Packaged: 2015-07-31 09:52:15 UTC; marcusgross

Package: Kernelheaping
Type: Package
Title: Kernel Density Estimation for Heaped and Rounded Data
Version: 1.2
Date: 2015-12-01
Depends: R (>= 2.15.0), evmix, MASS, ks, sparr
Author: Marcus Gross
Maintainer: Marcus Gross <marcus.gross@fu-berlin.de>
Description: In self-reported or anonymized 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. Additionally,
bivariate non-parametric density estimation for rounded data is supported.
License: GPL-2 | GPL-3
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2015-12-07 10:30:19 UTC; marcusgross
Repository: CRAN
Date/Publication: 2015-12-07 16:16:53

Package: Kernelheaping
Type: Package
Title: Kernel Density Estimation for Heaped and Rounded Data
Version: 1.5
Date: 2016-03-17
Depends: R (>= 2.15.0), evmix, MASS, ks, sparr
Imports: sp
Author: Marcus Gross
Maintainer: Marcus Gross <marcus.gross@fu-berlin.de>
Description: 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. Additionally,
bivariate non-parametric density estimation for rounded data is supported.
License: GPL-2 | GPL-3
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-03-21 15:32:04 UTC; marcusgross
Repository: CRAN
Date/Publication: 2016-03-21 22:59:52