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Kernelheaping  

Kernel Density Estimation for Heaped and Rounded Data
View on CRAN: Click here


Download and install Kernelheaping package within the R console
Install from CRAN:
install.packages("Kernelheaping")

Install from Github:
library("remotes")
install_github("cran/Kernelheaping")

Install by package version:
library("remotes")
install_version("Kernelheaping", "2.3.0")



Attach the package and use:
library("Kernelheaping")
Maintained by
Marcus Gross
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-01-27
Latest Update: 2022-01-26
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: 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.
How to cite:
Marcus Gross (2015). Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data. R package version 2.3.0, https://cran.r-project.org/web/packages/Kernelheaping
Previous versions and publish date:
0.2 (2015-01-27 12:10), 0.5 (2015-04-27 13:42), 0.9 (2015-06-13 01:30), 1.0 (2015-07-31 12:12), 1.2 (2015-12-07 16:16), 1.5 (2016-03-21 22:59), 1.6 (2016-04-16 00:10), 2.1.8 (2017-10-10 18:48), 2.2.0 (2018-07-31 17:10), 2.2.1 (2019-08-02 16:10), 2.2.2 (2020-02-21 11:40), 2.2.8 (2021-05-11 15:42)
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