c(
person("Jose M.", "Moyano", email = "i02momuj@uco.es", role = c("aut", "cre")),
person("Luciano", "Sanchez Ramos", email = "luciano@uniovi.es", role = "aut"),
person("Oliver", "Sanchez Marin", email = "oliversanchez718@gmail.com", role = "ctb")
)
Jose M. Moyano [aut, cre], Luciano Sanchez Ramos [aut], Oliver Sanchez Marin [ctb]
KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
It includes more than 100 algorithms for classification, regression and preprocess, which allows a more complete experimentation process.
For more information about KEEL, see <http://www.keel.es/>.
R6, XML, doParallel, foreach, gdata, RKEELjars (>= 1.0.14),
RKEELdata (>= 1.0.2), pmml, arules, Matrix

R6, XML, doParallel, foreach, gdata, RKEELjars (>= 1.0.14),
RKEELdata (>= 1.0.2), pmml, arules, Matrix, rJava

c(
person("Jose M.", "Moyano", email = "jmoyano@uco.es", role = c("aut", "cre")),
person("Luciano", "Sanchez Ramos", email = "luciano@uniovi.es", role = "aut"),
person("Oliver", "Sanchez Marin", email = "oliversanchez718@gmail.com", role = "ctb")
)
KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
It includes more than 100 algorithms for classification, regression, association rules and preprocess, which allows a more complete experimentation process.
For more information about KEEL, see <http://www.keel.es/>.

KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process.
For more information about KEEL, see <http://www.keel.es/>.