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: - 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 (< http://group.monolix.org/>).


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("saemix")

2.2 by Emmanuelle Comets, 9 months ago


Browse source code at https://github.com/cran/saemix


Authors: Emmanuelle Comets , Audrey Lavenu , Marc Lavielle (2017) <doi:10.18637/jss.v080.i03>


Documentation:   PDF Manual  


GPL (>= 2) license


Imports graphics, stats, methods

Suggests testthat


See at CRAN