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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