R-universe search: ripser

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tdaverse

ripserr:Calculate Persistent Homology with Ripser-Based Engines

Ports the Ripser <https://arxiv.org/abs/1908.02518> and Cubical Ripser <https://arxiv.org/abs/2005.12692> persistent homology calculation engines from C++. Can be used as a rapid calculation tool in topological data analysis pipelines.

Maintained by Raoul Wadhwa. Last updated 1 days ago.

algebraic-topology cohomology cpp cubical-complex persistent-homology pixel point-cloud r-language r-programming rcpp rips-complex ripser simplicial-complex simplicial-homology topological-data-analysis topology vietoris-complex voxel cpp

19.2 match 7 stars 5.80 score 6 scripts

rrrlw

TDAstats:Pipeline for Topological Data Analysis

A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) <doi:10.21105/joss.00860>. For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) <doi:10.1140/epjds/s13688-017-0109-5>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>. To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at <https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.

Maintained by Raoul Wadhwa. Last updated 3 years ago.

data-science ggplot2 homology homology-calculations homology-computation joss persistent-homology pipeline ripser tda topological-data-analysis topology topology-visualization visualization cpp

11.9 match 40 stars 8.30 score 46 scripts 4 dependents

shaelebrown

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.

Maintained by Shael Brown. Last updated 5 months ago.

cpp

3.8 match 16 stars 6.60 score 8 scripts