quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression

TL;DR

The paper introduces the R package quantreg.nonpar, which implements nonparametric quantile regression methods for estimating and inferring on partially linear models, providing point estimates and confidence intervals.

Contribution

It offers a comprehensive implementation of nonparametric quantile regression with inference tools, enhancing analysis capabilities in R for partially linear models.

Findings

Provides point estimates of the conditional quantile function.

Includes methods for confidence intervals using analytical and resampling techniques.

Demonstrates basic functionality of the package.

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.