Conley Wave A: DiD/cluster combo, sparse k-d-tree, callable metric

README.md

@@ -124,7 +124,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [Honest DiD](https://diff-diff.readthedocs.io/en/stable/api/honest_did.html) - Rambachan & Roth (2023) sensitivity analysis: robust CI under PT violations, breakdown values
 - [Pre-Trends Power Analysis](https://diff-diff.readthedocs.io/en/stable/api/pretrends.html) - Roth (2022) minimum detectable violation and power curves
 - [Power Analysis](https://diff-diff.readthedocs.io/en/stable/api/power.html) - analytical and simulation-based MDE, sample size, power curves for study design
-- Conley spatial HAC SE (`vcov_type="conley"`) on cross-sectional `LinearRegression` / `compute_robust_vcov` plus panel `MultiPeriodDiD` / `TwoWayFixedEffects` (with `conley_lag_cutoff` for within-unit Bartlett temporal HAC) - Conley (1999) spatial-correlation-aware SEs with parity vs R `conleyreg` on cross-sectional + panel fixtures
+- Conley spatial HAC SE (`vcov_type="conley"`) on cross-sectional `LinearRegression` / `compute_robust_vcov` plus panel `DifferenceInDifferences` / `MultiPeriodDiD` / `TwoWayFixedEffects` (with `conley_lag_cutoff` for within-unit Bartlett temporal HAC) - Conley (1999) spatial-correlation-aware SEs with parity vs R `conleyreg` on cross-sectional + panel fixtures, optional combined spatial + cluster product kernel via explicit `cluster=`, auto-activating sparse k-d-tree fast path for `n > 5_000`
 
 ## Survey Support
 

TODO.md

@@ -116,12 +116,8 @@ Deferred items from PR reviews that were not addressed before merge.
 | `HeterogeneousAdoptionDiD` time-varying dose on event study: Phase 2b REJECTS panels where `D_{g,t}` varies within a unit for `t >= F` (the aggregation uses `D_{g, F}` as the single regressor for all horizons, paper Appendix B.2 constant-dose convention). A follow-up PR could add a time-varying-dose estimator for these panels; current behavior is front-door rejection with a redirect to `ChaisemartinDHaultfoeuille`. | `diff_diff/had.py::_validate_had_panel_event_study` | Phase 2b | Low |
 | `HeterogeneousAdoptionDiD` repeated-cross-section support: paper Section 2 defines HAD on panel OR repeated cross-section, but Phase 2a is panel-only. RCS inputs (disjoint unit IDs between periods) are rejected by the balanced-panel validator with the generic "unit(s) do not appear in both periods" error. A follow-up PR will add an RCS identification path based on pre/post cell means (rather than unit-level first differences), with its own validator and a distinct `data_mode` / API surface. | `diff_diff/had.py::_validate_had_panel`, `diff_diff/had.py::_aggregate_first_difference` | Phase 2a | Medium |
 | SyntheticDiD: bootstrap cross-language parity anchor against R's default `synthdid::vcov(method="bootstrap")` (refit; rebinds `opts` per draw) or Julia `Synthdid.jl::src/vcov.jl::bootstrap_se` (refit by construction). Same-library validation (placebo-SE tracking, AER §6.3 MC truth) is in place; a cross-language anchor is desirable to bolster the methodology contract. Julia is the cleanest target — minimal wrapping work and refit-native vcov. Tolerance target: 1e-6 on Monte Carlo samples (different BLAS + RNG paths preclude 1e-10). The R-parity fixture from the previous release was deleted because it pinned the now-removed fixed-weight path. | `benchmarks/R/`, `benchmarks/julia/`, `tests/` | follow-up | Low |
-| `DifferenceInDifferences(vcov_type="conley")` support: Phase 2 lifted the panel rejection on `MultiPeriodDiD` / `TwoWayFixedEffects` but kept DiD rejected because `DiD.fit()` has no `unit` column declaration. A follow-up could either add a `unit` kwarg to `DiD.__init__` / `.fit()` and wire the block-decomposed sandwich, or document the redirect to `MultiPeriodDiD` permanently. | `diff_diff/estimators.py::DifferenceInDifferences.fit` | follow-up (spillover-conley) | Low |
-| Conley + cluster_ids combined product kernel `K_space(d_ij/h) · 1{cluster_i = cluster_j}`. Deferred to a follow-up PR per the Phase 2 scope decision (methodology PR only); tracked here for the next Conley wave. Currently raises `NotImplementedError` at the linalg validator (cross-sectional Conley + cluster) and at `TwoWayFixedEffects.fit` when the user sets `cluster=` explicitly. | `linalg.py::_validate_vcov_args`, `twfe.py::TwoWayFixedEffects.fit` | follow-up (spillover-conley) | Medium |
-| Conley sparse k-d-tree fast path via `scipy.spatial.cKDTree.query_ball_tree` for `n > 20_000`; lifts the dense O(n²) `UserWarning` that fires at the validator. Kernel must be compact-support (bartlett or uniform); callable metric not supported in the fast path. Performance-only; semantics unchanged. | `diff_diff/conley.py::_pairwise_distance_matrix`, `_compute_conley_vcov` | follow-up (spillover-conley) | Low |
 | Conley + survey weights / `survey_design`. Score-reweighted meat `s_i = w_i · X_i · ε_i` is mechanical, but PSU clustering interaction with the spatial kernel and replicate-weights variance under spatial correlation are non-trivial (Bertanha-Imbens 2014 covers cluster-sample but not the explicit Conley case). Phase 5 of the spillover-conley initiative; paper review prerequisite. Currently raises `NotImplementedError` at the linalg validator. | `linalg.py::_validate_vcov_args` | Phase 5 (spillover-conley) | Medium |
 | `SyntheticDiD(vcov_type="conley")` support. Currently raises `TypeError` at `__init__` because SyntheticDiD uses `variance_method ∈ {bootstrap, jackknife, placebo}` rather than the analytical sandwich that Conley plugs into. Wiring would require either reimplementing an analytical sandwich path for SyntheticDiD or designing a spatial-block bootstrap (new methodology, Politis-Romano 1994 territory). | `synthetic_did.py::SyntheticDiD` | follow-up (spillover-conley) | Low |
-| Validate user-supplied callable `conley_metric` for shape `(n, n)`, finiteness, non-negativity, and symmetry. Currently `np.asarray(metric(coords, coords))` is accepted unchecked; a malformed callable produces opaque matmul errors and a non-symmetric distance matrix produces a non-symmetric vcov. CI reviewer flagged as P2 M3 in PR #(spillover-conley). | `diff_diff/conley.py::_pairwise_distance_matrix`, `_compute_conley_vcov` | follow-up (spillover-conley) | Low |
 
 #### Performance