Introduction to Generalised Linear Mixed Models for Ecologists <https://prstats.org/course/introduction-to-generalised-linear-mixed-models-for-ecologists-mmie01/>Learn to build and interpret linear, generalised linear, and multilevel models for ecological data using R, lme4, and rstanarm in this five day applied training course.
https://prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-mmie02/ Join our intensive live-online workshop: *Bayesian Multilevel Modelling using brms for Ecologists*. If you work with ecological data and want to advance beyond standard regression into hierarchical modelling, spatial/temporal structure and full Bayesian workflows, this course is for you. *What you will learn* - A full foundation in Bayesian inference: priors, posteriors, credible intervals and uncertainty quantification. - How to fit and interpret generalised linear models (GLMs) and multilevel/hierarchical models using the brms package in R. - How to specify meaningful priors for ecological parameters and perform model criticism via posterior predictive checks, WAIC, LOO-CV and other diagnostics. - How to model non-Gaussian ecological responses (counts, proportions, zero-inflation), and account for spatial and temporal autocorrelation in your hierarchical models. - How to build multivariate or joint species-distribution models, interpret group-level effects, visualise hierarchical structures and communicate results effectively. *Who should attend* This course is designed for ecologists, data analysts, postgraduate researchers and early-career scientists who already have some experience with R and basic statistical concepts (e.g., mean, variance, correlation, linear regression). While prior exposure to GLMs is helpful, the essentials will be reviewed and built upon. *Format & logistics* - Live online sessions (5 days, approx. 6 hours per day) covering 30 hours of instruction, practical exercises and guided coding. - Sessions are recorded and made available so you can revisit content if you miss a live slot. - Participants are encouraged to bring their own ecological datasets where possible, for practical application. *Why this course stands out* - It bridges the gap between theoretical Bayesian modelling and applied ecological data analysis, focusing on workflows that ecologists can use in real research settings. - Emphasis on interpretation, visualisation and clear communication of uncertainty — not just model fitting. - Uses the brms package which allows flexible specification of hierarchical models while leveraging powerful backend tools (Stan) for Bayesian computation. *How to register / next steps* Visit the PR Stats website for full course details, upcoming dates and registration information. Early registration is recommended as places may be limited. If you have questions about suitability, software requirements (R, RStudio, brms package) or data prerequisites, contact the course organisers for guidance. https://prstats.org/course/bayesian-multilevel-modelling-using-brms-for-ecologists-mmie02/ -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
