Package: party
Title: A Laboratory for Recursive Partytioning
Date: $Date: 2008-10-28 18:57:51 +0100 (Tue, 28 Oct 2008) $
Version: 0.9-99
Author: Torsten Hothorn, Kurt Hornik and Achim Zeileis
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>
Description: A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available.
Depends: R (>= 2.0.1), methods, survival, grid, modeltools (>= 0.2-6), coin (>= 0.6-4),
zoo, sandwich (>= 1.1-1), strucchange, vcd
Suggests: ipred, mlbench, colorspace
LazyLoad: yes
LazyData: yes
License: GPL-2
Packaged: Tue Oct 28 19:49:54 2008; hothorn

Package: party
Title: A Laboratory for Recursive Partytioning
Date: 2009-01-13
Version: 0.9-991
Author: Torsten Hothorn, Kurt Hornik, Carolin Strobl and Achim Zeileis
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>
Description: A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available.
Depends: R (>= 2.0.1), methods, survival, grid, modeltools (>= 0.2-6), coin (>= 0.6-4),
zoo, sandwich (>= 1.1-1), strucchange, vcd
Suggests: ipred, mlbench, colorspace
LazyLoad: yes
LazyData: yes
License: GPL-2
Packaged: Tue Jan 13 21:12:39 2009; hothorn