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Changelog

Source: NEWS.md

songR 0.1.0

Core Features

  • Core SONG algorithm implemented in C++ via RcppArmadillo.
  • Incremental data visualization via update().
  • Projection of new points via predict().
  • Base R plot() and optional ggplot2::autoplot() methods.
  • Supports 2D and 3D output embeddings.
  • print() and summary() methods with codebook and graph statistics.

Data and Visualization

  • Bundled songR_blobs dataset: 8-cluster, 20D synthetic data (1600 points).
  • Viridis plasma color scale used throughout vignettes and tutorials.
  • All paper figures (Senanayake et al., 2021) reproducible via tutorial scripts.

Interactive App

  • Interactive Shiny app (run_songR_app()) for comparing SONG, t-SNE, and UMAP side-by-side.
  • Dark mode toggle with localStorage persistence.
  • Upload custom CSV/RDS data or use built-in datasets.
  • Full hyperparameter control, incremental update mode, and export options.

Vignettes and Tutorials

  • Two CRAN vignettes: “Introduction to songR” and “Getting Started with songR”.
  • pkgdown articles: “Reproducing Paper Figures” and “Interactive Shiny App”.
  • 13 tutorial scripts reproducing all figures and tables from Senanayake et al.
    1. on MNIST, Fashion-MNIST, Wong CyTOF (1.27M cells), COIL-20, and Samusik datasets.
  • Extra benchmarks: static AMI comparison, hyperparameter sensitivity sweeps, and runtime scaling analysis.