Thanks for your replies. To complement my own question: would  MCMCglmm do
the job?

On Mon, Jan 2, 2012 at 8:13 AM, Joshua Wiley <jwiley.psych@gmail.com> wrote:

> Hi Antonio,
>
> I am not familiar with antedependence models so no comment there.
>
> For factor analysis and that genre, I like OpenMx (also see sem and
> lavaan).  One thing I like about OpenMx is while it caters to SEM, it
> is a general purpose matrix optimizer, and it really is not difficult
> to access that power.  So in principal, you can have whatever matrices
> you want, roll your own objective function, and away it'll go.
>
> For BUGS you have a lot of options including: R2OpenBUGS and R2WinBUGS
> among others.
>
> Cheers,
>
> Josh
>
> On Mon, Jan 2, 2012 at 12:41 AM, Antonio P. Ramos
> <ramos.grad.student@gmail.com> wrote:
> > Hi all,
> >
> > I've trying to use R to fit some longitudinal models, mostly via lme and
> > nlme packages. However, it seems that many standard models are lacking,
> > such as antedependence models or factor analytic models for covariance
> > matrices. These models are readily available in SAS. Does an recommend
> > other packages for the job in R? I don't really care if I am in
> frequentist
> > or bayesian world as long as I have more modeling flexibility. I would
> also
> > be interested in doing that in WINBUGS/JAGS.
> >
> > All the best,
> >
> > Antonio.
> >
> >        [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
>
> --
> Joshua Wiley
> Ph.D. Student, Health Psychology
> Programmer Analyst II, Statistical Consulting Group
> University of California, Los Angeles
> https://joshuawiley.com/
>

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