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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Peer review:

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

On CRAN:

5 exports 30 stars 2.75 score 40 dependencies 2 dependents 9 scripts 383 downloads

Last updated 5 months agofrom:d1e1c7becf. Checks:OK: 7. Indexed: yes.

Exports:autoplotfind_tda_bwlookoutlookout_tspersisting_outliers

Dependencies:clicolorspacecpp11dplyrevdfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RANNRColorBrewerRcpprlangscalesstringistringrTDAstatstibbletidyrtidyselectutf8vctrsviridisLitewithr