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 |
Documentation:
Downloads:
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