Keywords per course

Course 1: Data exploration, regression, GLM and GAM: with introduction to R

Introduction to R. Outliers. Transformations. Collinearity. Multiple linear regression. Model selection. Visualising results. Poisson, negative binomial and binomial GLM and GAM. Overdispersion. ggplot2, mgcv.

Course 2: Introduction to mixed effects models and GLMM

Introduction to linear mixed effects models. Introduction to GLMM. Dealing with pseudoreplication. Nested data. Longitudinal data. This course can be taught using frequentist tools (nlme, lme4 and glmmTMB) or Bayesian tools (either JAGS or INLA).

Course 3: Introduction to zero-inflated models

Zero-inflated models for count data and continuous data. ZIP and ZAP models. Zero-inflated GLMMs for nested data. Analysis of zero-inflated proportional and binomial data.
This course can be taught using frequentist tools (pscl and glmmTMB) or Bayesian tools (either JAGS or INLA).

Course 4: Introduction to GAM and GAMM

Introduction to GAM. Poisson, negative binomial and binomial GAMs. Revision of mixed effects models. GAMM for nested data and non-linear relationships.
This course can be taught using frequentist tools (mgcv and gamm4) or Bayesian tools (either JAGS or INLA).

Course 5: Introduction to spatial and spatial-temporal models with R-INLA

Adding spatial and spatial-temporal correlation to regression models, GLMs and GLMMs using R-INLA. Introduction to Bayesian analysis.

Course 6: Time series analysis using R-INLA

We utilise R-INLA for the analysis of (multivariate) time series within the context of GLMs, GLMMs, GAMs and GAMMs.

Course 7: Workshop and combi-course

Combine the appropriate modules and use your own data sets during the course.

We also run the following courses: Introduction to R, data visualisation with R, and multivariate analysis with R.