2016/10/26 by Michael Lipsitz, Alexandre Belloni, Lipsitz, Michael +6 · 1 citation
Computer Science · Economics, Econometrics and Finance · Mathematics · #Computation (stat.CO) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Neural Networks and Applications #Statistical Methods and Inference #econ.EM #stat.CO
paper · pdf · doi:10.48550/arxiv.1610.08329
12 pages, 5 figures
arxiv created 2016/10/26 · openalex publication_date 2016/10/26 · arxiv updated 2017/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.