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 (http://group.monolix.org/).

Stochastic Approximation Expectation Maximization (SAEM)
Algorithm
Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03>
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
(<http://group.monolix.org/>).
'aaa_generics.R' 'SaemixData.R' 'SaemixModel.R' 'SaemixRes.R'
'SaemixObject.R' 'compute_LL.R' 'func_FIM.R' 'func_aux.R'
'func_distcond.R' 'func_plots.R' 'func_simulations.R' 'main.R'
'main_estep.R' 'main_initialiseMainAlgo.R' 'main_mstep.R'
'zzz.R'

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 (<http://group.monolix.org/>).