This package is considered a duplicate. The official version of this package is found at:https://sevvandi.r-universe.dev/lookout

Package: lookout 2.0.1.00

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]

lookout_2.0.1.00.tar.gz
lookout_2.0.1.00.zip(r-4.7)lookout_2.0.1.00.zip(r-4.6)lookout_2.0.1.00.zip(r-4.5)
lookout_2.0.1.00.tgz(r-4.6-any)lookout_2.0.1.00.tgz(r-4.5-any)
lookout_2.0.1.00.tar.gz(r-4.7-any)lookout_2.0.1.00.tar.gz(r-4.6-any)
lookout_2.0.1.00.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
lookout/json (API)
NEWS

# Install 'lookout' in R:

install.packages('lookout', repos = c('https://robjhyndman.r-universe.dev', 'https://cloud.r-project.org'))

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

Pkgdown/docs site:https://sevvandi.github.io

On CRAN:

5.48 score 29 stars 1 packages 10 scripts 570 downloads 6 exports 37 dependencies

Last updated from:caba04b4c2. Checks:9 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	152
source / vignettes	OK	179
linux-release-x86_64	OK	135
macos-release-arm64	OK	140
macos-oldrel-arm64	OK	107
windows-devel	OK	101
windows-release	OK	95
windows-oldrel	OK	90
wasm-release	OK	116

Exports:autoplot find_tda_bw lookout lookout_ts mvscale persisting_outliers

Dependencies:cli cpp11 DEoptimR dplyr evd farver generics ggplot2 glue gtable isoband labeling lifecycle magrittr mlpack pillar pkgconfig purrr R6 RANN RColorBrewer Rcpp RcppArmadillo RcppEnsmallen rlang robustbase S7 scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Plots outliers identified by lookout algorithm.	autoplot.lookoutliers
Plots outlier persistence for a range of significance levels.	autoplot.persistingoutliers
Identifies bandwidth for outlier detection.	find_tda_bw
Identifies outliers using the algorithm lookout.	lookout
Identifies outliers in univariate time series using the algorithm lookout.	lookout_ts
Compute robust multivariate scaled data	mvscale
Computes outlier persistence for a range of significance values.	persisting_outliers