adheaping: Characteristic-Function De-Heaping Density Estimation
Tuning-free kernel density estimation for heaped and rounded data
using a characteristic-function theory of heaping. Rounding to a grid is
convolution with a box followed by lattice sampling, so the density is
recovered by deconvolving the known box and tapering against a data-driven
noise floor. Provides a box-deconvolution de-heaping estimator, a
superposition variant, and a single combined estimator selected by a
band-capacity gate; blind grid, heaped-fraction, and mixed-grain readers;
and a spectral higher-order comb detector. Faithful base-R replicas of the
Heitjan-Rubin multiple-imputation and measurement-error deconvolution
methods are included for comparison, and the 'Kernelheaping' stochastic
expectation-maximization estimator is used when installed.
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