parallelDist: Parallel Distance Matrix Computation for R

An R software package providing a parallelized alternative to the native 'dist' function for calculating distance matrices. The package, authored by Alexander W. Eckert, is implemented primarily in C++ and supports 41 predefined distance functions from several R packages, as well as user-defined C++ functions. It leverages the RcppParallel and Armadillo libraries for optimized, multi-threaded matrix operations.

Use Cases

• Accelerating hierarchical clustering workflows based on the parallel computation of distance matrices.
• Improving performance of k-nearest neighbors (k-NN) algorithms based on fast distance calculations for high-dimensional data.
• Conducting time-series similarity analysis based on optimized Dynamic Time Warping (DTW) calculations mentioned in the description.
• Benchmarking custom distance metrics based on the support for user-defined C++ functions.

Strengths

• Implements 41 predefined distance functions from established R packages.
• Uses C++ and the RcppParallel library for parallelized computation to improve speed.
• Applies the Curiously Recurring Template Pattern (CRTP) to optimize Dynamic Time Warping calculations.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Software package release.