Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Tarn Duong
Title: ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Abstract: Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

Page views:: 19143. Submitted: 2007-03-04. Published: 2007-10-16.
Paper: ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R     Download PDF (Downloads: 21965)
Supplements:
ks_1.5.2.tar.gz: R source package Download (Downloads: 1572; 269KB)
v21i07.R.zip: v21i07.R: R example code from the paper Download (Downloads: 1476; 646B)
DOI: 10.18637/jss.v021.i07

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.