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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