Package: kdensity
Type: Package
Title: Kernel Density Estimation with Parametric Starts and Asymmetric
        Kernels
Version: 1.2.0
Authors@R: c(
    person("Jonas", "Moss", , "jonas.gjertsen@gmail.com", role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0002-6876-6964")),
    person("Martin", "Tveten", ,"tveten@nr.no", role = c("ctb"))
    )
Description: 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.
License: MIT + file LICENSE
URL: https://github.com/JonasMoss/kdensity
BugReports: https://github.com/JonasMoss/kdensity/issues
Encoding: UTF-8
Suggests: extraDistr, testthat, knitr, rmarkdown
Imports: univariateML
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)
Config/pak/sysreqs: cmake make
Repository: https://jonasmoss.r-universe.dev
Date/Publication: 2026-05-05 06:04:35 UTC
RemoteUrl: https://github.com/jonasmoss/kdensity
RemoteRef: HEAD
RemoteSha: fb8d28ca88170e4de0e67ec01f82c7109ec0067b
NeedsCompilation: no
Packaged: 2026-07-04 05:50:27 UTC; root
Author: Jonas Moss [aut, cre] (ORCID: <https://orcid.org/0000-0002-6876-6964>),
  Martin Tveten [ctb]
Maintainer: Jonas Moss <jonas.gjertsen@gmail.com>