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
rxode2 2.0.9/2.0.10
rxode2 2.0.9 has been released, and rxode2 2.0.10 will be released soon! I want to personally thank all those who have submitted issues, and helped with the development. Without the support rxode2 wouldn’t be the tool it is today. This is the first CRAN-visible release since rxode2 2.0.7 and I would like to highlight a few new interesting features: ‘rxode2’ can now have more flexible model functions The key features are:
Read more