Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.
Version: 3.0.4 Depends: R (≥ 3.5.0) Imports: parallel, doParallel, foreach, clue, rdist, parallelly, kernlab, iterators, methods, stats, utils, Rcpp (≥ 0.11.0) LinkingTo: Rcpp Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), TDAstats, reticulate, TDA, igraph Published: 2024-10-29 DOI: 10.32614/CRAN.package.TDApplied Author: Shael Brown [aut, cre], Dr. Reza Farivar [aut, fnd] Maintainer: Shael Brown <shaelebrown at gmail.com> BugReports: https://github.com/shaelebrown/TDApplied/issues License: GPL (≥ 3) URL: https://github.com/shaelebrown/TDApplied NeedsCompilation: yes Materials: README NEWS CRAN checks: TDApplied results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=TDApplied to link to this page.
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