Course flyer
HYBRID: This is an onsite course, but you can also join online via Zoom
Location: Burwood Corporate Centre, Deakin University, Burwood Campus, Melbourne, Australia
Dates and times: 23 - 27 February 2026. 09.00 - 16.00 (AEDT)
This course offers a practical introduction to the analysis of spatial, temporal, and spatial-temporal data using generalised linear models (GLMs) and generalised additive models (GAMs) in INLA.
We begin with how to add spatial structure to regression models using frequentist techniques, and then introduce Bayesian methods, focusing on how to implement models with spatial and temporal dependency using INLA. The course covers a range of data types and distributions, including Gaussian, Poisson, generalised Poisson, negative binomial, Bernoulli, and Tweedie. Participants will learn how to build models that incorporate spatial correlation, temporal trends, and spatio-temporal structure, and how to address practical challenges such as modelling in the presence of natural barriers (e.g., coastlines, forests) that prevent spatial correlation from extending freely across space, such as marine/terrestrial boundaries or fragmented habitats. We will also cover the use of more complex spatial meshes and multivariate likelihoods to accommodate study areas with isolated groups of sites.
We will utilise the R packages R-INLA and inlabru.
The course includes a 1-hour face-to-face video chat with the instructors.
Pre-required knowledge
Working knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial,
Bernoulli). This is a non-technical course. The course website provides preparatory materials, including on-demand videos and R scripts covering multiple linear regression, basic matrix notation, generalised linear models, model validation using DHARMa, and the explanation of variograms. If you are not familiar with these methods, please review them before the course begin
1 hour face-to-face
The course includes a 1-hour face-to-face video chat with the instructors (to be used after the course). You are invited to apply the statistical techniques discussed during the course on your own data and if you encounter any problems, you can ask questions during the 1-hour face-to-face chat.
A discussion board (access for 12 months) allows for interaction on course content between instructors and participants.
Course content
Module 1
- General introduction.
- Theory presentation on adding temporal dependency, and spatial dependency to a regression model using frequentist techniques.
- One exercise showing how to add spatial dependency to a regression model using frequentist tools.
- Brief introduction to Bayesian analysis. Conjugate priors. Diffuse versus informative priors.
- Short theory presentation on INLA.
Module 2
- Exercise showing how to execute a linear regression model in R-INLA and inlabru.
- Exercise showing how to add spatial correlation to a linear regression model using R-INLA and inlabru.
- Exercise showing how to execute a Poisson GLM in R-INLA and inlabru.
Module 3
- Exercise showing how to add spatial correlation to a Poisson GLM.
- Short theory presentation on GAMs.
- Exercise on executing a Gaussian GAM in R-INLA and and inlabru.
- Exercise on adding spatial correlation to a Gaussian GLM/GAM
Module 4
- Catching up
- Exercise showing how to add spatial correlation to a negative binomial GLM. With a barrier.
- Exercise showing how to add spatial correlation to a Bernoulli GLM.
- Exercise showing how to add temporal correlation to a GLM/GAM.
Module 5
- Exercise showing how to add spatial-temporal correlation to a Poisson or negative binomial GLM/GAM.
- Exercise showing how to add spatial-temporal correlation to a Tweedie GLM/GAM.
- Exercise showing how to add spatial-temporal correlation to a Bernoulli GLM/GAM.
We reserve the right to change the exercises. Pdf files of all theory material will be provided. All exercises consist of data sets and annotated R scripts. Access to the course website is for 12 months.
For terms and conditions, see: