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: Gross, M. and Rendtel, U. (2016) (
Package details 


Author  Marcus Gross [aut, cre], Kerstin Erfurth [ctb] 
Date of publication  20171010 16:48:26 UTC 
Maintainer  Marcus Gross <[email protected]> 
License  GPL2  GPL3 
Version  2.1.8 
Package repository  View on CRAN 
Installation 
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