Error in comments

Author: mgraffg

CompStats/metrics.py

@@ -307,7 +307,7 @@ def macro_f1(y_true, *y_pred, labels=None,
              sample_weight=None, zero_division='warn',
              num_samples: int=500, n_jobs: int=-1, use_tqdm=True,
              **kwargs):
-    """:py:class:`~CompStats.interface.Perf` with :py:func:`~sklearn.metrics.f1_score` (as :py:attr:`score_func`) with the parameteres needed to compute the macro score. The parameters not described can be found in :py:func:`~sklearn.metrics.macro_f1`
+    """:py:class:`~CompStats.interface.Perf` with :py:func:`~sklearn.metrics.f1_score` (as :py:attr:`score_func`) with the parameteres needed to compute the macro score. The parameters not described can be found in :py:func:`~sklearn.metrics.f1_score`
 
     :param y_true: True measurement or could be a pandas.DataFrame where column label 'y' corresponds to the true measurement. 
     :type y_true: numpy.ndarray or pandas.DataFrame 
@@ -332,7 +332,7 @@ def macro_recall(y_true, *y_pred, labels=None,
                  sample_weight=None, zero_division='warn',
                  num_samples: int=500, n_jobs: int=-1, use_tqdm=True,
                  **kwargs):
-    """:py:class:`~CompStats.interface.Perf` with :py:func:`~sklearn.metrics.recall_score` (as :py:attr:`score_func`) with the parameteres needed to compute the macro score. The parameters not described can be found in :py:func:`~sklearn.metrics.macro_recall`
+    """:py:class:`~CompStats.interface.Perf` with :py:func:`~sklearn.metrics.recall_score` (as :py:attr:`score_func`) with the parameteres needed to compute the macro score. The parameters not described can be found in :py:func:`~sklearn.metrics.recall_score`
 
     :param y_true: True measurement or could be a pandas.DataFrame where column label 'y' corresponds to the true measurement. 
     :type y_true: numpy.ndarray or pandas.DataFrame 
@@ -357,7 +357,7 @@ def macro_precision(y_true, *y_pred, labels=None,
                     sample_weight=None, zero_division='warn',
                     num_samples: int=500, n_jobs: int=-1, use_tqdm=True,
                     **kwargs):
-    """:py:class:`~CompStats.interface.Perf` with :py:func:`~sklearn.metrics.precision_score` (as :py:attr:`score_func`) with the parameteres needed to compute the macro score. The parameters not described can be found in :py:func:`~sklearn.metrics.macro_precision`
+    """:py:class:`~CompStats.interface.Perf` with :py:func:`~sklearn.metrics.precision_score` (as :py:attr:`score_func`) with the parameteres needed to compute the macro score. The parameters not described can be found in :py:func:`~sklearn.metrics.precision_score`
 
     :param y_true: True measurement or could be a pandas.DataFrame where column label 'y' corresponds to the true measurement. 
     :type y_true: numpy.ndarray or pandas.DataFrame 

quarto/CompStats.qmd

@@ -18,7 +18,7 @@ execute:
 Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. CompStats implements an evaluation methodology for statistically analyzing competition results and competition. CompStats offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. 
 :::
 
-::: {.card title='Installing using conda'}
+::: {.card title='Installing using conda' .flow}
 
 `CompStats` can be install using the conda package manager with the following instruction.
 
@@ -27,7 +27,7 @@ conda install --channel conda-forge CompStats
 ``` 
 ::: 
 
-::: {.card title='Installing using pip'} 
+::: {.card title='Installing using pip' .flow} 
 A more general approach to installing `CompStats` is through the use of the command pip, as illustrated in the following instruction.
 
 ```{sh}