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
RxODE and rxode2
RxODE vs rxode2 Since rxode2 came out recently, I am getting many questions about what is the difference between rxode2 and RxODE. I think the biggest reason for the question is – is this update going to break all the nice things I already do with RxODE? Or maybe why should I bother to change? I feel the same way when I have big changes in things I use. For me, I love the ability to pipe and change data with the tidyverse, and similar tools, but hate when they change things that affect my code.
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