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,...

Full description

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
Description
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

Similar Items