R/Kernelheaping.R

In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

#' Kernel Density Estimation for Heaped Data
#'
#' 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.
#'
#'
#' The most important function is \code{\link{dheaping}}. See the help and the attached examples on how to use the package.
#' @docType package
#' @name Kernelheaping
#' @import MASS ks sparr
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