The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community
International audienceWe consider some general mixed-effects diffusion models, in which the observat...
This Research Report is now published in the Journal: ESAIM: Mathematical Modelling and Numerical An...
International audienceNonlinear mixed effect models are classical tools to analyze nonlinear longitu...
The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effe...
saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect model
International audienceThis article focuses on parameter estimation of multilevel nonlinear mixed-eff...
DANS CETTE THESE NOUS NOUS INTERESSONS A L'ESTIMATION DE PARAMETRES DES MODELES MIXTES. CES MODELES ...
International audienceWe propose a new methodology for maximum likelihood estimation in mixtures of ...
International audienceAnalysis of count data from clinical trials using mixed effect analysis has re...
International audienceWe consider some general mixed-effects diffusion models, in which the observat...
This Research Report is now published in the Journal: ESAIM: Mathematical Modelling and Numerical An...
International audienceNonlinear mixed effect models are classical tools to analyze nonlinear longitu...
The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effe...
saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect model
International audienceThis article focuses on parameter estimation of multilevel nonlinear mixed-eff...
DANS CETTE THESE NOUS NOUS INTERESSONS A L'ESTIMATION DE PARAMETRES DES MODELES MIXTES. CES MODELES ...
International audienceWe propose a new methodology for maximum likelihood estimation in mixtures of ...
International audienceAnalysis of count data from clinical trials using mixed effect analysis has re...
International audienceWe consider some general mixed-effects diffusion models, in which the observat...
This Research Report is now published in the Journal: ESAIM: Mathematical Modelling and Numerical An...
International audienceNonlinear mixed effect models are classical tools to analyze nonlinear longitu...