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 (i) 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, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <>.

Version: 3.3
Depends: npde (≥ 3.2)
Imports: graphics, stats, methods, gridExtra, ggplot2, grid, rlang, mclust, scales, MASS
Suggests: testthat, survival
Published: 2024-03-05
DOI: 10.32614/CRAN.package.saemix
Author: Emmanuelle Comets [aut, cre], Audrey Lavenu [aut], Marc Lavielle [aut], Belhal Karimi [aut], Maud Delattre [ctb], Marilou Chanel [ctb], Johannes Ranke ORCID iD [ctb], Sofia Kaisaridi [ctb], Lucie Fayette [ctb]
Maintainer: Emmanuelle Comets <emmanuelle.comets at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: saemix citation info
In views: MixedModels
CRAN checks: saemix results


Reference manual: saemix.pdf


Package source: saemix_3.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): saemix_3.3.tgz, r-oldrel (arm64): saemix_3.3.tgz, r-release (x86_64): saemix_3.3.tgz, r-oldrel (x86_64): saemix_3.3.tgz
Old sources: saemix archive

Reverse dependencies:

Reverse imports: mkin, nlive, varTestnlme


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