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  "Title": "Application of Optimal Transport to Functional Data Analysis",
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  "Date": "2024-01-08",
  "Authors@R": "c(person(\"Valentina\", \"Masarotto\", role = c(\"aut\", \"cph\", \"cre\"),\nemail = \"v.masarotto@math.leidenuniv.nl\"),\nperson(\"Guido\", \"Masarotto\", role = c(\"aut\",\"cph\")))",
  "Author": "Valentina Masarotto [aut, cph, cre], Guido Masarotto [aut, cph]",
  "Maintainer": "Valentina Masarotto <v.masarotto@math.leidenuniv.nl>",
  "Description": "These functions were developed to support statistical\nanalysis on functional covariance operators. The package\ncontains functions to: - compute 2-Wasserstein distances\nbetween Gaussian Processes as in Masarotto, Panaretos & Zemel\n(2019) <doi:10.1007/s13171-018-0130-1>; - compute the\nWasserstein barycenter (Frechet mean) as in Masarotto,\nPanaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; -\nperform analysis of variance testing procedures for functional\ncovariances and tangent space principal component analysis of\ncovariance operators as in Masarotto, Panaretos & Zemel (2022)\n<arXiv:2212.04797>. - perform a soft-clustering based on the\nWasserstein distance where functional data are classified based\non their covariance structure as in Masarotto & Masarotto\n(2023) <doi:10.1111/sjos.12692>.",
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