I’ve translated most of the spatial capture-recapture models from the 2013 book “Spatial Capture-Recapture” by Andy Royle, Richard Chandler, Beth Gardner, and Rahel Sollmann from JAGS to Stan: https://github.com/mbjoseph/scr-stan
Spatial capture-recapture models are used to estimate density of (usually wildlife) populations monitored by camera traps, acoustic sensors, hair snares, and a variety of other methods. They extend traditional capture-recapture methods by explicitly modeling spatial activity centers of individuals, and in some cases, explicitly modeling movement over space and time at the individual level.
This plot might help provide some intuition:
Given some detections (black dots) of an individual in a trapping array (grey crosses), we can sample from the posterior density of the location of each individual’s activity center in space (the red 2d density), and estimate overall density in a spatial region (potentially as a function of landscape-level features).
Anecdotally the Stan implementations seem to be faster than JAGS, and Stan’s sampling diagnostics reveal issues with some of the more complex models.
This type of model might also be of interest to people who work on other types of latent point process models.