Kernelheaping: Kernel Density Estimation for Heaped Data

In self-reported data the user often encounters heaped data, i.e. data which are rounded to a different degree of coarseness. While this is mostly a minor problem in parametric density estimation the bias can be very large for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Additionally varying rounding probabilities (with the true value) and asymmetric rounding is estimable as well.

Version: 0.2
Depends: R (≥ 2.15.0), plyr, evmix, MASS
Published: 2015-01-27
Author: Marcus Gross
Maintainer: Marcus Gross <marcus.gross at fu-berlin.de>
License: GPL-2
NeedsCompilation: no
CRAN checks: Kernelheaping results

Downloads:

Reference manual: Kernelheaping.pdf
Package source: Kernelheaping_0.2.tar.gz
Windows binaries: r-devel: Kernelheaping_0.2.zip, r-release: Kernelheaping_0.2.zip, r-oldrel: Kernelheaping_0.2.zip
OS X Snow Leopard binaries: r-release: Kernelheaping_0.2.tgz, r-oldrel: Kernelheaping_0.2.tgz
OS X Mavericks binaries: r-release: Kernelheaping_0.2.tgz