Various docstring fixes

chainladder/development/constant.py

@@ -114,12 +114,14 @@ def __init__(self, patterns=None, style="ldf", callable_axis=0, groupby=None):
 
     def fit(self, X, y=None, sample_weight=None):
         """Fit the model with X.
+
         Parameters
         ----------
         X : Triangle-like
             Set of LDFs to which the munich adjustment will be applied.
         y : Ignored
         sample_weight : Ignored
+
         Returns
         -------
         self : object

chainladder/development/glm.py

@@ -41,22 +41,26 @@ class TweedieGLM(DevelopmentBase):
         Column name for the response variable of the GLM. If omitted, then the
         first column of the Triangle will be used.
     power: float, default=1
-            The power determines the underlying target distribution according
-            to the following table:
-            +-------+------------------------+
-            | Power | Distribution           |
-            +=======+========================+
-            | 0     | Normal                 |
-            +-------+------------------------+
-            | 1     | Poisson                |
-            +-------+------------------------+
-            | (1,2) | Compound Poisson Gamma |
-            +-------+------------------------+
-            | 2     | Gamma                  |
-            +-------+------------------------+
-            | 3     | Inverse Gaussian       |
-            +-------+------------------------+
-            For ``0 < power < 1``, no distribution exists.
+        The power determines the underlying target distribution according
+        to the following table:
+
+        .. list-table::
+           :header-rows: 1
+
+           * - Power
+             - Distribution
+           * - 0
+             - Normal
+           * - 1
+             - Poisson
+           * - (1,2)
+             - Compound Poisson Gamma
+           * - 2
+             - Gamma
+           * - 3
+             - Inverse Gaussian
+
+        For ``0 < power < 1``, no distribution exists.
     alpha: float, default=1
         Constant that multiplies the penalty term and thus determines the
         regularization strength. ``alpha = 0`` is equivalent to unpenalized

chainladder/utils/utility_functions.py

@@ -990,7 +990,8 @@ def model_diagnostics(
     - ``Run Off 1/2/3...``: Expected incremental emergence in successive future valuation periods (from ``full_expectation_``)
     - ``Apriori``: Expected ultimate for Benktander family of methods (from ``expectation_``)
 
-    Columns from the original Triangle are cross-joined into the index. ``Measure`` will contain all the columns from the original Triangle. 
+    Columns from the original Triangle are cross-joined into the index. 
+    ``Measure`` will contain all the columns from the original Triangle. 
     """
     from chainladder import Pipeline, Triangle
 

docs/user_guide/utilities.ipynb

@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e2c2fa7-9d32-404b-9121-51d06ac57324",
    "metadata": {},
    "source": [
     "# Utilities\n",
@@ -13,38 +12,54 @@
     "\n",
     "## Sample Datasets\n",
     "\n",
-    "A variety of datasets can be loaded using :func:`load_sample()`.  These are\n",
-    "sample datasets that are used in a variety of examples within this\n",
-    "documentation.\n",
-    "\n",
-    "\n",
-    "| Dataset   | Description                                          | \n",
-    "|-----------|------------------------------------------------------|\n",
-    "| abc       | ABC Data                                             |\n",
-    "| auto      | Auto Data                                            |\n",
-    "| berqsherm | Data from the Berquist Sherman paper                 |\n",
-    "| cc_sample | Sample Insurance Data for Cape Cod Method in Struhuss|\n",
-    "| clrd      | CAS Loss Reserving Database                          |\n",
-    "| genins    | General Insurance Data used in Clark                 |\n",
-    "| ia_sample | Sample data for Incremental Additive Method in Schmidt|\n",
-    "| liab      | more data|\n",
-    "| m3ir5     | more data|\n",
-    "| mcl       | Sample insurance data for Munich Adjustment in Quarg|\n",
-    "| mortgage  | more data|\n",
-    "| mw2008    | more data|\n",
-    "| mw2014    | more data|\n",
-    "| quarterly | Sample data to demonstrate changing Triangle grain|\n",
-    "| raa       | Sample data used in Mack Chainladder|\n",
-    "| ukmotor   | more data|\n",
-    "| usaa      | more data|\n",
-    "| usauto    | more data|\n",
+    "A variety of datasets can be loaded using `load_sample()`. These are sample\n",
+    "datasets that are used throughout the examples in this documentation. The full\n",
+    "list is available through `list_samples()`, which returns a table of every\n",
+    "bundled dataset along with its index, columns, and grain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import chainladder as cl\n",
     "\n",
+    "cl.list_samples()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Diagnostics\n",
     "\n",
+    "`model_diagnostics()` summarizes a fitted IBNR model into a single Triangle\n",
+    "whose columns are the diagnostic vectors of interest, such as the latest\n",
+    "diagonal, IBNR, and ultimate. It accepts a fitted estimator or a `Pipeline`,\n",
+    "and an optional `groupby` to summarize at a coarser index level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = cl.Chainladder().fit(cl.load_sample('raa'))\n",
+    "cl.model_diagnostics(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Chainladder Persistence\n",
     "\n",
-    "All estimators can be persisted to disk or database\n",
-    "using ``to_json`` or ``to_pickle``.  Restoring the estimator is as simple as\n",
-    "``cl.read_json`` or ``cl.read_pickle``.\n"
+    "All estimators can be persisted to disk or database using `to_json` or\n",
+    "`to_pickle`. Restoring the estimator is as simple as `cl.read_json` or\n",
+    "`cl.read_pickle`."
    ]
   },
   {