STAN is a new system for Bayesian inference, similar to BUGS and JAGS. I’ve played with it a bit and it’s quite promising, it really has the potential to make MCMC less of a pain (on simple models). I’ve written a short introduction to fitting psychometric functions using STAN and R, in case that’s useful to psychophysicists out there.
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Wow, this is fantastic! Thanks! Any further tutorials on Bayesian psychophysics you could recommend to R-challenged people out there?
I really can’t think of anything, sorry! There’s a lot of material on Bayesian regression (e.g. multilevel models) that applies directly to psychophysics if you know how to translate the jargon. Signal Detection Theory and psychometric function fitting are just generalised linear models, for example. The Gelman and Hill (2003) book on regression is good.