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 |
| In views: |
AnomalyDetection |
| CRAN checks: |
envoutliers results |
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