Packaged: 2022-08-24 16:21:13 UTC; jibaccount

Package: TDApplied
Type: Package
Title: Machine Learning and Inference for Topological Data Analysis
Version: 0.1.2
Authors@R: c(person("Shael", "Brown", email = "shaelebrown@gmail.com", role = c("aut","cre")),
person("Dr. Reza", "Farivar", email = "reza.farivar@mcgill.ca", role = c("aut","fnd")))
Author: Shael Brown [aut, cre],
Dr. Reza Farivar [aut, fnd]
Maintainer: Shael Brown <shaelebrown@gmail.com>
Description: 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 methods for machine learning
and inference for persistence diagrams which scale well.
Depends: R (>= 3.2.2)
Imports: parallel, doParallel, foreach, clue, rdist, parallelly,
kernlab, iterators, methods, stats, utils
License: GPL-3
URL: https://github.com/shaelebrown/TDApplied
BugReports: https://github.com/shaelebrown/TDApplied/issues
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.1.2
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TDA, TDAstats
VignetteBuilder: knitr
Config/testthat/edition: 3
Packaged: 2022-09-25 18:00:50 UTC; jibaccount
Repository: CRAN
Date/Publication: 2022-09-26 07:10:02 UTC

Package: TDApplied
Type: Package
Title: Machine Learning and Inference for Topological Data Analysis
Version: 0.1.3
Authors@R: c(person("Shael", "Brown", email = "shaelebrown@gmail.com", role = c("aut","cre")),
person("Dr. Reza", "Farivar", email = "reza.farivar@mcgill.ca", role = c("aut","fnd")))
Author: Shael Brown [aut, cre],
Dr. Reza Farivar [aut, fnd]
Maintainer: Shael Brown <shaelebrown@gmail.com>
Description: 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 methods for machine learning
and inference for persistence diagrams which scale well.
Depends: R (>= 3.2.2)
Imports: parallel, doParallel, foreach, clue, rdist, parallelly,
kernlab, iterators, methods, stats, utils
License: GPL-3
URL: https://github.com/shaelebrown/TDApplied
BugReports: https://github.com/shaelebrown/TDApplied/issues
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.1.2
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TDA, TDAstats
VignetteBuilder: knitr
Config/testthat/edition: 3
Packaged: 2022-09-27 17:59:54 UTC; jibaccount
Repository: CRAN
Date/Publication: 2022-10-02 16:30:02 UTC