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mvoutlier (version 2.1.4)

Multivariate Outlier Detection Based on Robust Methods

Description

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.

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Install

install.packages('mvoutlier')

Monthly Downloads

1,980

Version

2.1.4

License

GPL (>= 3)

Maintainer

P. Filzmoser

Last Published

February 26th, 2026

Functions in mvoutlier (2.1.4)

Interactive diagnostic plot for identifying local outliers

Data of illustrative example in paper (see below)

Plot Multivariate Outliers in a Map

Correlation Plot: robust versus classical bivariate correlation

Humus Layer (O-horizon) of the Kola Data

kola.background

Background map for the Kola project

Sign Method for Outlier Identification in High Dimensions - Simple Version

Univariate Presentation of Multivariate Outliers

Distance-Distance Plot

plot.mvoutlierCoDa

Plots for interpreting multivatiate outliers of CoDa

Kola background Plot

Sign Method for Outlier Identification in High Dimensions - Sophisticated Version

Diagnostic plot for identifying local outliers with fixed size of neighborhood

BSS background Plot

PCOut Method for Outlier Identification in High Dimensions

Diagnostic plot for identifying local outliers with varying size of neighborhood

Moss Layer of the Kola Data

Interpreting multivatiate outliers of CoDa

Bottom Layer of the BSS Data

C-horizon of the Kola Data

Chi-Square Plot

Data (Y coordinate) of illustrative example in paper (see below)

Adjusted Quantile Plot

B-horizon of the Kola Data

Data (X coordinate) of illustrative example in paper (see below)

Background map for the BSS project

Top Layer of the BSS Data

Adaptive reweighted estimator for multivariate location and scatter