Statistical Tools for Topological Data Analysis
Brittany T. Fasy, Jisu Kim, Fabrizio Lecci, Clement Maria,
David L. Millman, Vincent Rouvreau.
Tools for the statistical analysis of persistent homology and for
density clustering. For that, this package provides an R interface for the
efficient algorithms of the C++ libraries 'GUDHI' <http://gudhi.gforge.inria.fr/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.
FNN, Rcpp (>= 0.11.0), igraph, parallel, scales

Tools for the statistical analysis of persistent homology and for
density clustering. For that, this package provides an R interface for the
efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.

Tools for the statistical analysis of persistent homology and for
density clustering. For that, this package provides an R interface for the
efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.

c(person("Kisung", "You", role = c("aut", "cre"),email = "kyoustat@gmail.com",comment=c(ORCID="0000-0002-8584-459X")),person("Byeongsu","Yu", role="aut",email="byeongsu.yu@math.tamu.edu"))
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.
Rcpp, Rdpack, TDAstats, energy, ggplot2, maotai, stats, utils
Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>),
Byeongsu Yu [aut]