Package: demoKde 1.0.1

demoKde: Kernel Density Estimation for Demonstration Purposes

Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. The method used follows directly the definition, but gains efficiency by replacing the observations by frequencies in a very fine grid covering the sample range. A canonical reference is B. W. Silverman, (1998) <doi:10.1201/9781315140919>. NOTE: the density function in the stats package uses a more sophisticated method based on the fast Fourier transform and that function should be used if computational efficiency is a prime consideration.

Authors:Bill Venables

demoKde_1.0.1.tar.gz
demoKde_1.0.1.zip(r-4.7)demoKde_1.0.1.zip(r-4.6)demoKde_1.0.1.zip(r-4.5)
demoKde_1.0.1.tgz(r-4.6-any)demoKde_1.0.1.tgz(r-4.5-any)
demoKde_1.0.1.tar.gz(r-4.7-any)demoKde_1.0.1.tar.gz(r-4.6-any)
demoKde_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
demoKde/json (API)

# Install 'demoKde' in R:
install.packages('demoKde', repos = c('https://billvenables.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 packages 3 scripts 331 downloads 13 exports 0 dependencies

Last updated from:ccb4db51c4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK95
source / vignettesOK118
linux-release-x86_64OK95
macos-release-arm64OK102
macos-oldrel-arm64OK80
windows-develOK59
windows-releaseOK72
windows-oldrelOK81
wasm-releaseOK86

Exports:kdekernelBiweightkernelCosinekernelEpanechnikovkernelGaussiankernelLogistickernelOptCosinekernelRectangularkernelSquaredCosinekernelTriangularkernelTricubekernelTriweightkernelUniform

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Kernel density estimation demonstration and explorationdemoKde-package demoKde
Univariate kernel density estimation directly in R code.kde
Kernel functions for use with kdekernelBiweight kernelCosine kernelEpanechnikov kernelGaussian kernelLogistic kernelOptCosine kernelRectangular kernelSquaredCosine kernelTriangular kernelTricube kernelTriweight kernelUniform