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 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. License: GPL-2 | GPL-3 RoxygenNote: 7.1.0 NeedsCompilation: no Packaged: 2026-10-05 09:14:55 UTC; root Config/pak/sysreqs: make pari-gp Repository: https://mgross.r-universe.dev Date/Publication: 2022-01-26 17:42:52 UTC RemoteUrl: https://github.com/cran/Kernelheaping RemoteRef: HEAD RemoteSha: d916c0c7286124af4c9f4c5094ede84a7e3f9b2a