Ecological forecasting with R 📦’s {mvgam} and {brms}

Time series analysis and forecasting are standard goals in applied ecology. But most time series courses focus only on traditional forecasting models such as ARIMA or Exponential Smoothing. These models cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. Using the flexible and powerful Bayesian modelling software Stan, we can now meet this complexity head on. R packages such as {mvgam} and {brms} can build Stan code to specify ecologically appropriate models that include nonlinear effects, random effects and dynamic processes, all with simple interfaces that are familiar to most R users. In this course you will learn how to wrangle, visualize and explore ecological time series. You will also learn to use the {mvgam} and {brms} packages to analyse a diversity of ecological time series to gain useful insights and produce accurate forecasts. All course materials (presentations, practical exercises, data files, and commented R scripts) will be provided electronically to participants.

2025 R Workshops @ UQ

I have recently joined the R Workshops @ UQ team, which has been conducting several R Workshops at the University of Queensland each year since 2012. The team is composed of a wonderful group of quantitative ecologists who have learnt data analysis and data science through application to real-world problems in R. Because we are self-taught, we want to help others avoid some of the mistakes we made when we were setting out to learn and use R. We have now taught >1,500 students at these workshops, and we look forward to helping the next generation of R programmers and applied statisticians learn the skills they need to meet the demands of the modern research environment. The 2025 workshops, happening over 5 days in February, will cover the tidyverse, ggplot2, linear models, Generalized Linear Models, Generalized Additive Models, spatial and spatiotemporal modelling, multivariate modelling and much more!

Ecological forecasting with R 📦’s {mvgam} and {brms}

Time series analysis and forecasting are standard goals in applied ecology. But most time series courses focus only on traditional forecasting models such as ARIMA or Exponential Smoothing. These models cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. Using the flexible and powerful Bayesian modelling software Stan, we can now meet this complexity head on. R packages such as {mvgam} and {brms} can build Stan code to specify ecologically appropriate models that include nonlinear effects, random effects and dynamic processes, all with simple interfaces that are familiar to most R users. In this course you will learn how to wrangle, visualize and explore ecological time series. You will also learn to use the {mvgam} and {brms} packages to analyse a diversity of ecological time series to gain useful insights and produce accurate forecasts. All course materials (presentations, practical exercises, data files, and commented R scripts) will be provided electronically to participants.