saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. Documentation about 'saemix' is provided by a comprehensive user guide in the inst folder, and a reference concerning the methods is the paper by Comets, Lavenu and Lavielle (2017, <doi:10.18637/jss.v080.i03>). See 'citation("saemix")' for details.

Version: 2.4
Imports: graphics, stats, methods
Suggests: testthat (≥ 0.3)
Published: 2021-02-23
Author: Emmanuelle Comets ORCID iD [aut, cre], Audrey Lavenu [ctb], Marc Lavielle [ctb], Maud Delattre [ctb]
Maintainer: Emmanuelle Comets <emmanuelle.comets at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: saemix citation info
CRAN checks: saemix results


Reference manual: saemix.pdf


Package source: saemix_2.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): saemix_2.4.tgz, r-release (x86_64): saemix_2.4.tgz, r-oldrel: saemix_2.4.tgz
Old sources: saemix archive

Reverse dependencies:

Reverse imports: varTestnlme


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