Package: mvoutlier 2.1.4

mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.

Authors:Peter Filzmoser [aut, cre]

mvoutlier_2.1.4.tar.gz
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mvoutlier_2.1.4.tgz(r-4.6-any)mvoutlier_2.1.4.tgz(r-4.5-any)
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mvoutlier_2.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mvoutlier/json (API)

# Install 'mvoutlier' in R:
install.packages('mvoutlier', repos = c('https://petertuwien.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • bhorizon - B-horizon of the Kola Data
  • bss.background - Background map for the BSS project
  • bssbot - Bottom Layer of the BSS Data
  • bsstop - Top Layer of the BSS Data
  • chorizon - C-horizon of the Kola Data
  • dat - Data of illustrative example in paper
  • humus - Humus Layer (O-horizon) of the Kola Data
  • kola.background - Background map for the Kola project
  • moss - Moss Layer of the Kola Data
  • X - Data
  • Y - Data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.97 score 1 stars 5 packages 383 scripts 1.6k downloads 15 mentions 19 exports 3 dependencies

Last updated from:f2b871a7c9. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK103
source / vignettesOK142
linux-releaseOK87
macos-releaseOK110
macos-oldrelOK110
windows-develOK82
windows-releaseOK74
windows-oldrelOK66
wasm-releaseOK132

Exports:aq.plotarwchisq.plotcolor.plotcorr.plotdd.plotlocoutNeighborlocoutPercentlocoutSortmap.plotmvoutlier.CoDapbbpcoutpkbplot.mvoutlierCoDasign1sign2symbol.plotuni.plot

Dependencies:DEoptimRrobustbasesgeostat

Readme and manuals

Help Manual

Help pageTopics
Adjusted Quantile Plotaq.plot
Adaptive reweighted estimator for multivariate location and scatterarw
B-horizon of the Kola Databhorizon
Background map for the BSS projectbss.background
Bottom Layer of the BSS Databssbot
Top Layer of the BSS Databsstop
Chi-Square Plotchisq.plot
C-horizon of the Kola Datachorizon
Color Plotcolor.plot
Correlation Plot: robust versus classical bivariate correlationcorr.plot
Data of illustrative example in paper (see below)dat
Distance-Distance Plotdd.plot
Humus Layer (O-horizon) of the Kola Datahumus
Background map for the Kola projectkola.background
Diagnostic plot for identifying local outliers with varying size of neighborhoodlocoutNeighbor
Diagnostic plot for identifying local outliers with fixed size of neighborhoodlocoutPercent
Interactive diagnostic plot for identifying local outlierslocoutSort
Plot Multivariate Outliers in a Mapmap.plot
Moss Layer of the Kola Datamoss
Interpreting multivatiate outliers of CoDamvoutlier.CoDa
BSS background Plotpbb
PCOut Method for Outlier Identification in High Dimensionspcout
Kola background Plotpkb
Plots for interpreting multivatiate outliers of CoDaplot.mvoutlierCoDa
Sign Method for Outlier Identification in High Dimensions - Simple Versionsign1
Sign Method for Outlier Identification in High Dimensions - Sophisticated Versionsign2
Symbol Plotsymbol.plot
Univariate Presentation of Multivariate Outliersuni.plot
Data (X coordinate) of illustrative example in paper (see below)X
Data (Y coordinate) of illustrative example in paper (see below)Y