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:
demoKde_1.0.1.tar.gz
demoKde_1.0.1.zip(r-4.5)demoKde_1.0.1.zip(r-4.4)demoKde_1.0.1.zip(r-4.3)
demoKde_1.0.1.tgz(r-4.4-any)demoKde_1.0.1.tgz(r-4.3-any)
demoKde_1.0.1.tar.gz(r-4.5-noble)demoKde_1.0.1.tar.gz(r-4.4-noble)
demoKde_1.0.1.tgz(r-4.4-emscripten)demoKde_1.0.1.tgz(r-4.3-emscripten)
demoKde.pdf |demoKde.html✨
demoKde/json (API)
| # Install 'demoKde' in R: |
| install.packages('demoKde', repos = c('https://billvenables.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:ccb4db51c4. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-win | OK | Nov 12 2024 |
| R-4.5-linux | OK | Nov 12 2024 |
| R-4.4-win | OK | Nov 12 2024 |
| R-4.4-mac | OK | Nov 12 2024 |
| R-4.3-win | OK | Nov 12 2024 |
| R-4.3-mac | OK | Nov 12 2024 |
Exports:kdekernelBiweightkernelCosinekernelEpanechnikovkernelGaussiankernelLogistickernelOptCosinekernelRectangularkernelSquaredCosinekernelTriangularkernelTricubekernelTriweightkernelUniform
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Kernel density estimation demonstration and exploration | demoKde-package demoKde |
| Univariate kernel density estimation directly in R code. | kde |
| Kernel functions for use with kde | kernelBiweight kernelCosine kernelEpanechnikov kernelGaussian kernelLogistic kernelOptCosine kernelRectangular kernelSquaredCosine kernelTriangular kernelTricube kernelTriweight kernelUniform |
