Update index.md

docs/index.md

@@ -23,9 +23,9 @@ For more details about NeuMapper see "[NeuMapper: A Scalable Computational Frame
 
 ## **Related Projects** 
 
-- [Reciprocal Isomap](https://calebgeniesse.github.io/reciprocal_isomap) is a reciprocal variant of Isomap for robust non-linear dimensionality reduction in Python. `ReciprocalIsomap` was inspired by scikit-learn's implementation of Isomap, but the reciprocal variant enforces shared connectivity in the underlying *k*-nearest neighbors graph (i.e., two points are only considered neighbors if each is a neighbor of the other).
+- [Reciprocal Isomap](https://calebgeniesse.github.io/reciprocal_isomap) is a reciprocal variant of Isomap for robust non-linear dimensionality reduction in Python. The `ReciprocalIsomap` transformer was inspired by scikit-learn's implementation of Isomap, but the reciprocal variant enforces shared connectivity in the underlying *k*-nearest neighbors graph (i.e., two points are only considered neighbors if each is a neighbor of the other).
 
-- [Landmark Cover](https://calebgeniesse.github.io/landmark_cover) is a landmark-based cover for Mapper. `LandmarkCover` was designed for use with [KeplerMapper](https://kepler-mapper.scikit-tda.org/en/latest/), but rather than dividing an *extrinsic* space (e.g., low-dimensional projection) into overlapping hypercubes, the landmark-based approach directly partitions data points into overlapping subsets based on their *intrinsic* distances from pre-selected landmark points.
+- [Landmark Cover](https://calebgeniesse.github.io/landmark_cover) is a Python implementation of NeuMapper's landmark-based cover. The `LandmarkCover` transformer was designed for use with [KeplerMapper](https://kepler-mapper.scikit-tda.org/en/latest/), but rather than dividing an *extrinsic* space (e.g., low-dimensional projection) into overlapping hypercubes, the landmark-based approach directly partitions data points into overlapping subsets based on their *intrinsic* distances from pre-selected landmark points.
 
 - [DyNeuSR](https://braindynamicslab.github.io/dyneusr/) is a Python library for visualizing topological representations of neuroimaging data. The package combines visual web components with a high-level Python interface for interacting with, manipulating, and visualizing topological graph representations of functional brain activity.