Resources for Learning About and Using GAMs in R

In no particular order:

Slides from my talk at the New York R User's Group (this repo). This is a high-level overview of things that GAMs and mgcv can do. Video (~80 minutes) here: https://www.youtube.com/watch?v=q4_t8jXcQgc
Materials from our 2017 Ecological Society of America workshop on GAMs. These are designed for the interactive workshop but may still be useful. Target audience is graduate students with a little more statistical training. GLM knowledge a prerequisite. See the references page in particular
- The 2018 materials are almost exactly the same, but we are tracking issues in that repo for future improvement!
The essential GAMs reference is Simon Wood's Generalized Additive Models in R.
- Recently reviewed by Virgilio Gomez-Rubio in the Journal of Statistical Software
An online book by Michael Clark gives an a very nice short introduction to both GAM theory and use in R.
Gavin Simpsons's excellent, GAM-centric blog where he tries out new and little-used GAM formulations.
StackOverflow and Cross Validated tags for mgcv
- Home to Gavin's amazing Cross Validated answer on spatiotemporal modelling with GAMs
A post by Kim Larsen's GAMs on the Stitchfix Blog which explains GAMs and compares them to other methods for classification.
The gratia package by Gavin Simpson for using mgcv with ggplot2 and other useful and tidy helper functions, such as calculating spline derivatives and simulating from model posteriors. Here's a blog post introducing it.
Hierarchical Generalized Additive Models: an introduction with mgcv A paper by Eric J. Pedersen, David L. Miller, Gavin Simpson, and Noam Ross on fitting gams with heirarchical/mixed structures. GitHub repo here.
Modelling palaeoecological time series using generalized additive models, a paper by Gavin L. Simpson
Bayesian views of generalized additive modelling, a brief but useful write-up on Bayesian approaches and interpretations of GAMs by Dave Miller.
Generalised additive mixed models for dynamic analysis in linguistics: a practical introduction by Márton Sóskuthy
Simplified Integrated Nested Laplace Approximation by Simon N. Wood, details the ginla() function added in mgcv 1.8-27.