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


Reference manual: fdWasserstein.pdf


Package source: fdWasserstein_1.0.tar.gz
Windows binaries: r-devel: fdWasserstein_1.0.zip, r-release: fdWasserstein_1.0.zip, r-oldrel: fdWasserstein_1.0.zip
macOS binaries: r-release (arm64): fdWasserstein_1.0.tgz, r-oldrel (arm64): fdWasserstein_1.0.tgz, r-release (x86_64): fdWasserstein_1.0.tgz, r-oldrel (x86_64): fdWasserstein_1.0.tgz


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