`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

``````## 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)
#> 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.