Free online course on Generalised Linear Mixed Models (GLMMs) in R. Learn to analyse grouped ecological data using mixed-effects models in one day.
35 events found.
Expired Live Courses
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8 events,
Free online course introducing Generalised Linear Models (GLMs) in R. Ideal for ecologists and applied scientists working with binary or count data. 
Master the fundamentals of Generalised Linear Models in R 
Field Mapping and Species Identification for Ecologists – hands-on training in field data collection, GIS integration, and ecological survey methods. 
Learn advanced SDM and ENM techniques in R. Includes Maxent tuning, MESS and null models, and building mechanistic models and virtual species. 
A one-day live online course on zero-inflated models in R. Learn to model count data with excess zeros using ZIP, ZINB, and hurdle approaches, plus model diagnostics and interpretation. 
Learn decision trees, random forests, and boosted models in R. This one-day live online course covers CART, bagging, boosting, and model interpretation for applied data analysis. 
Learn applied Bayesian modelling in R with brms for ecological data. Build, fit, and interpret hierarchical and GLMMs in this hands-on workshop. |
7 events, |
7 events,
Data Visualisation in R using ggplot2 – online course covering scatterplots, histograms, bar plots, boxplots, density plots, line plots, heatmaps, maps, and advanced customisation for publication-quality graphics. |
7 events, |
7 events, |
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6 events,
Analyse ecological community data in R using VEGAN. Learn ordination, clustering, and multivariate statistics with real datasets. 
Model hierarchical ecological data using GLMMs in R. Covers lme4, brms, and Bayesian methods for ecologists. |
7 events,
This two-day hands-on course teaches researchers how to select, compare, and evaluate statistical models in R using cross-validation, information criteria (AIC, AICc, BIC), and regularization methods. Participants will learn model averaging, mixed effects model selection, and best practices for transparent, theory-driven analysis. |
7 events, |
6 events, |
6 events, |
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