DOC add more note links and minor doc improvements (#168)

src/model_diagnostics/calibration/identification.py

@@ -527,12 +527,13 @@ def compute_marginal(
 
     Notes
     -----
-    The marginal values iare computed as an estimation of:
+    The marginal values are computed as an estimation of:
 
-        - `y_obs`: \(\mathbb{E}(Y|features)\).
-        - `y_pred`: \(\mathbb{E}(m(X)|features)\).
+    - `y_obs`: \(\mathbb{E}(Y|feature)\)
+    - `y_pred`: \(\mathbb{E}(m(X)|feature)\)
 
-    Computationally that is more or less a group-by operation on a dataset.
+    with \(feature\) the column specified by `feature_name`.
+    Computationally that is more or less a group-by-aggregate operation on a dataset.
 
     The standard error for both are calculated in the standard way as
     \(\mathrm{SE} = \sqrt{\operatorname{Var}(\bar{Y})} = \frac{\sigma}{\sqrt{n}}\) and

src/model_diagnostics/calibration/plots.py

@@ -102,7 +102,7 @@ def plot_reliability_diagram(
         - Optimal selection of bins
         - Statistical consistent estimator
 
-    For details, refer to [Dimitriadis2021].
+    For details, refer to `[Dimitriadis2021]`.
 
     References
     ----------
@@ -326,7 +326,7 @@ def plot_bias(
     identification function, versus a feature. This is a good way to assess whether
     a model is conditionally calibrated or not. Well calibrated models have bias terms
     around zero.
-    See Notes for further details.
+    See [Notes](#notes) for further details.
 
     For numerical features, NaN are treated as Null values. Null values are always
     plotted as rightmost value on the x-axis and marked with a diamond instead of a
@@ -386,9 +386,10 @@ def plot_bias(
 
     Notes
     -----
+    [](){#notes}
     A model \(m(X)\) is conditionally calibrated iff \(E(V(m(X), Y))=0\) a.s. The
     empirical version, given some data, reads \(\frac{1}{n}\sum_i V(m(x_i), y_i)\).
-    See [FLM2022]`.
+    See `[FLM2022]`.
 
     References
     ----------