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. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

Version: 3.0.2
Depends: R (≥ 3.5.0)
Imports: parallel, doParallel, foreach, clue, rdist, parallelly, kernlab, iterators, methods, stats, utils, Rcpp (≥ 0.11.0)
LinkingTo: Rcpp
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), TDAstats, reticulate, igraph
Published: 2024-01-21
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:

Reference manual: TDApplied.pdf
Vignettes: Human Connectome Project Analysis
TDApplied Theory and Practice
Benchmarking and Speedups
Comparing Distance Calculations
Personalized Analyses with TDApplied

Downloads:

Package source: TDApplied_3.0.2.tar.gz
Windows binaries: r-devel: TDApplied_3.0.2.zip, r-release: TDApplied_3.0.2.zip, r-oldrel: TDApplied_3.0.2.zip
macOS binaries: r-release (arm64): TDApplied_3.0.2.tgz, r-oldrel (arm64): TDApplied_3.0.2.tgz, r-release (x86_64): TDApplied_3.0.2.tgz
Old sources: TDApplied archive

Linking:

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