Author: Graham Williams, Tridivesh Jena, Michael Hahsler (arules),
Zementis Inc, Hemant Ishwaran, Udaya B. Kogalur, Rajarshi Guha
glmnet, e1071, mlbench, arules, pmmlTransformations
Description: The Predictive Model Markup Language (PMML) is an XML-based
language which provides a way for applications to define statistical
and data mining models and to share models between PMML compliant
applications. More information about PMML and the Data Mining Group
The generated PMML can be imported into any PMML consuming application,
such as the Zementis ADAPA and UPPI scoring engines which allow for
predictive models built in R to be deployed and executed on site, in
the cloud (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal,
Sybase IQ, Teradata and Teradata Aster) or Hadoop (Datameer and Hive).

Suggests: ada, arules, glmnet, nnet, rpart, randomForestSRC,
randomForest, kernlab, e1071, mlbench, pmmlTransformations
The Predictive Model Markup Language (PMML) is an XML-based language
which provides a way for applications to define statistical and data
mining models and to share models between PMML compliant applications.
More information about PMML and the Data Mining Group can be found at

Author: Graham Williams, Tridivesh Jena, Wen Ching Lin, Michael Hahsler (arules),
Suggests: ada, amap, arules, glmnet, nnet, rattle, rpart,
randomForestSRC, randomForest, kernlab, e1071, mlbench,

Zementis Inc, Hemant Ishwaran, Udaya B. Kogalur, Rajarshi Guha, Dmitriy Bolotov
Suggests: ada, amap, arules, glmnet, nnet, rpart, randomForestSRC,
Imports: survival, methods, stats, utils

Author: Graham Williams, Tridivesh Jena, Wen Ching Lin, Michael Hahsler
(arules), Zementis Inc, Hemant Ishwaran, Udaya B. Kogalur, Rajarshi Guha,
language which provides a way for applications to define statistical and
data mining models and to share models between PMML compliant applications.
More information about PMML and the Data Mining Group can be found at http://
www.dmg.org. The generated PMML can be imported into any PMML consuming
application, such as the Zementis ADAPA and UPPI scoring engines which allow for
predictive models built in R to be deployed and executed on site, in the cloud
(Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata
and Teradata Aster) or Hadoop (Datameer and Hive).