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]

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envoutliers.pdf |envoutliers.html✨
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7 exports 0.00 score 67 dependencies 4 scripts 160 downloads

Last updated 4 years agofrom:033fb75502. Checks:OK: 1 NOTE: 1. Indexed: yes.

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

Dependencies:abindbackportsbootbroomcarcarDatachangepointclicolorspacecowplotcpp11DEoptimRDerivdoBydplyrecpfansifarvergenericsggplot2gluegtableismevisobandlabelinglatticelifecyclelme4lokernmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppEigenrlangrobustbasescalessfsmiscSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo