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Source code for hoi.utils.stats

     import pandas as pd
 
     hoi = np.asarray(hoi).squeeze()
+    if not hoi.ndim:
+        hoi = hoi.reshape(-1)
 
     # get order and multiplets
     if model:
diff --git a/auto_examples/it/plot_entropies.html b/auto_examples/it/plot_entropies.html
index cb68bb11..9c48a5a1 100644
--- a/auto_examples/it/plot_entropies.html
+++ b/auto_examples/it/plot_entropies.html
@@ -591,7 +591,7 @@ 
Entropy of data sampled from an exponential distributionplt.show()
-Comparison of entropy estimators when the data are sampled from a exponential distribution

Total running time of the script: (0 minutes 20.308 seconds)

+Comparison of entropy estimators when the data are sampled from a exponential distribution

Total running time of the script: (0 minutes 20.392 seconds)

 -2-dimensional multivariate normal, 4-dimensional multivariate normal, 6-dimensional multivariate normal, 8-dimensional multivariate normal

Total running time of the script: (0 minutes 31.995 seconds)

+2-dimensional multivariate normal, 4-dimensional multivariate normal, 6-dimensional multivariate normal, 8-dimensional multivariate normal

Total running time of the script: (0 minutes 32.313 seconds)

 -Comparison of entropy estimators when the data is high-dimensional, Mode: dense, Mode: sparse

Total running time of the script: (0 minutes 24.189 seconds)

+Comparison of entropy estimators when the data is high-dimensional, Mode: dense, Mode: sparse

Total running time of the script: (0 minutes 24.213 seconds)

 -Comparison of MI estimators when the data are sampled from a normal distribution

Total running time of the script: (0 minutes 20.547 seconds)

+Comparison of MI estimators when the data are sampled from a normal distribution

Total running time of the script: (0 minutes 20.688 seconds)

 -Comparison of MI estimators when the data is high-dimensional, Mode: dense, Mode: sparse

Total running time of the script: (0 minutes 29.919 seconds)

+Comparison of MI estimators when the data is high-dimensional, Mode: dense, Mode: sparse

Total running time of the script: (0 minutes 30.009 seconds)