kdensity (1.0.0)

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) . 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.

Maintainer: Jonas Moss
Author(s): Jonas Moss, Martin Tveten

License: MIT + file LICENSE

Uses: assertthat, EQL, knitr, rmarkdown, SkewHyperbolic, testthat, covr, extraDistr

Released over 1 year ago.