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
|Author||Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03>|
|Date of publication||2017-08-24 11:55:18 UTC|
|Maintainer||Emmanuelle Comets <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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