Fix Rust backend fallback issues (PR #115 review round 2)

- P1: solve_ols(skip_rank_check=True) now checks for None return from
  Rust and falls through to NumPy on numerical instability
- P2: compute_robust_vcov now catches "numerically unstable" errors
  and falls back to NumPy instead of propagating the error
- Remove PyO3-dependent tests from Rust that caused cargo test to fail
  without Python initialization

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: igerber

diff_diff/linalg.py

@@ -483,12 +483,15 @@ def solve_ols(
     # This saves O(nk²) QR overhead but won't detect rank-deficient matrices
     if skip_rank_check:
         if HAS_RUST_BACKEND and _rust_solve_ols is not None:
-            return _solve_ols_rust(
+            result = _solve_ols_rust(
                 X, y,
                 cluster_ids=cluster_ids,
                 return_vcov=return_vcov,
                 return_fitted=return_fitted,
             )
+            if result is not None:
+                return result
+            # Fall through to NumPy on numerical instability
         # Fall through to Python without rank check (user guarantees full rank)
         return _solve_ols_numpy(
             X, y,
@@ -761,14 +764,17 @@ def compute_robust_vcov(
         try:
             return _rust_compute_robust_vcov(X, residuals, cluster_ids_int)
         except ValueError as e:
-            # Translate Rust LAPACK errors to consistent Python error messages
+            # Translate Rust errors to consistent Python error messages or fallback
             error_msg = str(e)
             if "Matrix inversion failed" in error_msg:
                 raise ValueError(
                     "Design matrix is rank-deficient (singular X'X matrix). "
                     "This indicates perfect multicollinearity. Check your fixed effects "
                     "and covariates for linear dependencies."
                 ) from e
+            if "numerically unstable" in error_msg.lower():
+                # Fall back to NumPy on numerical instability
+                return _compute_robust_vcov_numpy(X, residuals, cluster_ids)
             raise
 
     # Fallback to NumPy implementation

rust/src/linalg.rs

@@ -441,43 +441,9 @@ mod tests {
         // not k, otherwise vt.t().dot(&s_inv_uty) will have mismatched dimensions
     }
 
-    #[test]
-    fn test_invert_symmetric_singular_matrix() {
-        // Create singular matrix: rows are linearly dependent
-        let a = array![
-            [1.0, 2.0, 3.0],
-            [2.0, 4.0, 6.0],  // = 2 * row 0
-            [3.0, 6.0, 9.0],  // = 3 * row 0
-        ];
-
-        // Should fail because matrix is singular (rank 1, not full rank 3)
-        let result = invert_symmetric(&a);
-        assert!(result.is_err(), "Singular matrix inversion should fail");
-
-        let err_msg = result.unwrap_err().to_string();
-        assert!(
-            err_msg.contains("singular") || err_msg.contains("unstable"),
-            "Error should mention singularity or instability: {}", err_msg
-        );
-    }
-
-    #[test]
-    fn test_invert_symmetric_near_singular_matrix() {
-        // Create near-singular matrix (high condition number)
-        let a = array![
-            [1.0, 1.0],
-            [1.0, 1.0 + 1e-15],  // Nearly identical rows
-        ];
-
-        // Should fail due to numerical instability (small pivot ratio triggers
-        // residual check which detects the inversion error)
-        let result = invert_symmetric(&a);
-        assert!(result.is_err(), "Near-singular matrix inversion should fail");
-
-        let err_msg = result.unwrap_err().to_string();
-        assert!(
-            err_msg.contains("singular") || err_msg.contains("unstable"),
-            "Error should mention singularity or instability: {}", err_msg
-        );
-    }
+    // Note: Singular and near-singular matrix tests removed because:
+    // 1. invert_symmetric() returns PyResult, which requires Python initialization
+    //    to create PyErr - `cargo test` without Python causes panic
+    // 2. These edge cases are tested at the Python integration level in
+    //    tests/test_linalg.py with proper fallback handling
 }