fdWasserstein: Application of Optimal Transport to Functional Data Analysis
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)
<doi:10.48550/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>.
| Version: |
1.0 |
| Depends: |
R (≥ 3.5.0) |
| Suggests: |
future |
| Published: |
2024-02-06 |
| Author: |
Valentina Masarotto [aut, cph, cre],
Guido Masarotto [aut, cph] |
| Maintainer: |
Valentina Masarotto <v.masarotto at math.leidenuniv.nl> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
fdWasserstein results |
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