jlmelville/uwot

An R package implementing the UMAP dimensionality reduction method.

What it solves

It provides an R implementation of the Uniform Manifold Approximation and Projection (UMAP) method, which is used for dimensionality reduction of high-dimensional data, making it easier to visualize and analyze.

How it works

The package translates the Python UMAP implementation into R, utilizing C++ code for performance. It supports basic dimensionality reduction, as well as supervised and metric (out-of-sample) learning extensions. It can integrate with optional packages like RcppHNSW and rnndescent to accelerate nearest neighbor searches.

Who it’s for

Data scientists and researchers using the R programming language who need to perform dimensionality reduction on complex datasets.

Highlights

  • Supports supervised and metric learning extensions.
  • Includes a umap2 function with improved default settings.
  • Compatible with faster nearest neighbor search methods like HNSW and nndescent.
  • Provides a text-based progress bar during execution when verbose mode is enabled.

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