Update README with new API findWithinBox()

Author: jlblancoc

CHANGELOG.md

@@ -1,3 +1,7 @@
+# nanoflann 1.8.0: Released Nov 16, 2025
+ - Some minor performance optimization changes. See [#271](https://github.com/jlblancoc/nanoflann/issues/271).
+ - New API: findWithinBox(). See [#272](https://github.com/jlblancoc/nanoflann/pull/272).
+
 # nanoflann 1.7.1: Released Mar 15, 2025
  - ResultSets::worstDist(): Fix a potential access to negative index in array (did not happen in practice, but static analysis tools correctly detected this possibility).
 

README.md

@@ -104,7 +104,8 @@ Refer to the examples below or to the C++ API of [nanoflann::KDTreeSingleIndexAd
     * [nanoflann::KDTreeSingleIndexAdaptor<>](https://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::radiusSearch()`
       * Finds all the neighbors to `query_point[0:dim-1]` within a maximum radius. The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. See an [example usage code](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_kdd_radius.cpp#L141).
     * [nanoflann::KDTreeSingleIndexAdaptor<>](https://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::radiusSearchCustomCallback()`
-	  * Can be used to receive a callback for each point found in range. This may be more efficient in some situations instead of building a huge vector of pairs with the results.
+    * Can be used to receive a callback for each point found in range. This may be more efficient in some situations instead of building a huge vector of pairs with the results.
+    * [nanoflann::KDTreeSingleIndexAdaptor<>](https://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::findWithinBox()` [New in 1.8.0]: Optimized search within a given axis-aligned bound box.
   * Working with 2D and 3D point clouds or N-dimensional data sets.
   * Working directly with `Eigen::Matrix<>` classes (matrices and vectors-of-vectors).
   * Working with dynamic point clouds without a need to rebuild entire kd-tree index.