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kdensity  

Kernel Density Estimation with Parametric Starts and Asymmetric Kernels
View on CRAN: Click here


Download and install kdensity package within the R console
Install from CRAN:
install.packages("kdensity")

Install from Github:
library("remotes")
install_github("cran/kdensity")

Install by package version:
library("remotes")
install_version("kdensity", "1.1.1")



Attach the package and use:
library("kdensity")
Maintained by
Jonas Moss
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-02-27
Latest Update: 2025-03-04
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) . 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) , the beta kernel of Chen (1999) , and the copula kernel of Jones & Henderson (2007) . User-supplied kernels, parametric starts, and bandwidths are supported.
How to cite:
Jonas Moss (2018). kdensity: Kernel Density Estimation with Parametric Starts and Asymmetric Kernels. R package version 1.1.1, https://cran.r-project.org/web/packages/kdensity. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0.0 (2018-02-27 18:28), 1.0.1 (2019-07-11 15:43), 1.1.0 (2020-09-30 11:00)
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Complete documentation for kdensity
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