Package: rrcov 1.7-6
Valentin Todorov
rrcov: Scalable Robust Estimators with High Breakdown Point
Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Authors:
rrcov_1.7-6.tar.gz
rrcov_1.7-6.tar.gz(r-4.5-noble)rrcov_1.7-6.tar.gz(r-4.4-noble)
rrcov_1.7-6.tgz(r-4.4-emscripten)rrcov_1.7-6.tgz(r-4.3-emscripten)
rrcov.pdf |rrcov.html✨
rrcov/json (API)
NEWS
| # Install 'rrcov' in R: |
| install.packages('rrcov', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/valentint/rrcov/issues
- Appalachia - Annual maximum streamflow in central Appalachia
- Cars - Consumer reports car data: dimensions
- Cascades - Annual precipitation totals for the North Cascades region
- OsloTransect - Oslo Transect Data
- bus - Automatic vehicle recognition data
- bushmiss - Campbell Bushfire Data with added missing data items
- diabetes - Reaven and Miller diabetes data
- fish - Fish Catch Data Set
- fruit - Fruit data set
- hemophilia - Hemophilia Data
- ionosphere - Johns Hopkins University Ionosphere database.
- lmom32 - Hosking and Wallis Data Set, Table 3.2
- lmom33 - Hosking and Wallis Data Set, Table 3.3
- machines - Computer Hardware
- maryo - Marona and Yohai Artificial Data
- octane - Octane data
- olitos - Olive Oil Data
- pottery - Archaic Greek Pottery data
- pottery.test - Archaic Greek Pottery data
- rice - Rice taste data
- salmon - Salmon data
- soil - Exchangable cations in forest soil data set
- un86 - United Nations Data - 1986
- wages - Wages and Hours
- wolves - Skull dimensions of the wolf _Canis lupus_ L.
Conda:r-rrcov-1.5_5(2025-03-25)
Last updated 7 months agofrom:8de2492280. Checks:2 OK, 1 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 04 2025 |
| R-4.5-linux-x86_64 | NOTE | Mar 04 2025 |
| R-4.4-linux-x86_64 | OK | Mar 04 2025 |
Exports:biplotCovCovClassicCovControlMcdCovControlMestCovControlMMestCovControlMrcdCovControlMveCovControlOgkCovControlSdeCovControlSestCovMcdcovMestCovMestCovMMestCovMrcdCovMveCovOgkCovRobustCovSdeCovSestgetCentergetCorrgetCovgetDatagetDetgetDistancegetEigenvaluesgetEllipsegetEvalsgetFlaggetLoadingsgetMethgetPrcompgetQuangetRawgetScalegetScoresgetSdevgetShapeisClassicisSingularLdaClassicLdaPPLindamtxconfusionmyscreeplotpca.distancespca.scoreplotPcaClassicPcaCovPcaGridPcaHubertPcaLocantorePcaProjplotpredictQdaClassicQdaCovrestimatescorePlotscreeplotshowsqrtmsummaryT2.testvecnormWilks.test
Dependencies:DEoptimRlatticemvtnormpcaPProbustbase
Citation
To cite package ‘rrcov’ in publications use:
Todorov V (2024). rrcov: Scalable Robust Estimators with High Breakdown Point. R package version 1.7-6, https://CRAN.R-project.org/package=rrcov.
Corresponding BibTeX entry:
@Manual{,
title = {rrcov: Scalable Robust Estimators with High Breakdown
Point},
author = {Valentin Todorov},
year = {2024},
note = {R package version 1.7-6},
url = {https://CRAN.R-project.org/package=rrcov},
}
Readme and manuals
rrcov: Scalable Robust Estimators with High Breakdown Pointrrcov
The package rrcov provides scalable robust estimators with high
breakdown point and covers a large number of robustified multivariate
analysis methods, starting with robust estimators for the multivariate
location and covariance matrix (MCD, MVE, S, MM, SD), the deterministic
versions of MCD, S and MM estimates and regularized versions (MRCD) for
high dimensions. These estimators are used to conduct robust principal
components analysis (PcaCov()), linear and quadratic discriminant
analysis (Linda(), Qda()), MANOVA. Projection pursuit algorithms for
PCA to be applied in high dimensions are also available (PcaHubert(),
PcaGrid() and PcaProj()).
Installation
The rrcov package is on CRAN (The Comprehensive R Archive Network) and
the latest release can be easily installed using the command
install.packages("rrcov")
library(rrcov)
Building from source
To install the latest stable development version from GitHub, you can pull this repository and install it using
## install.packages("remotes")
remotes::install_github("valentint/rrcov", build_opts = c("--no-build-vignettes"))
Of course, if you have already installed remotes, you can skip the
first line (I have commented it out).
Example
This is a basic example which shows you if the package is properly installed:
library(rrcov)
#> Loading required package: robustbase
#> Scalable Robust Estimators with High Breakdown Point (version 1.7-3)
data(hbk)
(out <- CovMcd(hbk))
#>
#> Call:
#> CovMcd(x = hbk)
#> -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5)
#>
#> Robust Estimate of Location:
#> X1 X2 X3 Y
#> 1.55833 1.80333 1.66000 -0.08667
#>
#> Robust Estimate of Covariance:
#> X1 X2 X3 Y
#> X1 1.58739 0.03129 0.21694 0.10748
#> X2 0.03129 1.60733 0.25612 0.02864
#> X3 0.21694 0.25612 1.47254 -0.18174
#> Y 0.10748 0.02864 -0.18174 0.44081
Community guidelines
Report issues and request features
If you experience any bugs or issues or if you have any suggestions for additional features, please submit an issue via the Issues tab of this repository. Please have a look at existing issues first to see if your problem or feature request has already been discussed.
Contribute to the package
If you want to contribute to the package, you can fork this repository and create a pull request after implementing the desired functionality.
Ask for help
If you need help using the package, or if you are interested in collaborations related to this project, please get in touch with the package maintainer.
