deamer: Deconvolution density estimation with adaptive methods for a variable prone to measurement error
Version 1.0

deamer provides deconvolution algorithms for the non-parametric estimation of the density f of an error-prone variable x with additive noise e. The model is y = x + e where the noisy variable y is observed, while x is unobserved. Estimation may be performed for i) a known density of the error ii) with an auxiliary sample of pure noise and iii) with an auxiliary sample of replicate (repeated) measurements. Estimation is performed using adaptive model selection and penalized contrasts.

Package details

AuthorJulien Stirnemann, Adeline Samson, Fabienne Comte. Contribution from Claire Lacour.
Date of publication2012-08-05 06:07:55
Maintainerj.stirnemann <[email protected]>
LicenseGPL
Version1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("deamer")

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deamer documentation built on May 30, 2017, 3:38 a.m.