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: | 2.2 |
| Imports: | graphics, stats, methods |
| Suggests: | testthat (≥ 0.3) |
| Published: | 2018-10-10 |
| Author: | Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03> |
| 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 |
| Reference manual: | saemix.pdf |
| Package source: | saemix_2.2.tar.gz |
| Windows binaries: | r-devel: saemix_2.2.zip, r-release: saemix_2.2.zip, r-oldrel: saemix_2.2.zip |
| OS X binaries: | r-release: saemix_2.2.tgz, r-oldrel: saemix_2.2.tgz |
| Old sources: | saemix archive |
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