diff --git a/chainladder/core/base.py b/chainladder/core/base.py
index 3660c4c1..8a05fcbc 100644
--- a/chainladder/core/base.py
+++ b/chainladder/core/base.py
@@ -176,7 +176,7 @@ def __init__(
         self.key_labels = index
         self.is_cumulative = cumulative
         self.valuation_date = data_agg["development"].max()
-        if not AUTO_SPARSE or array_backend == 'cupy':
+        if not AUTO_SPARSE or array_backend == "cupy":
             self.set_backend(array_backend, inplace=True)
         else:
             self = self._auto_sparse()
diff --git a/chainladder/core/slice.py b/chainladder/core/slice.py
index ebbc3598..3abf2eb8 100644
--- a/chainladder/core/slice.py
+++ b/chainladder/core/slice.py
@@ -53,11 +53,31 @@ def _contig_slice(arr):
             min_arr, max_arr = max_arr - 1, min_arr - 1 if min_arr else min_arr
         return slice(min_arr, max_arr, step)
 
+    def __setitem__(self, key, values):
+        if isinstance(values, TriangleSlicer):
+            if values.array_backend == "sparse":
+                values = values.set_backend("numpy").values
+            else:
+                values = values.values
+        key = tuple(
+            [slice(item, item + 1) if type(item) is int else item for item in key]
+        )
+        if self.obj.array_backend == "sparse":
+            self.obj.set_backend("numpy", inplace=True)
+            print(self.obj.values)
+            self.obj.values.__setitem__(normalize_index(key, self.obj.shape), values)
+            self.obj.set_backend("sparse", inplace=True)
+        else:
+            self.obj.values.__setitem__(normalize_index(key, self.obj.shape), values)
+
 
 class Location(_LocBase):
     """ class to generate .loc[] functionality """
 
     def __getitem__(self, key):
+        return self.get_idx(*self.format_key(key))
+
+    def format_key(self, key):
         key = (key,) if type(key) is not tuple else key
         key_mask = tuple([i if i is Ellipsis else 0 for i in key])
         if len(key_mask) < len(self.obj.shape) and Ellipsis not in key_mask:
@@ -93,7 +113,11 @@ def normalize(key, idx):
             return out
 
         key = [key[0]] + [normalize(key, 1), normalize(key, 2), normalize(key, 3)]
-        return self.get_idx(key, filter_idx)
+        return key, filter_idx
+
+    def __setitem__(self, key, values):
+        key = self.format_key(key)[0]
+        super().__setitem__(key, values)
 
 
 class Ilocation(_LocBase):
@@ -102,6 +126,10 @@ class Ilocation(_LocBase):
     def __getitem__(self, key):
         return self.get_idx(normalize_index(key, self.obj.shape))
 
+    def __setitem__(self, key, values):
+        key = normalize_index(key, self.obj.shape)
+        super().__setitem__(key, values)
+
 
 class TriangleSlicer:
     def __getitem__(self, key):
diff --git a/chainladder/core/tests/test_triangle.py b/chainladder/core/tests/test_triangle.py
index a94b4b1c..8254fe75 100644
--- a/chainladder/core/tests/test_triangle.py
+++ b/chainladder/core/tests/test_triangle.py
@@ -638,11 +638,14 @@ def test_init_vector():
     assert np.all(a.valuation == b.valuation)
     assert a.valuation_date == b.valuation_date
 
+
 def test_loc_ellipsis():
     assert tri == tri_gt
-    assert tri.loc['Aegis Grp'] == tri.loc['Adriatic Ins Co':'Aegis Grp'].loc['Aegis Grp']
-    assert tri.loc['Aegis Grp', ... , :] == tri.loc['Aegis Grp']
+    assert (
+        tri.loc["Aegis Grp"] == tri.loc["Adriatic Ins Co":"Aegis Grp"].loc["Aegis Grp"]
+    )
+    assert tri.loc["Aegis Grp", ..., :] == tri.loc["Aegis Grp"]
     assert tri.loc[..., 24:] == tri.loc[..., :, 24:]
     assert tri.loc[:, ..., 24:] == tri.loc[..., :, 24:]
-    assert tri.loc[:, 'CumPaidLoss'] == tri.loc[:, 'CumPaidLoss', ...]
-    assert tri.loc[..., 'CumPaidLoss', :, :] == tri.loc[:, 'CumPaidLoss', :, :]
+    assert tri.loc[:, "CumPaidLoss"] == tri.loc[:, "CumPaidLoss", ...]
+    assert tri.loc[..., "CumPaidLoss", :, :] == tri.loc[:, "CumPaidLoss", :, :]
diff --git a/chainladder/development/base.py b/chainladder/development/base.py
index db85b4cd..73436992 100644
--- a/chainladder/development/base.py
+++ b/chainladder/development/base.py
@@ -22,9 +22,11 @@ class Development(DevelopmentBase):
     n_periods : integer, optional (default=-1)
         number of origin periods to be used in the ldf average calculation. For
         all origin periods, set n_periods=-1
-    average : string, optional (default='volume')
+    average : string or float, optional (default='volume')
         type of averaging to use for ldf average calculation.  Options include
-        'volume', 'simple', and 'regression'
+        'volume', 'simple', and 'regression'. If numeric values are supplied,
+        then (2-average) in the style of Zehnwirth & Barnett is used
+        for the exponent of the regression weights.
     sigma_interpolation : string optional (default='log-linear')
         Options include 'log-linear' and 'mack'
     drop : tuple or list of tuples
@@ -260,7 +262,7 @@ def fit(self, X, y=None, sample_weight=None):
         weight_dict = {"regression": 0, "volume": 1, "simple": 2}
         x, y = tri_array[..., :-1], tri_array[..., 1:]
         val = xp.nan_to_num(
-            xp.array([weight_dict.get(item.lower(), 1) for item in self.average_])[
+            xp.array([weight_dict.get(item, item) for item in self.average_])[
                 None, None, None
             ]
             * (y * 0 + 1)
diff --git a/chainladder/methods/mack.py b/chainladder/methods/mack.py
index b5e295bd..0882b7df 100644
--- a/chainladder/methods/mack.py
+++ b/chainladder/methods/mack.py
@@ -103,7 +103,7 @@ def _get_full_std_err_(self, X=None):
         lxp = X.ldf_.get_array_module()
         full = getattr(X, "_full_triangle_", self.full_triangle_)
         avg = {"regression": 0, "volume": 1, "simple": 2}
-        avg = [avg[item] for item in X.average_]
+        avg = [avg.get(item, item) for item in X.average_]
         val = xp.broadcast_to(xp.array(avg + [avg[-1]]), X.shape)
         weight = xp.sqrt(full.values[..., : len(X.ddims)] ** (2 - val))
         obj.values = X.sigma_.values / num_to_nan(weight)
@@ -165,8 +165,8 @@ def total_mack_std_err_(self):
 
     def _get_total_mack_std_err_(self, obj):
         obj = obj.total_process_risk_ ** 2 + obj.total_parameter_risk_ ** 2
-        if obj.array_backend == 'sparse':
-            out = obj.set_backend('numpy').sqrt().values[..., 0, -1]
+        if obj.array_backend == "sparse":
+            out = obj.set_backend("numpy").sqrt().values[..., 0, -1]
         else:
             out = obj.sqrt().values[..., 0, -1]
         return pd.DataFrame(out, index=obj.index, columns=obj.columns)
diff --git a/chainladder/tails/base.py b/chainladder/tails/base.py
index 172b55f2..7fcbb5ef 100644
--- a/chainladder/tails/base.py
+++ b/chainladder/tails/base.py
@@ -57,7 +57,7 @@ def fit(self, X, y=None, sample_weight=None):
         self.sigma_.ddims = self.std_err_.ddims = np.concatenate(
             (obj.ldf_.ddims, np.array(["{}-9999".format(int(obj.ddims[-1]))]))
         )
-        if hasattr(obj, 'average_'):
+        if hasattr(obj, "average_"):
             self.average_ = obj.average_
         else:
             self.average_ = None
diff --git a/chainladder/tails/bondy.py b/chainladder/tails/bondy.py
index 8c176557..be05ac5c 100644
--- a/chainladder/tails/bondy.py
+++ b/chainladder/tails/bondy.py
@@ -74,7 +74,7 @@ def fit(self, X, y=None, sample_weight=None):
         backend = X.array_backend
         if X.array_backend == "cupy":
             X = X.set_backend("numpy", deep=True)
-        else :
+        else:
             X = X.set_backend("numpy")
         xp = X.get_array_module()
         super().fit(X, y, sample_weight)