Longitudinal models in R and WINBUGS or JAGS I've tried to use R to fit some longitudinal models, mostly via lmer 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. 
Would anyone recommend other packages for the job in R? I don't really care if I use to work in a frequentist or bayesian world as long as I have more modeling flexibility. I would also be interested in doing that in WINBUGS/JAGS. 
 A: Longitudinal and mixed models in BUGS is talked about in Ch. 10 of Bayesian Ideas and Data Analysis.  Below is a link to the book website which has some example code.
http://www.ics.uci.edu/~wjohnson/BIDA/BIDABook.html
A: I'm not sure what you mean by R not having "factor analytic models for covariance matrices" - can you clarify what you'd like to reproduce from SAS?  To my knowledge this is feasible with a lot of different packages in R.
Regarding antedependence models, there is a book on this very topic that has associated R code and examples, at the first author's website.
I'm not sure if WinBUGS will bring you any luck, but I'd start with the aforementioned textbook - it seems to be authoritative on antedependence models.  :)
A: I believe, with a slight learning curve, you could use one of the SEM packages in R: lavaan, OpenMX, or sem. I am just learning about SEM and these packages, but it does look to me that lavaan has a formula syntax that's much like other modeling (lm, lmer) in R, and SEM lets you do a lot of things with your covariance structure.
