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Authors and Citation

Authors

  • Matthew Fidler. Author, maintainer.

  • Rik Schoemaker. Contributor.

  • Kyle Baron. Contributor.

  • Thierry Wendling. Contributor.

  • Ted Grasella. Contributor.

  • C Weil. Contributor.

  • Yaning Wang. Contributor.

  • R O'Reilly. Contributor.

  • David D'Argenio. Contributor.

  • Rodriguez-Vera. Contributor.

  • D Gaver. Contributor.

  • Yuan Xiong. Contributor.

  • Wenping Wang. Author.

Citation

Source: inst/CITATION

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 2.0.9, https://CRAN.R-project.org/package=nlmixr. 

@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 2.0.9},
  url = {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. 

@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},
}

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. 

@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},
}