The R Journal: article published in 2021, volume 13:1

Benchmarking R packages for Calculation of Persistent Homology PDF download
Eashwar V. Somasundaram, Shael E. Brown, Adam Litzler, Jacob G. Scott and Raoul R. Wadhwa , The R Journal (2021) 13:1, pages 184-193.

Abstract Several persistent homology software libraries have been implemented in R. Specifically, the Dionysus, GUDHI, and Ripser libraries have been wrapped by the TDA and TDAstats CRAN packages. These software represent powerful analysis tools that are computationally expensive and, to our knowledge, have not been formally benchmarked. Here, we analyze runtime and memory growth for the 2 R packages and the 3 underlying libraries. We find that datasets with less than 3 dimensions can be evaluated with persistent homology fastest by the GUDHI library in the TDA package. For higher-dimensional datasets, the Ripser library in the TDAstats package is the fastest. Ripser and TDAstats are also the most memory-efficient tools to calculate persistent homology.

Received: 2020-05-01; online 2021-06-07, supplementary material, (151.5 Kb)
CRAN packages: TDA, TDAstats, readr, ggplot2, scatterplot3d, recexcavAAR, deldir, magick, bench, pryr
CRAN Task Views implied by cited CRAN packages: Graphics, Multivariate, Phylogenetics, Spatial, TeachingStatistics


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This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-033,
  author = {Eashwar V. Somasundaram and Shael E. Brown and Adam Litzler
          and Jacob G. Scott and Raoul R. Wadhwa},
  title = {{Benchmarking R packages for Calculation of Persistent
          Homology}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-033},
  url = {https://doi.org/10.32614/RJ-2021-033},
  pages = {184--193},
  volume = {13},
  number = {1}
}