add linearmodels covariance class

src/lineamodels_api.py

@@ -0,0 +1,56 @@
+from functools import cached_property
+from linearmodels.panel.covariance import HomoskedasticCovariance
+from linearmodels.typing import Float64Array, IntArray
+from typing import Union
+from boot_algo3 import boot_algo3
+
+class WildBootstrap(HomoskedasticCovariance):
+
+    def __init__(
+        self,
+        y: Float64Array,
+        x: Float64Array,
+        params: Float64Array,
+        entity_ids: Union[IntArray, None],
+        time_ids: Union[IntArray, None],
+        *,
+        debiased: bool = False,
+        extra_df: int = 0,
+    ):
+        self._y = y
+        self._x = x
+        self._params = params
+        self._entity_ids = entity_ids
+        self._time_ids = time_ids
+        self._debiased = debiased
+        self._extra_df = extra_df
+        self._nobs, self._nvar = x.shape
+        self._nobs_eff = self._nobs - extra_df
+        if debiased:
+            self._nobs_eff -= self._nvar
+        self._scale = self._nobs / self._nobs_eff
+        self._name = "Wild Bootstrap"
+
+    @property
+    def name(self) -> str:
+        """Covariance estimator name"""
+        return self._name
+
+    @property
+    def eps(self) -> Float64Array:
+        """Model residuals"""
+        return self._y - self._x @ self._params
+
+    @property
+    def s2(self) -> float:
+        """Error variance"""
+        eps = self.eps
+        return self._scale * float(eps.T @ eps) / self._nobs
+
+    @cached_property
+    def cov(self) -> Float64Array:
+        # add in bootalgo code
+
+    def deferred_cov(self) -> Float64Array:
+        """Covariance calculation deferred until executed"""
+        return self.cov