{ "_id": "6a211d9ecd65a98ecbd1db7f", "Package": "fdaoutlier", "Title": "Outlier Detection Tools for Functional Data Analysis", "Version": "0.2.1", "Authors@R": "c(person(given = \"Oluwasegun Taiwo\",\nfamily = \"Ojo\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"seguntaiwoojo@gmail.com\",\ncomment = c(ORCID = \"0000-0001-9629-6990\")),\nperson(given = \"Rosa Elvira\",\nfamily = \"Lillo\",\nrole = c(\"aut\"),\nemail = \"lillo@est-econ.uc3m.es\"),\nperson(given = \"Antonio\",\nfamily = \"Fernandez Anta\",\nrole = c(\"aut\", \"fnd\"),\nemail = \"antonio.fernandez@imdea.org\"))", "Description": "A collection of functions for outlier detection in\nfunctional data analysis. Methods implemented include\ndirectional outlyingness by Dai and Genton (2019)\n, MS-plot by Dai and Genton\n(2018) , total variation\ndepth and modified shape similarity index by Huang and Sun\n(2019) , and sequential\ntransformations by Dai et al. 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