saemix.internal

.First.lib

cutoff

cutoff.eps

cutoff.max

cutoff.res

normcdf

norminv

error

error.typ

transpsi

transphi

derivphi

dtransphi

compute.Uy

compute.LLy

conditional.distribution

trnd.mlx

gammarnd.mlx

tpdf.mlx

internal

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. Documentation about 'saemix' is provided by a comprehensive user guide in the inst folder, and a reference concerning the methods  is the paper by Comets, Lavenu and Lavielle (2017, <doi:10.18637/jss.v080.i03>). See 'citation("saemix")' for details.

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
Algorithm

Audrey Lavenu

Marc Lavielle

Maud Delattre

saemix.internal function

Internal saemix objects — saemix.internal

Internal saemix objects

saemix.internal: Internal saemix objects

Description

Usage

Arguments

Details