Package: fdaoutlier 0.2.1

fdaoutlier: 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) <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.

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

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fdaoutlier.pdf |fdaoutlier.html✨
fdaoutlier/json (API)
NEWS

# Install 'fdaoutlier' in R:
install.packages('fdaoutlier', repos = c('https://otsegun.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/otsegun/fdaoutlier/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

outlier-detection

4.56 score 4 stars 18 scripts 211 downloads 24 exports 1 dependencies

Last updated 1 years agofrom:c96c35b1a7. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-win-x86_64NOTENov 04 2024
R-4.5-linux-x86_64NOTENov 04 2024
R-4.4-win-x86_64NOTENov 04 2024
R-4.4-mac-x86_64NOTENov 04 2024
R-4.4-mac-aarch64NOTENov 04 2024
R-4.3-win-x86_64OKNov 04 2024
R-4.3-mac-x86_64OKNov 04 2024
R-4.3-mac-aarch64OKNov 04 2024

Exports:band_depthdir_outdirectional_quantileextremal_depthextreme_rank_lengthfunctional_boxplotlinfinity_depthmodified_band_depthmsplotmuodprojection_depthseq_transformsimulation_model1simulation_model2simulation_model3simulation_model4simulation_model5simulation_model6simulation_model7simulation_model8simulation_model9total_variation_depthtvd_msstvdmss

Dependencies:MASS

Simulation Models

Oluwasegun Ojo

Rendered fromsimulation_models.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2021-03-01
Started: 2021-03-01

Readme and manuals

Help Manual

Help pageTopics
Compute the band depth for a sample of curves/observations.band_depth
Dai & Genton (2019) Directional outlyingness for univariate or multivariate functional data.dir_out
Compute directional quantile outlyingness for a sample of discretely observed curvesdirectional_quantile
Compute extremal depth for functional dataextremal_depth
Compute the Extreme Rank Length Depth.extreme_rank_length
Functional Boxplot for a sample of functions.functional_boxplot
Compute F distribution factors for approximating the tail of the distribution of robust MCD distance.hardin_factor_numeric
Compute the L-infinity depth of a sample of curves/functions.linfinity_depth
Compute the modified band depth for a sample of curves/functions.modified_band_depth
Outlier Detection using Magnitude-Shape Plot (MS-Plot) based on the directional outlyingness for functional data.msplot
Massive Unsupervised Outlier Detection (MUOD)muod
Plot Data from simulation modelsplot_dtt
Random projection for multivariate dataprojection_depth
Find and classify outliers functional outliers using Sequential Transformationseq_transform
Convenience function for generating functional datasimulation_model1
Convenience function for generating functional datasimulation_model2
Convenience function for generating functional datasimulation_model3
Convenience function for generating functional datasimulation_model4
Convenience function for generating functional datasimulation_model5
Convenience function for generating functional datasimulation_model6
Convenience function for generating functional datasimulation_model7
Convenience function for generating functional datasimulation_model8
Convenience function for generating functional datasimulation_model9
Spanish Weather Dataspanish_weather
Total Variation Depth and Modified Shape Similarity Indextotal_variation_depth
Outlier detection using the total variation depth and modified shape similarity index.tvdmss tvd_mss
World Population Data by Countriesworld_population