Welcome
Why nlmixr?
The goal of nlmixr, or more accurately nlmixr2, is to support easy and robust nonlinear mixed effects models (NLMEMs) in R.
NLMEMs are used to help identify and explain the relationships between drug exposure, safety, and efficacy and the differences among population subgroups. Most often, they are built using longitudinal PK and pharmacodynamic (PD) data collected during clinical studies. These models characterize the relationships between dose, exposure and biomarker and/or clinical endpoint response over time, variability between individuals and groups, residual variability, and uncertainty.
NLMEM development in the pharmaceutical space is dominated by a small number of proprietary, commercial software tools. Although this kind of approach to software has some advantages, adopting an open-source, open-science paradigm also has benefits - third-party auditing or adjustments are possible, and the precise model-fitting methodology employed can be determined by anyone with the time and energy to review the source code. We see nlmixr2 being especially useful in being able to integrate into the rich R ecosystem, and it is well suited for use in scripted, literate-programming workflows of the kind flourishing in the R ecosystem by means of packages such as knitr and rmarkdown.
The nlmixr2 blog
Stepwise covariate modeling with nlmixr2scm
Watch on YouTube nlmixr2scm is on CRAN! Happily, nlmixr2scm, which does stepwise covariate modeling (SCM) for nlmixr2 models, is now on CRAN. It took a while - seems they’re very busy now that everyone is churning out packages with AI - but it’s finally there. install.packages("nlmixr2scm") If you’ve built covariate models in NONMEM, you’ve probably used scm in Perl-speaks-NONMEM. nlmixr2scm follows the same approach (Jonsson & Karlsson, 1998), and it works directly on an nlmixr2 fit.
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