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
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Package details 


Author  Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03> 
Date of publication  20170824 11:55:18 UTC 
Maintainer  Emmanuelle Comets <[email protected]> 
License  GPL (>= 2) 
Version  2.1 
Package repository  View on CRAN 
Installation 
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