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) convert persistence diagrams computed using the two main R packages for topological data analysis into a data frame, (2) implement fast versions of both distance and kernel calculations for pairs of persistence diagrams, (3) provide methods for machine learning and inference for persistence diagrams which scale well, (4) deliver a fast implementation of persistent homology via a python interface, and (5) contribute tools for the interpretation of persistence diagrams.

Version: 2.0.0
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: 2022-11-08
Author: Shael Brown [aut, cre], Dr. Reza Farivar [aut, fnd]
Maintainer: Shael Brown <shaelebrown at>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: TDApplied results


Reference manual: TDApplied.pdf
Vignettes: inference


Package source: TDApplied_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): TDApplied_2.0.0.tgz, r-oldrel (arm64): TDApplied_2.0.0.tgz, r-release (x86_64): TDApplied_2.0.0.tgz, r-oldrel (x86_64): TDApplied_2.0.0.tgz
Old sources: TDApplied archive


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