party v1.3-1

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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>.

Functions in party

Name Description
TreeControl Class Class "TreeControl"
ForestControl-class Class "ForestControl"
SplittingNode Class Class "SplittingNode"
plot.mob Visualization of MOB Trees
Plot BinaryTree Visualization of Binary Regression Trees
mob Model-based Recursive Partitioning
RandomForest-class Class "RandomForest"
mob_control Control Parameters for Model-based Partitioning
Transformations Function for Data Transformations
initVariableFrame-methods Set-up VariableFrame objects
prettytree Print a tree.
Initialize Methods Methods for Function initialize in Package `party'
party_intern Call internal functions.
Control Forest Hyper Parameters Control for Conditional Tree Forests
Panel Generating Functions Panel-Generators for Visualization of Party Trees
Conditional Inference Trees Conditional Inference Trees
varimp Variable Importance
reweight Re-fitting Models with New Weights
readingSkills Reading Skills
LearningSample Class Class "LearningSample"
BinaryTree Class Class "BinaryTree"
Fit Methods Fit `StatModel' Objects to Data
Control ctree Hyper Parameters Control for Conditional Inference Trees
cforest Random Forest
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Vignettes of party

Name
MOB.Rnw
MOB.Rout.save
party.Rnw
party.Rout.save
partyrefs.bib
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Last month downloads

Details

Date 2018-08-08
LinkingTo mvtnorm
LazyData yes
License GPL-2
URL http://party.R-forge.R-project.org
NeedsCompilation yes
Packaged 2018-08-08 11:57:17 UTC; hothorn
Repository CRAN
Date/Publication 2018-08-08 14:40:10 UTC

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