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 |