Nonlinear Mixed-Effects Modeling Programs in R

Gabriela Stegmann, Ross Jacobucci, Jeffrey R. Harring, Kevin Grimm

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations


In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using Stan). We briefly describe the approaches used, provide a sample code, and highlight strengths and weaknesses of each.

Original languageEnglish (US)
Pages (from-to)160-165
Number of pages6
JournalStructural Equation Modeling
Issue number1
StatePublished - Jan 2 2018


  • R software
  • mixed-effects model functions in R
  • mixed-effects modeling programs in R
  • nonlinear mixed-effects models

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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