Deconvolution Density Estimation using Penalized MLE
Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.

person(given = "Yun",family = "Cai",role = c("aut", "cre"),email = "Yun.Cai@dal.ca")
Performs penalized MLE deconvolution  estimation.

Sarah Colby <colby@lunenfeld.ca>, Sophia Lee, Juan Pablo
Lewinger, Shelley Bull <bull@lunenfeld.ca>
Extends the approach proposed by Firth (1993) for bias
reduction of MLEs in exponential family models to the
multinomial logistic regression model with general covariate
types.  Modification of the logistic regression score function
to remove first-order bias is equivalent to penalizing the
likelihood by the Jeffreys prior, and yields penalized maximum
likelihood estimates (PLEs) that always exist.  Hypothesis
testing is conducted via likelihood ratio statistics.  Profile
confidence intervals (CI) are constructed for the PLEs.

The Predictive Modelling Markup Language (PMML) is a language
for representing models, in an application independent way. Such models
can then be loaded into other applications supporting PMML, such as
Teradata Warehouse Miner and IBM's DB2.
This package is very much in the early stages of
development. Contributions of transformations of other R models are
welcome. Bug reports are also welcome.
Currently this package supports only rpart classification trees and
kmeans clusters. Also, only export of PMML is supported.
This package is part of the Rattle toolkit.

The Predictive Modelling Markup Language (PMML) is a language
for representing models, in an application independent way. Such models
can then be loaded into other applications supporting PMML, such as
Teradata Warehouse Miner and IBM's DB2.
The package is very much in the early stages of development, and
contributions (and bug reports) are welcome and encouraged!
Currently supported export of PMML: rpart classification trees,
randomSurvivorForest forest model, and kmeans clusters.
This package is part of the Rattle toolkit.