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) (<doi:10.1093/jssam/smw011>). Additionally, bivariate nonparametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>), as well as data aggregated on areas is supported.
Package details 


Author  Marcus Gross [aut, cre], Kerstin Erfurth [ctb] 
Maintainer  Marcus Gross <marcus.gross@inwtstatistics.de> 
License  GPL2  GPL3 
Version  2.2.2 
Package repository  View on CRAN 
Installation 
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