rrcov: Scalable Robust Estimators with High Breakdown Point
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
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.