decon: Deconvolution Estimation in Measurement Error Models

A collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.

Version: 1.3-4
Published: 2021-10-20
DOI: 10.32614/CRAN.package.decon
Author: Xiao-Feng Wang, Bin Wang
Maintainer: Xiao-Feng Wang <wangx6 at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: decon citation info
Materials: NEWS
CRAN checks: decon results [issues need fixing before 2024-06-27]


Reference manual: decon.pdf


Package source: decon_1.3-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): decon_1.3-4.tgz, r-oldrel (arm64): decon_1.3-4.tgz, r-release (x86_64): decon_1.3-4.tgz, r-oldrel (x86_64): decon_1.3-4.tgz
Old sources: decon archive

Reverse dependencies:

Reverse imports: lpme


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