Learn to analyse ecological field data with detection error using R. Work with point counts, ARU data, N-mixture models, distance sampling and time-removal methods.

Learn deep learning in R using the torch ecosystem. Build MLPs, CNNs and transformer models through hands-on coding and gain practical skills for real research workflows.

Species Distribution and Ecological Niche Modelling is an applied R course teaching standard workflows for building, evaluating, and interpreting SDMs.

Stable Isotope Mixing Models in R is an applied course teaching ecologists how to use SIBER, SIAR, and MixSIAR for dietary analysis.

Interactive Data Applications with Shiny is a practical R Shiny course for researchers focused on building, customising, and deploying interactive web applications from data analyses.

Learn Bayesian modelling with the R-INLA package. Build, fit, and interpret INLA models, define priors and latent effects, and apply INLA to real data in a five day course.

Bayesian Statistical Modelling with Stan and brms is an advanced R course for researchers covering Bayesian model building, diagnostics, and interpretation using Stan and brms.

Learn to analyse animal movement data using spatial methods, home range estimation, interaction metrics and resource or step selection models through hands-on training in R.

Learn ENM and SDM modelling in R. Apply tools like Maxent and Biomod2 to predict species distributions and environmental niches.

Visualise spatial data in R using the tmap package. Learn to create static and interactive maps, customise layouts, and publish high-quality visualisations.

Master Bayesian multilevel models in R with brms. Learn GLMs, priors, spatial/temporal autocorrelation, and species distribution modelling.

Learn spatial multiplexed proteomics data analysis with CODEX, CycIF, and MACSIMA. Master image processing, segmentation, phenotyping, and spatial analysis in R and Python.

Learn spatial ecological data visualisation in R. Master mapping, remote sensing, publication-quality graphics, accessible colour scales, and reproducible workflows.

Turn spatial data into clear, publication-quality maps using advanced cartography in R.

Analyse entire ecological communities with hierarchical models and joint species distribution modelling in R.