There is no changelog on web or in the release, one must look directly into the source code: http://sourceforge.net/p/mcmc-jags/code-0/ci/default/tree/NEWS...

I have added a rep function to the development version (future JAGS 4.0.0) as Matt and John have alluded to, this requires the second argument to be fixed so that the length of the resulting vector can be determined at compile time.

I don't do a lot of survival analysis (and you don't state which distribution you would like to use for this part - there are different options), but this code should get you started for the interval censoring part: library("runjags") # Your data t1 <- c(1.73, NA, NA, NA, NA,0.77,...

It appears (from information sent to me off-line) that the psrf is being calculated correctly, but is being reported out of sequence because some information is not available for some semi-stochastic monitored variables. The fact that this isn't being picked up by the software is a bug that I will...

Your lines zeros[i] <- 0 zeros[i] ~ dpois(zeros.mean[i]) cause a problem. In JAGS you can't redefine the given value of a variable. I think you should drop the line: zeros[i] <- 0 from your code...

You don't need to compute p in your data set at all. Just let it be a logical node in your model. I prefer the R2jags interface, which allows you to specify a BUGS model in the form of an R function ... jagsdata <- data.frame(y=rbinom(10, 500, 0.2), n=sample(500:600, 10),...

If you write your JAGS file as a brew package template you can do something like this: est.sd.mu <- <%= Xmu %> est.sd.beta <- <%= Xbeta %> est.sd.phi <- <%= Xphi %> [etc] Then then you process this with the brew function to generate the JAGS file, with the R...

Reason of th error: In my data, the response time is in millisecond and when the RT is divided by 1000 (seconds) it works. However, I haven't seen any documentation that limits the use of ms in dwiener module. But I can guess that somthing like NaN in R is...

Your lm example can also be written: m1 <- lm(gdp ~ -1 + rain + factor(ccode) + factor(ccode):year) The equivalent JAGS model would be: M <- function() { for(i in 1:N) { gdp[i] ~ dnorm(yhat[i], sd^-2) yhat[i] <- b0[ccode[i]] + b1*rain[i] + b2[ccode[i]]*year[i] } b1 ~ dnorm(0, 0.001) for (j...

The function update performs burn-in iterations for your Markov chain. If you fail to run those burn-in iterations, then your chain will not have converged to its stationary distribution and any samples you generate will not actually be from the correct posterior distribution. In short, you absolutely need to update...