envoutliers: Methods for Identification of Outliers in Environmental Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F20%3A43918071" target="_blank" >RIV/62156489:43110/20:43918071 - isvavai.cz</a>
Alternative codes found
RIV/44994575:_____/20:N0000120
Result on the web
<a href="https://cran.r-project.org/web/packages/envoutliers/index.html" target="_blank" >https://cran.r-project.org/web/packages/envoutliers/index.html</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
envoutliers: Methods for Identification of Outliers in Environmental Data
Original language description
Environmental data often include outliers that can significantly affect further modeling and data analysis. Although a number of outlier detection method has been proposed, their use is usually complicated by the assumption of the distribution or model of the analyzed data. However, environmental variables are quite often influenced by a lot of different factors and their distribution is difficult to estimate. The envoutliers package has been developed to provide users with a choice of recently presented, semi-parametric outlier detection methods that do not impose requirements on the distribution of the original data.
Czech name
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Czech description
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Project
<a href="/en/project/ED2.1.00%2F03.0064" target="_blank" >ED2.1.00/03.0064: Transport R&D Centre</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Internal product ID
Rpcg20200507
Technical parameters
Implementovaný R balíček je volně dostupný pro software R (R Core Team (2018). “R: A Language and Environment for Statistical Computing.” URL https://www.R-project.org/.) z Comprehensive R Archive Network (CRAN) na adrese http://CRAN.R-project.org/package=envoutliers.
Economical parameters
Software obsahuje implementaci tří semi-parametrických metod pro automatickou detekci odlehlých hodnot. Implementované metody jsou podrobně popsány v publikacích: 1. Čampulová M, Michálek J, Mikuška P, Bokal D (2018). “Nonparametric algorithm for identification of outliers in environmental data.” Journal of Chemometrics, 32, 453–463. 2. Čampulová M, Veselík P, Michálek J (2017). “Control chart and Six sigma based algorithms for identification of outliers in experimental data, with an application to particulate matter PM10.” Atmospheric Pollution Research. Doi=10.1016/j.apr.2017.01.004. 3. Holešovský J, Čampulová M, Michálek J (2018). “Semiparametric Outlier Detection in Nonstationary Times Series: Case Study for Atmospheric Pollution in Brno, Czech Republic.” Atmospheric Pollution Research, 9(1). Automatická detekce odlehlých hodnot je užitečná při validaci datových souborů velkého rozsahu, protože dochází k ušetření času a všechna data jsou vyhodnocena dle stejného kritéria.
Owner IČO
62156489
Owner name
Mendelova univerzita v Brně, Centrum dopravního výzkumu, v. v. i.

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