Is there a way to continue a R/JAGS MCMC chain that did not converge? I'm building a series of hierarchical models using R and JAGS, linked using the R2jags library.  The runs are fairly long -- from several hours to several days.  I've had the sad experience of running some chains that did not converge.  In that case, is there a way to extend the chain, rather than starting over?
 A: You can use autojags(currentmodel,n.iter,...) from R2jags.  You can specify the criteria for "convergence" based on $\hat R$.
A: If there is an option to choose a starting point--yes.  You can "continue" an MCMC chain simply by using the last point of the chain as a new starting point.
A: If a chain has not converged you don't want to use it for inference anyways. You can just continue sampling and save new samples for inference if you use rjags. Then the old iterations would be considered as burn-in. I assume you could do the same with R2jags.
A: I think ?update.jags will do the trick.
A: Abe,
Probably you have already solved this, but I think one can simply use the 'update' function provided in the package, as already suggested by Quantitative Historian.
Specifically you may want to use the 'update' function
"In JAGS, however, \updating" a chain in R is straightforward:
fit <- update(fit, 1000)
replaces fit with 1000 new draws (per chain), treating the
values in the original fit as burn-in"
from 
http://web.as.uky.edu/statistics/users/pbreheny/701/S13/notes/3-5.pdf
