The R Journal: article published in 2015, volume 7:2

QuantifQuantile: An R Package for Performing Quantile Regression Through Optimal Quantization
Isabelle Charlier, Davy Paindaveine and Jérôme Saracco , The R Journal (2015) 7:2, pages 65-80.

Abstract In quantile regression, various quantiles of a response variable Y are modelled as func tions of covariates (rather than its mean). An important application is the construction of reference curves/surfaces and conditional prediction intervals for Y. Recently, a nonparametric quantile regres sion method based on the concept of optimal quantization was proposed. This method competes very well with k-nearest neighbor, kernel, and spline methods. In this paper, we describe an R package, called QuantifQuantile, that allows to perform quantization-based quantile regression. We describe the various functions of the package and provide examples.

Received: 2014-09-28; online 2015-10-30
CRAN packages: quantreg, quantregGrowth, QuantifQuantile, rgl, quantregGrowth
CRAN Task Views implied by cited CRAN packages: Environmetrics, Econometrics, Graphics, Multivariate, Optimization, ReproducibleResearch, Robust, SocialSciences, SpatioTemporal, Survival

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

@article{RJ-2015-021,
  author = {Isabelle Charlier and Davy Paindaveine and Jérôme Saracco},
  title = {{QuantifQuantile: An R Package for Performing Quantile
          Regression Through Optimal Quantization}},
  year = {2015},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2015-021},
  url = {https://doi.org/10.32614/RJ-2015-021},
  pages = {65--80},
  volume = {7},
  number = {2}
}