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) .


Reference manual

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1.11.7 by Tarn Duong, a year ago

Browse source code at

Authors: Tarn Duong [aut, cre] , Matt Wand [ctb] , Jose Chacon [ctb] , Artur Gramacki [ctb]

Documentation:   PDF Manual  

Task views: Multivariate Statistics

GPL-2 | GPL-3 license

Imports FNN, kernlab, KernSmooth, Matrix, mclust, mgcv, multicool, mvtnorm

Suggests maps, MASS, misc3d, OceanView, oz, rgl

Imported by AsyK, BMTAR, GPareto, MaskJointDensity, NMADiagT, RandomCoefficients, Surrogate, TEAM, VIRF, birdring, cdcsis, curvHDR, feature, goffda, hdrcde, highriskzone, hypervolume, lg, logcondens, lsbs, motmot, multimode, oddstream, rainbow, raptr, rugarch, sNPLS, semiArtificial, simIReff, simukde, smoothROCtime, starvars, stray, tseriesEntropy.

Depended on by Kernelheaping, TPD, npphen.

Suggested by broom, condvis2, fdapace, httk, kernelboot, sensitivity, transport.

See at CRAN