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 (http://group.monolix.org/).

Version: 1.2
Imports: graphics, stats, methods
Published: 2014-02-25
Author: Emmanuelle Comets, Audrey Lavenu, Marc Lavielle.
Maintainer: Emmanuelle Comets <emmanuelle.comets at inserm.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: saemix citation info
CRAN checks: saemix results

Downloads:

Reference manual: saemix.pdf
Package source: saemix_1.2.tar.gz
MacOS X binary: saemix_1.2.tgz
Windows binary: saemix_1.2.zip
Old sources: saemix archive