Package: evtree
Title: Evolutionary Learning of Globally Optimal Trees
Version: 0.1-1
Date: 2011-10-02
Authors@R: c(person(given = "Thomas", family = "Grubinger", role =
c("aut", "cre"), email = "Thomas.Grubinger@i-med.ac.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email
= "Achim.Zeileis@R-project.org"), person(given = "Karl-Peter",
family = "Pfeiffer", role = "aut", email =
"Karl-Peter.Pfeiffer@i-med.ac.at"))
Author: Thomas Grubinger [aut, cre], Achim Zeileis [aut], Karl-Peter
Pfeiffer [aut]
Maintainer: Thomas Grubinger <Thomas.Grubinger@i-med.ac.at>
Description: Commonly used classification and regression tree methods
like the CART algorithm are recursive partitioning methods that
build the model in a forward stepwise search.  Although this
approach is known to be an efficient heuristic, the results of
recursive tree methods are only locally optimal, as splits are
chosen to maximize homogeneity at the next step only. An
alternative way to search over the parameter space of trees is
to use global optimization methods like evolutionary
algorithms. The evtree package implements an evolutionary
algorithm for learning globally optimal classification and
regression trees in R. CPU and memory-intensive tasks are fully
computed in C++ while the partykit package is leveraged to
represent the resulting trees in R, providing unified
infrastructure for summaries, visualizations, and predictions.
Depends: R (>= 2.11.0), partykit
Suggests: mlbench, kernlab, multcomp, rpart, party, xtable
LazyData: yes
License: GPL-2
Packaged: 2011-10-02 18:20:26 UTC; zeileis
Repository: CRAN
Date/Publication: 2011-10-02 19:24:26

Package: evtree
Title: Evolutionary Learning of Globally Optimal Trees
Version: 0.1-2
Date: 2012-04-11
Authors@R: c(person(given = "Thomas", family = "Grubinger", role =
c("aut", "cre"), email = "Thomas.Grubinger@i-med.ac.at"),
person(given = "Achim", family = "Zeileis", role = "aut", email
= "Achim.Zeileis@R-project.org"), person(given = "Karl-Peter",
family = "Pfeiffer", role = "aut", email =
"Karl-Peter.Pfeiffer@i-med.ac.at"))
Author: Thomas Grubinger [aut, cre], Achim Zeileis [aut], Karl-Peter
Pfeiffer [aut]
Maintainer: Thomas Grubinger <Thomas.Grubinger@i-med.ac.at>
Description: Commonly used classification and regression tree methods
like the CART algorithm are recursive partitioning methods that
build the model in a forward stepwise search.  Although this
approach is known to be an efficient heuristic, the results of
recursive tree methods are only locally optimal, as splits are
chosen to maximize homogeneity at the next step only. An
alternative way to search over the parameter space of trees is
to use global optimization methods like evolutionary
algorithms. The evtree package implements an evolutionary
algorithm for learning globally optimal classification and
regression trees in R. CPU and memory-intensive tasks are fully
computed in C++ while the partykit package is leveraged to
represent the resulting trees in R, providing unified
infrastructure for summaries, visualizations, and predictions.
Depends: R (>= 2.11.0), partykit
Suggests: mlbench, kernlab, multcomp, rpart, party, xtable
LazyData: yes
License: GPL-2
Packaged: 2012-04-11 11:02:54 UTC; zeileis
Repository: CRAN
Date/Publication: 2012-04-11 12:00:10