Nonlinear Mixed-Effects Modeling Programs in R. Issue 1 (2nd January 2018) - Stegmann, Gabriela; Jacobucci, Ross; Harring, Jeffrey R.; Grimm, Kevin J.
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.