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: 1.0.0
Imports: MASS, car, changepoint, ecp, graphics, ismev, lokern, robustbase, stats
Suggests: openair
Published: 2019-11-05
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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Reference manual: envoutliers.pdf
Package source: envoutliers_1.0.0.tar.gz
Windows binaries: r-devel: envoutliers_1.0.0.zip, r-devel-gcc8: envoutliers_1.0.0.zip, r-release: envoutliers_1.0.0.zip, r-oldrel: envoutliers_1.0.0.zip
OS X binaries: r-release: envoutliers_1.0.0.tgz, r-oldrel: envoutliers_1.0.0.tgz

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