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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 160-165 |
| Number of pages | 6 |
| Journal | Structural Equation Modeling |
| Volume | 25 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2 2018 |
Keywords
- R software
- mixed-effects model functions in R
- mixed-effects modeling programs in R
- nonlinear mixed-effects models
ASJC Scopus subject areas
- Decision Sciences(all)
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)