ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Hidden Markov models (HMMs) are flexible statistical models for time series observations driven by underlying states. Over the last decade, HMMs have become increasingly popular within the ecological community as they allow to uncover behavioural state dynamics from noisy sensor data. For example, a typical HMM-based analysis of say GPS locations or acceleration measurements could involve the investigation of internal (e.g. sex, size, age) and external (e.g. temperature, habitat) drivers of behavioural state occupancy.

This workshop will introduce the HMM framework, comprising a mix of theoretical lectures and hands-on practical components using R. In the theoretical sessions, the following topics will be covered:
 motivation & overview
 basic HMM formulation
 fitting an HMM to data
 model selection & model checking
 state decoding
 incorporating covariates, seasonality and random effects
 other model extensions

These techniques will be illustrated primarily using movement and acceleration data, but are applicable also to other ecological time series data (e.g. capture-recapture). In the practical sessions, we will focus on HMM analyses using the R packages moveHMM and momentuHMM, but will also showcase the use of hmmTMB. Basic knowledge of the free software R is helpful, but not required.

A basic understanding of statistics and probability calculus, as it would be taught in any introductory statistics class, is required. By the end of the course, participants will have a good understanding of what HMMs are and what they can be used for. Participants will also be prepared to tailor a suitable HMM to their data and to implement the corresponding analysis in R.