tdarec: A 'recipes' Extension for Persistent Homology and Its Vectorizations (original) (raw)
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 |
| 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 |
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