diff --git a/docs/API/estimators/ensemble/lexico_gradient_boosting.md b/docs/API/estimators/ensemble/lexico_gradient_boosting.md
index db5c8df..49c5eae 100644
--- a/docs/API/estimators/ensemble/lexico_gradient_boosting.md
+++ b/docs/API/estimators/ensemble/lexico_gradient_boosting.md
@@ -37,7 +37,7 @@ decision tree models capable of handling longitudinal data.
     2. **Cython Adaptation:** This implementation leverages a fork of Scikit-learn’s fast C++-powered
     decision tree to ensure that the Lexico Decision Tree is fast and efficient, avoiding the potential 
     slowdown of a from-scratch Python implementation. Further details on the algorithm can be found in the 
-    Cython adaptation available at `/scikit-longitudinal/scikit-learn/sklearn/tree/_splitter.pyx`, specifically in the `node_lexicoRF_split` function.
+    Cython adaptation available [here at Scikit-Lexicographical-Trees](https://github.com/simonprovost/scikit-lexicographical-trees/blob/21443b9dce51434b3198ccabac8bafc4698ce953/sklearn/tree/_splitter.pyx#L695) specifically in the `node_lexicoRF_split` function.
 
     For further scientific references, please refer to the Notes section.
 
diff --git a/docs/API/estimators/ensemble/lexico_random_forest.md b/docs/API/estimators/ensemble/lexico_random_forest.md
index 6cc5d0d..e36cd80 100644
--- a/docs/API/estimators/ensemble/lexico_random_forest.md
+++ b/docs/API/estimators/ensemble/lexico_random_forest.md
@@ -28,7 +28,7 @@ to select the best split at each node.
     Key Features:
     
     1. **Lexicographic Optimisation:** The approach prioritizes features based on both their information gain ratios and the recency of the data, favoring splits with more recent information.
-    2. **Cython Adaptation**: This implementation leverages a fork of Scikit-learn’s fast C++-powered decision tree to ensure that the Lexico Random Forest is fast and efficient, avoiding the potential slowdown of a from-scratch Python implementation. Further details on the algorithm can be found in the Cython adaptation available at `/scikit-longitudinal/scikit-learn/sklearn/tree/_splitter.pyx`, specifically in the `node_lexicoRF_split` function.
+    2. **Cython Adaptation**: This implementation leverages a fork of Scikit-learn’s fast C++-powered decision tree to ensure that the Lexico Random Forest is fast and efficient, avoiding the potential slowdown of a from-scratch Python implementation. Further details on the algorithm can be found in the Cython adaptation available [here at Scikit-Lexicographical-Trees](https://github.com/simonprovost/scikit-lexicographical-trees/blob/21443b9dce51434b3198ccabac8bafc4698ce953/sklearn/tree/_splitter.pyx#L695) specifically in the `node_lexicoRF_split` function.
 
     For further scientific references, please refer to the Notes section.
 
diff --git a/docs/API/estimators/trees/lexico_decision_tree_classifier.md b/docs/API/estimators/trees/lexico_decision_tree_classifier.md
index 962ef16..046ff1f 100644
--- a/docs/API/estimators/trees/lexico_decision_tree_classifier.md
+++ b/docs/API/estimators/trees/lexico_decision_tree_classifier.md
@@ -31,7 +31,7 @@ This implementation extends the traditional decision tree algorithm by incorpora
     and the recency of the data, favoring splits with more recent information.
     2. **Cython Adaptation**: This implementation leverages a fork of Scikit-learn’s fast C++-powered decision 
     tree to ensure that the Lexico Decision Tree is fast and efficient, avoiding the potential slowdown of a 
-    from-scratch Python implementation. Further details on the algorithm can be found in the Cython adaptation available at `/scikit-longitudinal/scikit-learn/sklearn/tree/_splitter.pyx`, specifically in the `node_lexicoRF_split` function.
+    from-scratch Python implementation. Further details on the algorithm can be found in the Cython adaptation available [here at Scikit-Lexicographical-Trees](https://github.com/simonprovost/scikit-lexicographical-trees/blob/21443b9dce51434b3198ccabac8bafc4698ce953/sklearn/tree/_splitter.pyx#L695) specifically in the `node_lexicoRF_split` function.
 
     For further scientific references, please refer to the Notes section.
 
diff --git a/docs/API/estimators/trees/lexico_decision_tree_regressor.md b/docs/API/estimators/trees/lexico_decision_tree_regressor.md
index d14c2e3..85af85a 100644
--- a/docs/API/estimators/trees/lexico_decision_tree_regressor.md
+++ b/docs/API/estimators/trees/lexico_decision_tree_regressor.md
@@ -159,7 +159,7 @@ The Lexico Decision Tree Regressor is an advanced regression model specifically
     Key Features:
     
     1. **Lexicographic Optimisation:** The approach prioritizes features based on both their information gain ratios and the recency of the data, favoring splits with more recent information.
-    2. **Cython Adaptation**: This implementation leverages a fork of Scikit-learn’s fast C++-powered decision tree to ensure that the Lexico Decision Tree is fast and efficient, avoiding the potential slowdown of a from-scratch Python implementation. Further details on the algorithm can be found in the Cython adaptation available at `/scikit-longitudinal/scikit-learn/sklearn/tree/_splitter.pyx`, specifically in the `node_lexicoRF_split` function.
+    2. **Cython Adaptation**: This implementation leverages a fork of Scikit-learn’s fast C++-powered decision tree to ensure that the Lexico Decision Tree is fast and efficient, avoiding the potential slowdown of a from-scratch Python implementation. Further details on the algorithm can be found in the Cython adaptation available [here at Scikit-Lexicographical-Trees](https://github.com/simonprovost/scikit-lexicographical-trees/blob/21443b9dce51434b3198ccabac8bafc4698ce953/sklearn/tree/_splitter.pyx#L695) specifically in the `node_lexicoRF_split` function.
 
     For further scientific references, please refer to the Notes section.