In selfreported 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 nonparametric 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 nonparametric density estimation for rounded data as well as data aggregated on areas is supported.
Package details 


Author  Marcus Gross 
Date of publication  20160416 00:09:59 
Maintainer  Marcus Gross <marcus.gross@fuberlin.de> 
License  GPL2  GPL3 
Version  1.6 
Package repository  View on CRAN 
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