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:	2.3.0
Depends:	R (≥ 2.15.0), MASS, ks, sparr
Imports:	sp, plyr, dplyr, fastmatch, fitdistrplus, GB2, magrittr, mvtnorm
Published:	2022-01-26
Author:	Marcus Gross [aut, cre], Lukas Fuchs [aut], Kerstin Erfurth [ctb]
Maintainer:	Marcus Gross <marcus.gross at inwt-statistics.de>
License:	GPL-2 | GPL-3
NeedsCompilation:	no
CRAN checks:	Kernelheaping results

Documentation:

Reference manual:

Kernelheaping.pdf

Downloads:

Package source:	Kernelheaping_2.3.0.tar.gz
Windows binaries:	r-devel: Kernelheaping_2.3.0.zip, r-release: Kernelheaping_2.3.0.zip, r-oldrel: Kernelheaping_2.3.0.zip
macOS binaries:	r-release (arm64): Kernelheaping_2.3.0.tgz, r-oldrel (arm64): Kernelheaping_2.3.0.tgz, r-release (x86_64): Kernelheaping_2.3.0.tgz, r-oldrel (x86_64): Kernelheaping_2.3.0.tgz
Old sources:	Kernelheaping archive

Reverse dependencies:

Reverse suggests:

smicd

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