Package: Kernelheaping
Type: Package
Title: Kernel Density Estimation for Heaped and Rounded Data
Authors@R: c(person("Marcus", "Gross",
  role = c("aut", "cre"), email = "marcus.gross@inwt-statistics.de"),
  person("Lukas", "Fuchs", role = "aut",
  email = "f.lukas@live.de"),
  person("Kerstin", "Erfurth", role = "ctb",
  email = "keri.erfurth@gmail.com"))
Version: 2.3.0
Date: 2022-01-26
Depends: R (>= 2.15.0), MASS, ks, sparr
Imports: sp, plyr, dplyr, fastmatch, fitdistrplus, GB2, magrittr,
        mvtnorm
Author: Marcus Gross [aut, cre],
  Lukas Fuchs [aut],
  Kerstin Erfurth [ctb]
Maintainer: Marcus Gross <marcus.gross@inwt-statistics.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: 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.
License: GPL-2 | GPL-3
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2022-01-26 18:23:59 UTC; M
Repository: CRAN
Date/Publication: 2022-01-26 18:42:52 UTC
Built: R 4.6.0; ; 'Thu, 12 Mar 2026 08:39:14 +0900'; unix
