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: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>). Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>), as well as data aggregated on areas is supported.

Version: 2.1.8
Depends: R (≥ 2.15.0), MASS, ks, sparr
Imports: sp, plyr, fastmatch, magrittr, mvtnorm
Published: 2017-10-10
Author: Marcus Gross [aut, cre], Kerstin Erfurth [ctb]
Maintainer: Marcus Gross <marcus.gross at>
License: GPL-2 | GPL-3
NeedsCompilation: no
CRAN checks: Kernelheaping results


Reference manual: Kernelheaping.pdf
Package source: Kernelheaping_2.1.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: Kernelheaping_2.1.8.tgz, r-oldrel: Kernelheaping_2.1.8.tgz
Old sources: Kernelheaping archive

Reverse dependencies:

Reverse suggests: smicd


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