diff --git a/netrd/utilities/entropy.py b/netrd/utilities/entropy.py
index dff68e2..9d6f3d7 100644
--- a/netrd/utilities/entropy.py
+++ b/netrd/utilities/entropy.py
@@ -15,7 +15,7 @@ from scipy.stats import entropy as sp_entropy
 
 
 def js_divergence(P, Q):
-    """Jenson-Shannon divergence between `P` and `Q`.
+    """Jensen-Shannon divergence between `P` and `Q`.
 
     Parameters
     ----------
@@ -35,8 +35,10 @@ def js_divergence(P, Q):
     return 0.5 * (sp_entropy(P, M, base=2) + sp_entropy(Q, M, base=2))
 
 
-def entropy(var):
-    """Return the Shannon entropy of a variable.
+def entropy_from_seq(var):
+    """Return the Shannon entropy of a variable. This differs from
+    Scipy's entropy by taking a sequence of observations as input
+    rather than a histogram or probability distribution.
 
     Parameters
     ----------
@@ -65,7 +67,7 @@ def joint_entropy(data):
     Returns
     -------
     float
-        Joint entrpoy of the variables of interests.
+        Joint entropy of the variables of interests.
 
     Notes
     -----

diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index 8837207..5b2ab79 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -8,7 +8,7 @@ Test utility functions.
 
 import numpy as np
 from netrd.utilities.entropy import categorized_data
-from netrd.utilities.entropy import entropy, joint_entropy, conditional_entropy
+from netrd.utilities.entropy import entropy_from_seq, joint_entropy, conditional_entropy
 from netrd.utilities import threshold
 
 
@@ -89,7 +89,7 @@ def test_entropies():
 
     """
     data = np.array([[1, 0, 0, 1, 1, 0, 1, 0], [0, 1, 0, 1, 1, 0, 1, 0]]).T
-    H = entropy(data[:, 0])
+    H = entropy_from_seq(data[:, 0])
     H_joint = joint_entropy(data)
     H_cond = conditional_entropy(data[:, 1, np.newaxis], data[:, 0, np.newaxis])
 

Clarify usage of utilities.entropy.entropy?
When I was refactoring the Dmeasure code, I looked at using our version of entropy instead of scipy's, but one is not a drop-in replacement for the other because they take different inputs - one takes a histogram and the other takes a sequence of values. It would touch a lot of files but but renaming our `entropy` to something like `entropy_from_sequence` would remove this ambiguity.
tests/test_utilities.py::test_thresholds tests/test_utilities.py::test_categorized_data tests/test_utilities.py::test_entropies