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) , MS-plot by Dai and Genton (2018) , total variation depth and modified shape similarity index by Huang and Sun (2019) , and sequential transformations by Dai et al. (2020)


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Reference manual

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install.packages("fdaoutlier")

0.2.0 by Oluwasegun Taiwo Ojo, 7 months ago


https://github.com/otsegun/fdaoutlier


Report a bug at https://github.com/otsegun/fdaoutlier/issues


Browse source code at https://github.com/cran/fdaoutlier


Authors: Oluwasegun Taiwo Ojo [aut, cre, cph] , Rosa Elvira Lillo [aut] , Antonio Fernandez Anta [aut, fnd]


Documentation:   PDF Manual  


Task views: Functional Data Analysis


GPL-3 license


Imports MASS

Suggests testthat, covr, spelling, knitr, rmarkdown


See at CRAN