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Quantreg.nonpar: an R package for performing nonparametric series quantile regression

Authors: Michael Lipsitz , Alexandre Belloni , Victor Chernozhukov and Ivan Fernandez-Val
Date: 06 June 2017
Type: cemmap Working Paper, CWP29/17
DOI: 10.1920/wp.cem.2017.2917


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.

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