Our two most popular courses are:

• Data exploration, regression, GLM and GAM. With an introduction to R.
• Introduction to linear mixed-effects models and GLMM.
  • We provide two versions of this course; a frequentist version and a Bayesian version using R-INLA. The frequentist course is easier, but the Bayesian version is useful if you (eventually) want to apply spatial models.

If you do not have spatial or temporal data, then we recommend attending the following courses within a time span of 3 years.

• Introduction to data exploration, regression, GLM and GAM. With introduction to R.
• Introduction to linear mixed-effects models and GLMM (frequentist version).
• Depending on whether you have zero-inflation and/or non-linear relationships you can then attend the ‘Introduction to zero-inflated models’ and/or ‘Introduction to GAM and GAMM’ courses.

If you have spatial or temporal data, then we recommend the following courses.

• Introduction to data exploration, regression, GLM and GAM. With introduction to R.
• Introduction to mixed effects models and GLMM (INLA version).
• Introduction to spatial and spatial-temporal models with R-INLA.
• Depending on whether you have zero-inflation, non-linear relationships and/or time series data, you can then attend the ‘Introduction to zero-inflated models’, ‘Introduction to GAM and GAMM’ and/or the ‘Time series analysis using R-INLA’ courses (INLA version).
• It is possible to skip the ‘Introduction to linear mixed-effects models and GLMM’ course before taking the INLA courses, but this requires some preparation.

Our courses are non-technical and are less suited for people interested in the underlying mathematics.