Package: envoutliers 1.1.0
Martina Campulova
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>).
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envoutliers_1.1.0.tar.gz
envoutliers_1.1.0.tar.gz(r-4.5-noble)envoutliers_1.1.0.tar.gz(r-4.4-noble)
envoutliers_1.1.0.tgz(r-4.4-emscripten)envoutliers_1.1.0.tgz(r-4.3-emscripten)
envoutliers.pdf |envoutliers.html✨
envoutliers/json (API)
NEWS
# Install 'envoutliers' in R: |
install.packages('envoutliers', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:033fb75502. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 04 2024 |
R-4.5-linux | NOTE | Dec 04 2024 |
Exports:KRDetect.outliers.changepointKRDetect.outliers.controlchartKRDetect.outliers.EVKRDetect.outliers.plotMRL.plotsmoothingstability.plot
Dependencies:abindbackportsbootbroomcarcarDatachangepointclicolorspacecowplotcpp11DEoptimRDerivdoBydplyrecpfansifarverFormulagenericsggplot2gluegtableismevisobandlabelinglatticelifecyclelme4lokernmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppEigenrlangrobustbasescalessfsmiscSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo