Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2025). nlmixr: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics. R package version 3.0.2, https://CRAN.R-project.org/package=nlmixr.
Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. https://doi.org/10.1002/psp4.12445.
Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930. https://doi.org/10.1002/psp4.12471.
Corresponding BibTeX entries:
@Manual{,
title = {{nlmixr}: Nonlinear Mixed Effects Models in Population
Pharmacokinetics and Pharmacodynamics},
author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and
Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun
Post and Wenping Wang},
year = {2025},
note = {R package version 3.0.2},
url = {https://CRAN.R-project.org/package=nlmixr},
}
@Article{,
title = {Nonlinear Mixed-Effects Model Development and Simulation
Using nlmixr and Related R Open-Source Packages},
author = {Matthew Fidler and Justin Wilkins and Richard
Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame
and Yuan Xiong and Wenping Wang},
journal = {CPT: Pharmacometrics \& Systems Pharmacology},
year = {2019},
volume = {8},
pages = {621--633},
number = {9},
month = {sep},
abstract = {nlmixr is a free and open-source R package for fitting
nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint
PK-PD, and quantitative systems pharmacology mixed-effects
models. Currently, nlmixr is capable of fitting both traditional
compartmental PK models as well as more complex models
implemented using ordinary differential equations. We believe
that, over time, it will become a capable, credible alternative
to commercial software tools, such as NONMEM, Monolix, and
Phoenix NLME.},
address = {Hoboken},
publisher = {John Wiley and Sons Inc.},
url = {https://doi.org/10.1002/psp4.12445},
}
@Article{,
title = {Performance of the SAEM and FOCEI Algorithms in the
Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr},
author = {Rik Schoemaker and Matthew Fidler and Christian Laveille
and Justin Wilkins and Richard Hooijmaijers and Teun Post and
Mirjam Trame and Yuan Xiong and Wenping Wang},
journal = {CPT: Pharmacometrics \& Systems Pharmacology},
year = {2019},
volume = {8},
pages = {923--930},
number = {12},
month = {dec},
abstract = {The free and open-source package nlmixr implements
pharmacometric nonlinear mixed effects model parameter estimation
in R. It provides a uniform language to define pharmacometric
models using ordinary differential equations. Performances of the
stochastic approximation expectation-maximization (SAEM) and
first order-conditional estimation with interaction (FOCEI)
algorithms in nlmixr were compared with those found in the
industry standards, Monolix and NONMEM, using the following two
scenarios: a simple model fit to 500 sparsely sampled data sets
and a range of more complex compartmental models with linear and
nonlinear clearance fit to data sets with rich sampling.
Estimation results obtained from nlmixr for FOCEI and SAEM
matched the corresponding output from NONMEM/FOCEI and
Monolix/SAEM closely both in terms of parameter estimates and
associated standard errors. These results indicate that nlmixr
may provide a viable alternative to existing tools for
pharmacometric parameter estimation.},
url = {https://doi.org/10.1002/psp4.12471},
}