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

conditional.distribution_c

conditional.distribution_d

center.eps

center.eta

normalise.eta

normalise.eta.svd

centerDist.NPcond

internal

The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) 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, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
Algorithm

Audrey Lavenu

Marc Lavielle

Belhal Karimi

Maud Delattre

Marilou Chanel

Johannes Ranke

Sofia Kaisaridi

Lucie Fayette

saemix.internal function

Internal saemix objects — saemix.internal

Internal saemix objects

saemix.internal: Internal saemix objects

Description

Usage

Arguments

Details