Fix circular import risk, add missing README params for PR #165 round 3

Duplicate _SPARSE_DENSE_THRESHOLD in two_stage_bootstrap.py to eliminate
cross-import from two_stage.py. Add bootstrap_weights param to README
ImputationDiD/TwoStageDiD constructors and n_bootstrap minimum note to TROP.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: igerber

README.md

@@ -1983,7 +1983,7 @@ TROP(
     max_iter=100,              # Max iterations for factor estimation
     tol=1e-6,                  # Convergence tolerance
     alpha=0.05,                # Significance level for CIs
-    n_bootstrap=200,           # Bootstrap replications
+    n_bootstrap=200,           # Bootstrap replications (minimum 2; TROP requires bootstrap for SEs)
     seed=None                  # Random seed
 )
 ```
@@ -2103,6 +2103,7 @@ ImputationDiD(
     alpha=0.05,                       # Significance level for CIs
     cluster=None,                     # Column for cluster-robust SEs
     n_bootstrap=0,                    # Bootstrap iterations (0 = analytical)
+    bootstrap_weights='rademacher',   # 'rademacher', 'mammen', or 'webb'
     seed=None,                        # Random seed
     rank_deficient_action='warn',     # 'warn', 'error', or 'silent'
     horizon_max=None,                 # Max event-study horizon
@@ -2157,6 +2158,7 @@ TwoStageDiD(
     alpha=0.05,                       # Significance level for CIs
     cluster=None,                     # Column for cluster-robust SEs (defaults to unit)
     n_bootstrap=0,                    # Bootstrap iterations (0 = analytical GMM SEs)
+    bootstrap_weights='rademacher',   # 'rademacher', 'mammen', or 'webb'
     seed=None,                        # Random seed
     rank_deficient_action='warn',     # 'warn', 'error', or 'silent'
     horizon_max=None,                 # Max event-study horizon

diff_diff/two_stage.py

@@ -31,7 +31,7 @@
 
 # Maximum number of elements before falling back to per-column sparse aggregation.
 # 10M float64 elements ≈ 80 MB peak allocation. Above this, per-column .getcol()
-# trades throughput for bounded memory.
+# trades throughput for bounded memory. Keep in sync with two_stage_bootstrap.py.
 _SPARSE_DENSE_THRESHOLD = 10_000_000
 
 from diff_diff.linalg import solve_ols

diff_diff/two_stage_bootstrap.py

@@ -15,7 +15,9 @@
 
 from diff_diff.linalg import solve_ols
 from diff_diff.staggered_bootstrap import _generate_bootstrap_weights_batch
-from diff_diff.two_stage import _SPARSE_DENSE_THRESHOLD
+# Maximum number of elements before falling back to per-column sparse aggregation.
+# Keep in sync with two_stage.py.
+_SPARSE_DENSE_THRESHOLD = 10_000_000
 from diff_diff.two_stage_results import TwoStageBootstrapResults
 
 __all__ = [