ks (1.11.6)

0 users

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. Chacon & Duong (2018) .

Maintainer: Tarn Duong
Author(s): Tarn Duong [aut, cre], Matt Wand [ctb], Jose Chacon [ctb], Artur Gramacki [ctb]

License: GPL-2 | GPL-3

Uses: FNN, kernlab, KernSmooth, Matrix, mclust, mgcv, multicool, mvtnorm, maps, misc3d, oz, rgl, MASS, OceanView
Reverse depends: curvHDR, feature, hdrcde, highriskzone, Kernelheaping, npphen, prim, rainbow, TPD, wild1
Reverse suggests: broom, condvis2, directlabels, fdapace, hdrcde, httk, Infusion, kernelboot, logcondens, rugarch, sensitivity, transport

Released 3 months ago.

45 previous versions



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of ks yet. Want to be the first? Write one now.

Related packages: FactoMineR, GPArotation, GenKern, Hmisc, ICSNP, ICS, JADE, KernSmooth, MCMCpack, MNP, Matrix, PTAk, PearsonICA, ROCR, SensoMineR, SparseM, SpatialNP, VGAM, YaleToolkit, abind(20 best matches, based on common tags.)

Search for ks on google, google scholar, r-help, r-devel.

Visit ks on R Graphical Manual.