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.

AuthorJulien Stirnemann, Adeline Samson, Fabienne Comte. Contribution from Claire Lacour.
Date of publication2012-08-05 06:07:55
Maintainerj.stirnemann <j.stirnemann@gmail.com>
LicenseGPL
Version1.0

View on CRAN

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.