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 errorsinvariables. 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 errorfree data to the deconvolution kernel estimation. Several methods for the selection of the datadriven 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), 124.
Package details 


Author  XiaoFeng Wang, Bin Wang 
Maintainer  XiaoFeng Wang <wangx6@ccf.org> 
License  GPL (>= 3) 
Version  1.24 
Package repository  View on CRAN 
Installation 
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