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

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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.

Author
Julien Stirnemann, Adeline Samson, Fabienne Comte. Contribution from Claire Lacour.
Date of publication
2012-08-05 06:07:55
Maintainer
j.stirnemann <j.stirnemann@gmail.com>
License
GPL
Version
1.0

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Man pages

deamerclass
Objects of class 'deamer'
deamer.ke
Density estimation with known error density
deamer-package
Non-parametric deconvolution density estimation of variables...
deamer.ro
Density estimation using an auxiliary sample of replicate...
deamer.se
Density estimation using an auxiliary sample of pure errors
laplace
Laplace distribution
mise
Mean integrated squared error

Files in this package

deamer
deamer/MD5
deamer/R
deamer/R/deconvolution_allfuncs.r
deamer/NAMESPACE
deamer/man
deamer/man/mise.Rd
deamer/man/laplace.Rd
deamer/man/deamerclass.Rd
deamer/man/deamer.se.Rd
deamer/man/deamer.ro.Rd
deamer/man/deamer.ke.Rd
deamer/man/deamer-package.Rd
deamer/DESCRIPTION