Package: mvoutlier 2.1.1
mvoutlier: Multivariate Outlier Detection Based on Robust Methods
Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.
Authors:
mvoutlier_2.1.1.tar.gz
mvoutlier_2.1.1.zip(r-4.5)mvoutlier_2.1.1.zip(r-4.4)mvoutlier_2.1.1.zip(r-4.3)
mvoutlier_2.1.1.tgz(r-4.4-any)mvoutlier_2.1.1.tgz(r-4.3-any)
mvoutlier_2.1.1.tar.gz(r-4.5-noble)mvoutlier_2.1.1.tar.gz(r-4.4-noble)
mvoutlier_2.1.1.tgz(r-4.4-emscripten)mvoutlier_2.1.1.tgz(r-4.3-emscripten)
mvoutlier.pdf |mvoutlier.html✨
mvoutlier/json (API)
# Install 'mvoutlier' in R: |
install.packages('mvoutlier', repos = c('https://petertuwien.r-universe.dev', 'https://cloud.r-project.org')) |
- X - Data
- Y - Data
- bhorizon - B-horizon of the Kola Data
- bss.background - Background map for the BSS project
- bssbot - Bottom Layer of the BSS Data
- bsstop - Top Layer of the BSS Data
- chorizon - C-horizon of the Kola Data
- dat - Data of illustrative example in paper
- humus - Humus Layer (O-horizon) of the Kola Data
- kola.background - Background map for the Kola project
- moss - Moss Layer of the Kola Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:f563386e4e. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 27 2024 |
R-4.5-win | NOTE | Oct 27 2024 |
R-4.5-linux | NOTE | Oct 27 2024 |
R-4.4-win | NOTE | Oct 27 2024 |
R-4.4-mac | NOTE | Oct 27 2024 |
R-4.3-win | NOTE | Oct 27 2024 |
R-4.3-mac | NOTE | Oct 27 2024 |
Exports:aq.plotarwchisq.plotcolor.plotcorr.plotdd.plotlocoutNeighborlocoutPercentlocoutSortmap.plotmvoutlier.CoDapbbpcoutpkbplot.mvoutlierCoDasign1sign2symbol.plotuni.plot
Dependencies:DEoptimRrobustbasesgeostat