Rfit: Rank-based Estimation for Linear Models

In the nineteen seventies, Jurečková and Jaeckel proposed rank estimation for linear models. Since that time, several authors have developed inference and diagnostic methods for these estimators. These rank-based estimators and their associated inference are highly efficient and are robust to outliers in response space. The methods include estimation of standard errors, tests of general linear hypotheses, confidence intervals, diagnostic procedures including studentized residuals, and measures of influential cases. We have developed an R package, Rfit, for computing of these robust procedures. In this paper we highlight the main features of the package. The package uses standard linear model syntax and includes many of the main inference and diagnostic functions.

John D. Kloke , Joseph W. McKean

CRAN packages used

Rfit, Rfit, MASS, quantreg

CRAN Task Views implied by cited packages

Econometrics, Environmetrics, Robust, SocialSciences, Distributions, Multivariate, NumericalMathematics, Optimization, Pharmacokinetics, Psychometrics, ReproducibleResearch, Survival


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For attribution, please cite this work as

Kloke & McKean, "The R Journal: Rfit: Rank-based Estimation for Linear Models", The R Journal, 2012

BibTeX citation

  author = {Kloke, John D. and McKean, Joseph W.},
  title = {The R Journal: Rfit: Rank-based Estimation for Linear Models},
  journal = {The R Journal},
  year = {2012},
  note = {https://doi.org/10.32614/RJ-2012-014},
  doi = {10.32614/RJ-2012-014},
  volume = {4},
  issue = {2},
  issn = {2073-4859},
  pages = {57-64}