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

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.

Version: 1.0
Published: 2012-08-05
Author: Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution from Claire Lacour.
Maintainer: j.stirnemann <j.stirnemann at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: deamer results

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Reference manual: deamer.pdf
Package source: deamer_1.0.tar.gz
Windows binaries: r-devel: deamer_1.0.zip, r-release: deamer_1.0.zip, r-oldrel: deamer_1.0.zip
OS X El Capitan binaries: r-release: deamer_1.0.tgz
OS X Mavericks binaries: r-oldrel: deamer_1.0.tgz

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