saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm
Version 2.1

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 ().

Package details

AuthorEmmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03>
Date of publication2017-08-24 11:55:18 UTC
MaintainerEmmanuelle Comets <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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saemix documentation built on Aug. 24, 2017, 5:19 p.m.