Package: party
Title: A Laboratory for Recursive Partytioning
Date: 2010-04-14
Version: 0.9-9995
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: 2010-05-19 17:11:12 UTC; hothorn
Repository: CRAN
Date/Publication: 2010-05-19 21:10:01

Package: party
Title: A Laboratory for Recursive Partytioning
Date: 2010-06-24
Version: 0.9-9996
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: 2010-06-24 11:31:12 UTC; hothorn
Repository: CRAN
Date/Publication: 2010-06-25 05:46:24