Kernelheaping (2.2.1)

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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) (). Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (), as well as data aggregated on areas is supported.

Maintainer: Marcus Gro
Author(s): Marcus Gross [aut, cre], Kerstin Erfurth [ctb]

License: GPL-2 | GPL-3

Uses: fastmatch, ks, magrittr, MASS, mvtnorm, plyr, sp, sparr
Reverse suggests: smicd

Released 6 months ago.

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