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

Emmanuelle Comets | Zendy; Audrey Lavenu | Zendy; Marc Lavielle | Zendy

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

Author(s) -

Emmanuelle Comets,

Audrey Lavenu,

Marc Lavielle

Publication year - 2017

Publication title -

journal of statistical software

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.636

H-Index - 145

ISSN - 1548-7660

DOI - 10.18637/jss.v080.i03

Subject(s) - r package , computer science , nonlinear system , maximum likelihood , estimation , stochastic approximation , algorithm , mathematical optimization , mixed model , mathematics , machine learning , statistics , computational science , key (lock) , physics , quantum mechanics , management , computer security , economics

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.

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