Package: saemix
Type: Package
Title: Stochastic Approximation Expectation Maximization (SAEM)
        Algorithm
Version: 2.1
Date: 2017-08-18
Author: Emmanuelle Comets, Audrey Lavenu, Marc Lavielle (2017) <doi:10.18637/jss.v080.i03> 
Maintainer: Emmanuelle Comets <emmanuelle.comets@inserm.fr>
Description: 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
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'
NeedsCompilation: no
Packaged: 2017-08-21 19:47:16 UTC; eco
Repository: CRAN
Date/Publication: 2017-08-24 11:55:18 UTC
Built: R 3.3.3; ; 2017-08-24 22:14:59 UTC; windows
