[<--methods

Methods for function <code>[&lt;-</code>, to allow replacements of the value of an element within an S4 object

methods

[&lt;--methods

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/).

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
algorithm

[&lt;--methods function

<dl> <dt><code>signature(x = "SaemixData")</code></dt><dd> replace elements of an object SaemixData</dd> <dt><code>signature(x = "SaemixModel")</code></dt><dd> replace elements of an object SaemixModel</dd> <dt><code>signature(x = "SaemixObject")</code></dt><dd> replace elements of an object SaemixObject</dd> <dt><code>signature(x = "SaemixRepData")</code></dt><dd> replace elements of an object SaemixRepData</dd> <dt><code>signature(x = "SaemixRes")</code></dt><dd> replace elements of an object SaemixRes</dd> <dt><code>signature(x = "SaemixSimData")</code></dt><dd> replace elements of an object SaemixSimData</dd> </dl>

[<--methods: Methods for "set" Function [<-

Description

Arguments

Methods