Stochastic Approximation Expectation Maximization (SAEM)
algorithm
Description
The SAEM 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 (linearization, 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://software.monolix.org/).