Nonlinear Mixed-Effects Modeling Programs in R

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

doi:10.1080/10705511.2017.1396187

Nonlinear Mixed-Effects Modeling Programs in R

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

Research output: Contribution to journal › Review article › peer-review

12 Scopus citations

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	https://doi.org/10.1080/10705511.2017.1396187
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)

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10.1080/10705511.2017.1396187

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title = "Nonlinear Mixed-Effects Modeling Programs in R",

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.",

keywords = "R software, mixed-effects model functions in R, mixed-effects modeling programs in R, nonlinear mixed-effects models",

author = "Gabriela Stegmann and Ross Jacobucci and Harring, {Jeffrey R.} and Kevin Grimm",

note = "Funding Information: This work was supported by National Science Foundation Grant REAL-1252463 awarded to the University of Virginia, David Grissmer (Principal Investigator), and Christopher Hulleman (Co-Principal Investigator). Funding Information: This work was supported by National Science Foundation Grant REAL-1252463 awarded to the University of Virginia, David Grissmer (Principal Investigator), and Christopher Hulleman (Co-Principal Investigator). This work was supported by National Science Foundation Grant REAL-1252463 awarded to the University of Virginia, David Grissmer (Principal Investigator), and Christopher Hulleman (Co-Principal Investigator).",

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doi = "10.1080/10705511.2017.1396187",

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AU - Jacobucci, Ross

AU - Harring, Jeffrey R.

AU - Grimm, Kevin

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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.

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Nonlinear Mixed-Effects Modeling Programs in R

Abstract

Keywords

ASJC Scopus subject areas

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