party

A Laboratory for Recursive Partytioning

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. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.

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Description file content

Package
party
Title
A Laboratory for Recursive Partytioning
Date
2018-08-08
Version
1.3-1
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. The methods are described in Hothorn et al. (2006) , Zeileis et al. (2008) and Strobl et al. (2007) .
Depends
R (>= 3.0.0), methods, grid, stats, mvtnorm (>= 1.0-2), modeltools (>= 0.2-21), strucchange
LinkingTo
mvtnorm
Imports
survival (>= 2.37-7), coin (>= 1.1-0), zoo, sandwich (>= 1.1-1)
Suggests
TH.data (>= 1.0-3), mlbench, colorspace, MASS, vcd, ipred, varImp
LazyData
yes
License
GPL-2
URL
NeedsCompilation
yes
Packaged
2018-08-08 11:57:17 UTC; hothorn
Author
Torsten Hothorn [aut, cre] (), Kurt Hornik [aut], Carolin Strobl [aut], Achim Zeileis [aut] ()
Maintainer
Torsten Hothorn
Repository
CRAN
Date/Publication
2018-08-08 14:40:10 UTC

install.packages('party')

1.3-1

4 months ago

http://party.R-forge.R-project.org

Torsten Hothorn

GPL-2

Depends on

R (>= 3.0.0), methods, grid, stats, mvtnorm (>= 1.0-2), modeltools (>= 0.2-21), strucchange

Imports

survival (>= 2.37-7), coin (>= 1.1-0), zoo, sandwich (>= 1.1-1)

Suggests

TH.data (>= 1.0-3), mlbench, colorspace, MASS, vcd, ipred, varImp

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