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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1 |
rolling source/ R- | fdaoutlier_0.2.1.tar.gz |
451.7 KiB |
0.2.1 |
latest source/ R- | fdaoutlier_0.2.1.tar.gz |
451.7 KiB |
0.2.1 |
2026-04-23 source/ R- | fdaoutlier_0.2.1.tar.gz |
451.7 KiB |
0.2.1 |
2026-04-09 windows/windows R-4.5 | fdaoutlier_0.2.1.zip |
742.3 KiB |