decon: Deconvolution Estimation in Measurement Error Models
Version 1.2-4

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.

Getting started

Package details

AuthorXiao-Feng Wang, Bin Wang
Date of publication2013-04-17 23:06:49
MaintainerXiao-Feng Wang <[email protected]>
LicenseGPL (>= 3)
Version1.2-4
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("decon")

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decon documentation built on May 30, 2017, 7:57 a.m.