nlmixr2 and NONMEM models; you can: Convert a NONMEM model to a rxode2 model

nlmixr2 family releases

This is another release of a group of nlmixr2-related packages. Feature Highlights There are a few things I would like to highlight in this release: Highly requested feature(s) A much requested feature has been added for rxode2; Diagonal zeros in the omega and sigma matrices are treated as zeros in the model. The corresponding omega and sigma matrices drop columns/rows where the diagonals are zero to create a new omega and sigma matrix for simulation.

Lag-time with NONMEM and nlmixr2

This is more of a methodology post, pointing out how things are done in nlmixr2 and how it likely doesn’t match what is done in NONMEM (and at least one reason why a drop-in replacement of rxode2 by another tool like PKPDsim, mrgsolve, or deSolve is not an easy project). For the impatient, adding focei lag time (and other dose-based events) have improved in stability for this release of nlmixr2.

nlmixr2 2.0.8 Objectively Surprising

Last time I blogged promised to talk about a few other things, including: Likelihood based on each observation (and how to get it) Standard Errors / Hessians, etc for between subject variabilities or etas (and how to get them) Hessians for the individual between subject variability is also used for the focei calculation. So, if you are impatient, I will give you brief instructions on where to get each component of the likelihood:

nlmixr2 2.0.8 log-likelihood

I am pretty excited abut the new nlmixr2 release (2.0.8). When I joined the the nlmixr2 team, I wanted to do a fancy heavy tailed, skewed model in an open source tool so I could figure out how to do even more with it. With this release, it is possible to do a heavy tailed (t-distribution dt()) skewed (coxBox(lambda)) distribution: my old wish is now possible with focei! A few other things that people may be interested in are:

nlmixr2 is here

Over the past half year, a lot of changes have been happening behind the scenes, and the time has finally come to reveal them! nlmixr2 nlmixr2 will be the version in active development going forward, taking over from nlmixr, starting with the current CRAN version, 2.0.6. Our new home on GitHub is here, and on CRAN, we’re here. The reasons for the name and format change are many, but most importantly, we’ve taken this step to improve overall user experience and to help us maintain the project more effectively.