
> 
> Jim Lindsey <jlindsey@luc.ac.be> writes:
> 
> > >     Jim> R itself has nothing for repeated measures. However, I am developing a
> > >     Jim> complete set of four libraries that will handle most any repeated
> > >     Jim> measures problem, normally distributed or other. This includes two
> > >     Jim> functions in one of the libraries that will do the so-called anova
> > >     Jim> approach plus ARMA. They will do everything in my Repeated
> > >     Jim> Measurements book (OUP 1993) and much more.
> > >     Jim> I am waiting until R stabilizes a bit before releasing them but will
> > >     Jim> soon be asking for volunteers to aid in preliminary testing.
> > >     Jim> Jim
> > > 
> ...
> > > 
> > > He was also badly missing S-plus's  aov(.) function...
> > > and I explained how he could  use   anova( lm(...) ) for FIXED effects
> > > anova, and that nothing is yet available for random (or mixed) effects.
> > > 
> > > Martin.
> > > 
> > Just a warning. These things are not as simple as aov. It should still
> > be implemented! Mine are general enough to do any nonlinear model for
> > both mean and variance (e.g.PKPD), generalized linear mixed models,
> > multivariate survival with any censoring pattern, completely
> > unbalanced data, etc. Hence, with this level of generality, the
> > interface is not always that simple: lists of time-varying covariates etc.
> >   Jim
> 
> Yes. We need aov().
> 
> Also note that Bates & Pinheiro are in the process of porting lme
> (linear mixed effects models) to R. From what I have seen, this does
> at least what SAS PROC MIXED does, only better...

My libraries also do all of SAS Proc Mixed. One difference with
respect to nlme is that I have not implemented models with more than
one random effect, and certainly not for nonlinear models i.e just
random intercept with nonlinear models, but any distribution, not just
normal. I am waiting impatiently for that aspect of nlme.
  Jim
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