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:	1.1.0
Imports:	assertthat, univariateML, EQL
Suggests:	extraDistr, SkewHyperbolic, testthat, covr, knitr, rmarkdown
Published:	2020-09-30
Author:	Jonas Moss, Martin Tveten
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:

Reference manual:	kdensity.pdf
Vignettes:	Tutorial for 'kdensity'

Downloads:

Package source:	kdensity_1.1.0.tar.gz
Windows binaries:	r-devel: kdensity_1.1.0.zip, r-release: kdensity_1.1.0.zip, r-oldrel: kdensity_1.1.0.zip
macOS binaries:	r-release (arm64): kdensity_1.1.0.tgz, r-oldrel (arm64): kdensity_1.1.0.tgz, r-release (x86_64): kdensity_1.1.0.tgz, r-oldrel (x86_64): kdensity_1.1.0.tgz
Old sources:	kdensity archive

Reverse dependencies:

Reverse imports:	mixComp, tscopula
Reverse suggests:	TreeDist

Linking:

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