The nlmixr team is delighted to announce that we’ll be running an nlmixr workshop at PAGE in Montreux on Tuesday 29 May 2018! Registration will open January 15th 2018. Mark your calendar! Places are limited…. https://www.eventbrite.co.uk/e/nlmixr-workshop-tickets-41316956128
nlmixr 0.9.0-2 is out! Now with extra useful Windows installers for 32-bit and 64-bit platforms, complete with dependencies. No more setup headaches from here on out! http://github.com/nlmixrdevelopment/nlmixr/releases/tag/v0.9.0-2
The nlmixr team is very proud to announce the first CRAN release of nlmixr (https://cran.r-project.org/web/packages/nlmixr/), an open source package for population PK and PKPD modelling. nlmixr builds on the RxODE package for simulation of nonlinear mixed effect models using ordinary differential equations, by implementing parameter estimation algorithms like nlme and
The nlmixr team is very proud to announce a major public release of nlmixr, an open source package for population PK and PKPD modelling. nlmixr builds on the RxODE package for simulation of nonlinear mixed effect models using ordinary differential equations, by implementing parameter estimation algorithms like nlme and SAEM
Part of the nlmixr team had the wonderful opportunity in June 2017 to train the next generation of pharmacometricians in Cuba. Wenping Wang and Mirjam N. Trame spent one week together with the Pharmacometrics group of Prof. Leyanis Rodriguez Vera from the College of Pharmacy at the University of Havana, Cuba.
nlmixr was featured on two posters at the recent PAGE conference in Budapest, Hungary. In one, by Rik Schoemaker and colleagues, the nlmixr team show comparisons between the built-in nlme, SAEM and FOCE-interaction methods, demonstrating that the nlmixr SAEM method in particular performs similarly to NONMEM’s FOCE-interaction method. In the
Growing up nestled in the heart of the Rocky Mountains, I had the opportunity to hike some of the most rugged mountains in the western United States. As I help code the nlmixr FOCEI likelihood surfaces I can’t help but draw some parallels between two of my hikes in these
There’s a distinct shortage of tools for performing effective pharmacometric data analysis. There’s NONMEM, the gold standard; there’s Monolix; and there’s Phoenix WinNonlin. All of these tools do a good enough job, but are united by two critical issues: first, they’re closed and proprietary, and second, they cost the equivalent of a