diff --git a/docs/conf.py b/docs/conf.py
index ae75c979d..d40c5344a 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -216,7 +216,8 @@
     "n_features_x",
     "n_features_y",
     "n_features_z",
-    'n_neighbors', 'one',
+    "n_neighbors",
+    "one",
 }
 
 # validation
diff --git a/sktree/experimental/mutual_info.py b/sktree/experimental/mutual_info.py
index 8c74610a1..a1820e715 100644
--- a/sktree/experimental/mutual_info.py
+++ b/sktree/experimental/mutual_info.py
@@ -164,8 +164,8 @@ def mutual_info_ksg(
         The number of neighbors to use in defining the radius, by default 0.2.
     metric : str
         Any distance metric accepted by :class:`sklearn.neighbors.NearestNeighbors`.
-        If 'forest' (default), then uses an :class:`UnsupervisedObliqueRandomForest`
-        to compute geodesic distances.
+        If 'forest' (default), then uses an
+        :class:`sktree.UnsupervisedObliqueRandomForest` to compute geodesic distances.
     algorithm : str, optional
         Method to use, by default 'knn'. Can be ('ball_tree', 'kd_tree', 'brute').
     n_jobs : int, optional
@@ -190,14 +190,16 @@ def mutual_info_ksg(
     4. Get the number of NN in Z subspace within radius 'r'
     5. Apply analytic solution for KSG estimate
 
-    For MI :footcite:`Kraskov_2004`, the analytical solution is::
-    .. math:: 
+    For MI, the analytical solution is:
+
+    .. math::
 
         \\psi(k) - E[(\\psi(n_x) + \\psi(n_y))] + \\psi(n)
 
-    For CMI :footcite:`Frenzel2007`m the analytical solution is::
-    .. math:: 
-    
+    For CMI, the analytical solution is:
+
+    .. math::
+
         \\psi(k) - E[(\\psi(n_{xz}) + \\psi(n_{yz}) - \\psi(n_{z}))]
 
     where :math:`\\psi` is the DiGamma function, and each expectation term
@@ -372,8 +374,8 @@ def _compute_nn(
         Method to use, by default 'knn'. Can be ('ball_tree', 'kd_tree', 'brute').
     metric : str
         Any distance metric accepted by :class:`sklearn.neighbors.NearestNeighbors`.
-        If 'forest', then uses an :class:`UnsupervisedObliqueRandomForest` to compute
-        geodesic distances.
+        If 'forest', then uses an :class:`sktree.UnsupervisedObliqueRandomForest`
+        to compute geodesic distances.
     k : int, optional
         The number of k-nearest neighbors to query, by default 1.
     n_jobs : int,
diff --git a/sktree/experimental/simulate.py b/sktree/experimental/simulate.py
index 33f59c9a5..17b56145f 100644
--- a/sktree/experimental/simulate.py
+++ b/sktree/experimental/simulate.py
@@ -22,7 +22,7 @@ def simulate_helix(
         The value of the smallest radius, by default 0.0.
     radius_b : int, optional
         The value of the largest radius, by default 1.0
-    obs_noise_func : scipy.stats.distribution, optional
+    obs_noise_func : Callable, optional
         By default None, which defaults to a Uniform distribution from
         (-0.005, 0.005). If passed in, then must be a callable that when
         called returns a random number denoting the noise.
diff --git a/sktree/neighbors.py b/sktree/neighbors.py
index 4421c6d41..1d6e1ed84 100644
--- a/sktree/neighbors.py
+++ b/sktree/neighbors.py
@@ -53,7 +53,7 @@ def fit(self, X, y=None):
 
         Returns
         -------
-        self : NearestNeighborsMetaEstimator
+        self : object
             Fitted estimator.
         """
         X, y = self._validate_data(X, y, accept_sparse="csc")