Hidden Markov Models for Movement, Acceleration and other ecological Data (an introduction using moveHMM and momentuHMM)
Learn how to analyse movement, acceleration, and other ecological time series using HMMs in R with moveHMM and momentuHMM.
Duration: 23 Hours
Format: Recorded ‘on-demand’ Format
£400Registration Fee
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- Overview
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Course Description
During this 24 hour workshop we will introduce the HMM framework, comprising a mix of theoretical lectures and hands-on practical components using R. 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.
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.
What You’ll Learn
During the course we will cover the following:
- Explain the principles of hidden Markov models (HMMs), including their probabilistic foundations and relationship to Markov chains.
- Simulate and visualise data from HMMs to build intuition about states, transitions, and observations.
- Fit HMMs to real ecological and movement data using R, and interpret the estimated parameters in a biological context.
- Apply model selection and model checking techniques to assess the fit and appropriateness of HMMs for specific datasets.
- Use HMMs with covariates and state decoding methods to gain insights into ecological processes and animal behaviour.
- Implement a complete HMM workflow from data simulation to model fitting, validation, and extension, and describe how advanced extensions can be applied in ecological research.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This workshop is designed for academics and postgraduate students interested in adding hidden Markov models (HMMs) to their methodological toolbox for analysing ecological data. A basic understanding of statistics and probability calculus—such as probability distributions, density functions, and conditional probability—is recommended. While only a basic familiarity with R is required, participants will be able to follow most of the workshop even without prior experience in R.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop.
Prof. Roland Langrock
Roland is a professor of statistics and data analysis at Bielefeld University, Germany, where he is extensively involved in teaching both introductory statistics courses and advanced statistical methods. His research focuses on the development of statistical approaches for state-switching time series models, in particular hidden Markov models (HMMs), and on their applications across ecology, sports, and economics. Within statistical ecology, he has published widely on the modelling of animal movement and behaviour, as well as on capture–recapture and distance sampling methods.
Education & Career
• PhD in Statistics (specialising in hidden Markov models and time series analysis)
• Professor of Statistics and Data Analysis, Bielefeld University
• International collaborations across ecology, sports science, and economics
Research Focus
Roland’s research is centred on both the theoretical and applied development of hidden Markov models and related state-switching time series methods. He is particularly interested in bridging methodological innovation with real-world applications, especially in ecological research on animal movement, capture–recapture, and behavioural analysis. His interdisciplinary work also extends to sports analytics and economic modelling.
Current Projects
• Development of new methods for hidden Markov models and related state-switching models
• Statistical modelling of animal movement and behaviour using biotelemetry data
• Advances in capture–recapture and distance sampling methodology
• Applied collaborations in sports performance analysis and economic time series modelling
Teaching & Skills
• Teaches foundational statistics and advanced statistical methods at Bielefeld University
• Specialist in hidden Markov models, state-switching models, and statistical ecology
• Strong advocate for combining rigorous methodology with applied problem-solving across disciplines
Links
• ResearchGate
• Google Scholar
• University Profile
Session 1- 01:00:00 – Motivation; overview
Session 2 – 01:00:00 – Preliminaries: probability calculus; Markov chains
Session 3 – 01:00:00 – The basic HMM formulation
Session 4 – 01:00:00 – Practical Session: Simulating data from an HMM
Session 5 – 01:00:00 – fitting an HMM to real data, part I
Session 6 – 01:00:00 – Fitting an HMM to real data, part II
Session 7 – 01:00:00 – fitting HMMs to movement and acceleration data
Session 8 – 02:00:00 – Practical session: Fitting an HMM to real data
Session 9 – 01:100:00 – Model selection; model checking
Session 10 – 01:00:00 – State decoding
Session 11 – 01:00:00 – Covariates
Session 12 – 02:00:00 – Practical session: Complete HMM workflow
Session 13 – 01:00:00 – Overview of extensions, part I
Session 14 – 01:15:00 – Overview of extensions, part II
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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