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) (
|Author||Marcus Gross [aut, cre], Kerstin Erfurth [ctb]|
|Date of publication||2017-10-10 16:48:26 UTC|
|Maintainer||Marcus Gross <[email protected]>|
|License||GPL-2 | GPL-3|
|Package repository||View on CRAN|
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