diff --git a/setup.py b/setup.py
index 5877c4ef..bcdf598b 100644
--- a/setup.py
+++ b/setup.py
@@ -59,7 +59,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.0.0.6",
+    version="5.0.0.7",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",
diff --git a/tigramite/independence_tests/cmiknn.py b/tigramite/independence_tests/cmiknn.py
index e04f1c49..92b480da 100644
--- a/tigramite/independence_tests/cmiknn.py
+++ b/tigramite/independence_tests/cmiknn.py
@@ -9,7 +9,7 @@
 import numpy as np
 from .independence_tests_base import CondIndTest
 from numba import jit
-
+import warnings
 
 class CMIknn(CondIndTest):
     r"""Conditional mutual information test based on nearest-neighbor estimator.
@@ -169,9 +169,10 @@ def _get_nearest_neighbors(self, array, xyz, knn):
             # array /= array.std(axis=1).reshape(dim, 1)
             # FIXME: If the time series is constant, return nan rather than
             # raising Exception
-            # if np.isnan(array).sum() != 0:
-            #     raise ValueError("nans after standardizing, "
-            #                      "possibly constant array!")
+            if np.any(std == 0.):
+                warnings.warn("Possibly constant array!")
+                # raise ValueError("nans after standardizing, "
+                #                  "possibly constant array!")
         elif self.transform == 'uniform':
             array = self._trafo2uniform(array)
         elif self.transform == 'ranks':
@@ -387,6 +388,8 @@ def get_conditional_entropy(self, array, xyz):
             # array /= array.std(axis=1).reshape(dim, 1)
             # FIXME: If the time series is constant, return nan rather than
             # raising Exception
+            if np.any(std == 0.):
+                warnings.warn("Possibly constant array!")
             # if np.isnan(array).sum() != 0:
             #     raise ValueError("nans after standardizing, "
             #                      "possibly constant array!")
diff --git a/tigramite/independence_tests/gpdc.py b/tigramite/independence_tests/gpdc.py
index fc7812e6..554aedce 100644
--- a/tigramite/independence_tests/gpdc.py
+++ b/tigramite/independence_tests/gpdc.py
@@ -216,6 +216,8 @@ def _get_single_residuals(self, array, target_var,
             for i in range(dim):
                 if std[i] != 0.:
                     array[i] /= std[i]
+            if np.any(std == 0.):
+                warnings.warn("Possibly constant array!")
             # array /= array.std(axis=1).reshape(dim, 1)
             # if np.isnan(array).sum() != 0:
             #     raise ValueError("nans after standardizing, "
diff --git a/tigramite/independence_tests/gpdc_torch.py b/tigramite/independence_tests/gpdc_torch.py
index 3efacb8c..26bd8714 100644
--- a/tigramite/independence_tests/gpdc_torch.py
+++ b/tigramite/independence_tests/gpdc_torch.py
@@ -221,6 +221,8 @@ def _get_single_residuals(self, array, target_var,
             for i in range(dim):
                 if std[i] != 0.:
                     array[i] /= std[i]
+            if np.any(std == 0.):
+                warnings.warn("Possibly constant array!")
             # array /= array.std(axis=1).reshape(dim, 1)
             # if np.isnan(array).any():
             #     raise ValueError("Nans after standardizing, "
diff --git a/tigramite/independence_tests/parcorr.py b/tigramite/independence_tests/parcorr.py
index 8a55a337..bdcccdf9 100644
--- a/tigramite/independence_tests/parcorr.py
+++ b/tigramite/independence_tests/parcorr.py
@@ -8,6 +8,7 @@
 from scipy import stats
 import numpy as np
 import sys
+import warnings
 
 from .independence_tests_base import CondIndTest
 
@@ -95,6 +96,8 @@ def _get_single_residuals(self, array, target_var,
             for i in range(dim):
                 if std[i] != 0.:
                     array[i] /= std[i]
+            if np.any(std == 0.):
+                warnings.warn("Possibly constant array!")
             # array /= array.std(axis=1).reshape(dim, 1)
             # if np.isnan(array).sum() != 0:
             #     raise ValueError("nans after standardizing, "