Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2024). nlmixr: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics. R package version 0.1.5, 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.

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.

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 = {2024},
    note = {R package version 0.1.5},
    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.},
  }

  @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.},
  }