Introduction | Reference | Usage | Arguments | Output | Example analyses | Normal linear models (t-testing, linear regression, (M)AN(C)OVA) | Univariate t testing | Multivariate t testing | Analysis of variance | Univariate and multivariate multiple regression | Testing independent group variances | Testing correlations | Meta-analyses | Exponential random graph models | Hypothesis testing using normal approximations | Running BF on a named vector | Logistic regression | Poisson regression

Introduction and learning objectives | The running example | What you will be able to do | Setup: packages, data, and model | The joint directional test in BFpack | Step 1 — Read the exact parameter names | Step 2 — Formulate the hypotheses | Results | Why not just test each coefficient and contrast separately? | References

Introduction and learning objectives | The running example | What you will be able to do | Setup: packages, seed, and data | The joint directional test in BFpack | Step 1 — Estimate the correlations per group | Step 2 — Read the exact parameter names | Step 3 — Formulate the hypotheses | Step 4 — Compute the Bayes factors | Results | Why not just test each correlation separately? | References

Introduction and learning objectives | Why go beyond the p-value? | What you will be able to do after this session | Setup: packages, seed, and data | The chemnet policy network | Preparing the network data | Build the network objects | A simplified ERGM to learn the workflow | Step 1 — Fit the model | Step 2 — Read the exact parameter names | Step 3 & 4 — Test a precise null: does preference similarity matter? | Testing competing constrained hypotheses | Reading the specification table | Changing the prior probabilities of hypotheses | The full model, reproducing the paper | Published results | Test 1 — no effect of preference similarity | Test 2 — the ranking of opportunity-structure effects | Reporting and interpretation checklist | References