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

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
Main Authors: Emmanuelle Comets, Audrey Lavenu, Marc Lavielle
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2017-08-01
Series:Journal of Statistical Software
Subjects:
nonlinear mixed effect models
stochastic approximation EM algorithm
pharmacokinetics
pharmacodynamics
theophylline
orange tree
S4 classes
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/2399
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spelling doaj-7dd46b7f783c40b3a6bf433a511efdce2020-11-25T00:29:48ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602017-08-0180114110.18637/jss.v080.i031139Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM AlgorithmEmmanuelle CometsAudrey LavenuMarc LavielleThe 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.https://www.jstatsoft.org/index.php/jss/article/view/2399nonlinear mixed effect modelsstochastic approximation EM algorithmpharmacokineticspharmacodynamicstheophyllineorange treeS4 classes
collection DOAJ
language English
format Article
sources DOAJ
author Emmanuelle Comets
Audrey Lavenu
Marc Lavielle
spellingShingle Emmanuelle Comets
Audrey Lavenu
Marc Lavielle
Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
Journal of Statistical Software
nonlinear mixed effect models
stochastic approximation EM algorithm
pharmacokinetics
pharmacodynamics
theophylline
orange tree
S4 classes
author_facet Emmanuelle Comets
Audrey Lavenu
Marc Lavielle
author_sort Emmanuelle Comets
title Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
title_short Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
title_full Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
title_fullStr Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
title_full_unstemmed Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
title_sort parameter estimation in nonlinear mixed effect models using saemix, an r implementation of the saem algorithm
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2017-08-01
description 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.
topic nonlinear mixed effect models
stochastic approximation EM algorithm
pharmacokinetics
pharmacodynamics
theophylline
orange tree
S4 classes
url https://www.jstatsoft.org/index.php/jss/article/view/2399
work_keys_str_mv AT emmanuellecomets parameterestimationinnonlinearmixedeffectmodelsusingsaemixanrimplementationofthesaemalgorithm
AT audreylavenu parameterestimationinnonlinearmixedeffectmodelsusingsaemixanrimplementationofthesaemalgorithm
AT marclavielle parameterestimationinnonlinearmixedeffectmodelsusingsaemixanrimplementationofthesaemalgorithm
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