Add links to samplingwrapper docs (arviz-devs#1582)

* add missing links in docs * update changelog * lint
utkarsh-maheshwari · May 27, 2021 · 58747e5 · 58747e5
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -16,6 +16,7 @@
 
 ### Deprecation
 * Deprecated `index_origin` and `order` arguments in `az.summary` ([1201](https://github.com/arviz-devs/arviz/pull/1201))
+* Improve documentation on experimental `SamplingWrapper` classes ([1582](https://github.com/arviz-devs/arviz/pull/1582))
 
 ### Documentation
 * Added "Label guide" page and API section for `arviz.labels` module ([1201](https://github.com/arviz-devs/arviz/pull/1201))

diff --git a/arviz/wrappers/base.py b/arviz/wrappers/base.py
@@ -12,7 +12,8 @@ class SamplingWrapper:
     functions requiring refitting like Leave Future Out or Simulation Based Calibration can be
     performed from ArviZ.
 
-    For more info on wrappers see :ref:`wrappers_api`
+    See other SamplingWrapper classes at :ref:`wrappers api section <wrappers_api>`. For
+    usage examples see user guide pages on :ref:`wrapper_guide`.
 
     Parameters
     ----------

diff --git a/arviz/wrappers/wrap_pystan.py b/arviz/wrappers/wrap_pystan.py
@@ -9,12 +9,17 @@ class PyStanSamplingWrapper(SamplingWrapper):
 
     See the documentation on  :class:`~arviz.SamplingWrapper` for a more detailed
     description. An example of ``PyStanSamplingWrapper`` usage can be found
-    in the :ref:`pystan_refitting` notebook.
+    in the :ref:`pystan_refitting` notebook. For usage examples of other wrappers
+    see the user guide pages on :ref:`wrapper_guide`.
 
     Warnings
     --------
     Sampling wrappers are an experimental feature in a very early stage. Please use them
     with caution.
+
+    See Also
+    --------
+    SamplingWrapper
     """
 
     def sel_observations(self, idx):

diff --git a/doc/source/user_guide/sampling_wrappers.md b/doc/source/user_guide/sampling_wrappers.md
@@ -1,3 +1,4 @@
+(wrapper_guide)=
 # Sampling wrappers
 Sampling wrappers allow ArviZ to call PPLs in order to perform a limited
 subset of their capabilities and calculate stats and diagnostics that require