fix typo of specicity

src/torchmetrics/functional/classification/__init__.py

@@ -119,7 +119,7 @@
     binary_specificity_at_sensitivity,
     multiclass_specificity_at_sensitivity,
     multilabel_specificity_at_sensitivity,
-    specicity_at_sensitivity,
+    specificity_at_sensitivity,
 )
 from torchmetrics.functional.classification.stat_scores import (
     binary_stat_scores,
@@ -211,7 +211,7 @@
     "binary_specificity_at_sensitivity",
     "multiclass_specificity_at_sensitivity",
     "multilabel_specificity_at_sensitivity",
-    "specicity_at_sensitivity",
+    "specificity_at_sensitivity",
     "binary_stat_scores",
     "multiclass_stat_scores",
     "multilabel_stat_scores",

src/torchmetrics/functional/classification/specificity_sensitivity.py

@@ -403,7 +403,7 @@ def multilabel_specificity_at_sensitivity(
     return _multilabel_specificity_at_sensitivity_compute(state, num_labels, thresholds, ignore_index, min_sensitivity)
 
 
-def specicity_at_sensitivity(
+def specificity_at_sensitivity(
     preds: Tensor,
     target: Tensor,
     task: Literal["binary", "multiclass", "multilabel"],
@@ -414,7 +414,7 @@ def specicity_at_sensitivity(
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
 ) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
-    r"""Compute the highest possible specicity value given the minimum sensitivity thresholds provided.
+    r"""Compute the highest possible specificity value given the minimum sensitivity thresholds provided.
 
     This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and
     the find the specificity for a given sensitivity level.