The R Journal: article published in 2013, volume 5:2

rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models
Yusuf K. Bilgic and Herbert Susmann , The R Journal (2013) 5:2, pages 71-79.

Abstract There is a lack of robust statistical analyses for random effects linear models. In practice, statistical analyses, including estimation, prediction and inference, are not reliable when data are unbalanced, of small size, contain outliers, or not normally distributed. It is fortunate that rank-based regression analysis is a robust nonparametric alternative to likelihood and least squares analysis. We propose an R package that calculates rank-based statistical analyses for twoand three-level random effects nested designs. In this package, a new algorithm which recursively obtains robust predictions for both scale and random effects is used, along with three rank-based fitting methods.

Received: 2013-02-04; online 2013-10-25
CRAN packages: aa, Rfit, rlme, lme4
CRAN Task Views implied by cited CRAN packages: Bayesian, Econometrics, Environmetrics, OfficialStatistics, Psychometrics, SocialSciences, SpatioTemporal

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2013-027,
  author = {Yusuf K. Bilgic and Herbert Susmann},
  title = {{rlme: An R Package for Rank-Based Estimation and Prediction
          in Random Effects Nested Models}},
  year = {2013},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2013-027},
  url = {https://doi.org/10.32614/RJ-2013-027},
  pages = {71--79},
  volume = {5},
  number = {2}
}