model {
# prior distributions
psi ~ dunif(0, 1)
p ~ dunif(0,1)
# zero-inflated binomial model for the augmented data
for(i in 1 : nind + nz){
z[i] ~ dbern(psi)
mu[i] <- z[i ]* p
y[i] ~ dbin(mu[i], J)
}
# N is a derived parameter under data augmentation
N<-sum(z[])
}