fdaoutlier: Outlier Detection Tools for Functional Data Analysis

A collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>, MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>, total variation depth and modified shape similarity index by Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection tools and depths for functional data like functional boxplot, (modified) band depth etc., are also available.

Package details

AuthorOluwasegun Taiwo Ojo [aut, cre, cph] (<https://orcid.org/0000-0001-9629-6990>), Rosa Elvira Lillo [aut], Antonio Fernandez Anta [aut, fnd]
MaintainerOluwasegun Taiwo Ojo <seguntaiwoojo@gmail.com>
URL https://github.com/otsegun/fdaoutlier
Package repositoryView on CRAN
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fdaoutlier documentation built on March 3, 2021, 1:07 a.m.