Closed krivit closed 3 years ago
Hi Pavel! Thanks for the notes. I have replaced those unit tests and made a few other minor changes in response. The new version will go to CRAN in a few days. I haven't used your predict
function yet, but will consider it in the future. It was more important to get this back out on CRAN for now. No worries about stepping on my toes -- I only implemented edgeprob
because this functionality was previously missing; but it's of course great if something like this is now available in ergm
!
Hi, Philip! We are hoping to release
ergm
4.0 in 2-3 weeks. The current release candidate code is at https://github.com/statnet/ergm/ .Here's what I am seeing for
btergm
:Looks like all the problems are stochastic, except for
test-gof.R
.I am seeing two issues there:
btergm
has been affected by some changes in the way curved ERGMs are handled, which breaksedgeprob()
.predict()
method forergm
(i.e.,? predict.ergm
). If you need it to be tweaked so that it could be used for this purpose, please let me know. (And, I think we've stepped on your toes there a bit; sorry about that.)ergm
had been fit on a composed block-diagonal network but the individual networks' predictions are done separately. I am not 100% sure what the best way is to handle that at the moment, except perhaps to construct anergm_model
for each subnetwork and use a separateetamap
for each statistic.ergm
withedges
and curvedgw*degree
to a network whose degree distribution is close to binomial is near-nonidentifiable, so it's not a great test case: some times estimation converges, other times it doesn't.Please let me know if I could be of help.