diff --git a/chainladder/core/common.py b/chainladder/core/common.py
index 5439c554..2545d806 100644
--- a/chainladder/core/common.py
+++ b/chainladder/core/common.py
@@ -33,6 +33,13 @@ def _get_full_triangle(full_expectation_, triangle_):
 
 class Common:
     """ Class that contains common properties of a "fitted" Triangle. """
+    @property
+    def has_ldf(self):
+        if hasattr(self, "ldf_"):
+            return True
+        else:
+            return False
+            
 
     @property
     def cdf_(self):
diff --git a/chainladder/core/triangle.py b/chainladder/core/triangle.py
index c264f549..57edbbd9 100644
--- a/chainladder/core/triangle.py
+++ b/chainladder/core/triangle.py
@@ -217,24 +217,28 @@ def incr_to_cum(self, inplace=False):
             if not self.is_cumulative:
                 if self.is_pattern:
                     values = xp.nan_to_num(self.values[..., ::-1])
+                    if self.array_backend == "sparse":
+                        xp = np
+                        values = self.set_backend('numpy').values
                     values[values == 0] = 1.0
                     values = xp.cumprod(values, -1)[..., ::-1]
                     self.values = values = values * self.nan_triangle
+                    if self.array_backend == "sparse":
+                        self.values = self.get_array_module()(self.values)
                 else:
-                    ddims = self.ddims
                     if self.array_backend != "sparse":
                         self.values = (
                             num_to_nan(xp.cumsum(xp.nan_to_num(self.values), 3))
                             * self.nan_triangle[None, None, ...]
                         )
                     else:
-                        l1 = lambda i: self.iloc[..., 0 : (i + 1)]
-                        l2 = lambda i: l1(i) * self.nan_triangle[..., i : i + 1]
-                        l3 = lambda i: l2(i).sum(3, auto_sparse=False, keepdims=True)
-                        self = concat(
-                            [l3(i).rename(3, [i]) for i in range(self.shape[-1])], 3,
-                        )
-                    self.ddims = ddims
+                        values = xp.nan_to_num(self.values)
+                        nan_triangle = xp.nan_to_num(self.nan_triangle)
+                        l1 = lambda i: values[..., 0 : (i + 1)]
+                        l2 = lambda i: l1(i) * nan_triangle[..., i : i + 1]
+                        l3 = lambda i: l2(i).sum(3, keepdims=True)
+                        out = [l3(i) for i in range(self.shape[-1])]
+                        self.values = num_to_nan(xp.concatenate(out, axis=3))
                 self.is_cumulative = True
             return self
         else:
diff --git a/chainladder/development/clark.py b/chainladder/development/clark.py
index 9370ce9d..5c8bb8d9 100644
--- a/chainladder/development/clark.py
+++ b/chainladder/development/clark.py
@@ -90,7 +90,7 @@ def G_(self, age):
         if type(age) == list:
             age = xp.array([age]).astype("float64")
         obj = self.incremental_act_.copy()
-        obj.odims = xp.array(["(All)"])
+        obj.odims = obj.odims[0:1]
         obj.values = 1 / self._G(age)
         obj.ddims = age
         return obj
@@ -185,8 +185,11 @@ def solver(x):
         cdf = self._G(
             latest_age - age_offset, theta=params[..., 1:2], omega=params[..., 0:1]
         )
-        obj.values = xp.repeat(cdf[..., :-1] / cdf[..., 1:], len(obj.odims), 2)
+        obj.values = cdf[..., :-1] / cdf[..., 1:]
         obj.ddims = X.link_ratio.ddims
+        obj.odims = obj.odims[0:1]
+        obj.is_pattern = True
+        obj.is_cumulative = False
         obj._set_slicers()
         self.ldf_ = obj
         self.ldf_.valuation_date = pd.to_datetime(ULT_VAL)
diff --git a/chainladder/development/munich.py b/chainladder/development/munich.py
index 8605510e..5bed586c 100644
--- a/chainladder/development/munich.py
+++ b/chainladder/development/munich.py
@@ -111,6 +111,7 @@ def fit(self, X, y=None, sample_weight=None):
         self.ldf_ = self._set_ldf(
             obj, self._get_mcl_cdf(obj, self.munich_full_triangle_)
         )
+        self.ldf_.is_cumulative = False
         self.ldf_.valuation_date = pd.to_datetime(ULT_VAL)
         self._map = {
             (list(X.columns).index(x)): (num % 2, num // 2)
diff --git a/chainladder/methods/base.py b/chainladder/methods/base.py
index 04ece3d9..de6c551f 100644
--- a/chainladder/methods/base.py
+++ b/chainladder/methods/base.py
@@ -140,7 +140,7 @@ def _include_process_variance(self):
         return process_var
 
     def validate_weight(self, X, sample_weight):
-        if sample_weight and X.shape[:-1] != sample_weight.shape[:-1]:
+        if sample_weight and X.shape[:-1] != sample_weight.shape[:-1] and sample_weight.shape[2] != 1 and sample_weight.shape[0] > 1:
             warnings.warn(
                 "X and sample_weight are not aligned.  Broadcasting may occur.\n"
             )
diff --git a/chainladder/utils/templates/triangle.yaml b/chainladder/utils/templates/triangle.yaml
index 3e348f11..65271a0e 100644
--- a/chainladder/utils/templates/triangle.yaml
+++ b/chainladder/utils/templates/triangle.yaml
@@ -92,7 +92,7 @@ Sheet:
       {% endfor %}
       {% endif %}
 
-      {% if triangle.ldf_ %}
+      {% if triangle.has_ldf %}
       - CSpacer:
           height: 1
       - Series:
diff --git a/docs/auto_examples/auto_examples_jupyter.zip b/docs/auto_examples/auto_examples_jupyter.zip
index 9efce133..301cbb09 100644
Binary files a/docs/auto_examples/auto_examples_jupyter.zip and b/docs/auto_examples/auto_examples_jupyter.zip differ
diff --git a/docs/auto_examples/auto_examples_python.zip b/docs/auto_examples/auto_examples_python.zip
index 3e6ea46f..80f0cdd5 100644
Binary files a/docs/auto_examples/auto_examples_python.zip and b/docs/auto_examples/auto_examples_python.zip differ
diff --git a/docs/auto_examples/images/sphx_glr_plot_bootstrap_001.png b/docs/auto_examples/images/sphx_glr_plot_bootstrap_001.png
index ba03d247..14bcc521 100644
Binary files a/docs/auto_examples/images/sphx_glr_plot_bootstrap_001.png and b/docs/auto_examples/images/sphx_glr_plot_bootstrap_001.png differ
diff --git a/docs/auto_examples/images/sphx_glr_plot_clarkldf_001.png b/docs/auto_examples/images/sphx_glr_plot_clarkldf_001.png
index 6e0a4b52..05406afc 100644
Binary files a/docs/auto_examples/images/sphx_glr_plot_clarkldf_001.png and b/docs/auto_examples/images/sphx_glr_plot_clarkldf_001.png differ
diff --git a/docs/auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png b/docs/auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
index 0b315d25..b6f25c9d 100644
Binary files a/docs/auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png and b/docs/auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png differ
diff --git a/docs/auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png b/docs/auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
index 4bc354d0..d96c043a 100644
Binary files a/docs/auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png and b/docs/auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png differ
diff --git a/docs/auto_examples/index.rst b/docs/auto_examples/index.rst
index 07a0d2dd..8b8da4dc 100644
--- a/docs/auto_examples/index.rst
+++ b/docs/auto_examples/index.rst
@@ -370,14 +370,14 @@ Examples
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -387,18 +387,18 @@ Examples
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_stochastic_bornferg
+   /auto_examples/plot_bootstrap
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
-     :alt: Clark Residual Plots
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
+     :alt: Stochastic Bornhuetter Ferguson
 
-     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
 
 .. raw:: html
 
@@ -408,18 +408,18 @@ Examples
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_clarkldf_resid
+   /auto_examples/plot_stochastic_bornferg
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
-     :alt: ODP Bootstrap Example
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
 
-     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
 
 .. raw:: html
 
@@ -429,7 +429,7 @@ Examples
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_bootstrap
+   /auto_examples/plot_clarkldf_resid
 
 .. raw:: html
 
diff --git a/docs/auto_examples/plot_bootstrap.ipynb b/docs/auto_examples/plot_bootstrap.ipynb
index 2d06f920..5965e8f7 100644
--- a/docs/auto_examples/plot_bootstrap.ipynb
+++ b/docs/auto_examples/plot_bootstrap.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import chainladder as cl\nimport matplotlib.pyplot as plt\n\n#  Grab a Triangle\ntri = cl.load_sample('genins')\n# Generate bootstrap samples\nsims = cl.BootstrapODPSample().fit_transform(tri)\n# Calculate LDF for each simulation\nsim_ldf = cl.Development().fit(sims).ldf_\nsim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]\n\n# Plot the Data\nfig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n# Plot 1\ntri.T.plot(ax=ax00, grid=True).set(title='Raw Data', xlabel='Development', ylabel='Incurred')\n# Plot 2\nsims.mean().T.plot(ax=ax01, grid=True).set(title='Mean Simulation', xlabel='Development')\n# Plot 3\nsim_ldf.T.plot(legend=False, color='lightgray', ax=ax10, grid=True).set(\n    title='Simulated LDF', xlabel='Development', ylabel='LDF')\ncl.Development().fit(tri).ldf_.drop_duplicates().T.plot(\n    legend=False, color='red', ax=ax10, grid=True)\n# Plot 4\nsim_ldf.T.loc['12-24'].plot(\n    kind='hist', bins=50, alpha=0.5, ax=ax11 , grid=True).set(\n    title='Age 12-24 LDF Distribution', xlabel='LDF');"
+        "import chainladder as cl\nimport matplotlib.pyplot as plt\n\n#  Grab a Triangle\ntri = cl.load_sample('genins')\n# Generate bootstrap samples\nsims = cl.BootstrapODPSample().fit_transform(tri)\n# Calculate LDF for each simulation\nsim_ldf = cl.Development().fit(sims).ldf_\n\n# Plot the Data\nfig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n# Plot 1\ntri.T.plot(ax=ax00, grid=True).set(title='Raw Data', xlabel='Development', ylabel='Incurred')\n# Plot 2\nsims.mean().T.plot(ax=ax01, grid=True).set(title='Mean Simulation', xlabel='Development')\n# Plot 3\nsim_ldf.T.plot(legend=False, color='lightgray', ax=ax10, grid=True).set(\n    title='Simulated LDF', xlabel='Development', ylabel='LDF')\ncl.Development().fit(tri).ldf_.drop_duplicates().T.plot(\n    legend=False, color='red', ax=ax10, grid=True)\n# Plot 4\nsim_ldf.T.loc['12-24'].plot(\n    kind='hist', bins=50, alpha=0.5, ax=ax11 , grid=True).set(\n    title='Age 12-24 LDF Distribution', xlabel='LDF');"
       ]
     }
   ],
diff --git a/docs/auto_examples/plot_bootstrap.py b/docs/auto_examples/plot_bootstrap.py
index 45034396..b3aaf25a 100644
--- a/docs/auto_examples/plot_bootstrap.py
+++ b/docs/auto_examples/plot_bootstrap.py
@@ -15,7 +15,6 @@
 sims = cl.BootstrapODPSample().fit_transform(tri)
 # Calculate LDF for each simulation
 sim_ldf = cl.Development().fit(sims).ldf_
-sim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]
 
 # Plot the Data
 fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))
diff --git a/docs/auto_examples/plot_bootstrap.py.md5 b/docs/auto_examples/plot_bootstrap.py.md5
index 8a4785f8..403232f3 100644
--- a/docs/auto_examples/plot_bootstrap.py.md5
+++ b/docs/auto_examples/plot_bootstrap.py.md5
@@ -1 +1 @@
-d69313408bdb72d6709775b307368f26
\ No newline at end of file
+a4b7163692b2b29b03d9eed31d4a4d32
\ No newline at end of file
diff --git a/docs/auto_examples/plot_bootstrap.rst b/docs/auto_examples/plot_bootstrap.rst
index fecdcf2b..4dd52b25 100644
--- a/docs/auto_examples/plot_bootstrap.rst
+++ b/docs/auto_examples/plot_bootstrap.rst
@@ -29,7 +29,7 @@ Bootstrap sampler and get various properties about parameter uncertainty.
 
  .. code-block:: none
 
-    c:\users\jboga\onedrive\documents\github\chainladder-python\chainladder\core\pandas.py:264: RuntimeWarning: Mean of empty slice
+    c:\users\jboga\onedrive\documents\github\chainladder-python\chainladder\core\pandas.py:280: RuntimeWarning: Mean of empty slice
       obj.values = func(obj.values, axis=axis, *args, **kwargs)
 
     [Text(0.5, 0, 'LDF'), Text(0.5, 1.0, 'Age 12-24 LDF Distribution')]
@@ -52,7 +52,6 @@ Bootstrap sampler and get various properties about parameter uncertainty.
     sims = cl.BootstrapODPSample().fit_transform(tri)
     # Calculate LDF for each simulation
     sim_ldf = cl.Development().fit(sims).ldf_
-    sim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]
 
     # Plot the Data
     fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))
@@ -73,7 +72,7 @@ Bootstrap sampler and get various properties about parameter uncertainty.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.300 seconds)
+   **Total running time of the script:** ( 0 minutes  2.244 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_bootstrap.py:
diff --git a/docs/auto_examples/plot_bootstrap_codeobj.pickle b/docs/auto_examples/plot_bootstrap_codeobj.pickle
index 663d2652..c6137928 100644
Binary files a/docs/auto_examples/plot_bootstrap_codeobj.pickle and b/docs/auto_examples/plot_bootstrap_codeobj.pickle differ
diff --git a/docs/auto_examples/plot_clarkldf.rst b/docs/auto_examples/plot_clarkldf.rst
index 61b93f45..ec2b247b 100644
--- a/docs/auto_examples/plot_clarkldf.rst
+++ b/docs/auto_examples/plot_clarkldf.rst
@@ -64,7 +64,7 @@ age.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.304 seconds)
+   **Total running time of the script:** ( 0 minutes  0.956 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_clarkldf.py:
diff --git a/docs/auto_examples/plot_clarkldf_codeobj.pickle b/docs/auto_examples/plot_clarkldf_codeobj.pickle
index cba64ceb..9392ae9b 100644
Binary files a/docs/auto_examples/plot_clarkldf_codeobj.pickle and b/docs/auto_examples/plot_clarkldf_codeobj.pickle differ
diff --git a/docs/auto_examples/plot_clarkldf_resid.rst b/docs/auto_examples/plot_clarkldf_resid.rst
index daec918d..2f24afc4 100644
--- a/docs/auto_examples/plot_clarkldf_resid.rst
+++ b/docs/auto_examples/plot_clarkldf_resid.rst
@@ -74,7 +74,7 @@ Clarks LDF Curve-Fitting paper (2003).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.376 seconds)
+   **Total running time of the script:** ( 0 minutes  0.345 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_clarkldf_resid.py:
diff --git a/docs/auto_examples/plot_clarkldf_resid_codeobj.pickle b/docs/auto_examples/plot_clarkldf_resid_codeobj.pickle
index f5069e8e..d7c09462 100644
Binary files a/docs/auto_examples/plot_clarkldf_resid_codeobj.pickle and b/docs/auto_examples/plot_clarkldf_resid_codeobj.pickle differ
diff --git a/docs/auto_examples/sg_execution_times.rst b/docs/auto_examples/sg_execution_times.rst
index 40341b1f..fcc91358 100644
--- a/docs/auto_examples/sg_execution_times.rst
+++ b/docs/auto_examples/sg_execution_times.rst
@@ -5,12 +5,14 @@
 
 Computation times
 =================
-**00:02.058** total execution time for **auto_examples** files:
+**00:03.546** total execution time for **auto_examples** files:
 
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` (``plot_stochastic_bornferg.py``)   | 00:01.796 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_bootstrap.py` (``plot_bootstrap.py``)                       | 00:02.244 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` (``plot_bf_apriori_from_cl.py``)     | 00:00.261 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_clarkldf.py` (``plot_clarkldf.py``)                         | 00:00.956 | 0.0 MB |
++-----------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py` (``plot_clarkldf_resid.py``)             | 00:00.345 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` (``plot_ave_analysis.py``)                 | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
@@ -20,18 +22,14 @@ Computation times
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_berqsherm_closure.py` (``plot_berqsherm_closure.py``)       | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_bondy_sensitivity.py` (``plot_bondy_sensitivity.py``)       | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` (``plot_bf_apriori_from_cl.py``)     | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_bootstrap.py` (``plot_bootstrap.py``)                       | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_bondy_sensitivity.py` (``plot_bondy_sensitivity.py``)       | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_bootstrap_comparison.py` (``plot_bootstrap_comparison.py``) | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_capecod.py` (``plot_capecod.py``)                           | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_clarkldf.py` (``plot_clarkldf.py``)                         | 00:00.000 | 0.0 MB |
-+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py` (``plot_clarkldf_resid.py``)             | 00:00.000 | 0.0 MB |
-+-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_development_periods.py` (``plot_development_periods.py``)   | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_exhibits.py` (``plot_exhibits.py``)                         | 00:00.000 | 0.0 MB |
@@ -50,6 +48,8 @@ Computation times
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_munich_resid.py` (``plot_munich_resid.py``)                 | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` (``plot_stochastic_bornferg.py``)   | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_tailcurve_compare.py` (``plot_tailcurve_compare.py``)       | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_triangle_from_pandas.py` (``plot_triangle_from_pandas.py``) | 00:00.000 | 0.0 MB |
diff --git a/docs/modules/generated/chainladder.Benktander.validate_weight.examples b/docs/modules/generated/chainladder.Benktander.validate_weight.examples
new file mode 100644
index 00000000..e69de29b
diff --git a/docs/modules/generated/chainladder.BootstrapODPSample.examples b/docs/modules/generated/chainladder.BootstrapODPSample.examples
index 9b80ff74..7f2082c8 100644
--- a/docs/modules/generated/chainladder.BootstrapODPSample.examples
+++ b/docs/modules/generated/chainladder.BootstrapODPSample.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.BootstrapODPSample``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -20,4 +20,4 @@ Examples using ``chainladder.BootstrapODPSample``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
diff --git a/docs/modules/generated/chainladder.BornhuetterFerguson.validate_weight.examples b/docs/modules/generated/chainladder.BornhuetterFerguson.validate_weight.examples
new file mode 100644
index 00000000..e69de29b
diff --git a/docs/modules/generated/chainladder.CapeCod.validate_weight.examples b/docs/modules/generated/chainladder.CapeCod.validate_weight.examples
new file mode 100644
index 00000000..e69de29b
diff --git a/docs/modules/generated/chainladder.Chainladder.validate_weight.examples b/docs/modules/generated/chainladder.Chainladder.validate_weight.examples
new file mode 100644
index 00000000..e69de29b
diff --git a/docs/modules/generated/chainladder.Common.T.examples b/docs/modules/generated/chainladder.Common.T.examples
index 36aff53d..032b8988 100644
--- a/docs/modules/generated/chainladder.Common.T.examples
+++ b/docs/modules/generated/chainladder.Common.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.Common.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.Common.examples b/docs/modules/generated/chainladder.Common.examples
index 9cb03671..169fe708 100644
--- a/docs/modules/generated/chainladder.Common.examples
+++ b/docs/modules/generated/chainladder.Common.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.Common``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the output of one method as the apriori selection...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png
-     :alt: Picking Bornhuetter-Ferguson Apriori
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,18 +20,18 @@ Examples using ``chainladder.Common``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -39,4 +39,23 @@ Examples using ``chainladder.Common``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.Development.examples b/docs/modules/generated/chainladder.Development.examples
index 6224f57a..d68cf6a2 100644
--- a/docs/modules/generated/chainladder.Development.examples
+++ b/docs/modules/generated/chainladder.Development.examples
@@ -3,101 +3,6 @@
 Examples using ``chainladder.Development``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Mack Chainladder method. ">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_mack_thumb.png
-     :alt: Mack Chainladder Example
-
-     :ref:`sphx_glr_auto_examples_plot_mack.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_mack.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates testing multiple number of periods in the development transformer to ...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_development_periods_thumb.png
-     :alt: Basic Assumption Tuning with Pipeline and Gridsearch
-
-     :ref:`sphx_glr_auto_examples_plot_development_periods.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_development_periods.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the adjustment to case reserves using the Berquist-Sherman method. A ...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_berqsherm_case_thumb.png
-     :alt: Berquist Sherman Case Reserve Adjustment
-
-     :ref:`sphx_glr_auto_examples_plot_berqsherm_case.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_berqsherm_case.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the usage of the TailBondy estimator as well as passing multiple scor...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bondy_sensitivity_thumb.png
-     :alt: Testing Sensitivity of Bondy Tail Assumptions
-
-     :ref:`sphx_glr_auto_examples_plot_bondy_sensitivity.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_bondy_sensitivity.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the relationship between the Chainladder and BornhuetterFerguson meth...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_benktander_thumb.png
-     :alt: Benktander: Relationship between Chainladder and BornhuetterFerguson
-
-     :ref:`sphx_glr_auto_examples_plot_benktander.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_benktander.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
@@ -116,60 +21,3 @@ Examples using ``chainladder.Development``
 .. only:: not html
 
  * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the adjustment to paid amounts and closed claim counts using the Berq...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_berqsherm_closure_thumb.png
-     :alt: Berquist-Sherman Disposal Rate Adjustment
-
-     :ref:`sphx_glr_auto_examples_plot_berqsherm_closure.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_berqsherm_closure.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to adjust LDFs by the relationship between Paid and Incurred usin...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_thumb.png
-     :alt: Munich Adjustment Example
-
-     :ref:`sphx_glr_auto_examples_plot_munich.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_munich.py`
-
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates some of the flexibility of the Excel outputs. It creates an Excel fil...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_exhibits_thumb.png
-     :alt: Sample Excel Exhibit functionality
-
-     :ref:`sphx_glr_auto_examples_plot_exhibits.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_exhibits.py`
diff --git a/docs/modules/generated/chainladder.DevelopmentBase.examples b/docs/modules/generated/chainladder.DevelopmentBase.examples
index 625957f9..aa8b0bcd 100644
--- a/docs/modules/generated/chainladder.DevelopmentBase.examples
+++ b/docs/modules/generated/chainladder.DevelopmentBase.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.DevelopmentBase``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,4 +20,42 @@ Examples using ``chainladder.DevelopmentBase``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
+
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.DevelopmentBase.lambda_.examples b/docs/modules/generated/chainladder.DevelopmentBase.lambda_.examples
new file mode 100644
index 00000000..dac8fff1
--- /dev/null
+++ b/docs/modules/generated/chainladder.DevelopmentBase.lambda_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.DevelopmentBase.lambda_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.DevelopmentBase.q_resids_.examples b/docs/modules/generated/chainladder.DevelopmentBase.q_resids_.examples
new file mode 100644
index 00000000..30a78dba
--- /dev/null
+++ b/docs/modules/generated/chainladder.DevelopmentBase.q_resids_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.DevelopmentBase.q_resids_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.DevelopmentBase.resids_.examples b/docs/modules/generated/chainladder.DevelopmentBase.resids_.examples
new file mode 100644
index 00000000..31188cec
--- /dev/null
+++ b/docs/modules/generated/chainladder.DevelopmentBase.resids_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.DevelopmentBase.resids_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.EstimatorIO.examples b/docs/modules/generated/chainladder.EstimatorIO.examples
index 350076ed..08b88158 100644
--- a/docs/modules/generated/chainladder.EstimatorIO.examples
+++ b/docs/modules/generated/chainladder.EstimatorIO.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.EstimatorIO``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the output of one method as the apriori selection...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png
-     :alt: Picking Bornhuetter-Ferguson Apriori
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,18 +20,18 @@ Examples using ``chainladder.EstimatorIO``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -39,4 +39,23 @@ Examples using ``chainladder.EstimatorIO``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.MackChainladder.validate_weight.examples b/docs/modules/generated/chainladder.MackChainladder.validate_weight.examples
new file mode 100644
index 00000000..e69de29b
diff --git a/docs/modules/generated/chainladder.Triangle.T.examples b/docs/modules/generated/chainladder.Triangle.T.examples
index 932ea0f4..1ba0d6ce 100644
--- a/docs/modules/generated/chainladder.Triangle.T.examples
+++ b/docs/modules/generated/chainladder.Triangle.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.Triangle.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.Triangle.examples b/docs/modules/generated/chainladder.Triangle.examples
index db3370e8..f00ad315 100644
--- a/docs/modules/generated/chainladder.Triangle.examples
+++ b/docs/modules/generated/chainladder.Triangle.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.Triangle``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the output of one method as the apriori selection...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png
-     :alt: Picking Bornhuetter-Ferguson Apriori
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,18 +20,18 @@ Examples using ``chainladder.Triangle``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -39,4 +39,4 @@ Examples using ``chainladder.Triangle``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
diff --git a/docs/modules/generated/chainladder.Triangle.sqrt.examples b/docs/modules/generated/chainladder.Triangle.sqrt.examples
new file mode 100644
index 00000000..e69de29b
diff --git a/docs/modules/generated/chainladder.TriangleBase.T.examples b/docs/modules/generated/chainladder.TriangleBase.T.examples
index c28d6e82..7363474d 100644
--- a/docs/modules/generated/chainladder.TriangleBase.T.examples
+++ b/docs/modules/generated/chainladder.TriangleBase.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.TriangleBase.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.TriangleDisplay.T.examples b/docs/modules/generated/chainladder.TriangleDisplay.T.examples
index 3a0d1c45..fe372033 100644
--- a/docs/modules/generated/chainladder.TriangleDisplay.T.examples
+++ b/docs/modules/generated/chainladder.TriangleDisplay.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.TriangleDisplay.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.TriangleDunders.T.examples b/docs/modules/generated/chainladder.TriangleDunders.T.examples
index ef8e505a..df6eeefb 100644
--- a/docs/modules/generated/chainladder.TriangleDunders.T.examples
+++ b/docs/modules/generated/chainladder.TriangleDunders.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.TriangleDunders.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.TriangleIO.T.examples b/docs/modules/generated/chainladder.TriangleIO.T.examples
index b37fb1eb..e57422a7 100644
--- a/docs/modules/generated/chainladder.TriangleIO.T.examples
+++ b/docs/modules/generated/chainladder.TriangleIO.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.TriangleIO.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.TrianglePandas.T.examples b/docs/modules/generated/chainladder.TrianglePandas.T.examples
index fb18d912..09137eaf 100644
--- a/docs/modules/generated/chainladder.TrianglePandas.T.examples
+++ b/docs/modules/generated/chainladder.TrianglePandas.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.TrianglePandas.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.TriangleSlicer.T.examples b/docs/modules/generated/chainladder.TriangleSlicer.T.examples
index 4d26709b..4c9d7bb5 100644
--- a/docs/modules/generated/chainladder.TriangleSlicer.T.examples
+++ b/docs/modules/generated/chainladder.TriangleSlicer.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.TriangleSlicer.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.Common.T.examples b/docs/modules/generated/chainladder.core.Common.T.examples
index e4f9e648..053ff7e1 100644
--- a/docs/modules/generated/chainladder.core.Common.T.examples
+++ b/docs/modules/generated/chainladder.core.Common.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.Common.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.Common.examples b/docs/modules/generated/chainladder.core.Common.examples
index 28aac82a..b8229075 100644
--- a/docs/modules/generated/chainladder.core.Common.examples
+++ b/docs/modules/generated/chainladder.core.Common.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.core.Common``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the output of one method as the apriori selection...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png
-     :alt: Picking Bornhuetter-Ferguson Apriori
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,18 +20,18 @@ Examples using ``chainladder.core.Common``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -39,4 +39,23 @@ Examples using ``chainladder.core.Common``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.core.EstimatorIO.examples b/docs/modules/generated/chainladder.core.EstimatorIO.examples
new file mode 100644
index 00000000..214aaf2e
--- /dev/null
+++ b/docs/modules/generated/chainladder.core.EstimatorIO.examples
@@ -0,0 +1,61 @@
+
+
+Examples using ``chainladder.core.EstimatorIO``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
+
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.core.TriangleBase.T.examples b/docs/modules/generated/chainladder.core.TriangleBase.T.examples
index 8695e9fb..2ce205b5 100644
--- a/docs/modules/generated/chainladder.core.TriangleBase.T.examples
+++ b/docs/modules/generated/chainladder.core.TriangleBase.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.TriangleBase.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.TriangleDisplay.T.examples b/docs/modules/generated/chainladder.core.TriangleDisplay.T.examples
index 986a1a0b..bfcb66d1 100644
--- a/docs/modules/generated/chainladder.core.TriangleDisplay.T.examples
+++ b/docs/modules/generated/chainladder.core.TriangleDisplay.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.TriangleDisplay.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.TriangleDunders.T.examples b/docs/modules/generated/chainladder.core.TriangleDunders.T.examples
index a79aeef2..980f03f1 100644
--- a/docs/modules/generated/chainladder.core.TriangleDunders.T.examples
+++ b/docs/modules/generated/chainladder.core.TriangleDunders.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.TriangleDunders.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.TriangleIO.T.examples b/docs/modules/generated/chainladder.core.TriangleIO.T.examples
index 1bed2f05..b4846ebd 100644
--- a/docs/modules/generated/chainladder.core.TriangleIO.T.examples
+++ b/docs/modules/generated/chainladder.core.TriangleIO.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.TriangleIO.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.TrianglePandas.T.examples b/docs/modules/generated/chainladder.core.TrianglePandas.T.examples
index 95c31cf9..4b7677ea 100644
--- a/docs/modules/generated/chainladder.core.TrianglePandas.T.examples
+++ b/docs/modules/generated/chainladder.core.TrianglePandas.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.TrianglePandas.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.TriangleSlicer.T.examples b/docs/modules/generated/chainladder.core.TriangleSlicer.T.examples
index 69d13bb6..b3bf3b32 100644
--- a/docs/modules/generated/chainladder.core.TriangleSlicer.T.examples
+++ b/docs/modules/generated/chainladder.core.TriangleSlicer.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.TriangleSlicer.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.base.TriangleBase.T.examples b/docs/modules/generated/chainladder.core.base.TriangleBase.T.examples
index 6ac6c2ee..a3969493 100644
--- a/docs/modules/generated/chainladder.core.base.TriangleBase.T.examples
+++ b/docs/modules/generated/chainladder.core.base.TriangleBase.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.base.TriangleBase.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.common.Common.T.examples b/docs/modules/generated/chainladder.core.common.Common.T.examples
index 4ac1ccfa..9766016b 100644
--- a/docs/modules/generated/chainladder.core.common.Common.T.examples
+++ b/docs/modules/generated/chainladder.core.common.Common.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.common.Common.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.common.Common.examples b/docs/modules/generated/chainladder.core.common.Common.examples
index e1552369..fccd5a43 100644
--- a/docs/modules/generated/chainladder.core.common.Common.examples
+++ b/docs/modules/generated/chainladder.core.common.Common.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.core.common.Common``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the output of one method as the apriori selection...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png
-     :alt: Picking Bornhuetter-Ferguson Apriori
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,18 +20,18 @@ Examples using ``chainladder.core.common.Common``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -39,4 +39,23 @@ Examples using ``chainladder.core.common.Common``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.core.display.TriangleDisplay.T.examples b/docs/modules/generated/chainladder.core.display.TriangleDisplay.T.examples
index f864becd..3fc8c447 100644
--- a/docs/modules/generated/chainladder.core.display.TriangleDisplay.T.examples
+++ b/docs/modules/generated/chainladder.core.display.TriangleDisplay.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.display.TriangleDisplay.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.dunders.TriangleDunders.T.examples b/docs/modules/generated/chainladder.core.dunders.TriangleDunders.T.examples
index 740db9c3..02662121 100644
--- a/docs/modules/generated/chainladder.core.dunders.TriangleDunders.T.examples
+++ b/docs/modules/generated/chainladder.core.dunders.TriangleDunders.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.dunders.TriangleDunders.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.io.EstimatorIO.examples b/docs/modules/generated/chainladder.core.io.EstimatorIO.examples
new file mode 100644
index 00000000..38754e80
--- /dev/null
+++ b/docs/modules/generated/chainladder.core.io.EstimatorIO.examples
@@ -0,0 +1,61 @@
+
+
+Examples using ``chainladder.core.io.EstimatorIO``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
+
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/modules/generated/chainladder.core.io.TriangleIO.T.examples b/docs/modules/generated/chainladder.core.io.TriangleIO.T.examples
index 982a5493..2d3c74ed 100644
--- a/docs/modules/generated/chainladder.core.io.TriangleIO.T.examples
+++ b/docs/modules/generated/chainladder.core.io.TriangleIO.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.io.TriangleIO.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.pandas.TrianglePandas.T.examples b/docs/modules/generated/chainladder.core.pandas.TrianglePandas.T.examples
index 4891c7c0..a02bbd03 100644
--- a/docs/modules/generated/chainladder.core.pandas.TrianglePandas.T.examples
+++ b/docs/modules/generated/chainladder.core.pandas.TrianglePandas.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.pandas.TrianglePandas.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.core.slice.TriangleSlicer.T.examples b/docs/modules/generated/chainladder.core.slice.TriangleSlicer.T.examples
index d10d8328..8ad623be 100644
--- a/docs/modules/generated/chainladder.core.slice.TriangleSlicer.T.examples
+++ b/docs/modules/generated/chainladder.core.slice.TriangleSlicer.T.examples
@@ -3,25 +3,6 @@
 Examples using ``chainladder.core.slice.TriangleSlicer.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
diff --git a/docs/modules/generated/chainladder.development.DevelopmentBase.lambda_.examples b/docs/modules/generated/chainladder.development.DevelopmentBase.lambda_.examples
new file mode 100644
index 00000000..80c62c2f
--- /dev/null
+++ b/docs/modules/generated/chainladder.development.DevelopmentBase.lambda_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.development.DevelopmentBase.lambda_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.development.DevelopmentBase.q_resids_.examples b/docs/modules/generated/chainladder.development.DevelopmentBase.q_resids_.examples
new file mode 100644
index 00000000..5771a770
--- /dev/null
+++ b/docs/modules/generated/chainladder.development.DevelopmentBase.q_resids_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.development.DevelopmentBase.q_resids_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.development.DevelopmentBase.resids_.examples b/docs/modules/generated/chainladder.development.DevelopmentBase.resids_.examples
new file mode 100644
index 00000000..2bfd0fe1
--- /dev/null
+++ b/docs/modules/generated/chainladder.development.DevelopmentBase.resids_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.development.DevelopmentBase.resids_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.development.base.DevelopmentBase.lambda_.examples b/docs/modules/generated/chainladder.development.base.DevelopmentBase.lambda_.examples
new file mode 100644
index 00000000..7c8762ed
--- /dev/null
+++ b/docs/modules/generated/chainladder.development.base.DevelopmentBase.lambda_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.development.base.DevelopmentBase.lambda_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.development.base.DevelopmentBase.q_resids_.examples b/docs/modules/generated/chainladder.development.base.DevelopmentBase.q_resids_.examples
new file mode 100644
index 00000000..d83088e7
--- /dev/null
+++ b/docs/modules/generated/chainladder.development.base.DevelopmentBase.q_resids_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.development.base.DevelopmentBase.q_resids_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.development.base.DevelopmentBase.resids_.examples b/docs/modules/generated/chainladder.development.base.DevelopmentBase.resids_.examples
new file mode 100644
index 00000000..e88b8eea
--- /dev/null
+++ b/docs/modules/generated/chainladder.development.base.DevelopmentBase.resids_.examples
@@ -0,0 +1,23 @@
+
+
+Examples using ``chainladder.development.base.DevelopmentBase.resids_``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the the residual correlation plots of the Munich Chai...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_munich_resid_thumb.png
+     :alt: Munich Chainladder Correlation Plots
+
+     :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_munich_resid.py`
diff --git a/docs/modules/generated/chainladder.load_sample.examples b/docs/modules/generated/chainladder.load_sample.examples
index 6554867d..7a7142f9 100644
--- a/docs/modules/generated/chainladder.load_sample.examples
+++ b/docs/modules/generated/chainladder.load_sample.examples
@@ -5,14 +5,14 @@ Examples using ``chainladder.load_sample``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the output of one method as the apriori selection...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates one of the attributes of the ClarkLDF. We can use the growth curve G_...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bf_apriori_from_cl_thumb.png
-     :alt: Picking Bornhuetter-Ferguson Apriori
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_thumb.png
+     :alt: Clark Growth Curves
 
-     :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
@@ -20,18 +20,18 @@ Examples using ``chainladder.load_sample``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py`
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf.py`
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -39,4 +39,23 @@ Examples using ``chainladder.load_sample``
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
+
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+
+.. raw:: html
+
+    </div>
+
+.. only:: not html
+
+ * :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
diff --git a/docs/tutorials/deterministic-tutorial.ipynb b/docs/tutorials/deterministic-tutorial.ipynb
index e819bf62..03917e59 100644
--- a/docs/tutorials/deterministic-tutorial.ipynb
+++ b/docs/tutorials/deterministic-tutorial.ipynb
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chainladder:0.7.5\n",
+      "chainladder:0.7.8\n",
       "pandas:1.0.3\n"
      ]
     }
@@ -101,8 +101,8 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>9999</th>\n",
+       "      <th></th>\n",
+       "      <th>2262</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -150,7 +150,7 @@
        "</table>"
       ],
       "text/plain": [
-       "              9999\n",
+       "              2262\n",
        "2001  4.016553e+06\n",
        "2002  5.594009e+06\n",
        "2003  5.537497e+06\n",
@@ -191,8 +191,8 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>9999</th>\n",
+       "      <th></th>\n",
+       "      <th>2262</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -240,7 +240,7 @@
        "</table>"
       ],
       "text/plain": [
-       "              9999\n",
+       "              2262\n",
        "2001  1.150899e+05\n",
        "2002  2.549240e+05\n",
        "2003  6.281822e+05\n",
@@ -280,7 +280,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>2001</th>\n",
        "      <th>2002</th>\n",
        "      <th>2003</th>\n",
@@ -616,7 +616,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -826,7 +826,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -990,7 +990,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -1155,7 +1155,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>2011</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1277,11 +1277,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (1, 2, 10, 10)\n",
-       "Index:      ['LOB']\n",
-       "Columns:    ['CumPaidLoss', 'EarnedPremNet']"
+       "                        Triangle Summary\n",
+       "Valuation:                       1997-12\n",
+       "Grain:                              OYDY\n",
+       "Shape:                    (1, 2, 10, 10)\n",
+       "Index:                             [LOB]\n",
+       "Columns:    [CumPaidLoss, EarnedPremNet]"
       ]
      },
      "execution_count": 12,
@@ -1393,7 +1394,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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8yOryRBkkQS5ECUrPKeDBaVtZH5PCvwe2oPf+V2DvIrjlTej8qNXliTJKhlaE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\n",
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9A4eWQa/J0GW82eWJYkyCXIhCsD0ykScXhgGw4NH2dD70BhxYBHe8Ct2eN7k6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\nw0mQCyGEh5MgF0IIDydBLoQQHu7/AWHabE2uNffDAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1472,7 +1473,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Gr5FjdZGPVBoqcgl6qZtWUDfhYc7w5rCqz+vEXaCLfKRyUZFL0Mo5mMXq6eM5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\nIPf/le6G114VOt0AAAAASUVORK5CYII=\n",
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ks3zqq1xiX2a/uxopt35GO13kI6WQilxCzr49O1g/7WWa75hMHIdZHtGWi+77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\nTkUuIhLiVOQiIiFORS4iEuL+P1c0dhSX0IuSAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1528,8 +1529,8 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>9999</th>\n",
+       "      <th></th>\n",
+       "      <th>2262</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1577,7 +1578,7 @@
        "</table>"
       ],
       "text/plain": [
-       "          9999\n",
+       "          2262\n",
        "1988  0.685686\n",
        "1989  0.685686\n",
        "1990  0.685686\n",
@@ -1653,8 +1654,8 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>9999</th>\n",
+       "      <th></th>\n",
+       "      <th>2262</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1702,7 +1703,7 @@
        "</table>"
       ],
       "text/plain": [
-       "          9999\n",
+       "          2262\n",
        "1988  0.685281\n",
        "1989  0.704094\n",
        "1990  0.690279\n",
@@ -1744,7 +1745,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1997</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1829,7 +1830,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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DBBWBvwdcXCRq5xXhXlFl5ZFlu/jfD4eYmdSNZ2YO8ugXndls4uqRsUzsG8mj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\ng9bWTq6EeyHgfJ9TsyPYwd5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9f4TpQ7vxt+kD3fpF5+Vl4sbzejK+bxceXbWHv36ynw93HuVvVw+gX0zrXPwk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\nhPBAEu5CCOGB/j8E3bXEer7R2QAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1856,7 +1857,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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6zskZou+B32dqZ48Nw/WuyPGYzfDTs9obaOvBEHX7pYud3oF6V6dUUWJ6NjuP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\ndxUbG1vhstaE+yZgBLBECNETrbmlVBL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NQB7wrZOI3WhLnuqA1ara5J9PdzmKuQUXxX0i\nMF1VJwI9gO+Av4ETwEn/kucYkO1vbx6RHsYboXYWLfIkIrlAT1UdCcwEbgN+w7t0fNg/rgSodBCv\nK0nnSUQGAV8Cpaq6zlnE7iSdK1Xtr6pjVHUMcBBvcNVZtOV/bzP+/56I3AfUuAm5JRfFfQ+wVkS2\nAMdVda2qVgI7gG0ishWoxiv6LwFPiMgPwCrgKQfxutIiT3gjhUIR2QGsBeao6lngfWCwiGwGngZe\ndRW0A23J03y8m2Zvi8gmEfnKWdRutCVXnVlb8rQUSBORbXj3KJ5xFfSlrCukMcaEkH2IyRhjQsiK\nuzHGhJAVd2OMCSEr7sYYE0JW3I0xJoSsuBtjTAhZcTfGmBD6H4FfMakkyTvmAAAAAElFTkSuQmCC\n",
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GV/BCaiEV7npzcoX7v4WwzrB0vDajlKIotU9qPCRvZ7nTQIJ9PejU2O+WNqfC3RY4e8CD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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1890,7 +1891,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ffz8Ax48fJycnh927dzNp0iR8fHwYMmQISUlJ1b4XDw8Plxk5z1VH8XM1Uk6OcZVy2nek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\ncVi0s0NqEp/+9idLUw/w5CWx9IkIcnY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VSzmQx/YDefztkl5OjcORmvtUwEdrPVwpNQx4EZgCoLXOAsYAKKWGA88A84A/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\n/bj/QsWUfhFEtW2aLs2idi0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WWgMXAluARcBXwNcNGbRoPJW19+z8Ej7+dZ+zw2lUxWUV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\nWiBJ7kII0QL9PwyXQtA2bSz7AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1924,7 +1925,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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rsG76L/fvX0uhmyem6Bkw9G4I7Ons6v5Ewl0IISpzKhN2Lqfrpjcg5wCFXu15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\n",
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rfZx58+Yxb968OohICNFYLItNooWnG2P6Ble5rdyhKoQQjUB+UQlf7jjKmL7BtPSqul4u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\nrkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1981,7 +1982,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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7RBf5SJmhIpewdiAjh7FfzuXhjP+jamQuBYM/pHzLi92OJRJUKnIJS3vSs/lg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\n",
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mkNtnNNOPNuX1kd+zPzWTS+JieO32NnSoH+toRBW5iMiZzH0BVn/AusZDeWBeHfakrKNN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\n5CIiIU5FLiIS4v4/mE8UR9NNfKMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/docs/tutorials/development-tutorial.ipynb b/docs/tutorials/development-tutorial.ipynb
index 210519c8..c0157027 100644
--- a/docs/tutorials/development-tutorial.ipynb
+++ b/docs/tutorials/development-tutorial.ipynb
@@ -19,7 +19,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chainladder:0.7.5\n",
+      "chainladder:0.7.8\n",
       "pandas:1.0.3\n"
      ]
     }
@@ -102,7 +102,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1982</th>\n",
        "      <th>1983</th>\n",
        "      <th>1984</th>\n",
@@ -116,7 +116,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>(All)</th>\n",
+       "      <th>1981</th>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
@@ -131,8 +131,8 @@
        "</table>"
       ],
       "text/plain": [
-       "        1982   1983   1984   1985   1986   1987   1988   1989   1990\n",
-       "(All)  False  False  False  False  False  False  False  False  False"
+       "       1982   1983   1984   1985   1986   1987   1988   1989   1990\n",
+       "1981  False  False  False  False  False  False  False  False  False"
       ]
      },
      "execution_count": 3,
@@ -228,7 +228,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12-24</th>\n",
        "      <th>24-36</th>\n",
        "      <th>36-48</th>\n",
@@ -288,7 +288,7 @@
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-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12-Ult</th>\n",
        "      <th>24-Ult</th>\n",
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@@ -390,7 +390,7 @@
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@@ -513,7 +513,7 @@
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@@ -575,7 +575,7 @@
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@@ -635,7 +635,7 @@
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@@ -831,7 +831,7 @@
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@@ -975,19 +975,12 @@
    "execution_count": 19,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\users\\jboga\\onedrive\\documents\\github\\chainladder-python\\chainladder\\development\\base.py:159: UserWarning: drop_high and drop_low cannot be computed when less than three LDFs are present. Ignoring exclusions in some cases.\n",
-      "  warnings.warn('drop_high and drop_low cannot be computed '\n"
+      "c:\\users\\jboga\\onedrive\\documents\\github\\chainladder-python\\chainladder\\development\\base.py:178: UserWarning: drop_high and drop_low cannot be computed when less than three LDFs are present. Ignoring exclusions in some cases.\n",
+      "  \"drop_high and drop_low cannot be computed \"\n"
      ]
     },
     {
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@@ -1056,7 +1049,7 @@
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@@ -1116,7 +1109,7 @@
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+       "      <th></th>\n",
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+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col4 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow5_col5 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col5 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow5_col6 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col6 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow5_col7 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col7 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow5_col8 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col8 {\n",
        "            background-color:  #fff7fb;\n",
        "            color:  #000000;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col0 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col0 {\n",
        "            background-color:  #dedcec;\n",
        "            color:  #000000;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col1 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col1 {\n",
        "            background-color:  #023858;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col2 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col2 {\n",
        "            background-color:  #4094c3;\n",
        "            color:  #000000;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col3 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col3 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col4 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col4 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col5 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col5 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col6 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col6 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col7 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col7 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow6_col8 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col8 {\n",
        "            background-color:  #fff7fb;\n",
        "            color:  #000000;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col0 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col0 {\n",
        "            background-color:  #0a73b2;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col1 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col1 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col2 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col2 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col3 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col3 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col4 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col4 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col5 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col5 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col6 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col6 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col7 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col7 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow7_col8 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col8 {\n",
        "            background-color:  #fff7fb;\n",
        "            color:  #000000;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col0 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col0 {\n",
        "            background-color:  #569dc8;\n",
        "            color:  #000000;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col1 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col1 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col2 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col2 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col3 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col3 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col4 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col4 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col5 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col5 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col6 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col6 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col7 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col7 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_90892974_df14_11ea_a279_e24c55f5b97erow8_col8 {\n",
+       "        }    #T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col8 {\n",
        "            background-color:  #fff7fb;\n",
        "            color:  #000000;\n",
-       "        }</style><table id=\"T_90892974_df14_11ea_a279_e24c55f5b97e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >12-24</th>        <th class=\"col_heading level0 col1\" >24-36</th>        <th class=\"col_heading level0 col2\" >36-48</th>        <th class=\"col_heading level0 col3\" >48-60</th>        <th class=\"col_heading level0 col4\" >60-72</th>        <th class=\"col_heading level0 col5\" >72-84</th>        <th class=\"col_heading level0 col6\" >84-96</th>        <th class=\"col_heading level0 col7\" >96-108</th>        <th class=\"col_heading level0 col8\" >108-120</th>    </tr></thead><tbody>\n",
+       "        }</style><table id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >12-24</th>        <th class=\"col_heading level0 col1\" >24-36</th>        <th class=\"col_heading level0 col2\" >36-48</th>        <th class=\"col_heading level0 col3\" >48-60</th>        <th class=\"col_heading level0 col4\" >60-72</th>        <th class=\"col_heading level0 col5\" >72-84</th>        <th class=\"col_heading level0 col6\" >84-96</th>        <th class=\"col_heading level0 col7\" >96-108</th>        <th class=\"col_heading level0 col8\" >108-120</th>    </tr></thead><tbody>\n",
        "                <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row0\" class=\"row_heading level0 row0\" >2001</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col0\" class=\"data row0 col0\" >3.1432</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col1\" class=\"data row0 col1\" >1.5428</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col2\" class=\"data row0 col2\" >1.2783</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row0\" class=\"row_heading level0 row0\" >2001</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col0\" class=\"data row0 col0\" >3.1432</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col1\" class=\"data row0 col1\" >1.5428</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col2\" class=\"data row0 col2\" >1.2783</td>\n",
        "                        <td></td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col4\" class=\"data row0 col4\" >1.2092</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col5\" class=\"data row0 col5\" >1.0441</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col6\" class=\"data row0 col6\" >1.0404</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col7\" class=\"data row0 col7\" >1.0630</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow0_col8\" class=\"data row0 col8\" >1.0177</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col4\" class=\"data row0 col4\" >1.2092</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col5\" class=\"data row0 col5\" >1.0441</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col6\" class=\"data row0 col6\" >1.0404</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col7\" class=\"data row0 col7\" >1.0630</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row0_col8\" class=\"data row0 col8\" >1.0177</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row1\" class=\"row_heading level0 row1\" >2002</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col0\" class=\"data row1 col0\" >3.5106</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col1\" class=\"data row1 col1\" >1.7555</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col2\" class=\"data row1 col2\" >1.5453</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col3\" class=\"data row1 col3\" >1.1329</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col4\" class=\"data row1 col4\" >1.0845</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col5\" class=\"data row1 col5\" >1.1281</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col6\" class=\"data row1 col6\" >1.0573</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow1_col7\" class=\"data row1 col7\" >1.0865</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row1\" class=\"row_heading level0 row1\" >2002</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col0\" class=\"data row1 col0\" >3.5106</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col1\" class=\"data row1 col1\" >1.7555</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col2\" class=\"data row1 col2\" >1.5453</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col3\" class=\"data row1 col3\" >1.1329</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col4\" class=\"data row1 col4\" >1.0845</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col5\" class=\"data row1 col5\" >1.1281</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col6\" class=\"data row1 col6\" >1.0573</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row1_col7\" class=\"data row1 col7\" >1.0865</td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row2\" class=\"row_heading level0 row2\" >2003</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col0\" class=\"data row2 col0\" >4.4485</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col1\" class=\"data row2 col1\" >1.7167</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col2\" class=\"data row2 col2\" >1.4583</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col3\" class=\"data row2 col3\" >1.2321</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col4\" class=\"data row2 col4\" >1.0369</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col5\" class=\"data row2 col5\" >1.1200</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow2_col6\" class=\"data row2 col6\" >1.0606</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row2\" class=\"row_heading level0 row2\" >2003</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col0\" class=\"data row2 col0\" >4.4485</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col1\" class=\"data row2 col1\" >1.7167</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col2\" class=\"data row2 col2\" >1.4583</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col3\" class=\"data row2 col3\" >1.2321</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col4\" class=\"data row2 col4\" >1.0369</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col5\" class=\"data row2 col5\" >1.1200</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row2_col6\" class=\"data row2 col6\" >1.0606</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row3\" class=\"row_heading level0 row3\" >2004</th>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row3\" class=\"row_heading level0 row3\" >2004</th>\n",
        "                        <td></td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow3_col1\" class=\"data row3 col1\" >1.5471</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row3_col1\" class=\"data row3 col1\" >1.5471</td>\n",
        "                        <td></td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow3_col3\" class=\"data row3 col3\" >1.0725</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow3_col4\" class=\"data row3 col4\" >1.0874</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow3_col5\" class=\"data row3 col5\" >1.0471</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row3_col3\" class=\"data row3 col3\" >1.0725</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row3_col4\" class=\"data row3 col4\" >1.0874</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row3_col5\" class=\"data row3 col5\" >1.0471</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row4\" class=\"row_heading level0 row4\" >2005</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow4_col0\" class=\"data row4 col0\" >2.5642</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow4_col1\" class=\"data row4 col1\" >1.8730</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow4_col2\" class=\"data row4 col2\" >1.3615</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow4_col3\" class=\"data row4 col3\" >1.1742</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow4_col4\" class=\"data row4 col4\" >1.1383</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row4\" class=\"row_heading level0 row4\" >2005</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row4_col0\" class=\"data row4 col0\" >2.5642</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row4_col1\" class=\"data row4 col1\" >1.8730</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row4_col2\" class=\"data row4 col2\" >1.3615</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row4_col3\" class=\"data row4 col3\" >1.1742</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row4_col4\" class=\"data row4 col4\" >1.1383</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row5\" class=\"row_heading level0 row5\" >2006</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow5_col0\" class=\"data row5 col0\" >3.3656</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow5_col1\" class=\"data row5 col1\" >1.6357</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow5_col2\" class=\"data row5 col2\" >1.3692</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow5_col3\" class=\"data row5 col3\" >1.2364</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row5\" class=\"row_heading level0 row5\" >2006</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col0\" class=\"data row5 col0\" >3.3656</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col1\" class=\"data row5 col1\" >1.6357</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col2\" class=\"data row5 col2\" >1.3692</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row5_col3\" class=\"data row5 col3\" >1.2364</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -1681,10 +1674,10 @@
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row6\" class=\"row_heading level0 row6\" >2007</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow6_col0\" class=\"data row6 col0\" >2.9228</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow6_col1\" class=\"data row6 col1\" >1.8781</td>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow6_col2\" class=\"data row6 col2\" >1.4394</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row6\" class=\"row_heading level0 row6\" >2007</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col0\" class=\"data row6 col0\" >2.9228</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col1\" class=\"data row6 col1\" >1.8781</td>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row6_col2\" class=\"data row6 col2\" >1.4394</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -1693,8 +1686,8 @@
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row7\" class=\"row_heading level0 row7\" >2008</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow7_col0\" class=\"data row7 col0\" >3.9533</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row7\" class=\"row_heading level0 row7\" >2008</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row7_col0\" class=\"data row7 col0\" >3.9533</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -1705,8 +1698,8 @@
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_90892974_df14_11ea_a279_e24c55f5b97elevel0_row8\" class=\"row_heading level0 row8\" >2009</th>\n",
-       "                        <td id=\"T_90892974_df14_11ea_a279_e24c55f5b97erow8_col0\" class=\"data row8 col0\" >3.6192</td>\n",
+       "                        <th id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0level0_row8\" class=\"row_heading level0 row8\" >2009</th>\n",
+       "                        <td id=\"T_860b9dde_14d6_11eb_986e_e290e4c56cc0row8_col0\" class=\"data row8 col0\" >3.6192</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -1749,7 +1742,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>2010</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1839,7 +1832,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12-Ult</th>\n",
        "      <th>24-Ult</th>\n",
        "      <th>36-Ult</th>\n",
@@ -1959,11 +1952,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (157, 1, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['CumPaidLoss']"
+       "            Triangle Summary\n",
+       "Valuation:           1997-12\n",
+       "Grain:                  OYDY\n",
+       "Shape:      (157, 1, 10, 10)\n",
+       "Index:         [GRNAME, LOB]\n",
+       "Columns:       [CumPaidLoss]"
       ]
      },
      "execution_count": 27,
@@ -2037,11 +2031,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2262-03\n",
-       "Grain:     OYDY\n",
-       "Shape:     (6, 1, 1, 9)\n",
-       "Index:      ['LOB']\n",
-       "Columns:    ['CumPaidLoss']"
+       "           Triangle Summary\n",
+       "Valuation:          2262-03\n",
+       "Grain:                 OYDY\n",
+       "Shape:         (6, 1, 1, 9)\n",
+       "Index:                [LOB]\n",
+       "Columns:      [CumPaidLoss]"
       ]
      },
      "execution_count": 28,
diff --git a/docs/tutorials/large-datasets.ipynb b/docs/tutorials/large-datasets.ipynb
index fd5f2f96..e71da93b 100644
--- a/docs/tutorials/large-datasets.ipynb
+++ b/docs/tutorials/large-datasets.ipynb
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chainladder:0.7.5\n",
+      "chainladder:0.7.8\n",
       "pandas:1.0.3\n"
      ]
     }
@@ -85,11 +85,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OMDM\n",
-       "Shape:     (34244, 4, 120, 120)\n",
-       "Index:      ['ClaimNo', 'Line', 'Type', 'ClaimLiability', 'Limit', 'Deductible']\n",
-       "Columns:    ['reportedCount', 'closedPaidCount', 'Paid', 'Incurred']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            2017-12\n",
+       "Grain:                                                   OMDM\n",
+       "Shape:                                   (34244, 4, 120, 120)\n",
+       "Index:      [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n",
+       "Columns:     [reportedCount, closedPaidCount, Paid, Incurred]"
       ]
      },
      "execution_count": 2,
@@ -178,10 +179,10 @@
     {
      "data": {
       "text/html": [
-       "<table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">coo</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(34244, 4, 120, 120)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">5750426</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.0029153657493932433</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">219.4M</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.0</td></tr></tbody></table>"
+       "<table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">coo</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(34244, 4, 120, 120)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">5750047</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.00291517360299939</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">219.3M</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.0</td></tr></tbody></table>"
       ],
       "text/plain": [
-       "<COO: shape=(34244, 4, 120, 120), dtype=float64, nnz=5750426, fill_value=nan>"
+       "<COO: shape=(34244, 4, 120, 120), dtype=float64, nnz=5750047, fill_value=nan>"
       ]
      },
      "execution_count": 5,
@@ -248,7 +249,7 @@
        "      <td>38339</td>\n",
        "      <td>Auto</td>\n",
        "      <td>PD</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>8000.0</td>\n",
        "      <td>1000</td>\n",
        "      <td>1.0</td>\n",
@@ -393,7 +394,7 @@
       ],
       "text/plain": [
        "     ClaimNo  Line Type  ClaimLiability    Limit  Deductible  reportedCount  \\\n",
-       "0      38339  Auto   PD            True   8000.0        1000            1.0   \n",
+       "0      38339  Auto   PD           False   8000.0        1000            1.0   \n",
        "1      38436  Auto   PD            True  15000.0        1000            1.0   \n",
        "2      38142  Auto   PD            True   8000.0        1000            1.0   \n",
        "3      38195  Auto   PD            True  20000.0        1000            1.0   \n",
@@ -445,7 +446,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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H/DYFCzLGdTJXarwb8XtjY8x04PfAzSJyYOQhqfHg8Nt/wWnZJCXn0yHCQafN\np+ZlRTospc5oJx2hG2M2A0VApjGmBvg24AYQkV8C9wIZwM+DozP/cG8H1MRRW7aPtM5upjvn09Qt\nHHbZ3FWgl5tTKpJOmtBF5MaT7L8NGPJDUDUx9XR20OztZKGkkGgncMDvx58dw5TUT54tq5QaO+N3\nOYKKmPoDgTN449IC8+f1ls2ihTo6VyrSNKGrU1Z7YB+IEJsxk26E/cbmU3N1/lypSNOEPki4yud2\ndnby2c9+lvnz57No0SI2bNgQkf6MhtqPdpHc3Ut6wlyaeoUal835s/SEIqUiTRP6IOEsn/v1r3+d\nffv2sWvXLt5++21efvnlse5O2Nm2xZGKcrJ9cSQ70mnyC74UITXeHenQlDrjaUIfJFzlcxMSEli1\nahUAMTExLFu2jJqamjHsyehoqqnG5/eTExOYP2/w26Rk6tpzpaJB1JbPffO3B2is9h63baSn/mdO\nS+KiG+aG3D5c5XNbW1v5n//5H+68885Tjjna1O0vBSA+fQa9CPuNRX66jguUigb6nziMcJXP9fv9\n3HjjjfzLv/wLs2bNCmOEkVF3oJQYyyImfQ7NvsD8+cxU/TNSKhpE7Qh9qJH0eCyfu379eubMmcPX\nvva10Qx5zNTtKyGr20FibDaVXRYNsZA7jotxKTWR6H/iIOEsn3v33XfT1tbGT3/601GLdyx1trfR\n2nCEPFegQnKjX0ibnoTLoXPoSkWDqB2hR8rbb7/N008/zZIlS1i6dCkA999/Pxs2bOCGG27gscce\nY/r06Tz33HP9j8nPz6e9vZ3e3l62bt3Ka6+9RkpKCt/73veYP39+/4esd9xxB7fdNn5Pqq0vC5xQ\nlJE4Ax825VgsnJEGHItsYEopQBP6J6xcuZLhSgq//vrrQ26vrKwccnsopYnHk7r9pRiEuLTZNPuF\nw06bK/PSoE0TulLRQKdcVMjqyw+QYTlxJ0+lyQc1Lpuz8vSSc0pFC03oKiQiQsOhg0x1TAWgyS+0\nJRryMxIjHJlSqo8mdBUST+Mxejo7yIqdjl8s6o1QMCMNh34gqlTU0Dl0FZKjlQcBSEmeSbNtUenQ\n6Ralos1JR+jGmMeNMQ3GmD3D7DfGmJ8ZY8qNMR8aY5aFP0wVaccqK4hxxATmz3ud1DosTehKRZlQ\nplyeAK44wf61wJzg13rgFyMPS0WbhsoK8txTMcZBk1+oddksztWErlQ0OWlCF5E3gOYTNLkaeEoC\n3gXSjDE54QpwrIWrfC7AFVdcwdlnn82iRYu4/fbbsSxrzPsTLg0V5eS4crHEoklsepNd5KbFRzos\npdQAJpS10saYfOBFEVk8xKT6x9wAACAASURBVL4XgY0i8lbw/uvAN0TkvSHaricwiic7O3v5li1b\njtufmppKQUHBsHGMtDhXKI4cOcKRI0dYunQpHo+Hiy++mM2bN/Pss8+Snp7OXXfdxY9//GNaW1v5\n7ne/S0dHBx9++CElJSWUlJTw4IMP9h+rvb2dlJQURISbb76Za665huuvv/4TfSkvL6etrW1U+zUS\n/u4uPvj1I3x20ufpSsphay+8l2fxb+cGErrX6yUpKSnCUYaH9iV6TaT+jKQvq1at2jncdZvD8aHo\nUMschnyVEJFNwCaAwsJCKSoqOm5/aWnpCWu1jEUtl+TkZObMmdN/e9GiRbS2tvLyyy9TXFxMcnIy\n69evp6ioiJ/85CckJyczZcoU6urqiImJOS6+vts+nw/btklISOjfNrAvcXFxnHPOOaPar5E4vOcD\nPgDiE7I5Zrk5bHysXJxPUdECAIqLixn8uxyvtC/RayL1Z7T6Eo6EXgNMG3A/D6gb6UH/8sQmGqoq\njttm+S2crtMfoU+eMYtVX14fcvtwlM+9/PLL2b59O2vXru0fnY83DYcOEutIwOlOxNtlUe2yuHaq\nzp8rFW3CsQ79D8AtwdUu5wNtIlIfhuNGVLjK57766qvU19fT09PDn//85zBGOHaOlO4l06QB4LGE\nOqfN4qmn/zNRSo2Ok47QjTGbgSIg0xhTA3wbcAOIyC+Bl4DPAOVAJ/CVcAQ21Eh6PJbPhcCUylVX\nXcULL7zAmjVrRivsUXP0QCn5jkCFxWa3hYlz6hmiSkWhkyZ0EbnxJPsF+OewRRRhJyufu2HDhpDK\n53q9XjweDzk5Ofj9fl566SUuuuii0Q4/7Hy9PbR52klJnIRPhOo4J4umJuoZokpFIT1TdJBwlc/N\nyMjgqquuoqenB8uyuPTSS7n99tsj1a3TdqyiHAHcyTl4LWGP7WNZbujvTpRSY0cT+iDhLJ+7Y8eO\ncIUVMTVvvwlAfOwUmnxw0O3jFv1AVKmopMW51Akd2VdCou0m0ZFAu9vG60DPEFUqSmlCVyfUcKSW\nbAmsaGlOdBHrcjA7Sz8QVSoaaUJXw/J1d9PW001iQmD9/V4XLMhJweXUPxulopH+Z6phHT1UDsbg\nTp6BX4SXvV6Wz0iPdFhKqWFoQlfDqtvzIQBJsXl4jdAsNoWa0JWKWprQ1bDq93xIXK+fFFcK7TGB\nbcvzNaErFa00oQ8SzvK5fa666ioWL/5Eocqod/TwIZKtOBIdDmrcDqZPSmByclykw1JKDUMT+iAu\nl4sHH3yQ0tJS3n33XR555BFKSkrYuHEjq1evpqysjNWrV7Nx40YgcFr/fffdx49+9KMhj/f73/9+\nXJb87PZ68XR2kBKXD8A2n0/nz5WKcprQB8nJyWHZssBV9JKTk1mwYAG1tbW88MILrFu3DoB169ax\ndetWABITE1m5ciVxcZ8cuXq9Xn784x9z9913j10HwuRIRRkA8SmBUsJ/6+3WhK5UlIvaM0Vb/+cg\nvXUdx22zLD9dztMPOWZqImlXzg65/UjL595zzz3867/+KwkJCacdc6QcPRhI6EmJ+XRjUYdQqPPn\nSkU1HaEPY6Tlc3fv3k15eTnXXnvtKEQ3+ur3lZDQ4yc9Jok6VzfJcS7mTh79SpdKqdMXtSP0oUbS\n46l87rZt29i5cyf5+fn4/X4aGhooKiqiuLh41OMPh4ZD5STZCSQ7DO+4Olk2PU8rLCoV5XSEPsjJ\nyucCIZXP/cd//Efq6uqorKzkrbfeYu7cueMmmft6e/C0tZIWX4Axhjf9On+u1HgQ0gjdGHMF8BDg\nBB4VkY2D9qcCzwDTg8f8kYj8Osyxjolwlc9duHBhpLowYi11tQAkJc8CYJcVw//VhK5U1AvlikVO\n4BFgDYHrh+4wxvxBREoGNPtnoERErjTGZAH7jTHPikjvqEQ9isJZPrdPfn4+e/bsGWloY6a5rgZ/\nfBKkZNJCNz3ORJZpQlcq6oUy5bICKBeRimCC3gIMnm8QINkYY4AkoBnwhzVSNWaaa2voyZlBWWI9\nh52tFOanE+c+/YtzK6XGRigJPReoHnC/JrhtoIeBBUAd8BFwp4jYYYlQjbmjFeXYsfFMtdPY6z7G\nhQWZkQ5JKRWCUObQh1raMHhO4nJgN3ApMBv4kzHmTRFpP+5AxqwH1kNg1cjgDwlTU1PxeDzDBmJZ\n1gn3jycD+9Ld3R1VH5hWVVXC5Olk26k8GVPJZ9qqKC6uGba91+uNqvhHQvsSvSZSf0arL6Ek9Bpg\n2oD7eQRG4gN9BdgYvGB0uTHmEDAf2D6wkYhsAjYBFBYWSlFR0XEHKS0tPeGyxLFatjgWBvYlLi6O\nc845J8IRBYhts+13m0Egw07moNtwy5WX4jzBksXi4mIG/y7HK+1L9JpI/RmtvoQy5bIDmGOMmWmM\niQG+CPxhUJvDwGoAY0w2MA+oCGegamy0Nx7DH59EmpVAs+lhbnb+CZO5Uip6nHSELiJ+Y8wdwKsE\nli0+LiJ7jTG3B/f/ErgPeMIY8xGBKZpviEjjKMatRkljzWGs+ARyrEkcdh3jopnzIx2SUipEIZ1Y\nJCIvichcEZktIt8LbvtlMJkjInUi8mkRWSIii0XkmdEMejSFs3xuUVER8+bNY+nSpSxdujSk+i+R\nVnlgPzic5EgaZXE1fHre3EiHpJQKkZ4pOki4y+c+++yz7N69m927d5+0XEA0OHywHIBsO5WS+DoK\nsk69jo1SKjI0oQ8SzvK541GTx0OsZYiTWOoneQicWqCUGg+itjjXyy+/zJEjR47bZlkWTufpn+Ay\nZcoU1q5dG3L7kZbPBfjKV76C0+nkuuuu4+677476BNnpjCXHSqbF9jN10vRIh6OUOgU6Qh/GSMvn\nQmC65aOPPuLNN9/kzTff5Omnnw5zlOF1tK4W2x1DLlnUO9pZPmVRpENSSp2CqB2hDzWSHk/lcwFy\ncwMn1CYnJ/OlL32J7du3c8stt4xq7CNxYG+g3kyOTOJddw1FM4siG5BS6pToCH2QcJXP9fv9NDYG\nVm76fD5efPHFqL9QdFV5OUaESZJEWXw1CzJDv7qTUiryonaEHinhKp87Y8YMLr/8cnw+H5Zlcdll\nl/EP//APkepWSBqO1JPojwExHE5qw+XQPw+lxhP9jx0knOVzd+7cGa6wRp2I4LFhhpVMmyUkZCVF\nOiSl1CnSKRcFwJHaGsTpIseRRbPtZ8G0WZEOSSl1ijShKwD27d4FwGRJp9bRykX5Z0c4IqXUqdKE\nrgCoKi8DEdIlkUMx9ZydvSDSISmlTpEmdAVAQ2MTST4HRhwcjm8kOWZilClW6kyiCV3h9/nowpBp\nJdFuCSZdLzen1HikCV1Rta8EcbnJdmTRZglTpmZEOiSl1GnQhD5IOMvn9vb2sn79eubOncv8+fN5\n/vnnx7w/odj/0YcATGYSzbaPcxfPiXBESqnToQl9kHCWz/3e977H5MmTOXDgACUlJVxyySVj3Z2Q\nVB86BMEzRA/G1bBsqtZwUWo8CimhG2OuMMbsN8aUG2M2DNOmyBiz2xiz1xjz1/CGOXbCWT738ccf\n5z/+4z8AcDgcZGZmjlEvTk1zezuJPoNLHJSmlJGTmBPpkJRSp+GkZ4oaY5zAI8AaAheM3mGM+YOI\nlAxokwb8HLhCRA4bY0Z8JYcDB+7D4y09bptl+XE6T//k1uSkBcyde0/I7UdSPre1tRWAe+65h+Li\nYmbPns3DDz9Mdnb2acc/Gnq6Oul1xTDZn4jHBv9UX9SX+FVKDS2UEfoKoFxEKkSkF9gCDK5M9SXg\n9yJyGEBEov9aaycx0vK5fr+fmpoaLrzwQt5//30uuOACvv71r49CpCNzuKwMcceQYybTYvuZNy83\n0iEppU5TKMPdXKB6wP0a4LxBbeYCbmNMMZAMPCQiTw0+kDFmPbAeAuVoi4uLj9ufmpqKx+MBICfn\nawx+4z/SC1wA/cc/EZ/Px+c//3muv/561qxZg8fjISsri7KyMqZMmcKRI0fIzMw87ljd3d309vb2\nb4uJiSEhIYHLLrsMj8fD2rVr+dWvftW/37Ks/tvd3d2f+FmMlfIP3gdgmpnMHncDCa3OU47F6/VG\nLP5w075Er4nUn9HqSygJfaj334OrV7mA5cBqIB7YZox5V0QOHPcgkU3AJoDCwkIpKio67iClpaUn\nrHc+FvXQRYR169axZMmS/vlvgGuuuYbnn3+eDRs28Mgjj3DttdceF0tcXBwxMTHHbbvyyivZuXMn\nl156KX/7299YvHhx//6BfYmLi+Occ84Z1X4N58D77+GwbNIkkX2JO/nqqtvJjD+1uf7i4mIG/y7H\nK+1L9JpI/RmtvoSS0GuAaQPu5wF1Q7RpFJEOoMMY8wZwNnCAcSZc5XMXLlzIAw88wM0338zXvvY1\nsrKy+PWvfx2pbg2rsd1Dao8D4zCUTio/5WSulIoeoST0HcAcY8xMoBb4IoE584FeAB42xriAGAJT\nMj8JZ6BjJZzlc2fMmMEbb7wRrtDCrrW1lV4Ms+x0OrBJnaUlc5Uaz06a0EXEb4y5A3gVcAKPi8he\nY8ztwf2/FJFSY8wrwIeADTwqIntGM3A1clVVVQBMN7kccbRzbu5ZEY5IKTUSIa0BFJGXgJcGbfvl\noPs/BH4YvtDUaDtYXo6xLKY5J/NK7B4WZy6NdEhKqRHQM0XPYJWHDpHUCwbDttT3WJShZ4gqNZ5p\nQj9DeTwe2r1eptipdNsWVZPrSI1NjXRYSqkR0IR+hur7ILfAkU+teJifvTCyASmlRkwT+hnq0KFD\nOG0hx2SxO/4QF00bfK6YUmq80YQ+SLjK53o8HpYuXdr/lZmZyde+9rWI9GkwEaG8vJy0HgcOHGxL\ne4+VuRdGOiyl1AhpQh8kXOVzk5OT2b17d//XjBkz+NznPheJLn3CsWPHaG9vJ9fOpNXnoS71CHnJ\neZEOSyk1QprQBwln+dw+ZWVlNDQ0cNFFF41+B0Jw8OBBAOY5Z1Pf20heWmTKDiilwuv0a9GOsnvK\natjj7Tpum+W3cLpOvzjX4qR47psT+kh0JOVzB9q8eTNf+MIXoqYsbXl5OSmOGFJJ5K/OGs7P0ekW\npSYCHaEPY6TlcwfasmULN954Y5giGxmfz0dVVRVZ3XH4bR/vTirlitma0JWaCKJ2hD7USHosqi1C\nIOldd9113HTTTf3z3tnZ2dTX15OTk0N9fT2TJ4d2DY8PPvgAv9/P8uXLRzPkkFVVVeH3+5nmz6ah\nu5ojue0UZE2KdFhKqTDQEfogIsKtt97KggULuOuuu/q3X3XVVTz55JMAPPnkk1x99eBrfAxt8+bN\nUTM6h8B0i8vpJN+RS31PI8TOx+GIjqkgpdTIRO0IPVLCWT4X4Le//S0vvfTSkM8VCeXl5WSZRFw4\n2e86SH7a2kiHpJQKE03og4SzfC5ARUVFOMIKi/b2dhobG1ncnYPH18w7+R9y+ZS7Tv5ApdS4oFMu\nZ5C+F56ZksORzsPgyGDelNH/TEIpNTY0oZ9BKisrcTvcZJk0anwHaW5byZzJmtCVmihCSujGmCuM\nMfuNMeXGmA0naHeuMcYyxlx/ugENN90xUY1lfysrK0n3JSC2RUXidjrdZ5ObFj9mz6+UGl0nTejG\nGCfwCLAWWAjcaIz5RGm+YLsHCFzZ6LTExcXR1NR0xiR1EaGpqemEZ5mGS3t7O83NzeT4U2noPgxZ\n8NmzpuoKF6UmkFA+FF0BlItIBYAxZgtwNVAyqN1XgeeBc083mLy8PGpqajh27NiQ+7u7u8ck+Y2F\nvr7ExcWRlzf6dVT6Ljc3k2xqO3bQ27uQzy2dOurPq5QaO6Ek9FygesD9GgIXge5njMkFrgUu5QQJ\n3RizHlgPgRN1iouLTylYr9dLUtLEuJDxwL70JdvRtH//fpziZJIk82HvTqoda2iv+IDiQyMfoXu9\n3lP+XUYr7Uv0mkj9Ga2+hJLQh/qPHzwn8lPgGyJinaheiYhsAjYBFBYWSlFRUYhhBhQXF3Oqj4lW\nY92XPXv2kOFPpMPXQsqk/Sw+9/usWrU4LMfW30t0mkh9gYnVn9HqSygJvQaYNuB+HlA3qE0hsCWY\nzDOBzxhj/CKyNSxRqhHxeDw0NjZSKLM42nGIxKx0rlw2PdJhKaXCLJSEvgOYY4yZCdQCXwS+NLCB\niMzsu22MeQJ4UZN59Oib0smVDA52vUF3/FlcpuvPlZpwTrrKRUT8wB0EVq+UAr8Vkb3GmNuNMbeP\ndoBq5Pbt24dTHKRYsVgpb5E09+KoKeWrlAqfkE79F5GXgJcGbfvlMG2/PPKwVLjU1tayZ88eFvhy\naO6sZVFWC8kXXhHpsJRSo0DPFJ3ARISXX36ZWFcM59pzaeg8iNM5gxk5WZEOTSk1CjShT2AfffQR\nNTU1zPQmEYOL5ri/0DP1gkiHpZQaJZrQJ6je3l7+9Kc/kTMlh6X+2TR2HyV3SjnTl3860qEppUaJ\nJvQJavv27Xg8HpYlFpDkSqG8dReF0sOk+RdHOjSl1CjRhD4BdXV18dZbbzGnoIBJe4Wm7joOJO+g\nN3k+xOpyRaUmKk3oE9C2bdvo7u7m/OyziSWWvW3vc8GUPSTMvSTSoSmlRpEm9AnG6/Wybds2Fi1c\nSNzfPDR111Pva+ISXwcJczShKzWRaUKfYN566y38fj+FsfMRr8VHrW/gmeTHYGD6+ZEOTyk1ivSa\nohNIU1MT27dv56y5i3H/rZ3a9oMc7arm2vxDgWQenxbpEJVSo0hH6BPIa6+9hsvlYml9DrgsdrS+\nge2exNlUYQpvjXR4SqlRpgl9gqioqGD//v2syFmCu8HiYO1r9FgNxCeDJGTCwqsiHaJSapRpQp8A\nbNvmlVdeITUllYKDKbjmxPGhpxJMLF9K/zNm2c3gio10mEqpUaYJfQJ47733aGho4IK4hbgdLt7c\n/RS9pg1S8kiPaYPlX4l0iEqpMaAJfZzzeDy8/vrrzJgyjdzD8ThXpFHdUI3DOZWbp/4vZu7lkD4j\n0mEqpcaAJvRx7rXXXsPv93OBpwDXpHj+8OpPEATis5lMDaz4h0iHqJQaIyEldGPMFcaY/caYcmPM\nhiH232SM+TD49Y4x5uzwh6oGO3ToEB999BHLMxeQ1OSgbU4HnoYjuGOXc0PuVshbAbNXRzpMpdQY\nOWlCN8Y4gUeAtcBC4EZjzMJBzQ4Bl4jIWcB9BC8ErUZPe3s7L7zwAqlxSSysyiT+U5N5+fmfYZxZ\nxBofuY7DsPoe0CsTKXXGCGWEvgIoF5EKEekFtgBXD2wgIu+ISEvw7rsELiStRklHRwdPP/00HZ4O\nLmmbT8o5Ofy1/HfYPh8JrkKun/4MMvMSmKmVFZU6k4SS0HOB6gH3a4LbhnMr8PJIglLDa21t5Zmn\nn6G5sZk1XUuYVjCD+sk1HPrbNpyxhTgSSshwNmNW3xvpUJVSY8yIyIkbGPN54HIRuS14/2ZghYh8\ndYi2q4CfAytFpGmI/euB9QDZ2dnLt2zZckrBer1ekpKSTukx0SqUvojUIXyE4dM0NjZSV1dHS0sL\nDgxres8iacYkDtgfUVn8Cg5nHonM5u/n/YCWjGXsXfwfY9STM+/3Ml5MpL7AxOrPSPqyatWqnSJS\nONS+UGq51ADTBtzPA+oGNzLGnAU8CqwdKpkDiMgmgvPrhYWFUlRUFMLTf6y4uJhTfUy0OlFfunuO\ncKjiIerqf4cxcZQfnExtTRtJznjO8c9kfuw0pt9yNiUVb1H55Cs4nbkkxBRxYe6juGITyLrlCYpS\ncqKiL+ON9iV6TaT+jFZfQknoO4A5xpiZQC3wReBLAxsYY6YDvwduFpEDYY/yDNDTc4yjDS/S2Pg6\nra07EBFaWs5hX+ksEvBzSe9C5sRPI/XyaXiyvDz/6//H0coKXI5pxMStJj73XRY5d8DaTTCGyVwp\nFT1OmtBFxG+MuQN4FXACj4vIXmPM7cH9vwTuBTKAn5vAqgr/cG8J1PF6exupqtpETe0z2HYPfv9k\njtTPp76+gBTnDM7rncxcO5f0S6fjzevi9ec3cfDD93HaTmITVoF7DqXZ1TwU8xQUfAbOuiHSXVJK\nRUhI5XNF5CXgpUHbfjng9m3AbeENbWIT8VBe/gOqa57Gtrtobp5HxcG5GHKYmzSdpd40MqxE4hdn\n4pnZySv/+59U7/0QhziJiTkHk3ghbUmNtLl38dPYhzGpefB3P9VlikqdwbQe+hjr6Cintu43WPaz\nVB3upblpNhUVC5kUN49LXNOZeiwRR7uT2MXpHE2s5c9v/YJjf6jA6UrCFX8xzpjFNCZWULVwGyuq\nyvmW60WYfhHc8BQkTIp095RSEaQJfQyIWDQce43q6idoa3sPEQfHjk3ncNVZTIlbxGU9OWS3pODM\njse7wENpzZuU/2EHYlsY5yRcCWtI9me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pWtDVSavZtweMITZ9Ml0Y9oqPz03X+XOlIk0Lej+hap/b0dHB5ZdfzsyZM5kz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\nyvGHQ3dnBz22GMZ6EnD6wDP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7g7F7i0hp+x+K5np47pZMbi38JQmvbeSihp+yL2YG2bes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\nmoiGHkIINfE3xfDudqQ0V6cAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -553,11 +554,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2262-03\n",
-       "Grain:     OMDM\n",
-       "Shape:     (34244, 2, 120, 1)\n",
-       "Index:      ['ClaimNo', 'Line', 'Type', 'ClaimLiability', 'Limit', 'Deductible']\n",
-       "Columns:    ['Paid', 'reportedCount']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            2262-03\n",
+       "Grain:                                                   OMDM\n",
+       "Shape:                                     (34244, 2, 120, 1)\n",
+       "Index:      [ClaimNo, Line, Type, ClaimLiability, Limit, D...\n",
+       "Columns:                                [Paid, reportedCount]"
       ]
      },
      "execution_count": 10,
@@ -586,7 +588,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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57y66E6PTHlORy0gp1WP+7gd+BlymlOpTSmWUUlngP4HLzMO7gRWO05cDPeb48iLjOeeI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\nq2CFUGQZOjlLhVBkOQ4DNVMth2fdJUamnEm6xXxVjMvIizg1k5YBm4H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xq6Ed9McnK+5TlclIKTVg/h4GfglcopQ6rpRKK6UywLeAS8zd+4EVOYcvBwbM8eUlxvOO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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -612,15 +614,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "paid_bf = cl.BornhuetterFerguson(apriori=40000).fit(\n",
     "    X=prism['Paid'].sum().incr_to_cum(),\n",
@@ -660,13 +654,6 @@
    "execution_count": 14,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'sparse._coo.core.COO'>\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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QLuhmZoVwQTczK4QLuplZIVzQzcwK4YJuZlYIF3Qzs0K4oJuZFcIF3cysEC7oZmaFcEE3\nMyuEC7qZWSFc0M3MCjGu3wGsnsnHXt52/FHbL+OQNtPMP/mdfc/QrRw58LqwHLmFbmZWCBd0M7NC\nuKCbmRXCfegduK/UzMYKt9DNzArhFrrZGNVp7xF6c/aT5cMtdDOzQnQs6JLOkfSwpFtbjJ8qaYmk\nm6rb8d2PaWZmndTpcjkXOAM4r80010TEvl1JZNaGuxnMWutY0CPil5Imj36Ul8vh15FmZmNFt/rQ\nd5N0s6QfS9q2S/M0M7NhUER0nii10C+LiO2ajFsbeDEinpK0D3BqRGzVYj7TgekAAwMDO8+YMaPt\ncuc8sKTt+IE1YNHS1uO3n7hO2+fXMdIMueTIIUM3cnTKUCeH10X3MtTJ4XVRP0OdHNOmTZsdEVOa\njRtxQW8y7XxgSkQ80m66KVOmxKxZs9rOq06XyylzWvca9eqCVO0y5JIjhwzdyFG3D93rol4Or4vu\nZaiToxvrQlLLgj7iLhdJG0lSdX/Xap6PjnS+ZmY2PB0Pikq6EJgKTJC0APg8sBpARJwFvBf4qKRl\nwFLgwKjT7Dczs66qc5bLBzqMP4N0WqOZmfWRfylqZlYIF3Qzs0K4oJuZFcIF3cysEC7oZmaFcEE3\nMyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXCBd3MrBAu6GZmhXBBNzMrhAu6mVkhXNDNzArh\ngm5mVggXdDOzQrigm5kVwgXdzKwQLuhmZoVwQTczK4QLuplZIVzQzcwK4YJuZlYIF3Qzs0K4oJuZ\nFcIF3cysEC7oZmaFcEE3MyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXCBd3MrBAu6GZmhXBB\nNzMrRMeCLukcSQ9LurXFeEk6TdI8SbdIemP3Y5qZWSd1WujnAnu1Gb83sFV1mw6cOfJYZmY2XB0L\nekT8EniszST7A+dFch2wrqSNuxXQzMzq6UYf+kTg/obHC6phZmbWQ4qIzhNJk4HLImK7JuMuB06K\niGurxz8Hjo6I2U2mnU7qlmFgYGDnGTNmtF3unAeWtB0/sAYsWtp6/PYT12n7/DpGmiGXHDlk6EaO\nThnq5PC66F6GOjm8LupnqJNj2rRpsyNiSrNx49rPupYFwKYNjycBDzabMCLOBs4GmDJlSkydOrXt\njA859vK244/afhmnzGn9EuZ/sP386xhphlxy5JChGzk6ZaiTw+uiexnq5PC6qJ9hpDm60eUyEzi4\nOtvlzcCSiFjYhfmamdkwdGyhS7oQmApMkLQA+DywGkBEnAVcAewDzAOeAQ4drbBmZtZax4IeER/o\nMD6Aj3ctkZmZvSL+paiZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXCBd3MrBAu6GZmhXBBNzMrhAu6\nmVkhXNDNzArhgm5mVggXdDOzQrigm5kVwgXdzKwQLuhmZoVwQTczK4QLuplZIVzQzcwK4YJuZlYI\nF3Qzs0K4oJuZFcIF3cysEC7oZmaFcEE3MyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXCBd3M\nrBAu6GZmhXBBNzMrhAu6mVkhXNDNzArhgm5mVggXdDOzQrigm5kVwgXdzKwQtQq6pL0k3SFpnqRj\nm4yfKmmJpJuq2/Hdj2pmZu2M6zSBpFWBbwDvABYA10uaGRG3D5n0mojYdxQymplZDXVa6LsC8yLi\n7oh4DpgB7D+6sczMbLjqFPSJwP0NjxdUw4baTdLNkn4saduupDMzs9oUEe0nkN4H7BkRh1ePDwJ2\njYgjG6ZZG3gxIp6StA9wakRs1WRe04HpAAMDAzvPmDGj7bLnPLCk7fiBNWDR0tbjt5+4Ttvn1zHS\nDLnkyCFDN3J0ylAnh9dF9zLUyeF1UT9DnRzTpk2bHRFTmo2rU9B3A06IiD2rx8cBRMRJbZ4zH5gS\nEY+0mmbKlCkxa9astsuefOzlbccftf0yTpnT+jDA/JPf2fb5dYw0Qy45csjQjRydMtTJ4XXRvQx1\ncnhd1M9QJ4eklgW9TpfL9cBWkraQtDpwIDBzyAI2kqTq/q7VfB+tMW8zM+uSjme5RMQySUcAPwFW\nBc6JiNskfaQafxbwXuCjkpYBS4EDo1PT38zMuqpjQQeIiCuAK4YMO6vh/hnAGd2NZmZmw+FfipqZ\nFcIF3cysEC7oZmaFcEE3MyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCbmRXCBd3MrBAu6GZmhXBB\nNzMrhAu6mVkhXNDNzArhgm5mVggXdDOzQrigm5kVwgXdzKwQLuhmZoVwQTczK4QLuplZIVzQzcwK\n4YJuZlYIF3Qzs0K4oJuZFcIF3cysEC7oZmaFcEE3MyuEC7qZWSFc0M3MCuGCbmZWCBd0M7NCuKCb\nmRXCBd3MrBAu6GZmhXBBNzMrhAu6mVkhXNDNzArhgm5mVohaBV3SXpLukDRP0rFNxkvSadX4WyS9\nsftRzcysnY4FXdKqwDeAvYFtgA9I2mbIZHsDW1W36cCZXc5pZmYd1Gmh7wrMi4i7I+I5YAaw/5Bp\n9gfOi+Q6YF1JG3c5q5mZtaGIaD+B9F5gr4g4vHp8EPCmiDiiYZrLgJMj4trq8c+BYyJi1pB5TSe1\n4AFeD9wxwvwTgEdGOI+RyiED5JEjhwyQR44cMkAeOXLIAHnk6EaGzSNiw2YjxtV4spoMG/otUGca\nIuJs4Oway6xF0qyImNKt+Y3VDLnkyCFDLjlyyJBLjhwy5JJjtDPU6XJZAGza8HgS8OArmMbMzEZR\nnYJ+PbCVpC0krQ4cCMwcMs1M4ODqbJc3A0siYmGXs5qZWRsdu1wiYpmkI4CfAKsC50TEbZI+Uo0/\nC7gC2AeYBzwDHDp6kV+ia903I5BDBsgjRw4ZII8cOWSAPHLkkAHyyDGqGToeFDUzs7HBvxQ1MyuE\nC7qZWSFc0M3MCuGCbmZWiDFT0CWtL+l4SYdXp0d+VtJlkr4qab0e5pgm6QxJ/y3p+5JOlrRlr5bf\nkGNPSWdKmlllOVPSXr3O0Yyk43u8vD0lHSZp8pDhH+rR8iXpAEnvq+7/ZXWxuo9J6utnTNIvery8\nCUMe/121LqZLavYDxNHK8deS1q/ubyjpPElzJH1P0qQeZfiapLf0YlnLlzlWznKRdAUwB1gb2Lq6\nfxHwDmCHiBh6fZnRyHAyMAD8HHg3cA/wB+BjwIkRcfFoZ6hy/BvwOuA80o+6IP2Y62Dgzoj4ZC9y\ntCLpvojYrEfLOhF4K3ADsB/wbxFxejXuhogY9St/Svom8GfA6sATwKuAH5FO5V3Uq/dD0i1DB5G2\nkzsAIuL/9CDD8nUu6XPA7sAFwL7Agoj4x9HOUC379ojYprr/PeA64GJgD+CDEfGOHmT4I3AvsCHw\nPeDCiLhxVBcaEWPiBtxU/RXwQLNxPcgwp+H+OOBX1f31gFt7uC7+0GK4SAW9FxmeaHF7EljWw3Ux\nBxhX3V+X9JuIr1ePb+zldgGsBjwKrN6wjczpRYZqeTOB84E3AJsDk4H7q/ub9yjDjQ33bwDWbFg3\nvVwXdzTcnz1kXK/qxY3V362A/wvcBvwe+DzwutFY5pjpcgFWqbpWNgXGD+5eS9qA1DLqhRcHd+OA\nTUg/tCIiHqf59WxGy7OSdm0yfBfg2R5lWAxsFRFrD7mtBfTyV8LjImIZQEQsJrXS15Z0Mb3bLgaX\n/zxwfaSrklLleqFHGYiIdwHfJ/14ZYeImA88HxH3RsS9PYqxhqSdJO0MrBoRT1fZnqeH6wK4StIX\nJK1R3X83pC5TYEmPMgRARNwZEV+MiG2BA4BXkxoeXVfn4ly5OIn07QbwIeA/JQXpGu3/0qMMJwI3\nSrqD1Ar6KKQ+OuDmHmUAOAQ4U9JarOhy2ZTUQj6kRxnOI7X8FjUZd0GPMgDcJentEXE1QES8ABwm\n6UvAe3qU4SFJ4yPiqYhYfhxD0kbAcz3KAEBE/FDST4EvSjqc3n2pDVoIfK26/5ikjSNiYdXwWtbD\nHEcAn2XFFV3/UdLTpK6wg3qU4WWNvIi4BbgFOG5UFljtEowJ1T/bUKTLEYwDdiR1v/SsRVi10P+c\ndI34xb1abossGwETSRvOgoh4qJ95+qFqgRERS5uMmxgRD/Q+1fLlr0nqcni4T8vfAdgt0uU5+qr6\n7L4qIp7pw7LXIe3JPdrj5Y6PiKd6uswxVtBF+ocbE0m7Mw8Cv4sevogcMrQj6Q0R8fvOU5adIZcc\nOWTIJUcOGXLJMVoZxkxBl/RXwDeBO4HBVtckYEvgYxHx05UhQye9PMMk5wy55MghQy45csiQS47R\nyjCW+tBPBfaoDvQsJ2kL0gGGrVeSDEg6rdUo0pkeK0WGXHLkkCGXHDlkyCVHPzKMpRb6ncDWg2c0\nNAxfHbg9Ikb9xz05ZKiW9yRwFPCnJqNPiYgJTYYXlyGXHDlkyCVHDhlyydGPDGOphX4OcL2kGaRz\nayGd2XEg8K2VKAOkfzpya0T8eugISSesRBlyyZFDhlxy5JAhlxw9zzBmWugAkrYB3kXDmR3AzIi4\nfSXLsD7wbD/OGMgpQy45csiQS44cMuSSox8ZxlRBNzOz1sbML0UlraN0IazfS3q0us2thvXqIEff\nM+SSI4cMueTIIUMuOXLIkEuOfmQYMwWddCGux4GpEbFBRGwATCP9BL0nF8XKJEO7HI/3MEcOGXLJ\nkUOGXHLkkCGXHD3PMGa6XCTdERGvH+640jLkkiOHDLnkyCFDLjlyyJBLjn5kGEst9HslHS1pYHCA\npAFJx7DijJOVIUMuOXLIkEuOHDLkkiOHDLnk6HmGsVTQ3w9sAFwt6XFJjwFXAeuTrmC2smTIJUcO\nGXLJkUOGXHLkkCGXHL3PED26PnE3bqQrHO4BjB8yfK+VKUMuOXLIkEuOHDLkkiOHDLnk6HWGnq3c\nLqyYT5AuhXkpMB/Yv2HcDStLhlxy5JAhlxw5ZMglRw4ZcsnRjww9WbldWjlzBr/lSP+JZRbwyepx\nz/4zTb8z5JIjhwy55MghQy45csiQS45+ZBhLP/1fNaprC0fEfElTgUskbQ49+29BOWTIJUcOGXLJ\nkUOGXHLkkCGXHD3PMJYOij4kacfBB9WK2heYAGy/EmXIJUcOGXLJkUOGXHLkkCGXHD3PMJbOQ59E\n+ufDL/uvPJLeEhG/Whky5JIjhwy55MghQy45csiQS45+ZBgzBd3MzNobS10uZmbWhgu6mVkhXNDN\nzArhgm5mVggXdDOzQvx/crvuuFr47kAAAAAASUVORK5CYII=\n",
@@ -701,10 +688,10 @@
      "data": {
       "text/plain": [
        "ClaimNo  Line  Type  ClaimLiability  Limit    Deductible\n",
-       "12138    Auto  PD    True            20000.0  1000              0.00000\n",
+       "12138    Auto  PD    False           20000.0  1000              0.00000\n",
        "12039    Auto  PD    True            15000.0  1000          11537.72996\n",
-       "12046    Auto  PD    True            8000.0   1000              0.00000\n",
-       "12112    Auto  PD    True            15000.0  1000              0.00000\n",
+       "12046    Auto  PD    False           8000.0   1000              0.00000\n",
+       "12112    Auto  PD    False           15000.0  1000              0.00000\n",
        "12125    Auto  PD    True            15000.0  1000          14000.00000\n",
        "Name: Selected Ultimate, dtype: float64"
       ]
@@ -733,7 +720,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD4CAYAAABMtfkzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/docs/tutorials/stochastic-tutorial.ipynb b/docs/tutorials/stochastic-tutorial.ipynb
index 1fc92adf..ece53d02 100644
--- a/docs/tutorials/stochastic-tutorial.ipynb
+++ b/docs/tutorials/stochastic-tutorial.ipynb
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chainladder:0.7.5\n",
+      "chainladder:0.7.8\n",
       "pandas:1.0.3\n"
      ]
     }
@@ -107,7 +107,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "    </tr>\n",
@@ -278,10 +278,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2887.</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Sat, 15 Aug 2020</td> <th>  Prob (F-statistic):</th>          <td>1.60e-11</td>\n",
+       "  <th>Date:</th>             <td>Thu, 22 Oct 2020</td> <th>  Prob (F-statistic):</th>          <td>1.60e-11</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>10:36:31</td>     <th>  Log-Likelihood:    </th>          <td> -107.89</td>\n",
+       "  <th>Time:</th>                 <td>20:48:13</td>     <th>  Log-Likelihood:    </th>          <td> -107.89</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>     9</td>      <th>  AIC:               </th>          <td>   217.8</td>\n",
@@ -327,8 +327,8 @@
        "Dep. Variable:                      y   R-squared (uncentered):                   0.997\n",
        "Model:                            WLS   Adj. R-squared (uncentered):              0.997\n",
        "Method:                 Least Squares   F-statistic:                              2887.\n",
-       "Date:                Sat, 15 Aug 2020   Prob (F-statistic):                    1.60e-11\n",
-       "Time:                        10:36:31   Log-Likelihood:                         -107.89\n",
+       "Date:                Thu, 22 Oct 2020   Prob (F-statistic):                    1.60e-11\n",
+       "Time:                        20:48:13   Log-Likelihood:                         -107.89\n",
        "No. Observations:                   9   AIC:                                      217.8\n",
        "Df Residuals:                       8   BIC:                                      218.0\n",
        "Df Model:                           1                                                  \n",
@@ -429,7 +429,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12-24</th>\n",
        "      <th>24-36</th>\n",
        "      <th>36-48</th>\n",
@@ -482,7 +482,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12-24</th>\n",
        "      <th>24-36</th>\n",
        "      <th>36-48</th>\n",
@@ -572,7 +572,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -857,14 +857,14 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>9999</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1988</th>\n",
-       "      <td>0</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1989</th>\n",
@@ -907,7 +907,7 @@
       ],
       "text/plain": [
        "              9999\n",
-       "1988      0.000000\n",
+       "1988           NaN\n",
        "1989   7312.634869\n",
        "1990  17838.223062\n",
        "1991  21813.683826\n",
@@ -946,7 +946,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>Latest</th>\n",
        "      <th>IBNR</th>\n",
        "      <th>Ultimate</th>\n",
@@ -959,7 +959,7 @@
        "      <td>1,241,715</td>\n",
        "      <td></td>\n",
        "      <td>1,241,715</td>\n",
-       "      <td>0</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1989</th>\n",
@@ -1029,7 +1029,7 @@
       ],
       "text/plain": [
        "         Latest          IBNR      Ultimate  Mack Std Err\n",
-       "1988  1241715.0           NaN  1.241715e+06      0.000000\n",
+       "1988  1241715.0           NaN  1.241715e+06           NaN\n",
        "1989  1308706.0  1.332126e+04  1.322027e+06   7312.634869\n",
        "1990  1394675.0  4.221037e+04  1.436885e+06  17838.223062\n",
        "1991  1414747.0  7.940888e+04  1.494156e+06  21813.683826\n",
@@ -1084,7 +1084,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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sAFwJzHD30UAbMNHMtgXOjeuNBy43s+a+ebiISJNJqkUwHlgK3A4MB74CTCa0CgAWAIcB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\n",
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xzr9NbGYj8W8Wj3P+bWIzG4lrBGZmBedEYGZWcE4EZmYF50TQZSo7f0tPMXSn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\n1O3lGLY+wQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1215,11 +1215,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (10000, 1, 10, 10)\n",
-       "Index:      ['LOB']\n",
-       "Columns:    ['CumPaidLoss']"
+       "              Triangle Summary\n",
+       "Valuation:             1997-12\n",
+       "Grain:                    OYDY\n",
+       "Shape:      (10000, 1, 10, 10)\n",
+       "Index:                   [LOB]\n",
+       "Columns:         [CumPaidLoss]"
       ]
      },
      "execution_count": 24,
@@ -1308,11 +1309,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2262-03\n",
-       "Grain:     OYDY\n",
-       "Shape:     (10000, 1, 1, 9)\n",
-       "Index:      ['LOB']\n",
-       "Columns:    ['CumPaidLoss']"
+       "            Triangle Summary\n",
+       "Valuation:           2262-03\n",
+       "Grain:                  OYDY\n",
+       "Shape:      (10000, 1, 1, 9)\n",
+       "Index:                 [LOB]\n",
+       "Columns:       [CumPaidLoss]"
       ]
      },
      "execution_count": 26,
@@ -1345,7 +1347,7 @@
     },
     {
      "data": {
-      "image/png": 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ippjZWOBKYCLQBcx091sS5bsDpwErgSvdfXYcvgBYGkd7zt1T7YweKM44zlrA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\ncdPNdusVAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1378,7 +1380,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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SKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpc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\nRFJFiUtERFJFiUtERFLl/wEtk8ytv56mxAAAAABJRU5ErkJggg==\n",
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SKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpcIiKSKkpc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\nIqmixCUiIqmixCUiIqny/0I8zK2PHQ7PAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1388,7 +1390,7 @@
     }
    ],
    "source": [
-    "mack_vs_bs = resampled_ldf[resampled_ldf.origin == resampled_ldf.origin.max()].std('index').to_frame().append(\n",
+    "mack_vs_bs = resampled_ldf.std('index').to_frame().append(\n",
     "    orig_dev.std_err_.to_frame()).T\n",
     "mack_vs_bs.columns = ['Mack', 'Bootstrap']\n",
     "mack_vs_bs.plot(kind='bar', title='Mack Regression Framework LDF Std Err vs Bootstrap Simulated LDF Std Err');"
@@ -1410,13 +1412,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "99%-ile of reserve estimate is 2,777,767.0\n"
+      "99%-ile of reserve estimate is 3,149,043.0\n"
      ]
     }
    ],
    "source": [
     "ibnr = cl.Chainladder().fit(samples).ibnr_.sum('origin')\n",
-    "ibnr_99 = ibnr.quantile(0.99).to_frame().values[0,0]\n",
+    "ibnr_99 = ibnr.quantile(q=0.99)\n",
     "print(\"99%-ile of reserve estimate is \" +'{:0,}'.format(round(ibnr_99,0)))"
    ]
   },
@@ -1434,7 +1436,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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2pw6JgxMM9A8AkE1lyQwFeXmU/Nh0Lpef9fLiZblcnvzo5NeUW78eywt1qFU8\nhWWlymtRh2w2y/O7nmfHjgSDg9W54CDuv+lKRJII3P2bZnZEUVGTu4+G0/3AQmABsLtonUJ5Wclk\nks7OztkKdZLu7u5It18NM63DXlcH7erjjMOCM4LDUstItu0CoIlmkm1JAFpaxqdnq7x4WUtLM81N\nk19Tbv16LM8MZUi2JWsWT2FZqfJa1CGRSHDIoYeweDEsXbqYaojjb3oqXV1dJZdV66qh4gbmDmAX\n0BdOTyyXKtPVQSLxVq2rhp40s1Xh9BnAZuBxYKWZtZnZQqCToCNZRESqqFpnBFcA682sFegGbnf3\nnJmtJUgKzcBV7j5UpXhERCQUWSJw9/8Cjg+nnwFOnmKd9cD6qGIQEZHp6YYyEZGY0xATIlJVvele\nBtJtbH0mO1Y2r3keqcQBGp66RpQIRKRqiscg+sxte49BdFDqAA1PXSNKBDFSfL9AsaLBIUUkhpQI\nYqT4foFixx9f/VhEpH6os1hEJOaUCEREYk6JQEQk5pQIRERiTolARCTmdNXQnHQQzz03uVSXiUq9\nGxlhyr9d3WgWLSWCOWhwcB6bNwfTe4b3MJwbBuAtK1P0htmgual57PGTmZHxcj2SUmppzx547LHJ\n5brRLFpKBHPccG6YjVs2ArD0qFPYuCW4m/Pc5eey6elNU5aL1EJvunevgxIYP2Dpz7TQ09c/Vp5K\npFiUWlSLMOckJQIRqbnioScKByUwfsDyitcdz39te3as/MxXnUF/Uk1Gs0WJQETqXn44xS13jD/j\nePToHAel1GQ0W3TVkIhIzCkRiIjEnJqGGszO9E7S2aAzLTNwAIMDLcD4eO6BZI2iE5FGpETQYNLZ\nNGvuWgPAyoWrx9pNC+O5AxxzjE70RKRySgRzSG+6F4Cmecmxad0XIHNZqRvQWloglwumBweXjK2j\nq4ympkTQAIofKNOf6WDlwtUAHJZaBgRnBIXL7wAO6zyZjVseAnRfgMxtpW5AO/748fLnn89y6KHB\ntK4ympoSQQ2UelJYqaOV4gfK9KZzbNwS7PyvfPdREUYpUt/K3YBWXN4yv4U9w3uY3zq/VqHWPSWC\nGij1pLDf/m09SlKkEtPdgFZcnhnKcNHrL2I4PcxAuo2tz2TH1i9cZFHclFQsLk1JSgR1pPg0t9QY\nQWrzF5m54sTxmdvGE0fhIovipqRicWlKqnkiMLNm4EvAa4EM8Mfuvq22Uc1MqaaeUkcZxUf4xTv8\niYO/3bb1NkBjAYlItGqeCIB3Am3ufoKZHQ98Hji7xjHNSLmHwldyhK8dvkjtTNXXAEF/w0C6da+m\nJAiak9rbDphTTUn1kAjeBFogxVAAAAWTSURBVHwPwN0fM7Pjonqj4puxYPyGrOKbsQYHl/DLX059\nJF98hJ/ODjKSHwFgZDhBb3oIgNaW1ik7pcqNAioitVGqrwGC3+aWnl/u1ZQEQXPSW1Y284PNkxPH\nmae10rN978TR1NRE67xmMtncpPLU/BGS7YN7lbc0tzDYlxy7WRQgO/oyfrZtkJcdfEAkiaZpdHR0\n9rc6A2b2FeCb7n5POP/fwCvdfWSq9bu6urYDU1w5LCIiZSxdsWLFkqkW1MMZQR/QUTTfXCoJAJSq\niIiI7Jt6GIvgEeBMgLCPYEttwxERiZd6OCP4FvBWM/sh0ARcWON4RERipeZ9BCIiUlv10DQkIiI1\npEQgIhJzSgQiIjFXD53FVWdmCeCrwBEEj/O6xt2/U7T83cCHgRzwFPABd6+rQX6mq0PRev8E7HT3\nj1U3wulV8D28Hrie4CKCF4DV7j5Ug1BLqqAO5wFXEPwtfdXd/7EWcZZjZi3AesAI4rzQ3Z8tWv4O\n4JPACEEd1tck0DIqqEMj/KbL1qFovVn/Tcf1jGA18JK7rwTOAG4oLDCzFHANcIq7nwgsBN5ekyjL\nK1mHAjO7FDi62oHNQLnvoYngR3GhuxfuPl9akyjLm+57uA44FTgJuMLM6nEAgncAuPtJBDv86wsL\nwkT3d8BpwMnAJWb2a7UIchrl6tAov+mSdSiI6jcd10TwL8AniuaLb2DLACe6e+G+73lAXR2FhsrV\nATM7ATge+HI1g5qhcnV4NfAS8GEzewhY5O5ezeAqVPZ7IDj6XAi0EZzZ1N1leu7+beCScHYp8GLR\n4k5gm7v3uvsw8DCwssohTmuaOjTEb3qaOkT6m45l05C7DwCYWQdwO3B10bI84RdgZn8CtAP31yDM\nssrVwcwOBf4S+F3gD2oRXyXK1QFYDJwI/Anwn8CdZtbl7t+veqBlTFMHgK1AF7AHuMPdd1U3wsq4\n+4iZfY3gb+b3ixYtAHYXzfcTJLa6U6oOjfKbhtJ1iPo3HdczAszscOAB4OvufuuEZc1mdh3wVuD3\n3L3ujuKgbB3eRbAjvRv4GPAeM7ug+hFOr0wdXiI4Ev2pu2cJmoZW1CLG6ZSqg5kdA5wFvIKgD+EQ\nM3tXTYKsgLufT3Amtt7MCiMnThwCpgOoy2QGJevQML9pKFmHSH/TsTwjMLOXAfcBHyxxhPllgtPJ\nd9Zbh1JBuTq4+1pgbbjeBcBvuvuGasc4nWm+h58D7Wb2qvD5FCuBG6sd43SmqcNuIA2k3T1nZv8L\n1F0fgZm9FzjM3f8aGATyBJ2VAN3AkWa2CBgA3kzQ71FXpqkDNMZvumQdov5Nx/LOYjP7AvCHwM+K\nitcD84Enwn+bGW/P/YK7f6uqQU6jXB3c/Z+K1ruA4I+mHq8aKlsHM3sL8FmCtvUfuvuHahBmWRXU\nYQ1wETAMPAtcHLa1143wqPMm4NeABMFnPh9oD+tQuGqomeCqoX+oWbAllKsDjfObLvs9FK13AbP8\nm45lIhARkXGx7SMQEZGAEoGISMwpEYiIxJwSgYhIzMXy8lERkUZkZm8E/sbdV5VZ5wLg/UAL8K/u\n/unptqszAhGRBmBmHwW+QjBcSal1lhEkgVXAG4DWcLyosnRGICLSGJ4FzgG+DmBmRxPcZNZEcCf+\nRQQDHD4BfA04FLg2vDO/LJ0RiIg0AHf/JlC8U18PXBY2E90NfJRgGIo3A+8Dfg/4opkdON22dUYg\nItKYOoEvmRkEdyI/A/wH8KC79wP9ZvZTgnGLHi+3ISUCEZHG5MAfuft/m9lJBE1BDlxmZm0EncWv\nAbZNtyElAhGRxvR+4ObwyWYA73P3Z8zsRuARgr6DT7v7zuk2pLGGRERiTp3FIiIxp0QgIhJzSgQi\nIjGnRCAiEnNKBCIiMadEICISc0oEIiIx93+djxwYxFe5AgAAAABJRU5ErkJggg==\n",
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D/cA6d0+Z2WqCotACXOHuiSrlJE2q2GmiAPF47XMRmYmqUgjcfRj4QJFFJxZZtxforUYe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\nibj/BbeUw7Hd1iWuAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1470,7 +1472,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1997</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1604,7 +1606,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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X84s833+eYTKcT2IWMWkm9IpjOya4doMuTbdJ02uaYOs5Hq7lyr4WCnMWM1U9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\nB8RuP0iCsstTjpuVSCSPG0dey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lEZNkwiyfGWlE6+drwRqBF+BarjSZlIUxu4ryiKqqTMSsuze7XtckKq9G2Ivn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\nRK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1631,6 +1633,13 @@
     "- Mack does impose more assumptions (i.e. constraints) on the reserve estimate making the Bootstrap approach more suitable in a broader set of applciations<br>\n",
     "- Both methods converge to their corresponding deterministic point estimates<br>"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/tutorials/tail-tutorial.ipynb b/docs/tutorials/tail-tutorial.ipynb
index afe28759..17d3cf1e 100644
--- a/docs/tutorials/tail-tutorial.ipynb
+++ b/docs/tutorials/tail-tutorial.ipynb
@@ -19,7 +19,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chainladder:0.7.5\n",
+      "chainladder:0.7.8\n",
       "pandas:1.0.3\n"
      ]
     }
@@ -91,7 +91,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>3-6</th>\n",
        "      <th>6-9</th>\n",
        "      <th>9-12</th>\n",
@@ -112,7 +112,7 @@
        "      <th>138-141</th>\n",
        "      <th>141-144</th>\n",
        "      <th>144-147</th>\n",
-       "      <th>147-Ult</th>\n",
+       "      <th>147-150</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -144,7 +144,7 @@
        "</table>"
       ],
       "text/plain": [
-       "          3-6       6-9      9-12     12-15     15-18     18-21     21-24     24-27     27-30     30-33     33-36     36-39    39-42    42-45     45-48     48-51    51-54     54-57     57-60     60-63     63-66     66-69     69-72     72-75     75-78     78-81     81-84     84-87     87-90     90-93     93-96     96-99    99-102   102-105   105-108   108-111   111-114   114-117  117-120  120-123   123-126  126-129  129-132  132-135   135-138   138-141   141-144   144-147  147-Ult\n",
+       "          3-6       6-9      9-12     12-15     15-18     18-21     21-24     24-27     27-30     30-33     33-36     36-39    39-42    42-45     45-48     48-51    51-54     54-57     57-60     60-63     63-66     66-69     69-72     72-75     75-78     78-81     81-84     84-87     87-90     90-93     93-96     96-99    99-102   102-105   105-108   108-111   111-114   114-117  117-120  120-123   123-126  126-129  129-132  132-135   135-138   138-141   141-144   144-147  147-150\n",
        "(All)  8.5625  3.554745  2.765914  1.933185  1.605535  1.401081  1.327029  1.165767  1.109803  1.077971  1.054556  1.046539  1.03534  1.03039  1.028639  1.017245  1.01494  1.010918  1.014197  1.008495  1.013206  1.008549  1.007365  1.005104  1.006258  1.009568  1.006634  1.002668  1.001661  1.002694  1.005374  1.001645  1.000838  1.001117  1.001673  1.000278  1.000835  0.999583      1.0      1.0  1.000911      1.0  1.00091      1.0  1.000119  1.000097  1.000079  1.000065  1.00029"
       ]
      },
@@ -305,7 +305,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>3-6</th>\n",
        "      <th>6-9</th>\n",
        "      <th>9-12</th>\n",
@@ -326,7 +326,7 @@
        "      <th>138-141</th>\n",
        "      <th>141-144</th>\n",
        "      <th>144-147</th>\n",
-       "      <th>147-Ult</th>\n",
+       "      <th>147-150</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -358,7 +358,7 @@
        "</table>"
       ],
       "text/plain": [
-       "            3-6       6-9      9-12     12-15     15-18     18-21    21-24     24-27     27-30     30-33     33-36    36-39     39-42     42-45     45-48     48-51     51-54     54-57    57-60     60-63     63-66     66-69    69-72     72-75     75-78     78-81     81-84     84-87     87-90    90-93     93-96     96-99    99-102   102-105   105-108   108-111   111-114   114-117   117-120   120-123  123-126   126-129   129-132   132-135   135-138   138-141   141-144   144-147   147-Ult\n",
+       "            3-6       6-9      9-12     12-15     15-18     18-21    21-24     24-27     27-30     30-33     33-36    36-39     39-42     42-45     45-48     48-51     51-54     54-57    57-60     60-63     63-66     66-69    69-72     72-75     75-78     78-81     81-84     84-87     87-90    90-93     93-96     96-99    99-102   102-105   105-108   108-111   111-114   114-117   117-120   120-123  123-126   126-129   129-132   132-135   135-138   138-141   141-144   144-147   147-150\n",
        "(All)  3.598765  2.476844  2.734072  1.468338  1.296558  1.182523  1.24184  1.045144  1.043969  1.036461  1.036835  1.02265  0.997308  1.032489  1.019202  0.997929  1.005544  1.011627  1.01173  1.003865  1.010892  1.009946  1.00807  0.999864  1.006029  1.008043  1.005945  0.999689  1.003302  1.00144  1.004519  1.000409  1.001669  1.001111  0.999168  0.999445  1.002501  0.997921  1.000417  0.999584      1.0  0.998182  1.002732  0.999092  1.000335  1.000291  1.000253  1.000219  1.001438"
       ]
      },
diff --git a/docs/tutorials/template-tutorial.ipynb b/docs/tutorials/template-tutorial.ipynb
index d9638b72..13cc8bb1 100644
--- a/docs/tutorials/template-tutorial.ipynb
+++ b/docs/tutorials/template-tutorial.ipynb
@@ -17,7 +17,7 @@
     {
      "data": {
       "text/plain": [
-       "'0.7.5'"
+       "'0.7.8'"
       ]
      },
      "execution_count": 1,
@@ -57,7 +57,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
diff --git a/docs/tutorials/triangle-tutorial.ipynb b/docs/tutorials/triangle-tutorial.ipynb
index 95a44d22..ea4af6a1 100644
--- a/docs/tutorials/triangle-tutorial.ipynb
+++ b/docs/tutorials/triangle-tutorial.ipynb
@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "chainladder:0.7.5\n",
+      "chainladder:0.7.8\n",
       "pandas:1.0.3\n"
      ]
     }
@@ -59,7 +59,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
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-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow7_col6 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row7_col6 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow7_col7 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row7_col7 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow7_col8 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row7_col8 {\n",
        "            background-color:  #fff5f0;\n",
        "            color:  #000000;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col0 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col0 {\n",
        "            background-color:  #fff5f0;\n",
        "            color:  #000000;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col1 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col1 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col2 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col2 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col3 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col3 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col4 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col4 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col5 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col5 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col6 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col6 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col7 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col7 {\n",
        "            background-color:  #000000;\n",
        "            color:  #f1f1f1;\n",
-       "        }    #T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col8 {\n",
+       "        }    #T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col8 {\n",
        "            background-color:  #fff5f0;\n",
        "            color:  #000000;\n",
-       "        }</style><table id=\"T_79060682_df14_11ea_a02d_e24c55f5b97e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >12-24</th>        <th class=\"col_heading level0 col1\" >24-36</th>        <th class=\"col_heading level0 col2\" >36-48</th>        <th class=\"col_heading level0 col3\" >48-60</th>        <th class=\"col_heading level0 col4\" >60-72</th>        <th class=\"col_heading level0 col5\" >72-84</th>        <th class=\"col_heading level0 col6\" >84-96</th>        <th class=\"col_heading level0 col7\" >96-108</th>        <th class=\"col_heading level0 col8\" >108-120</th>    </tr></thead><tbody>\n",
+       "        }</style><table id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >12-24</th>        <th class=\"col_heading level0 col1\" >24-36</th>        <th class=\"col_heading level0 col2\" >36-48</th>        <th class=\"col_heading level0 col3\" >48-60</th>        <th class=\"col_heading level0 col4\" >60-72</th>        <th class=\"col_heading level0 col5\" >72-84</th>        <th class=\"col_heading level0 col6\" >84-96</th>        <th class=\"col_heading level0 col7\" >96-108</th>        <th class=\"col_heading level0 col8\" >108-120</th>    </tr></thead><tbody>\n",
        "                <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row0\" class=\"row_heading level0 row0\" >1981</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col0\" class=\"data row0 col0\" >1.6498</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col1\" class=\"data row0 col1\" >1.3190</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col2\" class=\"data row0 col2\" >1.0823</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col3\" class=\"data row0 col3\" >1.1469</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col4\" class=\"data row0 col4\" >1.1951</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col5\" class=\"data row0 col5\" >1.1130</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col6\" class=\"data row0 col6\" >1.0333</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col7\" class=\"data row0 col7\" >1.0029</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow0_col8\" class=\"data row0 col8\" >1.0092</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row0\" class=\"row_heading level0 row0\" >1981</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col0\" class=\"data row0 col0\" >1.6498</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col1\" class=\"data row0 col1\" >1.3190</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col2\" class=\"data row0 col2\" >1.0823</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col3\" class=\"data row0 col3\" >1.1469</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col4\" class=\"data row0 col4\" >1.1951</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col5\" class=\"data row0 col5\" >1.1130</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col6\" class=\"data row0 col6\" >1.0333</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col7\" class=\"data row0 col7\" >1.0029</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row0_col8\" class=\"data row0 col8\" >1.0092</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row1\" class=\"row_heading level0 row1\" >1982</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col0\" class=\"data row1 col0\" >40.4245</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col1\" class=\"data row1 col1\" >1.2593</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col2\" class=\"data row1 col2\" >1.9766</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col3\" class=\"data row1 col3\" >1.2921</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col4\" class=\"data row1 col4\" >1.1318</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col5\" class=\"data row1 col5\" >0.9934</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col6\" class=\"data row1 col6\" >1.0434</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow1_col7\" class=\"data row1 col7\" >1.0331</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row1\" class=\"row_heading level0 row1\" >1982</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col0\" class=\"data row1 col0\" >40.4245</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col1\" class=\"data row1 col1\" >1.2593</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col2\" class=\"data row1 col2\" >1.9766</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col3\" class=\"data row1 col3\" >1.2921</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col4\" class=\"data row1 col4\" >1.1318</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col5\" class=\"data row1 col5\" >0.9934</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col6\" class=\"data row1 col6\" >1.0434</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row1_col7\" class=\"data row1 col7\" >1.0331</td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row2\" class=\"row_heading level0 row2\" >1983</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col0\" class=\"data row2 col0\" >2.6370</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col1\" class=\"data row2 col1\" >1.5428</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col2\" class=\"data row2 col2\" >1.1635</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col3\" class=\"data row2 col3\" >1.1607</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col4\" class=\"data row2 col4\" >1.1857</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col5\" class=\"data row2 col5\" >1.0292</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow2_col6\" class=\"data row2 col6\" >1.0264</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row2\" class=\"row_heading level0 row2\" >1983</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col0\" class=\"data row2 col0\" >2.6370</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col1\" class=\"data row2 col1\" >1.5428</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col2\" class=\"data row2 col2\" >1.1635</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col3\" class=\"data row2 col3\" >1.1607</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col4\" class=\"data row2 col4\" >1.1857</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col5\" class=\"data row2 col5\" >1.0292</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row2_col6\" class=\"data row2 col6\" >1.0264</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row3\" class=\"row_heading level0 row3\" >1984</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow3_col0\" class=\"data row3 col0\" >2.0433</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow3_col1\" class=\"data row3 col1\" >1.3644</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow3_col2\" class=\"data row3 col2\" >1.3489</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow3_col3\" class=\"data row3 col3\" >1.1015</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow3_col4\" class=\"data row3 col4\" >1.1135</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow3_col5\" class=\"data row3 col5\" >1.0377</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row3\" class=\"row_heading level0 row3\" >1984</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row3_col0\" class=\"data row3 col0\" >2.0433</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row3_col1\" class=\"data row3 col1\" >1.3644</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row3_col2\" class=\"data row3 col2\" >1.3489</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row3_col3\" class=\"data row3 col3\" >1.1015</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row3_col4\" class=\"data row3 col4\" >1.1135</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row3_col5\" class=\"data row3 col5\" >1.0377</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row4\" class=\"row_heading level0 row4\" >1985</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow4_col0\" class=\"data row4 col0\" >8.7592</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow4_col1\" class=\"data row4 col1\" >1.6556</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow4_col2\" class=\"data row4 col2\" >1.3999</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow4_col3\" class=\"data row4 col3\" >1.1708</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow4_col4\" class=\"data row4 col4\" >1.0087</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row4\" class=\"row_heading level0 row4\" >1985</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row4_col0\" class=\"data row4 col0\" >8.7592</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row4_col1\" class=\"data row4 col1\" >1.6556</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row4_col2\" class=\"data row4 col2\" >1.3999</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row4_col3\" class=\"data row4 col3\" >1.1708</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row4_col4\" class=\"data row4 col4\" >1.0087</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row5\" class=\"row_heading level0 row5\" >1986</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow5_col0\" class=\"data row5 col0\" >4.2597</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow5_col1\" class=\"data row5 col1\" >1.8157</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow5_col2\" class=\"data row5 col2\" >1.1054</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow5_col3\" class=\"data row5 col3\" >1.2255</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row5\" class=\"row_heading level0 row5\" >1986</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row5_col0\" class=\"data row5 col0\" >4.2597</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row5_col1\" class=\"data row5 col1\" >1.8157</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row5_col2\" class=\"data row5 col2\" >1.1054</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row5_col3\" class=\"data row5 col3\" >1.2255</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -563,10 +563,10 @@
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row6\" class=\"row_heading level0 row6\" >1987</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow6_col0\" class=\"data row6 col0\" >7.2172</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow6_col1\" class=\"data row6 col1\" >2.7229</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow6_col2\" class=\"data row6 col2\" >1.1250</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row6\" class=\"row_heading level0 row6\" >1987</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row6_col0\" class=\"data row6 col0\" >7.2172</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row6_col1\" class=\"data row6 col1\" >2.7229</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row6_col2\" class=\"data row6 col2\" >1.1250</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -575,9 +575,9 @@
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row7\" class=\"row_heading level0 row7\" >1988</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow7_col0\" class=\"data row7 col0\" >5.1421</td>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow7_col1\" class=\"data row7 col1\" >1.8874</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row7\" class=\"row_heading level0 row7\" >1988</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row7_col0\" class=\"data row7 col0\" >5.1421</td>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row7_col1\" class=\"data row7 col1\" >1.8874</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -587,8 +587,8 @@
        "                        <td></td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_79060682_df14_11ea_a02d_e24c55f5b97elevel0_row8\" class=\"row_heading level0 row8\" >1989</th>\n",
-       "                        <td id=\"T_79060682_df14_11ea_a02d_e24c55f5b97erow8_col0\" class=\"data row8 col0\" >1.7220</td>\n",
+       "                        <th id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0level0_row8\" class=\"row_heading level0 row8\" >1989</th>\n",
+       "                        <td id=\"T_1cda4042_14d6_11eb_9062_e290e4c56cc0row8_col0\" class=\"data row8 col0\" >1.7220</td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
        "                        <td></td>\n",
@@ -631,7 +631,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1990</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -837,11 +837,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (775, 6, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR', 'EarnedPremCeded', 'EarnedPremNet']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            1997-12\n",
+       "Grain:                                                   OYDY\n",
+       "Shape:                                       (775, 6, 10, 10)\n",
+       "Index:                                          [GRNAME, LOB]\n",
+       "Columns:    [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..."
       ]
      },
      "execution_count": 8,
@@ -958,7 +959,7 @@
       "text/plain": [
        "Index(['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR',\n",
        "       'EarnedPremCeded', 'EarnedPremNet'],\n",
-       "      dtype='object')"
+       "      dtype='object', name='columns')"
       ]
      },
      "execution_count": 10,
@@ -1073,11 +1074,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (775, 3, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['CumPaidLoss', 'IncurLoss', 'BulkLoss']"
+       "                              Triangle Summary\n",
+       "Valuation:                             1997-12\n",
+       "Grain:                                    OYDY\n",
+       "Shape:                        (775, 3, 10, 10)\n",
+       "Index:                           [GRNAME, LOB]\n",
+       "Columns:    [CumPaidLoss, IncurLoss, BulkLoss]"
       ]
      },
      "execution_count": 13,
@@ -1136,11 +1138,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (132, 6, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR', 'EarnedPremCeded', 'EarnedPremNet']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            1997-12\n",
+       "Grain:                                                   OYDY\n",
+       "Shape:                                       (132, 6, 10, 10)\n",
+       "Index:                                          [GRNAME, LOB]\n",
+       "Columns:    [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..."
       ]
      },
      "execution_count": 14,
@@ -1170,7 +1173,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -1388,11 +1391,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (775, 2, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['CaseIncurLoss', 'PaidToInc']"
+       "                      Triangle Summary\n",
+       "Valuation:                     1997-12\n",
+       "Grain:                            OYDY\n",
+       "Shape:                (775, 2, 10, 10)\n",
+       "Index:                   [GRNAME, LOB]\n",
+       "Columns:    [CaseIncurLoss, PaidToInc]"
       ]
      },
      "execution_count": 16,
@@ -1424,7 +1428,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -1641,11 +1645,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (6, 8, 10, 10)\n",
-       "Index:      ['LOB']\n",
-       "Columns:    ['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR', 'EarnedPremCeded', 'EarnedPremNet', 'CaseIncurLoss', 'PaidToInc']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            1997-12\n",
+       "Grain:                                                   OYDY\n",
+       "Shape:                                         (6, 8, 10, 10)\n",
+       "Index:                                                  [LOB]\n",
+       "Columns:    [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..."
       ]
      },
      "execution_count": 18,
@@ -1675,7 +1680,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -1903,11 +1908,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (775, 8, 1, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR', 'EarnedPremCeded', 'EarnedPremNet', 'CaseIncurLoss', 'PaidToInc']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            1997-12\n",
+       "Grain:                                                   OYDY\n",
+       "Shape:                                        (775, 8, 1, 10)\n",
+       "Index:                                          [GRNAME, LOB]\n",
+       "Columns:    [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..."
       ]
      },
      "execution_count": 20,
@@ -1937,7 +1943,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>12</th>\n",
        "      <th>24</th>\n",
        "      <th>36</th>\n",
@@ -2101,7 +2107,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
+       "      <th>origin</th>\n",
        "      <th>1988</th>\n",
        "      <th>1989</th>\n",
        "      <th>1990</th>\n",
@@ -2211,7 +2217,7 @@
        "</div>"
       ],
       "text/plain": [
-       "             1988     1989      1990      1991      1992      1993      1994  \\\n",
+       "origin       1988     1989      1990      1991      1992      1993      1994  \\\n",
        "LOB                                                                            \n",
        "comauto    626097   674441    718396    711762    731033    762039    768095   \n",
        "medmal     217239   222707    235717    275923    267007    276235    252449   \n",
@@ -2220,7 +2226,7 @@
        "prodliab   110973   112614    121255    100276     76059     94462    111264   \n",
        "wkcomp    1241715  1308706   1394675   1414747   1328801   1187581   1114842   \n",
        "\n",
-       "              1995     1996     1997  \n",
+       "origin        1995     1996     1997  \n",
        "LOB                                   \n",
        "comauto     675166   510191   272342  \n",
        "medmal      209222   107474    20361  \n",
@@ -2317,11 +2323,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (4, 9, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR', 'EarnedPremCeded', 'EarnedPremNet', 'CaseIncurLoss', 'PaidToInc', 'NetPaidLossRatio']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            1997-12\n",
+       "Grain:                                                   OYDY\n",
+       "Shape:                                         (4, 9, 10, 10)\n",
+       "Index:                                          [GRNAME, LOB]\n",
+       "Columns:    [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..."
       ]
      },
      "execution_count": 26,
@@ -2374,11 +2381,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 1997-12\n",
-       "Grain:     OYDY\n",
-       "Shape:     (31, 9, 10, 10)\n",
-       "Index:      ['GRNAME', 'LOB']\n",
-       "Columns:    ['IncurLoss', 'CumPaidLoss', 'BulkLoss', 'EarnedPremDIR', 'EarnedPremCeded', 'EarnedPremNet', 'CaseIncurLoss', 'PaidToInc', 'NetPaidLossRatio']"
+       "                                             Triangle Summary\n",
+       "Valuation:                                            1997-12\n",
+       "Grain:                                                   OYDY\n",
+       "Shape:                                        (31, 9, 10, 10)\n",
+       "Index:                                          [GRNAME, LOB]\n",
+       "Columns:    [IncurLoss, CumPaidLoss, BulkLoss, EarnedPremD..."
       ]
      },
      "execution_count": 27,
@@ -2436,7 +2444,7 @@
     {
      "data": {
       "text/plain": [
-       "\u001b[1;31mInit signature:\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTriangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevelopment\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevelopment_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marray_backend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+       "\u001b[1;31mInit signature:\u001b[0m \u001b[0mcl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTriangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevelopment\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevelopment_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marray_backend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpattern\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mDocstring:\u001b[0m     \n",
        "The core data structure of the chainladder package\n",
        "\n",
@@ -2753,7 +2761,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
@@ -4428,11 +4436,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OMDM\n",
-       "Shape:     (1, 2, 120, 120)\n",
-       "Index:      ['Total']\n",
-       "Columns:    ['Paid', 'Incurred']"
+       "            Triangle Summary\n",
+       "Valuation:           2017-12\n",
+       "Grain:                  OMDM\n",
+       "Shape:      (1, 2, 120, 120)\n",
+       "Index:               [Total]\n",
+       "Columns:    [Paid, Incurred]"
       ]
      },
      "execution_count": 32,
@@ -4465,7 +4474,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
@@ -6137,11 +6146,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OMDM\n",
-       "Shape:     (1, 2, 120, 120)\n",
-       "Index:      ['Total']\n",
-       "Columns:    ['Paid', 'Incurred']"
+       "            Triangle Summary\n",
+       "Valuation:           2017-12\n",
+       "Grain:                  OMDM\n",
+       "Shape:      (1, 2, 120, 120)\n",
+       "Index:               [Total]\n",
+       "Columns:    [Paid, Incurred]"
       ]
      },
      "execution_count": 34,
@@ -6204,11 +6214,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OYDM\n",
-       "Shape:     (1, 2, 10, 120)\n",
-       "Index:      ['Total']\n",
-       "Columns:    ['Paid', 'Incurred']"
+       "            Triangle Summary\n",
+       "Valuation:           2017-12\n",
+       "Grain:                  OYDM\n",
+       "Shape:       (1, 2, 10, 120)\n",
+       "Index:               [Total]\n",
+       "Columns:    [Paid, Incurred]"
       ]
      },
      "execution_count": 35,
@@ -6251,8 +6262,8 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>Premium</th>\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -6504,7 +6515,7 @@
        "</table>"
       ],
       "text/plain": [
-       "              Premium\n",
+       "                    1\n",
        "2008-01  4.187229e+07\n",
        "2008-02  4.354268e+07\n",
        "2008-03  3.592080e+07\n",
@@ -6690,11 +6701,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OMDM\n",
-       "Shape:     (2, 2, 120, 120)\n",
-       "Index:      ['Line']\n",
-       "Columns:    ['Paid', 'Incurred']"
+       "            Triangle Summary\n",
+       "Valuation:           2017-12\n",
+       "Grain:                  OMDM\n",
+       "Shape:      (2, 2, 120, 120)\n",
+       "Index:                [Line]\n",
+       "Columns:    [Paid, Incurred]"
       ]
      },
      "execution_count": 37,
@@ -6760,11 +6772,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OMDM\n",
-       "Shape:     (2, 2, 120, 120)\n",
-       "Index:      ['Line', 'Type']\n",
-       "Columns:    ['Paid', 'Incurred']"
+       "            Triangle Summary\n",
+       "Valuation:           2017-12\n",
+       "Grain:                  OMDM\n",
+       "Shape:      (2, 2, 120, 120)\n",
+       "Index:          [Line, Type]\n",
+       "Columns:    [Paid, Incurred]"
       ]
      },
      "execution_count": 38,
@@ -6806,7 +6819,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
@@ -8476,80 +8489,80 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>12</th>\n",
-       "      <th>24</th>\n",
-       "      <th>36</th>\n",
-       "      <th>48</th>\n",
-       "      <th>60</th>\n",
-       "      <th>72</th>\n",
-       "      <th>84</th>\n",
-       "      <th>96</th>\n",
-       "      <th>108</th>\n",
-       "      <th>120</th>\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>13</th>\n",
+       "      <th>25</th>\n",
+       "      <th>37</th>\n",
+       "      <th>49</th>\n",
+       "      <th>61</th>\n",
+       "      <th>73</th>\n",
+       "      <th>85</th>\n",
+       "      <th>97</th>\n",
+       "      <th>109</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
-       "      <td>3,404,254</td>\n",
-       "      <td>11,191,085</td>\n",
-       "      <td>74,613,012</td>\n",
-       "      <td>150,342,751</td>\n",
-       "      <td>150,982,873</td>\n",
+       "      <td></td>\n",
+       "      <td>4,141,279</td>\n",
+       "      <td>11,561,233</td>\n",
+       "      <td>82,569,225</td>\n",
+       "      <td>150,660,224</td>\n",
+       "      <td>151,018,056</td>\n",
        "      <td>151,152,726</td>\n",
        "      <td>151,228,872</td>\n",
        "      <td>151,264,806</td>\n",
        "      <td>151,277,217</td>\n",
-       "      <td>151,284,217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
-       "      <td>3,609,385</td>\n",
-       "      <td>11,002,927</td>\n",
-       "      <td>80,726,352</td>\n",
-       "      <td>156,970,789</td>\n",
-       "      <td>157,599,460</td>\n",
+       "      <td></td>\n",
+       "      <td>4,376,578</td>\n",
+       "      <td>11,474,186</td>\n",
+       "      <td>91,838,448</td>\n",
+       "      <td>157,002,754</td>\n",
+       "      <td>157,611,726</td>\n",
        "      <td>157,697,094</td>\n",
        "      <td>157,736,386</td>\n",
        "      <td>157,743,386</td>\n",
-       "      <td>157,748,735</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
-       "      <td>4,067,321</td>\n",
-       "      <td>12,396,777</td>\n",
-       "      <td>74,210,043</td>\n",
-       "      <td>161,049,586</td>\n",
-       "      <td>161,641,453</td>\n",
-       "      <td>161,787,135</td>\n",
+       "      <td></td>\n",
+       "      <td>4,881,766</td>\n",
+       "      <td>13,694,059</td>\n",
+       "      <td>89,686,543</td>\n",
+       "      <td>161,168,717</td>\n",
+       "      <td>161,649,819</td>\n",
+       "      <td>161,799,274</td>\n",
        "      <td>161,859,565</td>\n",
-       "      <td>161,870,156</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
-       "      <td>4,125,232</td>\n",
-       "      <td>13,183,144</td>\n",
-       "      <td>81,239,771</td>\n",
-       "      <td>161,412,913</td>\n",
-       "      <td>162,187,629</td>\n",
-       "      <td>162,417,460</td>\n",
-       "      <td>162,490,681</td>\n",
+       "      <td></td>\n",
+       "      <td>5,016,037</td>\n",
+       "      <td>14,305,817</td>\n",
+       "      <td>93,932,160</td>\n",
+       "      <td>161,748,542</td>\n",
+       "      <td>162,198,347</td>\n",
+       "      <td>162,421,747</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
-       "      <td>4,584,036</td>\n",
-       "      <td>14,001,178</td>\n",
-       "      <td>77,794,522</td>\n",
-       "      <td>152,118,384</td>\n",
-       "      <td>152,588,090</td>\n",
-       "      <td>152,819,473</td>\n",
+       "      <td></td>\n",
+       "      <td>5,628,248</td>\n",
+       "      <td>14,784,040</td>\n",
+       "      <td>88,124,227</td>\n",
+       "      <td>152,134,138</td>\n",
+       "      <td>152,618,680</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8557,11 +8570,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
-       "      <td>4,889,623</td>\n",
-       "      <td>14,607,742</td>\n",
-       "      <td>84,418,503</td>\n",
-       "      <td>161,110,312</td>\n",
-       "      <td>161,673,036</td>\n",
+       "      <td></td>\n",
+       "      <td>5,620,528</td>\n",
+       "      <td>15,593,880</td>\n",
+       "      <td>94,594,594</td>\n",
+       "      <td>161,176,188</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8570,10 +8583,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014</th>\n",
-       "      <td>5,546,158</td>\n",
-       "      <td>16,408,126</td>\n",
-       "      <td>77,256,792</td>\n",
-       "      <td>154,969,931</td>\n",
+       "      <td>18,194</td>\n",
+       "      <td>6,373,532</td>\n",
+       "      <td>17,649,391</td>\n",
+       "      <td>87,147,273</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8583,9 +8596,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015</th>\n",
-       "      <td>5,909,029</td>\n",
-       "      <td>17,427,611</td>\n",
-       "      <td>80,914,580</td>\n",
+       "      <td></td>\n",
+       "      <td>7,061,351</td>\n",
+       "      <td>18,550,289</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8596,8 +8609,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016</th>\n",
-       "      <td>6,080,962</td>\n",
-       "      <td>18,588,057</td>\n",
+       "      <td>3,214</td>\n",
+       "      <td>7,429,944</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8609,7 +8622,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2017</th>\n",
-       "      <td>6,396,536</td>\n",
+       "      <td>8,338</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8624,17 +8637,17 @@
        "</table>"
       ],
       "text/plain": [
-       "               12            24            36            48            60            72            84            96            108           120\n",
-       "2008  3.404254e+06  1.119109e+07  7.461301e+07  1.503428e+08  1.509829e+08  1.511527e+08  1.512289e+08  1.512648e+08  1.512772e+08  1.512842e+08\n",
-       "2009  3.609385e+06  1.100293e+07  8.072635e+07  1.569708e+08  1.575995e+08  1.576971e+08  1.577364e+08  1.577434e+08  1.577487e+08           NaN\n",
-       "2010  4.067321e+06  1.239678e+07  7.421004e+07  1.610496e+08  1.616415e+08  1.617871e+08  1.618596e+08  1.618702e+08           NaN           NaN\n",
-       "2011  4.125232e+06  1.318314e+07  8.123977e+07  1.614129e+08  1.621876e+08  1.624175e+08  1.624907e+08           NaN           NaN           NaN\n",
-       "2012  4.584036e+06  1.400118e+07  7.779452e+07  1.521184e+08  1.525881e+08  1.528195e+08           NaN           NaN           NaN           NaN\n",
-       "2013  4.889623e+06  1.460774e+07  8.441850e+07  1.611103e+08  1.616730e+08           NaN           NaN           NaN           NaN           NaN\n",
-       "2014  5.546158e+06  1.640813e+07  7.725679e+07  1.549699e+08           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2015  5.909029e+06  1.742761e+07  8.091458e+07           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2016  6.080962e+06  1.858806e+07           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2017  6.396536e+06           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN"
+       "               1             13            25            37            49            61            73            85            97            109\n",
+       "2008           NaN  4.141279e+06  1.156123e+07  8.256923e+07  1.506602e+08  1.510181e+08  1.511527e+08  1.512289e+08  1.512648e+08  1.512772e+08\n",
+       "2009           NaN  4.376578e+06  1.147419e+07  9.183845e+07  1.570028e+08  1.576117e+08  1.576971e+08  1.577364e+08  1.577434e+08           NaN\n",
+       "2010           NaN  4.881766e+06  1.369406e+07  8.968654e+07  1.611687e+08  1.616498e+08  1.617993e+08  1.618596e+08           NaN           NaN\n",
+       "2011           NaN  5.016037e+06  1.430582e+07  9.393216e+07  1.617485e+08  1.621983e+08  1.624217e+08           NaN           NaN           NaN\n",
+       "2012           NaN  5.628248e+06  1.478404e+07  8.812423e+07  1.521341e+08  1.526187e+08           NaN           NaN           NaN           NaN\n",
+       "2013           NaN  5.620528e+06  1.559388e+07  9.459459e+07  1.611762e+08           NaN           NaN           NaN           NaN           NaN\n",
+       "2014  18193.615004  6.373532e+06  1.764939e+07  8.714727e+07           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2015           NaN  7.061351e+06  1.855029e+07           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2016   3213.543940  7.429944e+06           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2017   8337.875863           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN"
       ]
      },
      "execution_count": 41,
@@ -8665,7 +8678,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
+       "      <th></th>\n",
        "      <th>2008</th>\n",
        "      <th>2009</th>\n",
        "      <th>2010</th>\n",
@@ -8681,55 +8694,55 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
-       "      <td>3,404,254</td>\n",
-       "      <td>11,191,085</td>\n",
-       "      <td>74,613,012</td>\n",
-       "      <td>150,342,751</td>\n",
-       "      <td>150,982,873</td>\n",
+       "      <td></td>\n",
+       "      <td>4,141,279</td>\n",
+       "      <td>11,561,233</td>\n",
+       "      <td>82,569,225</td>\n",
+       "      <td>150,660,224</td>\n",
+       "      <td>151,018,056</td>\n",
        "      <td>151,152,726</td>\n",
        "      <td>151,228,872</td>\n",
        "      <td>151,264,806</td>\n",
        "      <td>151,277,217</td>\n",
-       "      <td>151,284,217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td></td>\n",
-       "      <td>3,609,385</td>\n",
-       "      <td>11,002,927</td>\n",
-       "      <td>80,726,352</td>\n",
-       "      <td>156,970,789</td>\n",
-       "      <td>157,599,460</td>\n",
+       "      <td></td>\n",
+       "      <td>4,376,578</td>\n",
+       "      <td>11,474,186</td>\n",
+       "      <td>91,838,448</td>\n",
+       "      <td>157,002,754</td>\n",
+       "      <td>157,611,726</td>\n",
        "      <td>157,697,094</td>\n",
        "      <td>157,736,386</td>\n",
        "      <td>157,743,386</td>\n",
-       "      <td>157,748,735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>4,067,321</td>\n",
-       "      <td>12,396,777</td>\n",
-       "      <td>74,210,043</td>\n",
-       "      <td>161,049,586</td>\n",
-       "      <td>161,641,453</td>\n",
-       "      <td>161,787,135</td>\n",
+       "      <td></td>\n",
+       "      <td>4,881,766</td>\n",
+       "      <td>13,694,059</td>\n",
+       "      <td>89,686,543</td>\n",
+       "      <td>161,168,717</td>\n",
+       "      <td>161,649,819</td>\n",
+       "      <td>161,799,274</td>\n",
        "      <td>161,859,565</td>\n",
-       "      <td>161,870,156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>4,125,232</td>\n",
-       "      <td>13,183,144</td>\n",
-       "      <td>81,239,771</td>\n",
-       "      <td>161,412,913</td>\n",
-       "      <td>162,187,629</td>\n",
-       "      <td>162,417,460</td>\n",
-       "      <td>162,490,681</td>\n",
+       "      <td></td>\n",
+       "      <td>5,016,037</td>\n",
+       "      <td>14,305,817</td>\n",
+       "      <td>93,932,160</td>\n",
+       "      <td>161,748,542</td>\n",
+       "      <td>162,198,347</td>\n",
+       "      <td>162,421,747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
@@ -8737,12 +8750,12 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>4,584,036</td>\n",
-       "      <td>14,001,178</td>\n",
-       "      <td>77,794,522</td>\n",
-       "      <td>152,118,384</td>\n",
-       "      <td>152,588,090</td>\n",
-       "      <td>152,819,473</td>\n",
+       "      <td></td>\n",
+       "      <td>5,628,248</td>\n",
+       "      <td>14,784,040</td>\n",
+       "      <td>88,124,227</td>\n",
+       "      <td>152,134,138</td>\n",
+       "      <td>152,618,680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
@@ -8751,11 +8764,11 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>4,889,623</td>\n",
-       "      <td>14,607,742</td>\n",
-       "      <td>84,418,503</td>\n",
-       "      <td>161,110,312</td>\n",
-       "      <td>161,673,036</td>\n",
+       "      <td></td>\n",
+       "      <td>5,620,528</td>\n",
+       "      <td>15,593,880</td>\n",
+       "      <td>94,594,594</td>\n",
+       "      <td>161,176,188</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014</th>\n",
@@ -8765,10 +8778,10 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>5,546,158</td>\n",
-       "      <td>16,408,126</td>\n",
-       "      <td>77,256,792</td>\n",
-       "      <td>154,969,931</td>\n",
+       "      <td>18,194</td>\n",
+       "      <td>6,373,532</td>\n",
+       "      <td>17,649,391</td>\n",
+       "      <td>87,147,273</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015</th>\n",
@@ -8779,9 +8792,9 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>5,909,029</td>\n",
-       "      <td>17,427,611</td>\n",
-       "      <td>80,914,580</td>\n",
+       "      <td></td>\n",
+       "      <td>7,061,351</td>\n",
+       "      <td>18,550,289</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016</th>\n",
@@ -8793,8 +8806,8 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>6,080,962</td>\n",
-       "      <td>18,588,057</td>\n",
+       "      <td>3,214</td>\n",
+       "      <td>7,429,944</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2017</th>\n",
@@ -8807,23 +8820,23 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>6,396,536</td>\n",
+       "      <td>8,338</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "text/plain": [
-       "              2008          2009          2010          2011          2012          2013          2014          2015          2016          2017\n",
-       "2008  3.404254e+06  1.119109e+07  7.461301e+07  1.503428e+08  1.509829e+08  1.511527e+08  1.512289e+08  1.512648e+08  1.512772e+08  1.512842e+08\n",
-       "2009           NaN  3.609385e+06  1.100293e+07  8.072635e+07  1.569708e+08  1.575995e+08  1.576971e+08  1.577364e+08  1.577434e+08  1.577487e+08\n",
-       "2010           NaN           NaN  4.067321e+06  1.239678e+07  7.421004e+07  1.610496e+08  1.616415e+08  1.617871e+08  1.618596e+08  1.618702e+08\n",
-       "2011           NaN           NaN           NaN  4.125232e+06  1.318314e+07  8.123977e+07  1.614129e+08  1.621876e+08  1.624175e+08  1.624907e+08\n",
-       "2012           NaN           NaN           NaN           NaN  4.584036e+06  1.400118e+07  7.779452e+07  1.521184e+08  1.525881e+08  1.528195e+08\n",
-       "2013           NaN           NaN           NaN           NaN           NaN  4.889623e+06  1.460774e+07  8.441850e+07  1.611103e+08  1.616730e+08\n",
-       "2014           NaN           NaN           NaN           NaN           NaN           NaN  5.546158e+06  1.640813e+07  7.725679e+07  1.549699e+08\n",
-       "2015           NaN           NaN           NaN           NaN           NaN           NaN           NaN  5.909029e+06  1.742761e+07  8.091458e+07\n",
-       "2016           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN  6.080962e+06  1.858806e+07\n",
-       "2017           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN  6.396536e+06"
+       "      2008          2009          2010          2011          2012          2013          2014          2015          2016          2017\n",
+       "2008   NaN  4.141279e+06  1.156123e+07  8.256923e+07  1.506602e+08  1.510181e+08  1.511527e+08  1.512289e+08  1.512648e+08  1.512772e+08\n",
+       "2009   NaN           NaN  4.376578e+06  1.147419e+07  9.183845e+07  1.570028e+08  1.576117e+08  1.576971e+08  1.577364e+08  1.577434e+08\n",
+       "2010   NaN           NaN           NaN  4.881766e+06  1.369406e+07  8.968654e+07  1.611687e+08  1.616498e+08  1.617993e+08  1.618596e+08\n",
+       "2011   NaN           NaN           NaN           NaN  5.016037e+06  1.430582e+07  9.393216e+07  1.617485e+08  1.621983e+08  1.624217e+08\n",
+       "2012   NaN           NaN           NaN           NaN           NaN  5.628248e+06  1.478404e+07  8.812423e+07  1.521341e+08  1.526187e+08\n",
+       "2013   NaN           NaN           NaN           NaN           NaN           NaN  5.620528e+06  1.559388e+07  9.459459e+07  1.611762e+08\n",
+       "2014   NaN           NaN           NaN           NaN           NaN           NaN  1.819362e+04  6.373532e+06  1.764939e+07  8.714727e+07\n",
+       "2015   NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN  7.061351e+06  1.855029e+07\n",
+       "2016   NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN  3.213544e+03  7.429944e+06\n",
+       "2017   NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN  8.337876e+03"
       ]
      },
      "execution_count": 42,
@@ -8853,26 +8866,26 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>12</th>\n",
-       "      <th>24</th>\n",
-       "      <th>36</th>\n",
-       "      <th>48</th>\n",
-       "      <th>60</th>\n",
-       "      <th>72</th>\n",
-       "      <th>84</th>\n",
-       "      <th>96</th>\n",
-       "      <th>108</th>\n",
-       "      <th>120</th>\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>13</th>\n",
+       "      <th>25</th>\n",
+       "      <th>37</th>\n",
+       "      <th>49</th>\n",
+       "      <th>61</th>\n",
+       "      <th>73</th>\n",
+       "      <th>85</th>\n",
+       "      <th>97</th>\n",
+       "      <th>109</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td></td>\n",
+       "      <td></td>\n",
        "      <td>1,129,305</td>\n",
-       "      <td>61,658,874</td>\n",
-       "      <td>136,520,554</td>\n",
+       "      <td>69,559,190</td>\n",
        "      <td>136,800,554</td>\n",
        "      <td>136,800,554</td>\n",
        "      <td>136,800,554</td>\n",
@@ -8883,22 +8896,22 @@
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td></td>\n",
-       "      <td>187,292</td>\n",
-       "      <td>67,134,599</td>\n",
+       "      <td></td>\n",
+       "      <td>347,093</td>\n",
+       "      <td>78,135,230</td>\n",
        "      <td>142,267,946</td>\n",
        "      <td>142,547,946</td>\n",
        "      <td>142,547,946</td>\n",
        "      <td>142,547,946</td>\n",
        "      <td>142,547,946</td>\n",
-       "      <td>142,547,946</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td></td>\n",
-       "      <td>620,603</td>\n",
-       "      <td>59,082,456</td>\n",
-       "      <td>144,757,200</td>\n",
+       "      <td></td>\n",
+       "      <td>1,374,024</td>\n",
+       "      <td>74,458,951</td>\n",
        "      <td>144,757,200</td>\n",
        "      <td>144,757,200</td>\n",
        "      <td>144,757,200</td>\n",
@@ -8909,9 +8922,9 @@
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td></td>\n",
-       "      <td>503,296</td>\n",
-       "      <td>65,048,051</td>\n",
-       "      <td>143,971,618</td>\n",
+       "      <td></td>\n",
+       "      <td>1,117,817</td>\n",
+       "      <td>77,608,907</td>\n",
        "      <td>144,251,618</td>\n",
        "      <td>144,251,618</td>\n",
        "      <td>144,251,618</td>\n",
@@ -8922,9 +8935,9 @@
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td></td>\n",
-       "      <td>599,277</td>\n",
-       "      <td>60,536,642</td>\n",
-       "      <td>133,412,416</td>\n",
+       "      <td></td>\n",
+       "      <td>934,235</td>\n",
+       "      <td>70,725,245</td>\n",
        "      <td>133,412,416</td>\n",
        "      <td>133,412,416</td>\n",
        "      <td></td>\n",
@@ -8935,9 +8948,9 @@
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td></td>\n",
-       "      <td>536,303</td>\n",
-       "      <td>66,443,157</td>\n",
-       "      <td>141,645,782</td>\n",
+       "      <td></td>\n",
+       "      <td>957,449</td>\n",
+       "      <td>76,482,983</td>\n",
        "      <td>141,645,782</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8948,9 +8961,9 @@
        "    <tr>\n",
        "      <th>2014</th>\n",
        "      <td></td>\n",
-       "      <td>965,973</td>\n",
-       "      <td>57,394,263</td>\n",
-       "      <td>133,542,848</td>\n",
+       "      <td></td>\n",
+       "      <td>1,493,213</td>\n",
+       "      <td>67,089,980</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8961,8 +8974,8 @@
        "    <tr>\n",
        "      <th>2015</th>\n",
        "      <td></td>\n",
-       "      <td>371,015</td>\n",
-       "      <td>59,047,107</td>\n",
+       "      <td></td>\n",
+       "      <td>884,928</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -8974,7 +8987,7 @@
        "    <tr>\n",
        "      <th>2016</th>\n",
        "      <td></td>\n",
-       "      <td>640,179</td>\n",
+       "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -9001,17 +9014,17 @@
        "</table>"
       ],
       "text/plain": [
-       "      12            24            36            48            60            72            84            96            108           120\n",
-       "2008  NaN  1.129305e+06  6.165887e+07  1.365206e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08\n",
-       "2009  NaN  1.872920e+05  6.713460e+07  1.422679e+08  1.425479e+08  1.425479e+08  1.425479e+08  1.425479e+08  1.425479e+08           NaN\n",
-       "2010  NaN  6.206026e+05  5.908246e+07  1.447572e+08  1.447572e+08  1.447572e+08  1.447572e+08  1.447572e+08           NaN           NaN\n",
-       "2011  NaN  5.032956e+05  6.504805e+07  1.439716e+08  1.442516e+08  1.442516e+08  1.442516e+08           NaN           NaN           NaN\n",
-       "2012  NaN  5.992768e+05  6.053664e+07  1.334124e+08  1.334124e+08  1.334124e+08           NaN           NaN           NaN           NaN\n",
-       "2013  NaN  5.363029e+05  6.644316e+07  1.416458e+08  1.416458e+08           NaN           NaN           NaN           NaN           NaN\n",
-       "2014  NaN  9.659729e+05  5.739426e+07  1.335428e+08           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2015  NaN  3.710149e+05  5.904711e+07           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2016  NaN  6.401785e+05           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2017  NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN"
+       "      1    13            25            37            49            61            73            85            97            109\n",
+       "2008  NaN  NaN  1.129305e+06  6.955919e+07  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08\n",
+       "2009  NaN  NaN  3.470930e+05  7.813523e+07  1.422679e+08  1.425479e+08  1.425479e+08  1.425479e+08  1.425479e+08           NaN\n",
+       "2010  NaN  NaN  1.374024e+06  7.445895e+07  1.447572e+08  1.447572e+08  1.447572e+08  1.447572e+08           NaN           NaN\n",
+       "2011  NaN  NaN  1.117817e+06  7.760891e+07  1.442516e+08  1.442516e+08  1.442516e+08           NaN           NaN           NaN\n",
+       "2012  NaN  NaN  9.342348e+05  7.072524e+07  1.334124e+08  1.334124e+08           NaN           NaN           NaN           NaN\n",
+       "2013  NaN  NaN  9.574485e+05  7.648298e+07  1.416458e+08           NaN           NaN           NaN           NaN           NaN\n",
+       "2014  NaN  NaN  1.493213e+06  6.708998e+07           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2015  NaN  NaN  8.849275e+05           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2016  NaN  NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2017  NaN  NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN"
       ]
      },
      "execution_count": 43,
@@ -9047,24 +9060,22 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Origin</th>\n",
-       "      <th>24</th>\n",
-       "      <th>36</th>\n",
-       "      <th>48</th>\n",
-       "      <th>60</th>\n",
-       "      <th>72</th>\n",
-       "      <th>84</th>\n",
-       "      <th>96</th>\n",
-       "      <th>108</th>\n",
-       "      <th>120</th>\n",
+       "      <th></th>\n",
+       "      <th>25</th>\n",
+       "      <th>37</th>\n",
+       "      <th>49</th>\n",
+       "      <th>61</th>\n",
+       "      <th>73</th>\n",
+       "      <th>85</th>\n",
+       "      <th>97</th>\n",
+       "      <th>109</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td>1,129,305</td>\n",
-       "      <td>61,658,874</td>\n",
-       "      <td>136,520,554</td>\n",
+       "      <td>69,559,190</td>\n",
        "      <td>136,800,554</td>\n",
        "      <td>136,800,554</td>\n",
        "      <td>136,800,554</td>\n",
@@ -9074,21 +9085,19 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
-       "      <td>187,292</td>\n",
-       "      <td>67,134,599</td>\n",
+       "      <td>347,093</td>\n",
+       "      <td>78,135,230</td>\n",
        "      <td>142,267,946</td>\n",
        "      <td>142,547,946</td>\n",
        "      <td>142,547,946</td>\n",
        "      <td>142,547,946</td>\n",
        "      <td>142,547,946</td>\n",
-       "      <td>142,547,946</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
-       "      <td>620,603</td>\n",
-       "      <td>59,082,456</td>\n",
-       "      <td>144,757,200</td>\n",
+       "      <td>1,374,024</td>\n",
+       "      <td>74,458,951</td>\n",
        "      <td>144,757,200</td>\n",
        "      <td>144,757,200</td>\n",
        "      <td>144,757,200</td>\n",
@@ -9098,9 +9107,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
-       "      <td>503,296</td>\n",
-       "      <td>65,048,051</td>\n",
-       "      <td>143,971,618</td>\n",
+       "      <td>1,117,817</td>\n",
+       "      <td>77,608,907</td>\n",
        "      <td>144,251,618</td>\n",
        "      <td>144,251,618</td>\n",
        "      <td>144,251,618</td>\n",
@@ -9110,9 +9118,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
-       "      <td>599,277</td>\n",
-       "      <td>60,536,642</td>\n",
-       "      <td>133,412,416</td>\n",
+       "      <td>934,235</td>\n",
+       "      <td>70,725,245</td>\n",
        "      <td>133,412,416</td>\n",
        "      <td>133,412,416</td>\n",
        "      <td></td>\n",
@@ -9122,9 +9129,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
-       "      <td>536,303</td>\n",
-       "      <td>66,443,157</td>\n",
-       "      <td>141,645,782</td>\n",
+       "      <td>957,449</td>\n",
+       "      <td>76,482,983</td>\n",
        "      <td>141,645,782</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -9134,9 +9140,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014</th>\n",
-       "      <td>965,973</td>\n",
-       "      <td>57,394,263</td>\n",
-       "      <td>133,542,848</td>\n",
+       "      <td>1,493,213</td>\n",
+       "      <td>67,089,980</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -9146,20 +9151,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015</th>\n",
-       "      <td>371,015</td>\n",
-       "      <td>59,047,107</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>640,179</td>\n",
-       "      <td></td>\n",
+       "      <td>884,928</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -9172,16 +9164,15 @@
        "</table>"
       ],
       "text/plain": [
-       "               24            36            48            60            72            84            96            108           120\n",
-       "2008  1.129305e+06  6.165887e+07  1.365206e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08\n",
-       "2009  1.872920e+05  6.713460e+07  1.422679e+08  1.425479e+08  1.425479e+08  1.425479e+08  1.425479e+08  1.425479e+08           NaN\n",
-       "2010  6.206026e+05  5.908246e+07  1.447572e+08  1.447572e+08  1.447572e+08  1.447572e+08  1.447572e+08           NaN           NaN\n",
-       "2011  5.032956e+05  6.504805e+07  1.439716e+08  1.442516e+08  1.442516e+08  1.442516e+08           NaN           NaN           NaN\n",
-       "2012  5.992768e+05  6.053664e+07  1.334124e+08  1.334124e+08  1.334124e+08           NaN           NaN           NaN           NaN\n",
-       "2013  5.363029e+05  6.644316e+07  1.416458e+08  1.416458e+08           NaN           NaN           NaN           NaN           NaN\n",
-       "2014  9.659729e+05  5.739426e+07  1.335428e+08           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2015  3.710149e+05  5.904711e+07           NaN           NaN           NaN           NaN           NaN           NaN           NaN\n",
-       "2016  6.401785e+05           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN"
+       "               25            37            49            61            73            85            97            109\n",
+       "2008  1.129305e+06  6.955919e+07  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08  1.368006e+08\n",
+       "2009  3.470930e+05  7.813523e+07  1.422679e+08  1.425479e+08  1.425479e+08  1.425479e+08  1.425479e+08           NaN\n",
+       "2010  1.374024e+06  7.445895e+07  1.447572e+08  1.447572e+08  1.447572e+08  1.447572e+08           NaN           NaN\n",
+       "2011  1.117817e+06  7.760891e+07  1.442516e+08  1.442516e+08  1.442516e+08           NaN           NaN           NaN\n",
+       "2012  9.342348e+05  7.072524e+07  1.334124e+08  1.334124e+08           NaN           NaN           NaN           NaN\n",
+       "2013  9.574485e+05  7.648298e+07  1.416458e+08           NaN           NaN           NaN           NaN           NaN\n",
+       "2014  1.493213e+06  6.708998e+07           NaN           NaN           NaN           NaN           NaN           NaN\n",
+       "2015  8.849275e+05           NaN           NaN           NaN           NaN           NaN           NaN           NaN"
       ]
      },
      "execution_count": 44,
@@ -9340,11 +9331,12 @@
        "</table>"
       ],
       "text/plain": [
-       "Valuation: 2017-12\n",
-       "Grain:     OMDM\n",
-       "Shape:     (2, 2, 120, 120)\n",
-       "Index:      ['Line', 'Type']\n",
-       "Columns:    ['Paid', 'Incurred']"
+       "            Triangle Summary\n",
+       "Valuation:           2017-12\n",
+       "Grain:                  OMDM\n",
+       "Shape:      (2, 2, 120, 120)\n",
+       "Index:          [Line, Type]\n",
+       "Columns:    [Paid, Incurred]"
       ]
      },
      "execution_count": 49,
@@ -9387,7 +9379,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
+       "      <th>development</th>\n",
        "      <th>2015-07</th>\n",
        "      <th>2015-08</th>\n",
        "      <th>2015-09</th>\n",
@@ -9417,10 +9409,10 @@
        "</div>"
       ],
       "text/plain": [
-       "       2015-07   2015-08   2015-09\n",
-       "Line                              \n",
-       "Auto   2096673   1811862   2056103\n",
-       "Home  12961113  10167525  10825431"
+       "development   2015-07   2015-08   2015-09\n",
+       "Line                                     \n",
+       "Auto          2096673   1811862   2056103\n",
+       "Home         12961113  10167525  10825431"
       ]
      },
      "execution_count": 50,
diff --git a/examples/plot_bootstrap.py b/examples/plot_bootstrap.py
index 45034396..b3aaf25a 100644
--- a/examples/plot_bootstrap.py
+++ b/examples/plot_bootstrap.py
@@ -15,7 +15,6 @@
 sims = cl.BootstrapODPSample().fit_transform(tri)
 # Calculate LDF for each simulation
 sim_ldf = cl.Development().fit(sims).ldf_
-sim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]
 
 # Plot the Data
 fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))