Kernel Density Estimation with Parametric Starts and Asymmetric Kernels R package kdensity version 1.2.0 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. Software Imports: univariateML Suggests: extraDistr, testthat, knitr, rmarkdown Jonas Moss <jonas.gjertsen@gmail.com> Comprehensive R Archive Network (CRAN) Jonas Moss [aut, cre] (ORCID: <https://orcid.org/0000-0002-6876-6964>), Martin Tveten [ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=kdensity/LICENSE) 2026-05-04 application/tgz https://CRAN.R-project.org/package=kdensity doi:10.32614/CRAN.package.kdensity