revss

revss is an R package which implements the estimation techniques described in Rousseeuw & Verboven (2002) for the location and scale of very small samples.

Citation

If you use the package, please cite it as:

Avraham Adler (2020). revss: Robust Estimation in Very Small Samples. R package version 1.0.4. doi: 10.5281/zenodo.5874911 https://CRAN.R-project.org/package=revss

A BibTeX entry for LaTeX users is:

  @Manual{,
    title = {revss: Robust Estimation in Very Small Samples},
    author = {Avraham Adler},
    year = {2020},
    url = {https://CRAN.R-project.org/package=revss},
    doi = {10.5281/zenodo.5874911},
    note = {R package version 1.0.4},
  }

Acknowledgements

The author is grateful Dr. Peter Rousseeuw for his response to this MathExchange question about the implementation.

Contributions

Please ensure that all contributions comply with both R and CRAN standards for packages.

Versioning

This project attempts to follow Semantic Versioning

Changelog

This project attempts to follow the changelog system at Keep a CHANGELOG

Dependancies

This project intends to have as few dependancies as possible. Please consider that when writing code.

Style

Please conform to this coding style guide as best possible.

Documentation

Please provide valid .Rd files and not roxygen-style documentation.

Tests

Please review the current test suite and supply similar tinytest-compatible unit tests for all added functionality.

Submission

If you would like to contribute to the project, it may be prudent to first contact the maintainer via email. A request or suggestion may be raised as an issue as well. To supply a pull request (PR), please:

1. Fork the project and then clone into your own local repository
2. Create a branch in your repository in which you will make your changes
3. Ideally use -s to sign-off on commits under the Developer Certificate of Origin.
4. If possible, sign commits using a GPG key.
5. Push that branch and then create a pull request

At this point, the PR will be discussed and eventually accepted or rejected by the lead maintainer.

Roadmap

Major

• There are no plans for major changes in 2022

Minor

• Achieve OpenSSF Best Practices silver status

Security

Expectations

This package is a calculation engine and requires no secrets or private information. It is checked for memory leaks prior to releases to CRAN using ASAN/UBSBAN. Dissemination is handled by CRAN. Bugs are reported via the tracker and handled as soon as possible.

Assurance

The threat model is that a malicious actor would “poison” the package code by adding in elements having nothing to do with the package’s purpose but which would be used for malicious purposes. This is protected against by having the email account of the maintainer—used for verification by CRAN—protected by a physical 2FA device (Yubikey) which is carried by the lead maintainer.