[R-pkgs] TDApplied: Machine Learning and Inference for Topological Data Analysis

[Shael Brown]

Dear R users,

I am very excited to announce my new package TDApplied is now available on
CRAN.

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 three 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, and (3) provide scalable
methods for machine learning and inference for persistence diagrams.

For details and examples please check out the github page
https://github.com/shaelebrown/TDApplied or the README file on CRAN
https://cran.r-project.org/web/packages/TDApplied/readme/README.html

I sincerely hope that you will enjoy using this package and that it helps
the field of topological data analysis progress in both academia and
industry.

Best regards,
Shael

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