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envoutliers  

Methods for Identification of Outliers in Environmental Data
View on CRAN: Click here


Download and install envoutliers package within the R console
Install from CRAN:
install.packages("envoutliers")

Install from Github:
library("remotes")
install_github("cran/envoutliers")

Install by package version:
library("remotes")
install_version("envoutliers", "1.1.0")



Attach the package and use:
library("envoutliers")
Maintained by
Martina Campulova
[Scholar Profile | Author Map]
First Published: 2019-11-05
Latest Update: 2020-05-07
Description:
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) ) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) ) and the third method (Holesovsky, Campulova and Michalek (2018) ) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) ).
How to cite:
Martina Campulova (2019). envoutliers: Methods for Identification of Outliers in Environmental Data. R package version 1.1.0, https://cran.r-project.org/web/packages/envoutliers. Accessed 10 May. 2025.
Previous versions and publish date:
1.0.0 (2019-11-05 16:20)
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Complete documentation for envoutliers
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