decon: Deconvolution Estimation in Measurement Error Models

This package contains 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: 1.2-4
Published: 2013-04-17
Author: Xiao-Feng Wang, Bin Wang
Maintainer: Xiao-Feng Wang <wangx6 at ccf.org>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: decon citation info
Materials: NEWS
CRAN checks: decon results

Downloads:

Reference manual: decon.pdf
Package source: decon_1.2-4.tar.gz
Windows binaries: r-devel: decon_1.2-4.zip, r-release: decon_1.2-4.zip, r-oldrel: decon_1.2-4.zip
OS X Snow Leopard binaries: r-release: decon_1.2-4.tgz, r-oldrel: decon_1.2-4.tgz
OS X Mavericks binaries: r-release: decon_1.2-4.tgz
Old sources: decon archive