TDAstats | R pipeline for computing persistent homology | Data Visualization library

by rrrlw R Version: v0.3.2 License: GPL-3.0

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | TDAstats Summary

TDAstats is a R library typically used in Analytics, Data Visualization, Docker applications. TDAstats has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

TDAstats is an R pipeline for computing persistent homology in topological data analysis.

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Quality

Security

License

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Support

TDAstats has a low active ecosystem.

It has 23 star(s) with 5 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 13 have been closed. On average issues are closed in 101 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TDAstats is v0.3.2

Quality

TDAstats has no bugs reported.

Security

TDAstats has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

TDAstats is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

TDAstats releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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TDAstats Key Features

No Key Features are available at this moment for TDAstats.

TDAstats Examples and Code Snippets

TDAstats: topological data analysis in R ,Sample code

Lines of Code : 16

License : Strong Copyleft (GPL-3.0)

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# load TDAstats
library("TDAstats")

# load sample datasets
data("unif2d")
data("circle2d")

# calculate persistent homology for both datasets
unif.phom <- calculate_homology(unif2d, dim = 1)
circ.phom <- calculate_homology(circle2d, dim = 1)

TDAstats: topological data analysis in R ,Installation

Lines of Code : 8

License : Strong Copyleft (GPL-3.0)

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# install from CRAN
install.packages("TDAstats")

# install development version from GitHub
devtools::install_github("rrrlw/TDAstats")

# install development version with vignettes/tutorials
devtools::install_github("rrrlw/TDAstats", build_vignettes

Community Discussions

Trending Discussions on TDAstats

How can I improve the performance of this R code?

QUESTION

How can I improve the performance of this R code?

Asked 2021-Jan-05 at 13:16

I'm having trouble with the performance of this code. I have to loop through 2225 columns and calculate a betti_number. I'm wondering if there is some way to speed this up. I'd like a list of betti numbers at the end so I can add it to an excel file.

...

ANSWER

Answered 2021-Jan-05 at 13:16

You'll have to check if the betti numbers make sense, I couldn't get your code to work 100%. For the same reason I also haven't done any benchmarks, but I expect it to give some improvement. The main issues I see with your code are a number of unnecessary steps (mainly remappings) and use of inefficient data structures (use vector, matrix and array when possible).

Source https://stackoverflow.com/questions/65577758

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TDAstats

To install TDAstats, run the following R code:.

Support

To contribute to TDAstats, you can create issues for any bugs/suggestions on the issues page. You can also fork the TDAstats repository and create pull requests to add features you think will be useful for users.

Find more information at: