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 (<>).

Tests Vignettes

Available Snapshots

This version of saemix can be found in the following snapshots:


Imports/Depends/LinkingTo/Enhances (6)
  • gridExtra
  • ggplot2
  • rlang
  • mclust
  • scales
  • npde >= 3.2
  • Suggests (1)
  • testthat
  • Version History