Handouts, Tutorials & Tools
Our workshops support all IFAS disciplines and include downloadable handouts, step-by-step tutorials, and interactive apps.
Learn with hands-on, open-source workflows
Learn statistical and computational methods with hands-on, open-source workflows in R, Python, Jupyter Notebooks, Google Colab, and Shiny. Our workshops support all IFAS disciplines—Agriculture, Natural Resources, Environmental Science, Food Science, and interdisciplinary projects— and include downloadable handouts, step-by-step tutorials, and interactive apps.
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Linear Mixed-Effects Models (LMM)
Learn how to specify, fit, diagnose, and interpret LMMs. Choose the format that works for you:
- LMMs workshop in R: Open workshop.html
- Google Colab notebook: Download
LMM_workshop_colab.ipynb - Jupyter notebook: Download
LMM_workshop_jupyter.ipynb - Interactive Shiny app: Run the LMMs app
Topics: fixed vs random effects, model formulae, contrasts, diagnostics (residuals, Q–Q, Shapiro/Levene), and reporting (tables, R²).
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Randomized Complete Block Design (RCBD)
Randomized Complete Block Design (RCBD).
Plan and analyze RCBD experiments: define treatments and blocks, fit the model, and review assumptions and contrasts.- Interactive Shiny app: Run the RCBD app
Outputs include ANOVA tables, diagnostic plots, multiple comparisons, and effect visualizations.
- Interactive Shiny app: Run the RCBD app
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Statistical Methods Finder
Statistical Methods Finder
Not sure which analysis to use? Answer a few questions about your study design (response type, factors/blocks, repeated measures), and get suggested methods with example R code.
- Interactive Shiny app: QuickStatSel
Suggestions may include t/ANOVA/ANCOVA, GLMs/GLMMs, LMMs, nonparametrics, and post-hoc comparisons.