TDApplied: Machine Learning and Inference for Topological Data Analysis
Topological data analysis is a powerful tool for finding non-linear global structure
in whole datasets. 'TDApplied' aims to bridge topological data analysis with data, statistical
and machine learning practitioners so that more analyses may benefit from the
power of topological data analysis. The main tool of topological data analysis is
persistent homology, which computes a shape descriptor of a dataset, called
a persistence diagram. There are five goals of this package: (1) deliver a fast implementation
of persistent homology via a python interface, (2) convert persistence diagrams
computed using the two main R packages for topological data analysis into a data frame,
(3) implement fast versions of both distance and kernel calculations
for pairs of persistence diagrams, (4) contribute tools for the interpretation of
persistence diagrams, and (5) provide parallelized methods for machine learning
and inference for persistence diagrams.
| Version: |
2.0.3 |
| Depends: |
R (≥ 3.2.2) |
| Imports: |
parallel, doParallel, foreach, clue, rdist, parallelly, kernlab, iterators, methods, stats, utils |
| Suggests: |
rmarkdown, knitr, testthat (≥ 3.0.0), TDA, TDAstats, reticulate |
| Published: |
2023-01-15 |
| Author: |
Shael Brown [aut, cre],
Dr. Reza Farivar [aut, fnd] |
| Maintainer: |
Shael Brown <shaelebrown at gmail.com> |
| BugReports: |
https://github.com/shaelebrown/TDApplied/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/shaelebrown/TDApplied |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
TDApplied results |
Documentation:
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