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fdWasserstein (version 1.0)

Application of Optimal Transport to Functional Data Analysis

Description

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) ; - compute the Wasserstein barycenter (Frechet mean) as in Masarotto, Panaretos & Zemel (2019) ; - perform analysis of variance testing procedures for functional covariances and tangent space principal component analysis of covariance operators as in Masarotto, Panaretos & Zemel (2022) . - perform a soft-clustering based on the Wasserstein distance where functional data are classified based on their covariance structure as in Masarotto & Masarotto (2023) .

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Version

Install

install.packages('fdWasserstein')

Monthly Downloads

145

Version

1.0

License

GPL-3

Maintainer

Valentina Masarotto

Last Published

February 6th, 2024

Functions in fdWasserstein (1.0)

wassersteinTest

A permutation or bootstrap test based on optimal transport maps.
gaussBary

Wasserstein barycenter between Gaussian Processes
Phoneme

Phoneme data
dwasserstein

2-Wasserstein distance
tangentPCA

Tangent space principal component analysis
wassersteinCluster

Soft clustering of covariance operators.
fdWasserstein-package

tools:::Rd_package_title("fdWasserstein")