A package containing functions developed to support statistical analysis on functional covariance operators. In particular,
Function dwasserstein computes the
Wasserstein-Procrustes distance between two covariances.
Function gaussBary computes the Frechet mean of
K covariances with respect to the Procrustes metrics
(equivalently, the Wasserstein barycenter of centered Gaussian
processes with corresponding covariances) via steepest gradient
descent. See Masarotto, Panaretos & Zemel (2019).
Function tangentPCA performs the tangent space
principal component analysis considered in Masarotto, Panaretos &
Zemel (2022).
Function wassersteinTest lets to test the null
hypothesis that K covariances are equal using the methodology suggested by
Masarotto, Panaretos & Zemel (2022).
Function wassersteinCluster implements the soft
partion procedure proposed by Masarotto & Masarotto (2023).
tools:::Rd_package_author("fdWasserstein")
Maintainer: tools:::Rd_package_maintainer("fdWasserstein")
Masarotto, V., Panaretos, V.M. & Zemel, Y. (2019) "Procrustes Metrics on Covariance Operators and Optimal Transportation of Gaussian Processes", Sankhya A 81, 172-213 tools:::Rd_expr_doi("10.1007/s13171-018-0130-1")
Masarotto, V., Panaretos, V.M. & Zemel, Y. (2022) "Transportation-Based Functional ANOVA and PCA for Covariance Operators", arXiv, https://arxiv.org/abs/2212.04797
Masarotto, V. & Masarotto, G. (2023) "Covariance-based soft clustering of functional data based on the Wasserstein-Procrustes metric", Scandinavian Journal of Statistics, tools:::Rd_expr_doi("10.1111/sjos.12692").