ks: Kernel Smoothing

Kernel smoothers for univariate and multivariate data, including densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Duong (2017) <doi:10.18637/jss.v021.i07>.

Version: 1.11.3
Depends: R (≥ 2.10.0)
Imports: FNN (≥ 1.1), kernlab, KernSmooth (≥ 2.22), Matrix, mclust, mgcv, multicool, mvtnorm (≥ 1.0-0)
Suggests: maps, MASS, misc3d (≥ 0.4-0), OceanView, oz, rgl (≥ 0.66)
Published: 2018-07-25
Author: Tarn Duong
Maintainer: Tarn Duong <tarn.duong at gmail.com>
License: GPL-2 | GPL-3
URL: http://www.mvstat.net/mvksa
NeedsCompilation: yes
Materials: ChangeLog
In views: Multivariate
CRAN checks: ks results


Reference manual: ks.pdf
Vignettes: kde
Package source: ks_1.11.3.tar.gz
Windows binaries: r-devel: ks_1.11.3.zip, r-release: ks_1.11.3.zip, r-oldrel: ks_1.11.3.zip
OS X binaries: r-release: ks_1.11.3.tgz, r-oldrel: ks_1.11.3.tgz
Old sources: ks archive

Reverse dependencies:

Reverse depends: Kernelheaping, npphen, TPD
Reverse imports: birdring, cdcsis, curvHDR, feature, GPareto, hdrcde, highriskzone, hypervolume, lg, logcondens, MaskJointDensity, multimode, rainbow, raptr, rugarch, semiArtificial, simIReff, simukde, sNPLS, tseriesEntropy
Reverse suggests: broom, fdapace, httk, kernelboot, sensitivity, transport


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