Pipeline for Topological Data Analysis

A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) . For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) . To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) . To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at < https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) .


Reference manual

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0.4.1 by Raoul Wadhwa, 2 years ago


Report a bug at https://github.com/rrrlw/TDAstats/issues

Browse source code at https://github.com/cran/TDAstats

Authors: Raoul Wadhwa [aut, cre] , Andrew Dhawan [aut] , Drew Williamson [aut] , Jacob Scott [aut] , Jason Cory Brunson [ctb] , Shota Ochi [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2, Rcpp

Suggests testthat, knitr, rmarkdown, covr

Linking to Rcpp

System requirements: C++11

Imported by TDAkit, lookout.

See at CRAN