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 (<>).


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2.3 by Emmanuelle Comets, a year ago

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

Documentation:   PDF Manual  

GPL (>= 2) license

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