R: Multivariate Outlier Detection Based on Robust Methods

# Multivariate Outlier Detection Based on Robust Methods

## Documentation for package ‘mvoutlier’ version 2.1.4

- DESCRIPTION file

## Help Pages

aq.plot | Adjusted Quantile Plot
arw | Adaptive reweighted estimator for multivariate location and scatter
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
chisq.plot | Chi-Square Plot
chorizon | C-horizon of the Kola Data
color.plot | Color Plot
corr.plot | Correlation Plot: robust versus classical bivariate correlation
dat | Data of illustrative example in paper (see below)
dd.plot | Distance-Distance Plot
humus | Humus Layer (O-horizon) of the Kola Data
kola.background | Background map for the Kola project
locoutNeighbor | Diagnostic plot for identifying local outliers with varying size of neighborhood
locoutPercent | Diagnostic plot for identifying local outliers with fixed size of neighborhood
locoutSort | Interactive diagnostic plot for identifying local outliers
map.plot | Plot Multivariate Outliers in a Map
moss | Moss Layer of the Kola Data
mvoutlier.CoDa | Interpreting multivatiate outliers of CoDa
pbb | BSS background Plot
pcout | PCOut Method for Outlier Identification in High Dimensions
pkb | Kola background Plot
plot.mvoutlierCoDa | Plots for interpreting multivatiate outliers of CoDa
sign1 | Sign Method for Outlier Identification in High Dimensions - Simple Version
sign2 | Sign Method for Outlier Identification in High Dimensions - Sophisticated Version
symbol.plot | Symbol Plot
uni.plot | Univariate Presentation of Multivariate Outliers
X | Data (X coordinate) of illustrative example in paper (see below)
Y | Data (Y coordinate) of illustrative example in paper (see below)