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>).

Version:	1.1.0
Imports:	MASS, car, changepoint, ecp, graphics, ismev, lokern, robustbase, stats
Suggests:	openair
Published:	2020-05-07
Author:	Martina Campulova [cre], Martina Campulova [aut], Roman Campula [ctb]
Maintainer:	Martina Campulova <martina.campulova at mendelu.cz>
License:	GPL-2
NeedsCompilation:	no
Citation:	envoutliers citation info
Materials:	NEWS
CRAN checks:	envoutliers results

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envoutliers.pdf

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Package source:	envoutliers_1.1.0.tar.gz
Windows binaries:	r-devel: envoutliers_1.1.0.zip, r-release: envoutliers_1.1.0.zip, r-oldrel: envoutliers_1.1.0.zip
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