Merge pull request #243 from igerber/docs/todo-cleanup

Clean up TODO.md: remove resolved items, update line counts

Author: igerber

TODO.md

@@ -19,25 +19,28 @@ Current limitations that may affect users:
 
 ### Large Module Files
 
-Target: < 1000 lines per module for maintainability.
+Target: < 1000 lines per module for maintainability. Updated 2026-03-29.
 
 | File | Lines | Action |
 |------|-------|--------|
-| `trop.py` | 862 | Split done — trop_global.py (990), trop_local.py (1000) |
-| `utils.py` | 1838 | Monitor |
-| `staggered.py` | 1785 | Monitor |
-| `imputation.py` | 1756 | Monitor |
-| `visualization.py` | 1727 | Monitor — growing but cohesive |
-| `linalg.py` | 1727 | Monitor — unified backend, splitting would hurt cohesion |
-| `triple_diff.py` | 1581 | Monitor |
+| `power.py` | 2588 | Consider splitting (power analysis + MDE + sample size) |
+| `linalg.py` | 2289 | Monitor — unified backend, splitting would hurt cohesion |
+| `staggered.py` | 2275 | Monitor — grew with survey support |
+| `imputation.py` | 2009 | Monitor |
+| `triple_diff.py` | 1921 | Monitor |
+| `utils.py` | 1902 | Monitor |
+| `two_stage.py` | 1708 | Monitor |
+| `survey.py` | 1646 | Monitor — grew with Phase 6 features |
+| `continuous_did.py` | 1626 | Monitor |
 | `honest_did.py` | 1511 | Acceptable |
-| `two_stage.py` | 1451 | Acceptable |
-| `power.py` | 1350 | Acceptable |
-| `prep.py` | 1242 | Acceptable |
-| `sun_abraham.py` | 1162 | Acceptable |
-| `continuous_did.py` | 1155 | Acceptable |
-| `estimators.py` | 1147 | Acceptable |
-| `pretrends.py` | 1104 | Acceptable |
+| `sun_abraham.py` | 1540 | Acceptable |
+| `estimators.py` | 1357 | Acceptable |
+| `trop_local.py` | 1261 | Acceptable |
+| `trop_global.py` | 1251 | Acceptable |
+| `prep.py` | 1225 | Acceptable |
+| `pretrends.py` | 1105 | Acceptable |
+| `trop.py` | 981 | Split done — trop_global.py + trop_local.py |
+| `visualization/` | 4172 | Subpackage (split across 7 files) — OK |
 
 ---
 
@@ -49,20 +52,13 @@ Deferred items from PR reviews that were not addressed before merge.
 
 | Issue | Location | PR | Priority |
 |-------|----------|----|----------|
-| ImputationDiD dense `(A0'A0).toarray()` scales O((U+T+K)^2), OOM risk on large panels | `imputation.py` | #141 | Medium (deferred — only triggers when sparse solver fails; fixing requires sparse least-squares alternatives) |
-| EfficientDiD: API docs / tutorial page for new public estimator | `docs/` | #192 | Medium |
+| ImputationDiD dense `(A0'A0).toarray()` scales O((U+T+K)^2), OOM risk on large panels | `imputation.py` | #141 | Medium (deferred — only triggers when sparse solver fails) |
 | Multi-absorb weighted demeaning needs iterative alternating projections for N > 1 absorbed FE with survey weights; unweighted multi-absorb also uses single-pass (pre-existing, exact only for balanced panels) | `estimators.py` | #218 | Medium |
-| Replicate-weight survey df — **Resolved**. `df_survey = rank(replicate_weights) - 1` matching R's `survey::degf()`. For IF paths, `n_valid - 1` when dropped replicates reduce effective count. | `survey.py` | #238 | Resolved |
-| CallawaySantAnna survey: strata/PSU/FPC — **Resolved**. Aggregated SEs (overall, event study, group) use `compute_survey_if_variance()`. Bootstrap uses PSU-level multiplier weights. | `staggered.py` | #237 | Resolved |
-| CallawaySantAnna survey + covariates + IPW/DR: DRDID panel nuisance-estimation IF corrections not implemented. Currently gated with NotImplementedError. Regression method with covariates works (has WLS nuisance IF correction). | `staggered.py` | #233 | Medium |
-| SyntheticDiD/TROP survey: strata/PSU/FPC — **Resolved**. Rao-Wu rescaled bootstrap implemented for both. TROP uses cross-classified pseudo-strata. Rust TROP remains pweight-only (Python fallback for full design). | `synthetic_did.py`, `trop.py` | — | Resolved |
-| EfficientDiD hausman_pretest() clustered covariance stale `n_cl` — **Resolved**. Recompute `n_cl` and remap indices after `row_finite` filtering via `np.unique(return_inverse=True)`. | `efficient_did.py` | #230 | Resolved |
-| EfficientDiD `control_group="last_cohort"` trims at `last_g - anticipation` but REGISTRY says `t >= last_g`. With `anticipation=0` (default) these are identical. With `anticipation>0`, code is arguably more conservative (excludes anticipation-contaminated periods). Either align REGISTRY with code or change code to `t < last_g` — needs design decision. | `efficient_did.py` | #230 | Low |
-| TripleDifference power: `generate_ddd_data` is a fixed 2×2×2 cross-sectional DGP — no multi-period or unbalanced-group support. Add a `generate_ddd_panel_data` for panel DDD power analysis. | `prep_dgp.py`, `power.py` | #208 | Low |
-| ContinuousDiD event-study aggregation anticipation filter — **Resolved**. `_aggregate_event_study()` now filters `e < -anticipation` when `anticipation > 0`, matching CallawaySantAnna behavior. Bootstrap paths also filtered. | `continuous_did.py` | #226 | Resolved |
-| Survey design resolution/collapse patterns are inconsistent across panel estimators — ContinuousDiD rebuilds unit-level design in SE code, EfficientDiD builds once in fit(), StackedDiD re-resolves on stacked data; extract shared helpers for panel-to-unit collapse, post-filter re-resolution, and metadata recomputation | `continuous_did.py`, `efficient_did.py`, `stacked_did.py` | #226 | Low |
-| Survey metadata formatting dedup — **Resolved**. Extracted `_format_survey_block()` helper in `results.py`, replaced 13 occurrences across 11 files. | `results.py` + 10 results files | — | Resolved |
-| TROP: `fit()` and `_fit_global()` share ~150 lines of near-identical data setup (panel pivoting, absorbing-state validation, first-treatment detection, effective rank, NaN warnings). Both bootstrap methods also duplicate the stratified resampling loop. Extract shared helpers to eliminate cross-file sync risk. | `trop.py`, `trop_global.py`, `trop_local.py` | — | Low |
+| CallawaySantAnna survey + covariates + IPW/DR: DRDID panel nuisance-estimation IF corrections not implemented. Currently gated with NotImplementedError. Regression method with covariates works. | `staggered.py` | #233 | Medium — tracked in Survey Phase 7a |
+| EfficientDiD `control_group="last_cohort"` trims at `last_g - anticipation` but REGISTRY says `t >= last_g`. With `anticipation=0` (default) these are identical. Needs design decision for `anticipation>0`. | `efficient_did.py` | #230 | Low |
+| TripleDifference power: `generate_ddd_data` is a fixed 2×2×2 cross-sectional DGP — no multi-period or unbalanced-group support. | `prep_dgp.py`, `power.py` | #208 | Low |
+| Survey design resolution/collapse patterns inconsistent across panel estimators — extract shared helpers for panel-to-unit collapse, post-filter re-resolution, metadata recomputation | `continuous_did.py`, `efficient_did.py`, `stacked_did.py` | #226 | Low |
+| TROP: `fit()` and `_fit_global()` share ~150 lines of near-identical data setup. Extract shared helpers to eliminate cross-file sync risk. | `trop.py`, `trop_global.py`, `trop_local.py` | — | Low |
 
 #### Performance
 
@@ -75,12 +71,11 @@ Deferred items from PR reviews that were not addressed before merge.
 
 | Issue | Location | PR | Priority |
 |-------|----------|----|----------|
+| Plotly renderers silently ignore styling kwargs (marker, markersize, linewidth, capsize, ci_linewidth) that the matplotlib backend honors; thread them through or reject when `backend="plotly"` | `visualization/_event_study.py`, `_diagnostic.py`, `_power.py` | #222 | Medium |
 | R comparison tests spawn separate `Rscript` per test (slow CI) | `tests/test_methodology_twfe.py:294` | #139 | Low |
 | CS R helpers hard-code `xformla = ~ 1`; no covariate-adjusted R benchmark for IRLS path | `tests/test_methodology_callaway.py` | #202 | Low |
-| ~376 `duplicate object description` Sphinx warnings — caused by autodoc `:members:` on dataclass attributes within manual API pages (not from autosummary stubs); fix requires restructuring `docs/api/*.rst` pages to avoid documenting the same attribute via both `:members:` and inline `autosummary` tables | `docs/api/*.rst` | — | Low |
-| Plotly renderers silently ignore styling kwargs (marker, markersize, linewidth, capsize, ci_linewidth) that the matplotlib backend honors; thread them through or reject when `backend="plotly"` | `visualization/_event_study.py`, `_diagnostic.py`, `_power.py` | #222 | Medium |
-| Survey bootstrap test coverage — **Resolved**. Added FPC census zero-variance, single-PSU NaN, full-design bootstrap for CS/ContinuousDiD/EfficientDiD, and TROP Rao-Wu vs block bootstrap equivalence tests. | `tests/test_survey_phase*.py` | — | Resolved |
-| Doc-snippet smoke tests only cover `.rst` files; new `.txt` AI guides are outside CI validation | `tests/test_doc_snippets.py` | #239 | Low |
+| ~376 `duplicate object description` Sphinx warnings — restructure `docs/api/*.rst` to avoid duplicate `:members:` + `autosummary` | `docs/api/*.rst` | — | Low |
+| Doc-snippet smoke tests only cover `.rst` files; `.txt` AI guides outside CI validation | `tests/test_doc_snippets.py` | #239 | Low |
 
 ---
 
@@ -164,11 +159,11 @@ Spurious RuntimeWarnings ("divide by zero", "overflow", "invalid value") are emi
 
 Features in R's `did` package that block porting additional tests:
 
-| Feature | R tests blocked | Priority |
-|---------|----------------|----------|
-| Repeated cross-sections (`panel=FALSE`) | ~7 tests in test-att_gt.R + test-user_bug_fixes.R | Medium |
-| Sampling/population weights | 7 tests incl. all JEL replication | Medium |
-| Calendar time aggregation | 1 test in test-att_gt.R | Low |
+| Feature | R tests blocked | Priority | Status |
+|---------|----------------|----------|--------|
+| Repeated cross-sections (`panel=FALSE`) | ~7 tests in test-att_gt.R + test-user_bug_fixes.R | High | Planned — Survey Phase 7b |
+| Sampling/population weights | 7 tests incl. all JEL replication | Medium | Mostly resolved (Phases 1-6); CS IPW/DR + covariates + survey in Phase 7a |
+| Calendar time aggregation | 1 test in test-att_gt.R | Low | |
 
 ---