Kernel Density Estimation for Heaped and Rounded Data R package Kernelheaping version 2.3.0 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. Software Depends: R (>= 2.15.0), MASS, ks, sparr Imports: sp, plyr, dplyr, fastmatch, fitdistrplus, GB2, magrittr, mvtnorm Marcus Gross <marcus.gross@inwt-statistics.de> Comprehensive R Archive Network (CRAN) Marcus Gross [aut, cre], Lukas Fuchs [aut], Kerstin Erfurth [ctb] GPL-2 GPL-3 2022-01-26 application/tgz https://CRAN.R-project.org/package=Kernelheaping doi:10.32614/CRAN.package.Kernelheaping