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 |
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Keywords
- mixed-effects model functions in R
- mixed-effects modeling programs in R
- nonlinear mixed-effects models
- R software
ASJC Scopus subject areas
- Decision Sciences(all)
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)
Cite this
Nonlinear Mixed-Effects Modeling Programs in R. / Stegmann, Gabriela; Jacobucci, Ross; Harring, Jeffrey R.; Grimm, Kevin.
In: Structural Equation Modeling, Vol. 25, No. 1, 02.01.2018, p. 160-165.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Nonlinear Mixed-Effects Modeling Programs in R
AU - Stegmann, Gabriela
AU - Jacobucci, Ross
AU - Harring, Jeffrey R.
AU - Grimm, Kevin
PY - 2018/1/2
Y1 - 2018/1/2
N2 - 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.
AB - 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.
KW - mixed-effects model functions in R
KW - mixed-effects modeling programs in R
KW - nonlinear mixed-effects models
KW - R software
UR - http://www.scopus.com/inward/record.url?scp=85039156127&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039156127&partnerID=8YFLogxK
U2 - 10.1080/10705511.2017.1396187
DO - 10.1080/10705511.2017.1396187
M3 - Review article
AN - SCOPUS:85039156127
VL - 25
SP - 160
EP - 165
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 1
ER -