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.
|Depends:||R (≥ 2.15.0), MASS, ks, sparr|
|Maintainer:||Marcus Gross <marcus.gross at fu-berlin.de>|
|License:||GPL-2 | GPL-3|
|CRAN checks:||Kernelheaping results|
|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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