Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

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.

Getting started

Package details

AuthorMarcus Gross [aut, cre], Kerstin Erfurth [ctb]
MaintainerMarcus Gross <marcus.gross@inwt-statistics.de>
LicenseGPL-2 | GPL-3
Version2.2.2
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("Kernelheaping")

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Kernelheaping documentation built on Feb. 21, 2020, 5:07 p.m.