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.github/PULL_REQUEST_TEMPLATE.md

@@ -1,6 +1,6 @@
 ## Description
 
-Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
+Please include a summary of the change and which issue is fixed. Also include relevant motivation and context. List any dependencies that are required for this change.
 
 Fixes # (issue)
 
@@ -15,7 +15,7 @@ Please delete options that are not relevant.
 
 ## How Has This Been Tested?
 
-Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration.
+Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Also list any relevant details for your test configuration.
 
 - [ ] Test A
 - [ ] Test B

CHANGELOG.md

@@ -76,4 +76,4 @@ All notable changes to this project will be documented in this file.
 - Added a badge for the code style (Ruff) in the README.
 - Added a badge for the license in the README.
 - Added CI/CD pipelines for PyPI deployment (including test coverage, compiling extensions and wheels, and uploading to PyPI).
-- Resolving issues with compiling Cython extensions on Windows and MacOS.
+- Resolving issues with compiling Cython extensions on Windows and macOS.

README.md

@@ -72,7 +72,7 @@ print(labels)
 
 ## Documentation
 
-You can find the full documentation for Radius Clustering [here](https://contrib.scikit-learn.org/radius_clustering/).
+See the [full documentation for Radius Clustering](https://contrib.scikit-learn.org/radius_clustering/).
 
 ### Building the documentation
 

docs/source/details.rst

@@ -28,7 +28,7 @@ The Radius Clustering package implements a clustering algorithm based on the Min
 The MDS problem is a well-known NP-Hard problem in graph theory, and it has been proven to be linked to the clustering
 under radius constraint problem. The MDS problem is defined as follows:
 
-Given a undirected weighted graph :math:`G = (V,E)` where :math:`V` is a set of vertices and :math:`E` is a set of edges,
+Given an undirected weighted graph :math:`G = (V,E)` where :math:`V` is a set of vertices and :math:`E` is a set of edges,
 a dominating set :math:`D` is a subset of :math:`V` such that every vertex in :math:`V` is either in :math:`D` or
 adjacent to a vertex in :math:`D`. The goal is to find a dominating set :math:`D` such that the number of vertices in
 :math:`D` is minimized. This problem is known to be NP-Hard.

docs/source/installation.rst

@@ -67,7 +67,7 @@ Installing from PyPI
             python -m pip show radius-clustering # show radius-clustering version and location
             python -m pip freeze             # show all installed packages in the environment
 
-    .. tab-item:: MacOS
+    .. tab-item:: macOS
       :class-label: tab-4
       :sync: macos
 
@@ -183,7 +183,7 @@ To install Radius Clustering from the source, you need to have a C and C++ compi
           In Build Tools, install C++ toolchain. Ensure that it is added to the system PATH.
           You are now ready to install Radius Clustering from source.
 
-    .. tab-item:: MacOS
+    .. tab-item:: macOS
       :class-label: tab-4
       :sync: macos
 

examples/plot_iris_example.py

@@ -255,7 +255,7 @@ def get_order_labels(kmeans, rad, data):
 t_kmeans = time.time() - t0
 
 # %%
-# Reapllying the same process as before
+# Reapplying the same process as before
 # --------------------------------------
 
 rad_centers_index = np.array(rad.centers_)