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

On CRAN:

outlier-detection

24 exports 4 stars 1.18 score 1 dependencies 15 scripts 236 downloads

Last updated 11 months agofrom:c96c35b1a7. Checks:OK: 4 NOTE: 5. Indexed: yes.

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