saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

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 (<>).

Version: 2.1
Imports: graphics, stats, methods
Published: 2017-08-24
Author: Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03>
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_2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: saemix_2.1.tgz
OS X Mavericks binaries: r-oldrel: saemix_2.1.tgz
Old sources: saemix archive


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