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.
| Version: |
2.3.0 |
| Depends: |
R (≥ 2.15.0), MASS, ks, sparr |
| Imports: |
sp, plyr, dplyr, fastmatch, fitdistrplus, GB2, magrittr, mvtnorm |
| Published: |
2022-01-26 |
| DOI: |
10.32614/CRAN.package.Kernelheaping |
| Author: |
Marcus Gross [aut, cre],
Lukas Fuchs [aut],
Kerstin Erfurth [ctb] |
| Maintainer: |
Marcus Gross <marcus.gross at inwt-statistics.de> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
Kernelheaping results |
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