`rrcov`

: Scalable Robust Estimators with High Breakdown PointThe 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()`

).

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)
```

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" --no-build-vignettes)
```

Of course, if you have already installed `remotes`

, you can skip the first line (I have commented it out).

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 1.6-1)
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
#> 1.50345 1.85345 1.68276 -0.06552
#>
#> Robust Estimate of Covariance:
#> X1 X2 X3 Y
#> X1 1.56742 0.15447 0.28699 0.16560
#> X2 0.15447 1.60912 0.22130 -0.01917
#> X3 0.28699 0.22130 1.55468 -0.21853
#> Y 0.16560 -0.01917 -0.21853 0.45091
```