1. Overview | 2. Installation | 3. Quick start | 4. Understanding homogeneity-of-variance testing | 5. Data input | 5.1 Data frame and formula | 5.2 A list of numeric vectors | 6. The individual tests | 6.1 Ansari--Bradley test | 6.2 Bartlett's test | 6.3 Brown--Forsythe test | 6.4 Fligner--Killeen test | 6.5 't Lam's G test | 6.6 Levene's test | 6.7 O'Brien's test | 6.8 O'Neill--Mathews test | 7. Selecting a test with check_var_equal() | 8. Automatic assessment with is_var_equal() | 9. Why multiple tests can disagree | 10. Inspecting the data before testing | 11. Built-in datasets | 11.1 roGFP | 11.2 CYCB1 | 12. Benchmarking and method performance | Scenario 1: normally-distributed data, moderate sample size, no outlier | Scenario 2: normally-distributed data, small sample size, no outlier | Scenario 3: normally-distributed data, moderate sample size, with outliers | Practical interpretation | Summary | References

1. Overview | 2. Installation | 3. Quick start | The function return a list which includes 5 components: | 4. Interface | 5. Describe the data | 6. Omnibus / a priori test | 6.1 Parametric | 6.2 Nonparametric | ART-ANOVA | Kruskal--Wallis test | 7. Post hoc analysis | 7.1 Parametric | REGWQ | Tukey HSD | Tukey--Kramer | Games--Howell | 7.2 Nonparametric | Dunn's test | 8. Automatic Workflow | 9. Planned contrasts | Scheffé | 10. Compact letter displays | 11. Effect sizes | Hedges' g | Partial-ω2 | 12. Datasets | 13. Interpretation notes | Statistical significance is not the same as practical importance | CLDs are a summary, not the primary inferential result | Do not mechanically transform data merely to obtain a desired p-value | Summary | References

1. Overview | 2. Installation | 3. Quick start | 4. Understanding the subsets | 5. Summary tables | 6. Obtaining detailed output | 7. Customizing the Venn diagram | 7.1 Ellipse lines and fills | 7.2 Set labels | 7.3 Subset labels | 7.4 Counts and percentages | 8. Working with ellipse paths | 9. Set operations on Venn regions | 9.1 Intersection | 9.2 Union | 9.3 Set difference | 9.4 Chaining operations | 10. Case study: RNA-seq analysis | 10.1 Prerequisites | 10.2 Background information | 10.3 Loss-of-function (Set C) | 10.4 High-dosage recovery (Subset BCD) | 11. Practical recommendations | Choose venny() when: | Choose venn_summary() when: | Use ellipse-path operations when: | Summary

1. Overview | 2. Installation | 3. Quick start | 4. Understanding normality testing | 5. Available tests | 5.1. Anderson--Darling test | 5.2. Cramér--von Mises test | 5.3. Lilliefors test | 5.4. D'Agostino--Pearson test | 5.5. Jarque--Bera test | 5.6. Shapiro-family tests | Original Shapiro--Wilk | Shapiro--Francia | Shapiro--Wilk with Royston's modifications | Very large samples | 6. Testing multiple groups | 7. Skewness | 8. Kurtosis | 9. Interpreting results | Summary | References