Links

Noam’s NYR Meetup Talk (Video, Slides): This is an ~80 minute talk that is mostly an overview of all the different model types that mgcv can fit, including different basis types, distributions, and options for bigger data and fully Bayesian approaches
Gavin’s GAM-centric blog: Lots of post of experimenting with new features, packages, and edge cases for GAMs.
A post on GAMs by Kim Larsen at Stitchfix: This introduces GAMs from a machine-learning perspective.
Stack Overflow questions and answers about mgcv (many answered by Gavin!)
The qgam package for quantile regression with GAMs.
The brms package which uses Stan to fit full Bayesian models using Stan for MCMC, including mgcv-type smooths.

Bibliography

Here we’ll list some useful papers and books on the topics covered in the course.

Wood, S. N. (2017). Generalized Additive Models: An Introduction with R, Second Edition. CRC Press.
Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric Regression. Cambridge University Press.
Hastie, T. J., & Tibshirani, R. J. (1990). Generalized Additive Models. Taylor & Francis.

Wood, S. N. (2003). Thin plate regression splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology).
Reiss, P. T., & Ogden, R. T. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 505–523.
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), 3–36. http://doi.org/10.1111/j.1467-9868.2010.00749.x
Wood, S. N., Pya, N., & Säfken, B. (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 1–45. http://doi.org/10.1080/01621459.2016.1180986

Marra, G., & Wood, S. N. (2012). Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics, 39(1), 53–74. http://doi.org/10.1111/j.1467-9469.2011.00760
Simpson, G. L. (2011) Additive modelling and the HadCRUT3v global mean temperature series. fromthebottomoftheheap.net http://www.fromthebottomoftheheap.net/2011/06/12/additive-modelling-and-the-hadcrut3v-global-mean-temperature-series/
Simpson, G. L. (2014) Simultaneous confidence intervals for derivatives of splines in GAMs. fromthebottomoftheheap.net http://www.fromthebottomoftheheap.net/2014/06/16/simultaneous-confidence-intervals-for-derivatives/

Dunn, P. K., & Smyth, G. K. (1996). Randomized Quantile Residuals. Journal of Computational and Graphical Statistics, 5(3), 236–244. http://doi.org/10.1080/10618600.1996.10477470

Foster, S. D., & Bravington, M. V. (2012). A Poisson–Gamma model for analysis of ecological non-negative continuous data. Environmental and Ecological Statistics, 20(4), 533–552. http://doi.org/10.1007/s10651-012-0233-0
Shono, H. (2008). Application of the Tweedie distribution to zero-catch data in CPUE analysis. Fisheries Research, 93(1-2), 154–162. http://doi.org/10.1016/j.fishres.2008.03.006

Augustin, N. H., Musio, M., Wilpert, von, K., Kublin, E., Wood, S. N., & Schumacher, M. (2009). Modeling Spatiotemporal Forest Health Monitoring Data. Journal of the American Statistical Association, 104(487), 899–911. http://doi.org/10.1198/jasa.2009.ap07058
Augustin, N. H., Trenkel, V. M., Wood, S. N., & Lorance, P. (2013). Space-time modelling of blue ling for fisheries stock management. Environmetrics, 24(2), 109–119. http://doi.org/10.1002/env.2196
Miller, D. L., Burt, M. L., Rexstad, E. A., & Thomas, L. (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001–1010. http://doi.org/10.1111/2041-210X.12105
Wood, S. N., Bravington, M. V., & Hedley, S. L. (2008). Soap film smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 931–955. http://doi.org/10.1111/j.1467-9868.2008.00665.x
Course notes from Dave’s spatial modelling course at Duke available here