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.
| Version: | 0.2.1 |
| Depends: | R (≥ 2.10) |
| Imports: | MASS |
| Suggests: | testthat (≥ 2.1.0),covr,spelling,knitr,rmarkdown |
| Published: | 2023-09-30 |
| DOI: | 10.32614/CRAN.package.fdaoutlier |
| Author: | Oluwasegun Taiwo Ojo [aut, cre, cph], Rosa Elvira Lillo [aut], Antonio Fernandez Anta [aut, fnd] |
| Maintainer: | Oluwasegun Taiwo Ojo <seguntaiwoojo at gmail.com> |
| BugReports: | https://github.com/otsegun/fdaoutlier/issues |
| License: | GPL-3 |
| URL: | https://github.com/otsegun/fdaoutlier |
| NeedsCompilation: | yes |
| Language: | en-US |
| Materials: | README,NEWS |
| In views: | AnomalyDetection,FunctionalData |
| CRAN checks: | fdaoutlier results |
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