Topological data analysis is a powerful tool for finding non-linear global structure
in whole datasets. 'TDApplied' aims to bridge topological data analysis with data, statistical
and machine learning practitioners so that more analyses may benefit from the
power of topological data analysis. The main tool of topological data analysis is
persistent homology, which computes a shape descriptor of a dataset, called
a persistence diagram. There are three goals of this package: (1) convert persistence diagrams
computed using the two main R packages for topological data analysis into a data frame,
(2) implement fast versions of both distance and kernel calculations
for pairs of persistence diagrams, and (3) provide methods for machine learning
and inference for persistence diagrams which scale well.

Topological data analysis is a powerful tool for finding non-linear global structure
in whole datasets. 'TDApplied' aims to bridge topological data analysis with data, statistical
and machine learning practitioners so that more analyses may benefit from the
power of topological data analysis. The main tool of topological data analysis is
persistent homology, which computes a shape descriptor of a dataset, called
a persistence diagram. There are five goals of this package: (1) convert persistence diagrams
computed using the two main R packages for topological data analysis into a data frame,
(2) implement fast versions of both distance and kernel calculations
for pairs of persistence diagrams, (3) provide methods for machine learning
and inference for persistence diagrams which scale well, (4) deliver a fast implementation
of persistent homology via a python interface, and (5) contribute tools for the interpretation of
persistence diagrams.
rmarkdown, knitr, testthat (>= 3.0.0), TDA, TDAstats,
reticulate

c(
person("Raoul", "Wadhwa", email = "raoulwadhwa@gmail.com", role = c("aut", "cre")),
person("Andrew", "Dhawan", role = "aut"),
person("Drew", "Williamson", role = "aut"),
person("Jacob", "Scott", role = "aut"))
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.
For a general background on computing persistent homology for
topological data analysis, see Otter et al. (2017)
<doi:10.1140/epjds/s13688-017-0109-5>.
To learn more about how the permutation test is used for
nonparametric statistical inference in topological data analysis,
read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
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>.
Raoul Wadhwa [aut, cre],
Andrew Dhawan [aut],
Drew Williamson [aut],
Jacob Scott [aut]