A 'recipes' Extension for Persistent Homology and Its Vectorizations R package tdarec version 0.2.1 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. Software 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: cli, ripserr (>= 0.1.1), TDA, TDAvec (>= 0.1.4), testthat (>= 3.0.0), modeldata, tdaunif, knitr (>= 1.20), rmarkdown (>= 1.10), tidymodels, ranger Jason Cory Brunson <cornelioid@gmail.com> Comprehensive R Archive Network (CRAN) Jason Cory Brunson [cre, aut] GPL (>= 3) 2026-04-10 application/tgz https://CRAN.R-project.org/package=tdarec doi:10.32614/CRAN.package.tdarec