Which R package for estimating a multilevel Weibull regression model? Could you recommend an R package for estimating a (frequentist) multilevel Weibull regression model? 
I need to model random intercepts, random slopes, as well as a cross-classified structure. 
UPDATE:
It seems like there is currently no "easy" solution for that. I decided to leave it for now by estimating a multilevel discrete hazard model with glmer and a multilevel Cox PH model with coxme (proposed by EddieMcGoldrick). With regards to the latter, I have still to figure out if implementing a cross-classified structure is possible.
 A: You can add frailty terms in the survreg function, but that will not help you if you want random slopes. 
If you don't need to use Weibull, you could use the coxme package for fitting a Cox regression model with random intercepts and slopes. Or a discrete time approach using a mixed effects logit with the lmer package. 
A: I am not aware of any R package that can do this.  You could possibly coerce the lme4 or nlme packages to do it, if you wrote your own functions for that particular family.  Another (related) possibility is to use AD Model Builder (ADMB).  This is a flexible optimizer and forms the basis of the glmmADMB package in R (which although it implements quite a few families, does not implement the Weibull distribution).  
Perhaps you can explain why this must use a frequentist approach?  If you really want that, another more "canned" approach could possibly be done in SAS using proc nlmixed which allows random effects models where you specify your own (log) likelihood function.  I work out an extensive example of this for random coefficient zero-inflated Poisson models here.  That might not quite work as I have since updated the dataset that is based on (it was a simulated multilevel dataset).
