{ "_id": "6a12b42cacfb0bcc41d1a765", "Package": "wwntests", "Type": "Package", "Title": "Hypothesis Tests for Functional Time Series", "Version": "1.1.0", "Authors@R": "c(\nperson(\"Mihyun\", \"Kim\", email = \"mihyun.kim@mail.wvu.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Daniel\", \"Petoukhov\", email = \"dvpetouk@uwaterloo.ca\", role = c(\"aut\")))", "Maintainer": "Mihyun Kim ", "Description": "Provides a collection of white noise hypothesis tests for\nfunctional time series and related visualizations. These\ninclude tests based on the norms of autocovariance operators\nthat are built under both strong and weak white noise\nassumptions. Additionally, tests based on the spectral density\noperator and on principal component dimensional reduction are\nincluded, which are built under strong white noise assumptions.\nAlso, this package provides goodness-of-fit tests for\nfunctional autoregressive of order 1 models. These methods are\ndescribed in Kokoszka et al. (2017)\n, Characiejus and Rice (2019)\n, Gabrys and Kokoszka (2007)\n, and Kim et al. 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