The R Journal: article published in 2016, volume 8:1

Variable Clustering in High-Dimensional Linear Regression: The R Package clere
Loïc Yengo, Julien Jacques, Christophe Biernacki and Mickael Canouil , The R Journal (2016) 8:1, pages 92-106.

Abstract Dimension reduction is one of the biggest challenges in high-dimensional regression models. We recently introduced a new methodology based on variable clustering as a means to reduce dimen sionality. We present here the R package clere that implements some refinements of this methodology. An overview of the package functionalities as well as examples to run an analysis are described. Numerical experiments on real data were performed to illustrate the good predictive performance of our parsimonious method compared to standard dimension reduction approaches.

Received: 2015-03-26; online 2016-04-03
CRAN packages: glmnet, spikeslab, clere, Rcpp, RcppEigen, lasso2, flare
CRAN Task Views implied by cited CRAN packages: MachineLearning, NumericalMathematics, Bayesian, HighPerformanceComputing, Survival

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

@article{RJ-2016-006,
  author = {Loïc Yengo and Julien Jacques and Christophe Biernacki and
          Mickael Canouil},
  title = {{Variable Clustering in High-Dimensional Linear Regression:
          The R Package clere}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-006},
  url = {https://doi.org/10.32614/RJ-2016-006},
  pages = {92--106},
  volume = {8},
  number = {1}
}