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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1 |
rolling linux/jammy R-4.5 | tdarec_0.2.1.tar.gz |
886.2 KiB |
0.2.1 |
rolling linux/noble R-4.5 | tdarec_0.2.1.tar.gz |
886.0 KiB |
0.2.1 |
rolling source/ R- | tdarec_0.2.1.tar.gz |
836.7 KiB |
0.2.1 |
latest linux/jammy R-4.5 | tdarec_0.2.1.tar.gz |
886.2 KiB |
0.2.1 |
latest linux/noble R-4.5 | tdarec_0.2.1.tar.gz |
886.0 KiB |
0.2.1 |
latest source/ R- | tdarec_0.2.1.tar.gz |
836.7 KiB |
0.2.1 |
2026-04-26 source/ R- | tdarec_0.2.1.tar.gz |
836.7 KiB |
0.2.1 |
2026-04-23 source/ R- | tdarec_0.2.1.tar.gz |
836.7 KiB |
0.2.0 |
2026-04-09 windows/windows R-4.5 | tdarec_0.2.0.zip |
810.9 KiB |