TDAstats

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) <doi:10.21105/joss.00860>. 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>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.

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Description file content

Package
TDAstats
Type
Package
Title
Pipeline for Topological Data Analysis
Version
0.4.0
Description
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 . This package has been published as Wadhwa et al. (2018) .
License
GPL-3
Encoding
UTF-8
LazyData
true
Depends
R (>= 3.3)
Imports
ggplot2 (>= 2.2.1), Rcpp (>= 0.12.15)
URL
BugReports
https://github.com/rrrlw/TDAstats/issues
LinkingTo
Rcpp
RoxygenNote
6.1.0
SystemRequirements
C++11
Suggests
testthat (>= 2.0.0), knitr, rmarkdown, covr
VignetteBuilder
knitr
NeedsCompilation
yes
Packaged
2018-11-05 16:49:30 UTC; WADHWAR
Author
Raoul Wadhwa [aut, cre], Andrew Dhawan [aut], Drew Williamson [aut], Jacob Scott [aut]
Maintainer
Raoul Wadhwa
Repository
CRAN
Date/Publication
2018-11-05 19:20:11 UTC

install.packages('TDAstats')

0.4.0

12 days ago

https://github.com/rrrlw/TDAstats

Raoul Wadhwa

GPL-3

Depends on

R (>= 3.3)

Imports

ggplot2 (>= 2.2.1), Rcpp (>= 0.12.15)

Suggests

testthat (>= 2.0.0), knitr, rmarkdown, covr

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