[R-pkgs] saemix: SAEM algorithm for parameter estimation in non-linear mixed-effect models (version 0.96)

[Emmanuelle Comets]

	saemix implements the SAEM (stochastic approximation EM) algorithm for 
parameter estimation in non-linear mixed effect models, used to model 
longitudinal data.

	Longitudinal data are particularly prominent in pharmacokinetics (study of drug 
concentrations versus time) and pharmacodynamics (study of drug effect versus 
time), but the SAEM algorithm has also been successfully applied in many other 
areas and we would like to encourage you to try saemix.

	More details can be found in the user guide included in the package, which 
encloses a section showing different examples using SAEMIX.

	As always, I would be very grateful for comments and suggestions, and would 
welcome any feed-back.

			Emmanuelle Comets

Authors: Emmanuelle Comets, Audrey Lavenu and Marc Lavielle
-- 
Statistiquement, tout s'explique.
Personnellement, tout se complique.
         (Daniel Pennac)