In all tutorials and relevant lecture slides, provide links to mvgam vignettes

Day 1

• Link to the ESA talk rather than the Stat Rethinking Categories and Curves
• Skip over ETS and ARMA slides in lecture
• Skip through the Beta example as well
• Introduce the portal example when first talking about random effects; then pause, switch to live coding and show how to do this in mgcv (provide syntax to do it in brms, mvgam and glm as well); show how to use marginaleffects to make hindcast predictions; emphasize how to create smooth plots; emphasize how to take realisations of fitted functions so they can be used for more custom plots or analyses
• Create an extra exercise script that shows how to handle nested and crossed random effects in mgcv: https://stats.stackexchange.com/questions/618715/building-the-right-gam-model-struggling-with-the-jump-from-lmer/618760#618760
• Switch to live coding for the yearly smooth, again showing in mgcv; use gratia to show the basis; extract residuals and plot against another predictor to start building in the idea of a workflow
• Link to Nishan's paper in the tutorial and include the cheatsheet pdf in the supplied files

Day 2

• Link to the Oceania EcoForecasting seminar
• Skip over the tscount AR example slides
• For the dynamic Beta GAM, pause and switch to live coding; show how to do something similar in mgcv with the RW MRF basis; talk about tensor products and introduce the idea of ti() decompositions to help build strategies (link to some of Gavin's Stackoverflow descriptions and the gam.models help page as well)
• Skip over the enforcing stationarity Student T example
• In the dynamic coefficient example, switch to live coding; show how to do something similar in mgcv with the RW MRF basis; mention how one could use the same principles for spatially varying coefficients
• Use Hilbert GP in dynamic coefficient mvgam example
• In tutorial fix typo: "except the length scale was changed to 3" should be "except the length scale was changed to 4"
• Subtitles should use "GAM" rather than "GLM"; i.e. "A standard Poisson GLM" should be "A standard Poisson GAM"
• Link to MRFtools
• Include the cheatsheet pdf in the supplied files

Day 3

• Link to the forecasting vignette and to Juniper's paper on forecast evaluation
• Skip over expectations entirely and reduce down the conditional predictions part
• At the types of predictions, switch to live coding and show something cool / interesting (distributed lags in mgcv)
• Same at probabilistic forecast evaluation; perhaps show a strategy to estimate a decaying effect of some treatment (time since treatment example, or time-varying dispersion with a distributional model)
• Remove stochastic trend extrapolation completely, and talk about loo_compare instead; relate back to the cheatsheet
• Include the cheatsheet pdf in the supplied files

Day 4

• At hierarchical dist lags, switch to live coding and show something cool / interesting (phylogenetically informed intercepts and nonlinear functions in mgcv; illustrate prediction by excluding certain terms to show how the trend is built of additive functions)
• At multivariate forecast evaluation, switch to live coding and show time-varying seasonality in mvgam

Day 5

• Pick a more simple and useful dataset for groups to analyse

Be sure to create PDFs of all lectures again, once finalised