kdensity: Kernel Density Estimation with Parametric Starts and Asymmetric
Kernels
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 + file LICENSE |
| URL: |
https://github.com/JonasMoss/kdensity |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
kdensity results |
Documentation:
Downloads:
Reverse dependencies:
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