kdensity: Kernel Density Estimation with Parametric Starts and AsymmetricKernels
Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.
| Version: | 1.1.1 |
| Imports: | assertthat,univariateML,EQL |
| Suggests: | extraDistr,SkewHyperbolic,testthat,covr,knitr,rmarkdown |
| Published: | 2025-03-04 |
| DOI: | 10.32614/CRAN.package.kdensity |
| Author: | Jonas Moss [aut, cre], Martin Tveten [ctb] |
| Maintainer: | Jonas Moss <jonas.gjertsen at gmail.com> |
| BugReports: | https://github.com/JonasMoss/kdensity/issues |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/JonasMoss/kdensity |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | kdensity results |
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