total_variation_depth

This function computes the total variation depth (tvd) and the modified shape similarity index (mss)
proposed in Huang and Sun (2019) tools:::Rd_expr_doi("10.1080/00401706.2019.1574241").

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.

Oluwasegun Taiwo Ojo

fdaoutlier

Outlier Detection Tools for Functional Data Analysis

Rosa Elvira Lillo

Antonio Fernandez Anta

total_variation_depth function

<dl><dt>dts</dt>
<dd>A matrix or dataframe of size \(n\) observations/curves by \(p\) domain/evaluation
points.</dd></dl>

Arguments

Author

Total Variation Depth and Modified Shape Similarity Index — total_variation_depth

<dl>

<dt>dts</dt>
<dd>A matrix or dataframe of size \(n\) observations/curves by \(p\) domain/evaluation
points.</dd>

</dl>

total_variation_depth: Total Variation Depth and Modified Shape Similarity Index

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples