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
DOI: 10.32614/CRAN.package.envoutliers
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


Reference manual: envoutliers.pdf


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
macOS binaries: r-release (arm64): envoutliers_1.1.0.tgz, r-oldrel (arm64): envoutliers_1.1.0.tgz, r-release (x86_64): envoutliers_1.1.0.tgz, r-oldrel (x86_64): envoutliers_1.1.0.tgz
Old sources: envoutliers archive


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