fdaoutlier0.2.1 package

Outlier Detection Tools for Functional Data Analysis

band_depth

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

dir_out

Dai & Genton (2019) Directional outlyingness for univariate or multiva...

modified_band_depth

Compute the modified band depth for a sample of curves/functions.

msplot

Outlier Detection using Magnitude-Shape Plot (MS-Plot) based on the di...

simulation_model3

Convenience function for generating functional data

simulation_model4

Convenience function for generating functional data

simulation_model5

Convenience function for generating functional data

simulation_model6

Convenience function for generating functional data

simulation_model7

Convenience function for generating functional data

simulation_model8

Convenience function for generating functional data

simulation_model9

Convenience function for generating functional data

directional_quantile

Compute directional quantile outlyingness for a sample of discretely o...

extremal_depth

Compute extremal depth for functional data

extreme_rank_length

Compute the Extreme Rank Length Depth.

functional_boxplot

Functional Boxplot for a sample of functions.

hardin_factor_numeric

Compute F distribution factors for approximating the tail of the distr...

linfinity_depth

Compute the L-infinity depth of a sample of curves/functions.

muod

Massive Unsupervised Outlier Detection (MUOD)

plot_dtt

Plot Data from simulation models

projection_depth

Random projection for multivariate data

seq_transform

Find and classify outliers functional outliers using Sequential Transf...

simulation_model1

Convenience function for generating functional data

simulation_model2

Convenience function for generating functional data

total_variation_depth

Total Variation Depth and Modified Shape Similarity Index

tvdmss

Outlier detection using the total variation depth and modified shape s...

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.

  • Maintainer: Oluwasegun Taiwo Ojo
  • License: GPL-3
  • Last published: 2023-09-30