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':
<https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.
