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: 0.2.0
Depends: R (≥ 2.10)
Imports: MASS
Suggests: testthat (≥ 2.1.0), covr, spelling, knitr, rmarkdown
Published: 2021-03-02
Author: Oluwasegun Taiwo Ojo ORCID iD [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: FunctionalData
CRAN checks: fdaoutlier results


Reference manual: fdaoutlier.pdf
Vignettes: Simulation Models
Package source: fdaoutlier_0.2.0.tar.gz
Windows binaries: r-devel: fdaoutlier_0.1.1.zip, r-release: fdaoutlier_0.1.1.zip, r-oldrel: fdaoutlier_0.1.1.zip
macOS binaries: r-release: fdaoutlier_0.1.1.tgz, r-oldrel: fdaoutlier_0.1.1.tgz
Old sources: fdaoutlier archive


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