Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly 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. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well. Additionally, bivariate non-parametric density estimation for rounded data as well as data aggregated on areas is supported.

Version: 1.6
Depends: R (≥ 2.15.0), MASS, ks, sparr
Imports: sp, plyr
Published: 2016-04-16
Author: Marcus Gross
Maintainer: Marcus Gross <marcus.gross at fu-berlin.de>
License: GPL-2 | GPL-3
NeedsCompilation: no
CRAN checks: Kernelheaping results

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Reference manual: Kernelheaping.pdf
Package source: Kernelheaping_1.6.tar.gz
Windows binaries: r-devel: Kernelheaping_1.6.zip, r-release: Kernelheaping_1.6.zip, r-oldrel: Kernelheaping_1.6.zip
OS X El Capitan binaries: r-release: Kernelheaping_1.6.tgz
OS X Mavericks binaries: r-oldrel: Kernelheaping_1.6.tgz
Old sources: Kernelheaping archive

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