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 |