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: | 0.1.0 |
| 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: | 2025-05-26 |
| 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 |
| Reference manual: | tdarec.pdf |
| Vignettes: |
Tuning persistent homological hyperparameters (source) |
| Package source: | tdarec_0.1.0.tar.gz |
| Windows binaries: | r-devel: tdarec_0.1.0.zip, r-release: tdarec_0.1.0.zip, r-oldrel: tdarec_0.1.0.zip |
| macOS binaries: | r-release (arm64): tdarec_0.1.0.tgz, r-oldrel (arm64): tdarec_0.1.0.tgz, r-release (x86_64): tdarec_0.1.0.tgz, r-oldrel (x86_64): tdarec_0.1.0.tgz |
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