These workshops are a follow-up of the course on “Spatial, temporal and spatial-temporal models using R-INLA” by Alain Zuur and Elena Ieno (Highland Statistics Ltd.). The main goal is the get people using R-INLA with their own data in a workshop setting so they can tap into the knowledge of others. The workshops are not a copy of the Highstat course but elaborate certain topics. We also introduce the
HackMD, a place to share your code related to these workshops.
Fitting models with only fixed effects, random intercepts and first order random walk.
INLA for fixed effects, random intercepts and random walks.
Fitting models with spatial correlation
- Bachl, F. et al, 2019 inlabru: an R package for Bayesian spatial modelling from ecological survey data https://doi.org/10.1111/2041-210X.13168
- Blangiardo, M. and Cameletti, M. 2015 Spatial and Spatio-temporal Bayesian Models with R-INLA https://sites.google.com/a/r-inla.org/stbook/ ISBN: 978-1-118-32655-8
- Gómez-Rubio, V. 2019 Bayesian inference with INLA and R-INLA https://becarioprecario.bitbucket.io/inla-gitbook/index.html
- Krainski, E. et al 2018 Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA https://becarioprecario.bitbucket.io/spde-gitbook/index.html
- Wang, X. et al 2018 Bayesian Regression Modeling with INLA ISBN: 978-1-498-72725-9
- Zuur, A. et al 2017 Beginner’s Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA http://highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation ISBN: 978-0-957-17419-1
- Zuur, A. and Ieno, E. 2018 Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. Volume II: GAM and Zero-Inflated Models http://highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation ISBN: 978-0-957-17414-6