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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 |
| 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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