Package: lookout 0.1.5

Sevvandi Kandanaarachchi

lookout: Leave One Out Kernel Density Estimates for Outlier Detection

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.

Authors:Sevvandi Kandanaarachchi [aut, cre], Rob Hyndman [aut], Chris Fraley [ctb]

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lookout.pdf |lookout.html✨
lookout/json (API)

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

Peer review:

Bug tracker:https://github.com/sevvandi/lookout/issues

On CRAN:

5.26 score 30 stars 2 packages 9 scripts 283 downloads 5 exports 40 dependencies

Last updated 7 months agofrom:d1e1c7becf. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:autoplotfind_tda_bwlookoutlookout_tspersisting_outliers

Dependencies:clicolorspacecpp11dplyrevdfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RANNRColorBrewerRcpprlangscalesstringistringrTDAstatstibbletidyrtidyselectutf8vctrsviridisLitewithr