Dear colleagues,
We are pleased to announce the next run of our hybrid course:
*
*
*GLMs and GAMs with Spatial, Temporal or Spatial-Temporal Correlation
using INLA
16–20 February 2026, Murdoch University, Perth (or join online)
*
This applied, hands-on course introduces participants to modelling
spatial, temporal, and spatial-temporal dependency using inlabru. The
focus is on practical implementation rather than theory, making the
methods accessible for ecological, environmental, fisheries, and other
applied sciences.
Across five modules, we cover:
* How to incorporate spatial and temporal correlation into linear
models, GLMs, and GAMs.
* Bayesian modelling using INLA, including priors, latent fields, and
interpretation.
* GLMs and GAMs for Gaussian, Poisson, negative binomial, Bernoulli,
Tweedie, and generalised Poisson data.
* Building spatial meshes, including barrier models for coastlines and
fragmented habitats.
* Using multi-likelihood models when analyses require shared latent
effects.
* Practical exercises using R-INLA and inlabru for continuous, count,
and binary data.
All modules include data sets and fully annotated R scripts.
Participants receive 12 months of access to course materials, on-demand
videos, and PDFs of all theory presentations.
A free 1-hour consultancy meeting to discuss your own data is also included.
Course details:
https://www.highstat.com/Courses/Flyers/Flyer2026_02_Murdoch_SpatTempGLMGAM.pdf
Venue: Murdoch University, Perth (hybrid format).
Dates & times: 16–20 February 2026, 09.00–16.00 (AWST).
Price: £500 (VAT rules vary by participant location).
Instructors: Dr. Alain Zuur and Dr. Elena Ieno
Registration:
https://www.highstat.com/index.php/joine-an-onsite-course
Please feel free to share this information with colleagues who may
benefit from attending.
Kind regards,
Alain Zuur
Highland Statistics Ltd.
[[alternative HTML version deleted]]
_______________________________________________
R-sig-ecology mailing list
[email protected]
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
