The R Package Ecosystem for Robust Statistics

In the last few years, the number of R packages implementing different robust statistical methods have increased substantially. There are now numerous packages for computing robust multivariate location and scatter, robust multivariate analysis like principal components and discriminant analysis, robust linear models, and other algorithms dedicated to cope with outliers and other irregularities in the data. This abundance of package options may be overwhelming for both beginners and more experienced R users. Here we provide an overview of the most important 25 R packages for different tasks. As metrics for the importance of each package, we consider its maturity and history, the number of total and average monthly downloads from CRAN (The Comprehensive R Archive Network), and the number of reverse dependencies. Then we briefly describe what each of these package does. After that we elaborate on the several above-mentioned topics of robust statistics, presenting the methodology and the implementation in R and illustrating the application on real data examples. Particular attention is paid to the robust methods and algorithms suitable for high-dimensional data. The code for all examples is accessible on the GitHub repository .