TY - JOUR
T1 - Nonlinear Mixed-Effects Modeling Programs in R
AU - Stegmann, Gabriela
AU - Jacobucci, Ross
AU - Harring, Jeffrey R.
AU - Grimm, Kevin
N1 - 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).
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 - R software
KW - mixed-effects model functions in R
KW - mixed-effects modeling programs in R
KW - nonlinear mixed-effects models
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U2 - 10.1080/10705511.2017.1396187
DO - 10.1080/10705511.2017.1396187
M3 - Review article
AN - SCOPUS:85039156127
SN - 1070-5511
VL - 25
SP - 160
EP - 165
JO - Structural Equation Modeling
JF - Structural Equation Modeling
IS - 1
ER -