R PMML Support: BetteR than EveR!

PMML, the Predictive Model Markup Language, has become the de-facto standard to represent not only predictive models, but also data pre- and post-processing. In so doing, it allows for the interchange of models among different tools and environments, avoiding proprietary issues and incompatibilities. R PMML Package
The PMML Package exports a variety of predictive models form R to PMML. The PMML package itself was conceived at first as part of Togaware's data mining toolkit Rattle. Although it can easily be accessed through Rattle's GUI, it can also be accessed directly in R.
To download the PMML Package from CRAN, the R Archive, click HERE. Extended PMML Support Traditionally, the PMML Package offered support for the following data mining algorithms: ksvm(kernlab): Support Vector Machines nnet: Neural Networks rpart: C&RT Decision Trees lm & glm (stats): Linear and Binary Logistic Regression Models arules: Association Rules kmeans and hclust: Clustering Model Recently, it has been expanded to support: multinom (nnet): Multinomial Logistic Regression Models; glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions randomForest: Random Forest Models for classification and regression coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards ada: Stochastic Boosting naiveBayes (e1071): Naive Bayes Classifiers svm (e1071): Support Vector Machines Once exported in PMML, your R model can be readily deployed in the Zementis ADAPA Scoring Engine, where it can be put to work immediately.