pmml: Generate PMML for Various Models

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 <http://>. 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).

Version: 1.5.4
Depends: XML
Imports: methods, stats, utils, stringr
Suggests: ada, amap, arules, gbm, glmnet, neighbr, nnet, rpart, randomForestSRC (≤ 2.5.0), randomForest, kernlab, e1071, testthat, survival, xgboost, pmmlTransformations (≥ 1.3.1)
Published: 2018-01-08
Author: Graham Williams, Tridivesh Jena, Wen Ching Lin, Michael Hahsler (arules), Zementis Inc, Hemant Ishwaran, Udaya B. Kogalur, Rajarshi Guha, Dmitriy Bolotov
Maintainer: Tridivesh Jena <rpmmlsupport at>
License: GPL (≥ 2.1)
NeedsCompilation: no
Materials: ChangeLog
In views: ModelDeployment
CRAN checks: pmml results


Reference manual: pmml.pdf
Package source: pmml_1.5.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: pmml_1.5.4.tgz, r-oldrel: pmml_1.5.4.tgz
Old sources: pmml archive

Reverse dependencies:

Reverse imports: fpmoutliers
Reverse suggests: arules, partykit, pmmlTransformations, rattle


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