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envoutliers (version 1.1.0)

Methods for Identification of Outliers in Environmental Data

Description

Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) ) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) ) and the third method (Holesovsky, Campulova and Michalek (2018) ) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) ).

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Version

Install

install.packages('envoutliers')

Monthly Downloads

193

Version

1.1.0

License

GPL-2

Maintainer

Martina Campulova

Last Published

May 7th, 2020

Functions in envoutliers (1.1.0)

Moment.gpd.fit

Moment estimates of GP distribution parameters - Only intended for developer use
boxcoxTransform

Box-Cox transformation of data - Only intended for developer use
MRL.plot

Mean residual life (MRL) plot
changepoint

Changepoint analysis - Only intended for developer use
changepoint.plot

Changepoint outlier detection plot - Only intended for developer use
EV.plot

Extreme value outlier detection plot - Only intended for developer use
KRDetect.outliers.changepoint

Identification of outliers using changepoint analysis
KRDetect.outliers.plot

Outlier detection plot
KRDetect.outliers.controlchart

Identification of outliers using control charts
KRDetect.outliers.EV

Identification of outliers using extreme value theory
extremal.index.censored

Extremal index estimation (Holesovsky and Fusek, 2020) - Only intended for developer use
extremal.index.gomes

Extremal index estimation (Gomes, 1993) - Only intended for developer use
control.limits.x

Limits for control chart x - Only intended for developer use
summary.KRDetect

Summary of the outlier detection results
stability.plot

Stability plot
control.limits.s

Limits for control chart s - Only intended for developer use
extremal.index.sliding.blocks

Extremal index estimation (Northrop, 2015) - Only intended for developer use
find.L

Parameter L for Chebyshev inequality based outlier detection - Only intended for developer use
extremal.index.runs

Extremal index estimation (Smith and Weissman, 1994) - Only intended for developer use
extremal.index.intervals

Extremal index estimation (Ferro and Segers, 2003) - Only intended for developer use
find.alpha

Parameter alpha for Quantiles of normal distribution based outlier detection - Only intended for developer use
chebyshev.inequality.detect

Chebyshev inequality based identification of outliers on segments - Only intended for developer use
controlchart.plot

Control chart outliers detection plot - Only intended for developer use
grubbs.test

Outlier detection using Grubbs test - Only intended for developer use
grubbs.detect

Grubbs test based identification of outliers on segments - Only intended for developer use
smoothing

Kernel regression smoothing
mc.right

Right medcouple (RMC) - Only intended for developer use
mc.left

Left medcouple (LMC) - Only intended for developer use
control.limits.R

Limits for control chart R - Only intended for developer use
segment.length.control

Segment length control - Only intended for developer use
get.norm

Table of Control Charts Constants - Only intended for developer use
plot.KRDetect

Outlier detection plot
extremal.index.Kgaps

Extremal index estimation (Suveges and Davison, 2010) - Only intended for developer use
mc.test

Robust medcouple MC-LR test - Only intended for developer use
normal.distr.quantiles.detect

Normal distribution based identification of outliers on segments - Only intended for developer use
return.level.est

Return level estimation - Only intended for developer use