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. It (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>.
