diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index fb09e31c..5d39ed25 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -31,7 +31,7 @@ You can contribute in many ways:
 
 ### Report Bugs
 
-Report bugs at <https://github.com/richford/AFQ-Insight/issues>.
+Report bugs at <https://github.com/tractometry/AFQ-Insight/issues>.
 
 If you are reporting a bug, please include:
 
@@ -64,7 +64,7 @@ articles, and such.
 ### Submit Feedback
 
 The best way to send feedback is to file an issue at
-<https://github.com/richford/AFQ-Insight/issues>.
+<https://github.com/tractometry/AFQ-Insight/issues>.
 
 If you are proposing a feature:
 
diff --git a/afqinsight/_serial_bagging.py b/afqinsight/_serial_bagging.py
index f9536b0e..62de2f71 100644
--- a/afqinsight/_serial_bagging.py
+++ b/afqinsight/_serial_bagging.py
@@ -23,8 +23,9 @@
     _parallel_predict_regression,
 )
 from sklearn.ensemble._base import _partition_estimators
-from sklearn.utils import check_array, check_random_state, indices_to_mask, resample
-from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils import check_array, check_random_state, resample
+from sklearn.utils._mask import indices_to_mask
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.random import sample_without_replacement
 from sklearn.utils.validation import (
     _check_sample_weight,
@@ -610,7 +611,7 @@ def predict_log_proba(self, X):
         else:
             return np.log(self.predict_proba(X))
 
-    @if_delegate_has_method(delegate="base_estimator")
+    @available_if(lambda est: hasattr(est.base_estimator, "decision_function"))
     def decision_function(self, X):
         """Average of the decision functions of the base classifiers.
 
diff --git a/setup.cfg b/setup.cfg
index cd5cd0bf..93b8a131 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -33,18 +33,18 @@ setup_requires =
     setuptools_scm
 python_requires = >=3.10
 install_requires =
-    scipy==1.13.1
-    dipy>=1.0.0
-    matplotlib<3.9
+    scipy==1.14.1
+    dipy==1.9.0
+    matplotlib==3.9.3
     groupyr>=0.3.4
-    numpy==1.23.5
-    pandas==2.1.4
+    numpy==1.26.4
+    pandas==2.2.3
     requests
-    seaborn==0.13.0
-    scikit-learn==1.2.1
+    seaborn==0.13.2
+    scikit-learn==1.5.2
     sklearn_pandas>=2.0.0
     tqdm
-    statsmodels==0.14.0
+    statsmodels==0.14.4
 zip_safe = False
 include_package_data = True
 packages = find: