tdarec: A 'recipes' Extension for Persistent Homology and Its Vectorizations

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

Documentation:

Reference manual:	tdarec.pdf
Vignettes:	Tuning persistent homological hyperparameters (source)

Downloads:

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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