1. High-dimensional inference ($p \geq NT$): debiasedATT() | A worked example | Why debiased, and how well it covers | An asymptotic alternative: the wild bootstrap | Simultaneous bands at $p \geq NT$ | Controlled high-dimensional truth | References

1. Simultaneous confidence intervals via simultaneousCIs() | The multiplier-bootstrap method: method = "bootstrap" | Simultaneous bands are the default: the ci_type argument | 2. Selection consistency as an implicit zero-effect test | Why this is stronger than a confidence interval on an unregularized estimator | The q < 1 caveat | A controlled-truth worked example | Scope: this is the post-treatment zero-effect test | References

Introduction | Background | Package Usage | Simulated Data Example | A "Real Data" Example | Testing the zero-effect null | Per-cohort, per-event-time, and per-(cohort, time) accessors | Plotting the effects | Tidy outputs with broom | Conclusion | References

Introduction | Simulation Workflow Using Piping | Step 1: Generate Simulation Coefficients | Step 2: Simulate Panel Data | Step 3: Run the FETWFE Estimator on Simulated Data | Step 4: Extract True Treatment Effects | Combining the Workflow in One Pipeline | Covariate-dependent cohort assignment | Multinomial-logit DGP | Ordered-logit DGP | Effect of assignment strength | Nonlinear propensity via interactions | Truth derivation under non-marginal DGPs | Targeted-sparsity coefficients for high-dimensional DGPs | Conclusion

1. The headline: tight Gaussian CIs by default under (Ψ-IF) | 1.1 Which propensity-score estimators satisfy (Ψ-IF)? | 1.2 What changed in v1.12.0 | 2. The two variance pieces and how the package exposes them | 2.1 $V_1$: regression-coefficient variance | 2.2 $V_2$: propensity-score variance | 2.3 The combined variance: tilde_v_N and the paper's catalogue | 2.4 A running example | 3. Experimental: cluster-robust standard errors via se_type = "cluster" | 3.1 The formula | 3.2 Why we call this experimental | 3.3 Worked cluster comparison | 4. Choosing the bridge penalty parameter lambda | References

From did | From etwfe | What fetwfe adds | References

What a "fusion structure" is | fusion_structure = "cohort" (the default) | fusion_structure = "event_study" | Two meanings of "event study" --- read this first | A side-by-side demo | When to prefer each | Supplying your own fusion matrix D_N (advanced) | The row/column ordering | Worked example | Staying inside the theoretical guarantees | References

Introduction | Setup: simulating panel data | etwfe(): extended TWFE without penalty | betwfe(): bridge-penalized ETWFE | No-covariate setting | Visualizing event-time effects | When to use which | References