Parameter Estimation in Nonlinear Mixed Effect Models Using saemix , an R Implementation of the SAEM Algorithm

The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation-maximisation (SAEM) algorithm. In the present paper we describe the main features of the package,...

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Bibliographic Details
Published in:Journal of statistical software 2017, Vol.80 (3), p.1-41
Main Authors: Comets, Emmanuelle, Lavenu, Audrey, Lavielle, Marc
Format: Article
Language:English
Subjects:
Life Sciences
nonlinear mixed effect models
orange tree
pharmacodynamics
pharmacokinetics
Quantitative Methods
S4 classes
Santé publique et épidémiologie
stochastic approximation EM algorithm
theophylline
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Summary:The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation-maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community.
ISSN:1548-7660
1548-7660
DOI:10.18637/jss.v080.i03