Running large population PK/PD analyses on laptops and desktops often requires long computational times. This is quite tedious. In addition, when using parallel computing on your machine, it can slow it down for a while, creating further nuisances. Outsourcing computation to the cloud is a solution to this problem. Among the various cloud providers, Amazon Web Service (AWS) is one of the most famous and used by industries in various fields.
nlmixr2 2.1.0 was released and I promised to talk about the new features. One of the things that can impact many peoples work-flow is new estimation methods for population-only data. Many people use population-only estimation methods before changing the model to a mixed effect model, so I believe these can be useful for many people trying to find the best model to the data at hand. I will talk about the new ones (and why you may want to use them).
nlmixr2 2.1.0 was released 9 Jan 2024 which requires the newest (and recently released) engine packages nlmixr2est 2.2.0 (released 12 Dec 2023) and rxode2 2.0.10 (released 13 Dec 2023). I will be blogging about a few new features but want to mention a few now: More flexible mu referencing User defined functions can now be used with nlmixr2 and rxode2 Event handling changes Many new estimation methods for population only fitting The truncated changelog for these packages are below:
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
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.
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.
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:
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:
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.