band_depth

This function computes the band depth of López-Pintado and Romo (2009)
tools:::Rd_expr_doi("10.1198/jasa.2009.0108"). Bands
of 2 functions are always considered using the fast algorithm of Sun et al. (2012)
tools:::Rd_expr_doi("10.1002/sta4.8").

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

band_depth function

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

Arguments

Compute the band depth for a sample of curves/observations. — band_depth

<dl>

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

</dl>

band_depth: Compute the band depth for a sample of curves/observations.

Description

Usage

Value

Arguments

References

Examples