nlmixr2 2.1.2/ rxode2 2.1.3

Both nlmixr2 and rxode2 have been updated, the below describes all the nlmixr2 related packages (maintained by the nlmixr2 team). Most of items in this release are bug fixes. One of the changes will make random number generation platform independent. Unfortunately, this means simulations from within rxode2/nlmixr2 will have different numbers drawn from random distributions but I think platform independence is important enough to push this change through. Versions of new packages nlmixr2 2.

nlmixr2/rxode2 user functions

One of the exciting new features of the recent rxode2 is user functions. This allows you to define your own R functions for use in nlmixr2 or rxode2. This new feature can really help make your code more concise by reusing custom transformations or apply more complex routines. This can call R functions directly, but at a cost – single threaded and slower execution. However, you can reduce the cost by converting the R functions to C automatically with rxFun().

nlmixr2/rxode2 steady state changes

One of the things that I changed in the last release was steady state. Once I created the nonmem2rx package, I searched for NONMEM control streams that we could test the import from, especially those with attached data. I ran across nmtests that uses NONMEM to test against mrgsolve and how it behaves. During that test I noticed that rxode2 did not follow the convention of NONMEM in lagged steady state doses.

nlmixr2, tidyverse and RStudio on AWS

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+ new estimation methods

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).