Package: weird 2.1.0.9000

weird: Functions and Data Sets for "That's Weird: Anomaly Detection Using R" by Rob J Hyndman

All functions and data sets required for the examples in the book Hyndman (2026) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>. All packages needed to run the examples are also loaded.

Authors:Rob Hyndman [aut, cre, cph], RStudio [cph]

weird_2.1.0.9000.tar.gz
weird_2.1.0.9000.zip(r-4.7)weird_2.1.0.9000.zip(r-4.6)weird_2.1.0.9000.zip(r-4.5)
weird_2.1.0.9000.tgz(r-4.6-any)weird_2.1.0.9000.tgz(r-4.5-any)
weird_2.1.0.9000.tar.gz(r-4.7-any)weird_2.1.0.9000.tar.gz(r-4.6-any)
weird_2.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
weird/json (API)

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

Bug tracker:https://github.com/robjhyndman/weird/issues

Pkgdown/docs site:https://pkg.robjhyndman.com

Datasets:
  • cricket_batting - Cricket batting data for international test players
  • fr_mortality - French mortality rates by age and sex
  • gun_deaths - Gun ownership and homicide rates by country
  • n01 - Multivariate standard normal data
  • oldfaithful - Old faithful eruption data

On CRAN:

Conda:

6.34 score 26 stars 35 scripts 293 downloads 27 exports 58 dependencies

Last updated from:538fe9942c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK264
source / vignettesOK202
linux-release-x86_64OK273
macos-release-arm64OK207
macos-oldrel-arm64OK158
windows-develOK145
windows-releaseOK165
windows-oldrelOK139
wasm-releaseOK147

Exports:augmentautoplotbiplot_projectionchauvenet_anomaliesdensity_dfdist_densitydist_kdedist_mclustdixon_anomaliesfetch_air_qualityfetch_wine_reviewsgg_bagplotgg_densitygg_hdrboxplotglosh_scoresgrubbs_anomalieshampel_anomalieshdr_tablekde_bandwidthlof_scoresmvscaleoutlier_mappeirce_anomaliesstray_anomaliesstray_scoressurprisalssurprisals_prob

Dependencies:aplpackbackportsbroomclicolorspacecpp11dbscanDEoptimRdistributionaldplyrevdfarverFNNgenericsggplot2gluegtableisobandkernlabKernSmoothkslabelinglatticelifecyclemagrittrMatrixmclustmgcvmlpackmulticoolmvtnormnlmenumDerivpcaPPpillarpkgconfigpracmapurrrR6RANNRColorBrewerRcppRcppArmadilloRcppEnsmallenrlangrobustbaseS7scalesstraystringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Augment data with results from a robust principal component analysisaugment.Pca
Biplot of a two-dimensional projectionbiplot_projection
Cricket batting data for international test playerscricket_batting
Convert distributional object to a data framedensity_df
Create distributional object based on a specified densitydist_density
Create distributional object based on a kernel density estimatedist_kde
Convert Gaussian mixture model to a distributional objectdist_mclust
Air quality data for 12 Beijing monitoring stations from 2013 to 2017air_quality fetch_air_quality
Wine prices and pointsfetch_wine_reviews wine_reviews
French mortality rates by age and sexfr_mortality
Bagplotgg_bagplot
Produce ggplot of densities from distributional objects in 1 or 2 dimensionsgg_density
HDR plotgg_hdrboxplot
GLOSH scoresglosh_scores
Statistical tests for anomalies using Grubbs' test and Dixon's testdixon_anomalies grubbs_anomalies
Gun ownership and homicide rates by countrygun_deaths
Identify anomalies using the Hampel filterhampel_anomalies
Table of Highest Density Regionshdr_table
Robust bandwidth estimation for kernel density estimationkde_bandwidth
Local outlier factorslof_scores
Compute robust multivariate scaled datamvscale
Multivariate standard normal datan01
Old faithful eruption dataoldfaithful
Outlier map from a projection or principal component analysisoutlier_map
Anomalies according to Peirce's and Chauvenet's criteriachauvenet_anomalies peirce_anomalies
Stray anomaliesstray_anomalies
Stray scoresstray_scores
Surprisals and surprisal probabilitiessurprisals surprisals_prob
Surprisals and surprisal probabilities computed from a modelsurprisals.gam surprisals.glm surprisals.lm surprisals_prob.gam surprisals_prob.glm surprisals_prob.lm
Surprisals and surprisal probabilities computed from datasurprisals.data.frame surprisals.matrix surprisals.numeric surprisals_prob.data.frame surprisals_prob.matrix surprisals_prob.numeric