Nonlinear Mixed-Effects Modeling Programs in R. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Stegmann, Gabriela; Jacobucci, Ross; Harring, Jeffrey R.; Grimm, Kevin J.
• Is Part Of: Structural equation modeling. Volume 25:Issue 1(2018)
• Page Start: 160
• Page End: 165
• Publication Date: 2018-01-02
• Subjects: nonlinear mixed-effects models -- R software -- mixed-effects model functions in R -- mixed-effects modeling programs in R; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1396187
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : 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.