diff --git a/src/pysearchlight/__init__.py b/src/pysearchlight/__init__.py
index c594f17..9f4518b 100644
--- a/src/pysearchlight/__init__.py
+++ b/src/pysearchlight/__init__.py
@@ -1 +1,3 @@
-from .sl import Searchlight
+from .sl import SearchLight
+
+__all__ = ["SearchLight"]
diff --git a/src/pysearchlight/examples.py b/src/pysearchlight/examples.py
index 82ac31d..c09f7ef 100644
--- a/src/pysearchlight/examples.py
+++ b/src/pysearchlight/examples.py
@@ -1,10 +1,15 @@
 import numpy as np
-import itertools
 from sklearn.model_selection import cross_val_score
 from sklearn.svm import SVC
+from typing import Union, List
+from numpy.typing import NDArray
 
 
-def fit_clf(data, labels, clf=SVC()):
+def fit_clf(
+    data: NDArray[np.floating],
+    labels: Union[NDArray, List[NDArray]],
+    clf: SVC = SVC(),
+) -> float:
     """
     Returns classification accuracy for a single voxel (cross-validated). Labels can either be
     a numpy array or a list of numpy arrays (in which case the classification accuracies for every
diff --git a/src/pysearchlight/sl.py b/src/pysearchlight/sl.py
index cddbf49..69cd3cb 100644
--- a/src/pysearchlight/sl.py
+++ b/src/pysearchlight/sl.py
@@ -3,14 +3,13 @@
 from joblib import Parallel, delayed
 import tqdm
 from numba import jit
-from typing import List, Callable, Tuple
+from typing import List, Callable, Tuple, Union, Optional
+from numpy.typing import NDArray
 
 
 @jit(nopython=True, cache=True)
-def get_searchlight_data(data, coords):
-    """
-    Returns data in a sphere with radius self.radius at position x, y, z
-    """
+def get_searchlight_data(data: NDArray[np.floating], coords: NDArray[np.int64]) -> NDArray[np.floating]:
+    """Return all voxel data for a list of sphere coordinates."""
     # Get coordinates of all voxels in sphere
     out = np.zeros((len(coords), len(coords[0]), data.shape[1]))
     for i, sphere_coords in enumerate(coords):
@@ -26,8 +25,12 @@ class SearchLight:
     """
 
     def __init__(
-        self, data: np.array, sl_fn: Callable, radius: int, mask: np.array = None
-    ):
+        self,
+        data: NDArray[np.floating],
+        sl_fn: Callable[[NDArray[np.floating]], Union[float, NDArray[np.floating]]],
+        radius: int,
+        mask: Optional[NDArray[np.int_]] = None,
+    ) -> None:
         """
         Parameters
         ----------
@@ -52,12 +55,12 @@ def __init__(
 
     def fit(
         self,
-        coords: List[Tuple] = None,
+        coords: Optional[List[Tuple[int, int, int]]] = None,
         output_size: int = 1,
         n_jobs: int = 1,
         n_chunks: int = 1,
         verbose: int = 1,
-    ) -> np.array:
+    ) -> NDArray[np.floating]:
         """
         Returns a 3D array with the classification accuracy for each voxel.
 
@@ -116,7 +119,7 @@ def fit(
 
         return results
 
-    def get_sphere_coords(self, x, y, z):
+    def get_sphere_coords(self, x: int, y: int, z: int) -> NDArray[np.int_]:
         """
         Returns coordinates of all voxels in a sphere with radius self.radius
         at position x, y, z