Stochastic Approximation Expectation Maximization (SAEM) algorithm 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/). Software Depends: methods Imports: graphics, stats Emmanuelle Comets <emmanuelle.comets@inserm.fr> Emmanuelle Comets, Audrey Lavenu, Marc Lavielle. GPL (>= 2) 2011-07-02 application/tgz http://CRAN.R-project.org/package=saemix