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
Built: R 2.13.2; i386-pc-mingw32; 2012-02-25 02:27:20 UTC; windows
Archs: i386, x64
