Topological data analytic methods in machine learning rely on vectorizations of the persistence diagrams that encode persistent homology, as surveyed by Ali &al (2000) <doi:10.48550/arXiv.2212.09703>. Persistent homology can be computed using 'TDA' and 'ripserr' and vectorized using 'TDAvec'. The Tidymodels package collection modularizes machine learning in R for straightforward extensibility; see Kuhn & Silge (2022, ISBN:978-1-4920-9644-3). These 'recipe' steps and 'dials' tuners make efficient algorithms for computing and vectorizing persistence diagrams available for Tidymodels workflows.
Version: 0.2.0 Depends: R (≥ 3.5.0), recipes (≥ 0.1.17), dials Imports: rlang (≥ 1.1.0), vctrs (≥ 0.5.0), scales, tibble, purrr (≥ 1.0.0), tidyr, magrittr Suggests: ripserr (≥ 0.1.1), TDA, TDAvec (≥ 0.1.4), testthat (≥ 3.0.0), modeldata, tdaunif, knitr (≥ 1.20), rmarkdown (≥ 1.10), tidymodels, ranger Published: 2025-06-20 DOI: 10.32614/CRAN.package.tdarec Author: Jason Cory Brunson [cre, aut] Maintainer: Jason Cory Brunson <cornelioid at gmail.com> BugReports: https://github.com/tdaverse/tdarec/issues License: GPL (≥ 3) URL: https://github.com/tdaverse/tdarec NeedsCompilation: no Materials: README NEWS CRAN checks: tdarec results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=tdarec to link to this page.
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