  Hidden Markov models (HMMs) are popular time series model in many fields
including ecology, economics and genetics. HMMs can be defined over discrete or
continuous time, though here we only cover the former. In the field of movement
ecology in particular, HMMs have become a popular tool for the analysis of
movement data because of their ability to connect observed movement data to an
underlying latent process, generally interpreted as the animal's unobserved
behavior. Further, we model the tendency to persist in a given behavior over
time. Notation presented here will generally follow the format of Zucchini et
al. (2016) and cover HMMs applied in an unsupervised case to animal movement
data, specifically positional data. We provide Stan code to analyze movement
data of the wild haggis as presented first in Michelot et al. (2016).
