The R Journal: article published in 2016, volume 8:2

quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression
Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov and Iván Fernández-Val , The R Journal (2016) 8:2, pages 370-381.

Abstract The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.

Received: 2016-04-30; online 2016-11-21
CRAN packages: quantreg.nonpar, quantreg, QuantifQuantile, quantregGrowth, fda
CRAN Task Views implied by cited CRAN packages: Environmetrics, Econometrics, Optimization, ReproducibleResearch, Robust, SocialSciences, Survival

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

@article{RJ-2016-052,
  author = {Michael Lipsitz and Alexandre Belloni and Victor
          Chernozhukov and Iván Fernández-Val},
  title = {{quantreg.nonpar: An R Package for Performing Nonparametric
          Series Quantile Regression}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-052},
  url = {https://doi.org/10.32614/RJ-2016-052},
  pages = {370--381},
  volume = {8},
  number = {2}
}