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Update README with hyperlink for inphr website and function help.
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‎README.Rmd‎

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[![R-CMD-check](https://github.com/tdaverse/inphr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tdaverse/inphr/actions/workflows/R-CMD-check.yaml)
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<!-- badges: end -->
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The goal of {inphr} is to provide a set of functions for performing null
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hypothesis testing on samples of persistence diagrams using the theory of
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permutations. Currently, only two-sample testing is implemented. Inputs can be
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either samples of persistence diagrams themselves or vectorizations. In the
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former case, they are embedded in a metric space using either the Bottleneck or
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Wasserstein distance. In the former case, persistence data becomes functional
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data and inference is performed using tools available in the
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[{fdatest}](https://permaverse.github.io/fdatest/) package.
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The goal of [{inphr}](https://tdaverse.github.io/inphr/) is to provide a set of
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functions for performing null hypothesis testing on samples of persistence
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diagrams using the theory of permutations. Currently, only two-sample testing is
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implemented. Inputs can be either samples of persistence diagrams themselves or
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vectorizations. In the former case, they are embedded in a metric space using
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either the Bottleneck or Wasserstein distance. In the former case, persistence
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data becomes functional data and inference is performed using tools available in
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the [{fdatest}](https://permaverse.github.io/fdatest/) package.
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## Installation
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noisy samples of trefoil knots, while the third set contains persistence
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diagrams computed from noisy samples of 2-armed Archimedean spirals. Each set
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contains 24 persistence diagrams, each computed from a sample of 120 points
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sampled from the respective shape, with Gaussian noise added (standard
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deviation = 0.05). The persistence diagrams were computed using the
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[`TDA::ripsDiag()`] function with a maximum scale of 6 and up to dimension 2.
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You can use the `two_sample_test()` function to perform a two-sample test on
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these persistence diagrams. For example, to test whether the first 5
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persistence diagrams from the first set are significantly different from the
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first 5 persistence diagrams from the second set, you can run:
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sampled from the respective shape, with Gaussian noise added (standard deviation
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= 0.05). The persistence diagrams were computed using the
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[`TDA::ripsDiag()`](https://www.rdocumentation.org/packages/TDA/versions/1.9.1/topics/ripsDiag)
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function with a maximum scale of 6 and up to dimension 2.
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You can use the
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[`two_sample_test()`](https://tdaverse.github.io/inphr/reference/two_sample_test.html)
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function to perform a two-sample test on these persistence diagrams. For
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example, to test whether the first 5 persistence diagrams from the first set are
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significantly different from the first 5 persistence diagrams from the second
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set, you can run:
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```{r}
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two_sample_test(trefoils1[1:5], trefoils2[1:5], B = 100L)
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## Contributions
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### Code of Conduct
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Contributions are welcome! Please feel free to open an issue or a pull request
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if you have any suggestions or improvements. The package is still in its early
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stages, so any feedback is appreciated.
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### Code of Conduct
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Please note that the {inphr} project is released with a [Contributor Code of
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Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By
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contributing to this project, you agree to abide by its terms.

‎README.md‎

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[![R-CMD-check](https://github.com/tdaverse/inphr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tdaverse/inphr/actions/workflows/R-CMD-check.yaml)
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<!-- badges: end -->
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The goal of {inphr} is to provide a set of functions for performing null
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hypothesis testing on samples of persistence diagrams using the theory
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of permutations. Currently, only two-sample testing is implemented.
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Inputs can be either samples of persistence diagrams themselves or
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vectorizations. In the former case, they are embedded in a metric space
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using either the Bottleneck or Wasserstein distance. In the former case,
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persistence data becomes functional data and inference is performed
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using tools available in the
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The goal of [{inphr}](https://tdaverse.github.io/inphr/) is to provide a
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set of functions for performing null hypothesis testing on samples of
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persistence diagrams using the theory of permutations. Currently, only
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two-sample testing is implemented. Inputs can be either samples of
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persistence diagrams themselves or vectorizations. In the former case,
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they are embedded in a metric space using either the Bottleneck or
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Wasserstein distance. In the former case, persistence data becomes
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functional data and inference is performed using tools available in the
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[{fdatest}](https://permaverse.github.io/fdatest/) package.
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## Installation
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Archimedean spirals. Each set contains 24 persistence diagrams, each
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computed from a sample of 120 points sampled from the respective shape,
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with Gaussian noise added (standard deviation = 0.05). The persistence
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diagrams were computed using the \[`TDA::ripsDiag()`\] function with a
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maximum scale of 6 and up to dimension 2. You can use the
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`two_sample_test()` function to perform a two-sample test on these
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persistence diagrams. For example, to test whether the first 5
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persistence diagrams from the first set are significantly different from
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the first 5 persistence diagrams from the second set, you can run:
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diagrams were computed using the
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[`TDA::ripsDiag()`](https://www.rdocumentation.org/packages/TDA/versions/1.9.1/topics/ripsDiag)
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function with a maximum scale of 6 and up to dimension 2.
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You can use the
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[`two_sample_test()`](https://tdaverse.github.io/inphr/reference/two_sample_test.html)
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function to perform a two-sample test on these persistence diagrams. For
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example, to test whether the first 5 persistence diagrams from the first
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set are significantly different from the first 5 persistence diagrams
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from the second set, you can run:
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``` r
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two_sample_test(trefoils1[1:5], trefoils2[1:5], B = 100L)
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## Contributions
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### Code of Conduct
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Contributions are welcome! Please feel free to open an issue or a pull
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request if you have any suggestions or improvements. The package is
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still in its early stages, so any feedback is appreciated.
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### Code of Conduct
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Please note that the {inphr} project is released with a [Contributor
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Code of
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Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).

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