diff --git a/dianna/methods/lime_tabular.py b/dianna/methods/lime_tabular.py
index b95e8d92..839e0f70 100644
--- a/dianna/methods/lime_tabular.py
+++ b/dianna/methods/lime_tabular.py
@@ -1,4 +1,6 @@
 """LIME tabular explainer."""
+from typing import Iterable
+from typing import Union
 import numpy as np
 from lime.lime_tabular import LimeTabularExplainer
 from dianna import utils
@@ -9,18 +11,18 @@ class LIMETabular:
 
     def __init__(
         self,
-        training_data,
-        mode='classification',
-        feature_names=None,
-        categorical_features=None,
-        kernel_width=25,
-        kernel=None,
-        verbose=False,
-        class_names=None,
-        feature_selection='auto',
-        random_state=None,
+        training_data: np.array,
+        mode: str = "classification",
+        feature_names: list[int] = None,
+        categorical_features: list[int] = None,
+        kernel_width: int = 25,
+        kernel: callable = None,
+        verbose: bool = False,
+        class_names: list[str] = None,
+        feature_selection: str = "auto",
+        random_state: int = None,
         **kwargs,
-    ):
+    ) -> None:
         """Initializes Lime explainer.
 
         For numerical features, perturb them by sampling from a Normal(0,1) and
@@ -37,9 +39,9 @@ def __init__(
         Args:
             training_data (np.array): numpy 2d array
             mode (str, optional): "classification" or "regression"
-            feature_names (strings, optional): list of names corresponding to the columns
+            feature_names (list(str), optional): list of names corresponding to the columns
                            in the training data.
-            categorical_features (ints, optional): list of indices corresponding to the
+            categorical_features (list(int), optional): list of indices corresponding to the
                                                    categorical columns. Values in these
                                                    columns MUST be integers.
             kernel_width (int, optional): kernel width
@@ -49,15 +51,14 @@ def __init__(
                                          the classifier is using. If not present, class names
                                          will be '0', '1', ...
             feature_selection (str, optional): feature selection
-            discretize_continuous (bool, optional): if True, all non-categorical features
-                                                    will be discretized into quartiles.
             random_state (int or np.RandomState, optional): seed or random state
             kwargs: These parameters are passed on
 
         """
         self.mode = mode
         init_instance_kwargs = utils.get_kwargs_applicable_to_function(
-            LimeTabularExplainer, kwargs)
+            LimeTabularExplainer, kwargs
+        )
 
         # temporary solution for setting num_features and top_labels
         self.num_features = len(feature_names)
@@ -79,12 +80,12 @@ def __init__(
 
     def explain(
         self,
-        model_or_function,
-        input_tabular,
-        labels=(1, ),
-        num_samples=5000,
+        model_or_function: Union[str, callable],
+        input_tabular: np.array,
+        labels: Iterable[int] = (1,),
+        num_samples: int = 5000,
         **kwargs,
-    ):
+    ) -> np.array:
         """Run the LIME explainer.
 
         Args:
@@ -103,7 +104,8 @@ def explain(
         """
         # run the explanation.
         explain_instance_kwargs = utils.get_kwargs_applicable_to_function(
-            self.explainer.explain_instance, kwargs)
+            self.explainer.explain_instance, kwargs
+        )
         runner = utils.get_function(model_or_function)
 
         explanation = self.explainer.explain_instance(
@@ -116,11 +118,11 @@ def explain(
             **explain_instance_kwargs,
         )
 
-        if self.mode == 'regression':
+        if self.mode == "regression":
             local_exp = sorted(explanation.local_exp[1])
             saliency = [i[1] for i in local_exp]
 
-        elif self.mode == 'classification':
+        elif self.mode == "classification":
             # extract scores from lime explainer
             saliency = []
             for i in range(self.top_labels):