Dear R users,
I am very excited to announce TDApplied version 2.0.0 is now available on
CRAN, with some significant improvements to the previous version.
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.
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 updated version and that it
helps the field of topological data analysis progress in both academia and
industry.
Best regards,
Shael
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