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)


Reference manual

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0.2.0 by Oluwasegun Taiwo Ojo, a year ago


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