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: 1.0.0
Imports: assertthat, EQL, knitr, rmarkdown
Suggests: extraDistr, SkewHyperbolic, testthat, covr
Published: 2018-02-27
Author: Jonas Moss, Martin Tveten
Maintainer: Jonas Moss <jonas.gjertsen at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: kdensity results


Reference manual: kdensity.pdf
Vignettes: Tutorial for 'kdensity'
Package source: kdensity_1.0.0.tar.gz
Windows binaries: r-devel: kdensity_1.0.0.zip, r-release: kdensity_1.0.0.zip, r-oldrel: kdensity_1.0.0.zip
OS X binaries: r-release: kdensity_1.0.0.tgz, r-oldrel: kdensity_1.0.0.tgz


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