The hottest ticket in R will be @gavinsimpson's live stream on What's New in Generalized Additive Models in R

2026-03-06 (17:00–19:00 CET) at https://youtube.com/live/A9U8e1KdlU4?feature=share

• what GAMs are and how they work
• recent {mgcv} updates (incl. Hierarchical GAMs)
• new features in {gratia}
• deeper inference with {marginaleffects}

Post questions at https://github.com/gavinsimpson/gratia/discussions/categories/q-a?discussions_q=is%3Aopen+category%3AQ%26A+label%3Alivestream

#RStats #mgcv #gratia #statistics #GAMs

📈 Yes you can do that in mgcv update

big thanks to Zachary Susswein for spotting that my code was out of date in my neighbourhood cross-validation examples: https://calgary.converged.yt/articles/ncv.html https://calgary.converged.yt/articles/ncv_timeseries.html

They are now up-to-date, as is the helper package mgcvUtils: https://github.com/dill/mgcvUtils

#mgcvchat #mgcv

#quarto #rstats friends who use github action to publish articles:

it's currently taking github actions ~30 mins to publish my little #mgcv help site (https://calgary.converged.yt/). This seems to be because it's installing a lot of R packages from source.

What's the current state-of-the-art to get these things to render quickly? (And using minimal power.)

(I'd like to not use github but I would also like to encourage PRs etc from folks without a huge overhead from them, so let's stick to github-based solutions for now.)

new (out for a while but sitting in my browser from before Christmas) paper in Biometrika from Benjamin Säfken, Thomas Kneib and Simon Wood on smoothing parameter degrees of freedom

Green OA @ Edinburgh https://www.pure.ed.ac.uk/ws/portalfiles/portal/475921820/asae052.pdf

#mgcvchat #mgcv

#mgcv mini-lifehack:

(assuming you have multithreading enabled) you can get a rough idea of what's happening when fitting a big model by looking at your CPU usage. If only 1 core is being used, the model is still "building" (assembling of design/penalty matrices), once you switch to all cores, then you're actually fitting the model. Sometimes that first model construction phase can take a long time (with a very big model), so it'll probably take a very very long time to fit. So buckle-up.

#mgcvchat

spending some more time thinking about neighbourhood cross-validation in #mgcv (see original post here: https://calgary.converged.yt/articles/ncv.html), but for time series.

Pretty nice to be able to get back to a yearly trend here without needing to specify an autoregressive structure. We just need to specify a cross-validation scheme and the autocorrelation is "dealt with" during fitting.

Full post on this soon. #mgcvchat #rstats

Ok, a more *specific* #mgcv #GAM question: When using tensor product interaction terms with `ti()`, do the knots have to match? E.g. do I have to do ti(x, k = 10) + ti(y, k = 20) + ti(x,y k = c(10, 20))? Or can the knots in the interaction term be whatever? Would I want them to be different for some reason?

#rstats