Package: envoutliers 1.1.0

Martina Campulova

envoutliers: Methods for Identification of Outliers in Environmental Data

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) <doi:10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <doi:10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <doi:10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <doi:10.1016/j.apr.2017.06.005>).

Authors:Martina Campulova [cre], Martina Campulova [aut], Roman Campula [ctb]

envoutliers_1.1.0.tar.gz
envoutliers_1.1.0.zip(r-4.7-any)envoutliers_1.1.0.zip(r-4.6-any)envoutliers_1.1.0.zip(r-4.5-any)
envoutliers_1.1.0.tgz(r-4.6-any)envoutliers_1.1.0.tgz(r-4.5-any)
envoutliers_1.1.0.tar.gz(r-4.7-any)envoutliers_1.1.0.tar.gz(r-4.6-any)
envoutliers_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
envoutliers/json (API)

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

Conda:

archivedpackagesr-universe

1.70 score 5 stars 4 scripts 416 downloads 7 exports 75 dependencies

Last updated from:f94c416b9d (on package/envoutliers). Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE154
source / vignettesOK222
linux-releaseNOTE185
macos-releaseNOTE112
macos-oldrelNOTE129
windows-develNOTE102
windows-releaseNOTE113
windows-oldrelNOTE103
wasm-releaseOK118

Exports:KRDetect.outliers.changepointKRDetect.outliers.controlchartKRDetect.outliers.EVKRDetect.outliers.plotMRL.plotsmoothingstability.plot

Dependencies:abindbackportsbootbroomcarcarDatachangepointclicolorspacecowplotcpp11DEoptimRDerivdoBydplyrecpfarverforecastFormulafracdiffgenericsggplot2gluegtableismevisobandlabelinglatticelifecyclelme4lmtestlokernmagrittrMASSMatrixMatrixModelsmgcvminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrobustbaseS7scalessfsmiscSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Box-Cox transformation of data - Only intended for developer useboxcoxTransform
Changepoint analysis - Only intended for developer usechangepoint
Changepoint outlier detection plot - Only intended for developer usechangepoint.plot
Chebyshev inequality based identification of outliers on segments - Only intended for developer usechebyshev.inequality.detect
Limits for control chart _R_ - Only intended for developer usecontrol.limits.R
Limits for control chart _s_ - Only intended for developer usecontrol.limits.s
Limits for control chart _x_ - Only intended for developer usecontrol.limits.x
Control chart outliers detection plot - Only intended for developer usecontrolchart.plot
Extreme value outlier detection plot - Only intended for developer useEV.plot
Extremal index estimation (Holesovsky and Fusek, 2020) - Only intended for developer useextremal.index.censored
Extremal index estimation (Gomes, 1993) - Only intended for developer useextremal.index.gomes
Extremal index estimation (Ferro and Segers, 2003) - Only intended for developer useextremal.index.intervals
Extremal index estimation (Suveges and Davison, 2010) - Only intended for developer useextremal.index.Kgaps
Extremal index estimation (Smith and Weissman, 1994) - Only intended for developer useextremal.index.runs
Extremal index estimation (Northrop, 2015) - Only intended for developer useextremal.index.sliding.blocks
Parameter _alpha_ for Quantiles of normal distribution based outlier detection - Only intended for developer usefind.alpha
Parameter L for Chebyshev inequality based outlier detection - Only intended for developer usefind.L
Table of Control Charts Constants - Only intended for developer useget.norm
Grubbs test based identification of outliers on segments - Only intended for developer usegrubbs.detect
Outlier detection using Grubbs test - Only intended for developer usegrubbs.test
Identification of outliers using changepoint analysisKRDetect.outliers.changepoint
Identification of outliers using control chartsKRDetect.outliers.controlchart
Identification of outliers using extreme value theoryKRDetect.outliers.EV
Outlier detection plotKRDetect.outliers.plot
Left medcouple (LMC) - Only intended for developer usemc.left
Right medcouple (RMC) - Only intended for developer usemc.right
Robust medcouple MC-LR test - Only intended for developer usemc.test
Moment estimates of GP distribution parameters - Only intended for developer useMoment.gpd.fit
Mean residual life (MRL) plotMRL.plot
Normal distribution based identification of outliers on segments - Only intended for developer usenormal.distr.quantiles.detect
Outlier detection plotplot.KRDetect
Return level estimation - Only intended for developer usereturn.level.est
Segment length control - Only intended for developer usesegment.length.control
Kernel regression smoothingsmoothing
Stability plotstability.plot
Summary of the outlier detection resultssummary.KRDetect