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 HastingsMetropolis 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/>).
Package details 


Author  Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03> 
Maintainer  Emmanuelle Comets <[email protected]> 
License  GPL (>= 2) 
Version  2.3 
Package repository  View on CRAN 
Installation 
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