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  "Title": "Realistic Confidence Intervals for Non-Stationary Extreme Value\nStatistics",
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  "Authors@R": "c(person(c(\"Olivier\", \"C.\"), \"Pasche\", \nemail = \"olivier_pasche@alumni.epfl.ch\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-1202-9199\"))\n)",
  "Description": "This framework provides versatile algorithms to\nefficiently infer confidence intervals for extreme value\nstatistics, such as extreme quantiles and return levels, that\nare representative of the asymmetric uncertainty spread, using\nextreme value theory extrapolation and the profile likelihood\n(see e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>).\nUnlike existing algorithms, the CI endpoints are found without\nthe need for a strict prespecified range, can be\ncovariate-dependent, and can be based on weighted samples. This\npackage is motivated by Zeder et al. (2023)\n<doi:10.1029/2023GL104090> and by Pasche et al. (2026)\n<doi:10.1007/s10687-026-00536-9>.",
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  "Date/Publication": "2026-05-13 15:34:04 UTC",
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