diff --git a/docs/source/conf.py b/docs/source/conf.py
index 47f6c77..fceaabb 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -73,4 +73,4 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
+html_static_path = []
diff --git a/peak_performance/models.py b/peak_performance/models.py
index 91a3a20..9a9532f 100644
--- a/peak_performance/models.py
+++ b/peak_performance/models.py
@@ -29,12 +29,19 @@
 
 
 class ModelType(str, Enum):
-    """Class containing all implemented model types."""
+    """Enum of default model types."""
 
     Normal = "normal"
+    """Shape of a Gaussian Normal PDF."""
+
     SkewNormal = "skew_normal"
+    """Shape of a skewed Normal PDF."""
+
     DoubleNormal = "double_normal"
+    """Superposition of two ``Normal`` peaks."""
+
     DoubleSkewNormal = "double_skew_normal"
+    """Superposition of two ``SkewedNormal`` peaks."""
 
 
 def guess_noise(intensity):
@@ -393,10 +400,9 @@ def std_skew_calculation(scale, alpha):
         Skewness parameter of the skew normal distribution.
 
     Returns
-    ----------
+    -------
     std
         Standard deviation of a skew normal distribution.
-    -------
     """
     return np.sqrt(scale**2 * (1 - (2 * alpha**2) / ((alpha**2 + 1) * np.pi)))
 
@@ -415,7 +421,7 @@ def mean_skew_calculation(loc, scale, alpha):
         Skewness parameter of the skew normal distribution.
 
     Returns
-    ----------
+    -------
     mean
         Arithmetic mean of a skew normal distribution.
     """
@@ -489,7 +495,7 @@ def height_calculation(area, loc, scale, alpha, mode_skew):
         Mode of the skew normal distribution.
 
     Returns
-    ----------
+    -------
     mean
         Arithmetic mean of a skew normal distribution.
     """
diff --git a/peak_performance/pipeline.py b/peak_performance/pipeline.py
index d13b021..bfa9cb3 100644
--- a/peak_performance/pipeline.py
+++ b/peak_performance/pipeline.py
@@ -1187,7 +1187,7 @@ def pipeline(
         Data format (suffix) of the raw data, default is '.npy'.
 
     Returns
-    ----------
+    -------
     path_results
         Path variable pointing to the newly created folder for this batch.
     """
@@ -1224,7 +1224,7 @@ def pipeline_restart(
         Path variable pointing to the directory of the broken PeakPerformance batch
 
     Returns
-    ----------
+    -------
     path_results_new
         Path variable pointing to the newly created folder for the restarted batch.
     """
@@ -1323,7 +1323,7 @@ def parse_files_for_model_selection(signals: pandas.DataFrame) -> Dict[str, str]
         DataFrame containing the signals tab of Template.xlsx.
 
     Returns
-    ----------
+    -------
     files_for_selection
         Dict with file names as keys and unique identifiers as values.
     """
@@ -1430,7 +1430,7 @@ def model_selection_check(
         to be accepted.
 
     Returns
-    ----------
+    -------
     selected_model
         Name of the selected model type.
     """
@@ -1473,7 +1473,7 @@ def selection_loop(
         "waic": widely applicable information criterion)
 
     Returns
-    ----------
+    -------
     result_df
         DataFrame containing the ranking and scores of the model selection.
     model_dict
@@ -1565,7 +1565,7 @@ def model_selection(path_raw_data: Union[str, os.PathLike], *, ic: str = "loo"):
         "waic": widely applicable information criterion)
 
     Returns
-    ----------
+    -------
     comparison_results
         DataFrame containing all rankings from model selection.
     model_dict