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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
DOI: 10.32614/CRAN.package.Kernelheaping
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=Kernelheaping to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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