Fast Kernel Smoothing in R with Applications to Projection Pursuit

Fast Kernel Smoothing in R with Applications to Projection Pursuit

kernel smoothing, nonparametric, density estimation, regression, projection pursuit, independent component analysis, R

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.

David P. Hofmeyr

https://www.jstatsoft.org/article/view/v101i03

Stellenbosch University

January 2022

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