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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.
Getting started
Browse package contents
Package details |
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| Author | Oluwasegun Taiwo Ojo [aut, cre, cph] (<https://orcid.org/0000-0001-9629-6990>), Rosa Elvira Lillo [aut], Antonio Fernandez Anta [aut, fnd] |
| Maintainer | Oluwasegun Taiwo Ojo <seguntaiwoojo@gmail.com> |
| License | GPL-3 |
| Version | 0.2.1 |
| URL | https://github.com/otsegun/fdaoutlier |
| Package repository | View on CRAN |
| Installation |
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