Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
Version: | 0.1.0 |
Imports: | Rcpp, Rdpack, TDAstats, energy, ggplot2, maotai, stats, utils |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2021-03-11 |
Author: | Kisung You |
Maintainer: | Kisung You <kyoustat at gmail.com> |
License: | MIT + file LICENSE |
NeedsCompilation: | yes |
Materials: | NEWS |
CRAN checks: | TDAkit results |
Reference manual: | TDAkit.pdf |
Package source: | TDAkit_0.1.0.tar.gz |
Windows binaries: | r-devel: TDAkit_0.1.0.zip, r-release: TDAkit_0.1.0.zip, r-oldrel: TDAkit_0.1.0.zip |
macOS binaries: | r-release: TDAkit_0.1.0.tgz, r-oldrel: TDAkit_0.1.0.tgz |
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