dwasserstein

Computes the 2-Wasserstein distance between the (covariance) matrices A and B.

These functions were developed to support statistical analysis on functional covariance operators.
The package contains functions to:
- compute 2-Wasserstein distances between Gaussian Processes as in
Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
- compute the Wasserstein barycenter (Frechet mean) as in Masarotto,
Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>;
- perform analysis of variance testing procedures for functional
covariances and tangent space principal component analysis of
covariance operators as in Masarotto, Panaretos & Zemel (2022)
<arXiv:2212.04797>.
- perform a soft-clustering based on the Wasserstein distance where
functional data are classified based on their covariance structure
as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.

Valentina Masarotto

fdWasserstein

Application of Optimal Transport to Functional Data Analysis

Guido Masarotto

dwasserstein function

<dl> <dt>A,B</dt>
<dd>Two symmetric positive semi-definite matrices.</dd></dl>

Arguments

Valentina Masarotto, Guido Masarotto

Author

2-Wasserstein distance — dwasserstein

<dl>

 <dt>A,B</dt>
<dd>Two symmetric positive semi-definite matrices.</dd>

</dl>

dwasserstein: 2-Wasserstein distance

Description

Usage

Value

Arguments

Author

References

See Also

Examples