party v1.0-21


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by Torsten Hothorn

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.

Functions in party

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


Date 2015-06-06
LinkingTo mvtnorm
LazyData yes
License GPL-2
NeedsCompilation yes
Packaged 2015-06-06 09:39:40 UTC; zeileis
Repository CRAN
Date/Publication 2015-06-06 13:04:03

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