Package: saemix
Type: Package
Title: Stochastic Approximation Expectation Maximization (SAEM)
        Algorithm
Version: 2.3
Date: 2019-12-07
Author: Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03> 
Maintainer: Emmanuelle Comets <emmanuelle.comets@inserm.fr>
Description: 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/>).
License: GPL (>= 2)
LazyLoad: yes
LazyData: yes
Suggests: testthat (>= 0.3)
Imports: graphics, stats, methods
Collate: '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'
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2019-12-06 16:34:53 UTC; eco
Repository: CRAN
Date/Publication: 2019-12-06 17:10:02 UTC
Built: R 3.5.3; ; 2020-04-22 04:30:57 UTC; windows
