Fast Kernel Smoothing in R with Applications to Projection Pursuit

This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions. The fast kernel computations are based on recursive expressions invol...

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Bibliographic Details
Main Author: David P. Hofmeyr
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2022-01-01
Series:Journal of Statistical Software
Subjects:
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/4026
Description
Summary:This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions. The fast kernel computations are based on recursive expressions involving the order statistics, which allows for exact evaluation of kernel smoothers at all sample points in log-linear time. In addition to general purpose kernel smoothing functions, the package offers purpose built and readyto-use implementations of popular kernel-type estimators. On top of these basic smoothing problems, this paper focuses on projection pursuit problems in which the projection index is based on kernel-type estimators of functionals of the projected density.
ISSN:1548-7660