deamer provides deconvolution algorithms for the nonparametric estimation of the density f of an errorprone 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 


Author  Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution from Claire Lacour. 
Date of publication  20120805 06:07:55 
Maintainer  j.stirnemann <[email protected]> 
License  GPL 
Version  1.0 
Package repository  View on CRAN 
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