Package: fdWasserstein 1.0

Valentina Masarotto

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) <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>.

Authors:Valentina Masarotto [aut, cph, cre], Guido Masarotto [aut, cph]

fdWasserstein_1.0.tar.gz
fdWasserstein_1.0.tar.gz(r-4.5-noble)fdWasserstein_1.0.tar.gz(r-4.4-noble)
fdWasserstein_1.0.tgz(r-4.4-emscripten)fdWasserstein_1.0.tgz(r-4.3-emscripten)
fdWasserstein.pdf |fdWasserstein.html✨
fdWasserstein/json (API)

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

6 exports 0.00 score 0 dependencies 139 downloads

Last updated 7 months agofrom:df061e9a72. Checks:OK: 2. Indexed: yes.

Exports:dwassersteingaussBarytangentPCAtrimmedAverageSilhouettewassersteinClusterwassersteinTest

Dependencies: