This repository contains a development version of the companion package to the 2nd edition of the book Robust Statistics: Theory and Methods, by Ricardo Maronna, Doug Martin, Victor Yohai and Matias Salibian-Barrera.


Bug reports

We use GitHub issues to track and solve potential bugs in our package. When submitting your bug report please:

  1. Check if your issue / bug has been fixed by trying to reproduce it using the latest version of the package.

  2. Isolate the problem by creating a minimal reproducible example (see below)

  3. Create an issue for this repository. Refer to this page for instructions on how to create a GitHub issue.

A good bug report should not require others to contact you to find more information. Please try to be as detailed as possible in your report. What is your environment? What steps will reproduce the issue? What outcome did you expect and what outcome did you get?


A short and descriptive bug report title

A summary of the issue and the OS environment in which it occurs. Include the steps required to reproduce the bug.

  1. This is the first step
  2. This is the second step
  3. Further steps, etc.

Any other information you want to share that is relevant to the issue being reported. This might include the lines of code that you have identified as causing the bug, and potential solutions.

Minimal reproducible examples

(This section is adapted from Rob Hyndman’s notes on minimal reproducible examples).

A Minimal reproducible example (MRE) is intended to reproduce an error using the smallest amount of code possible. To check that your MRE code is reproducible, try running it in a fresh R session before you submit the issue. Using minimal reproducible examples saves package developers time in wading through messy code that is not relevant to the apparent bug.

A MRE should consist of a single R script file that can be run without error in a fresh R session, and should contain the following three sections:

Please remove anything that is not necessary to reproduce the problem.

Try to use one of the built-in datasets if possible. If you need to include some data, then use dput() so the data can be included as part of the same text file. In most cases, you do not need to include all of your data, just a small subset that will allow the problem to be reproduced.

If you randomly generate some data, use set.seed(somenumber).

Please spend time adding comments so we can understand your code quickly.