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party (version 1.3-14)

A Laboratory for Recursive Partytioning

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) .

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Version

Install

install.packages('party')

Monthly Downloads

25,688

Version

1.3-14

License

GPL-2

Maintainer

Last Published

November 28th, 2023

Functions in party (1.3-14)

Transformations

Function for Data Transformations
Plot BinaryTree

Visualization of Binary Regression Trees
plot.mob

Visualization of MOB Trees
reweight

Re-fitting Models with New Weights
mob

Model-based Recursive Partitioning
mob_control

Control Parameters for Model-based Partitioning
varimp

Variable Importance
Conditional Inference Trees

Conditional Inference Trees
ForestControl-class

Class "ForestControl"
Control ctree Hyper Parameters

Control for Conditional Inference Trees
Fit Methods

Fit `StatModel' Objects to Data
Panel Generating Functions

Panel-Generators for Visualization of Party Trees
party_intern

Call internal functions.
initVariableFrame-methods

Set-up VariableFrame objects
readingSkills

Reading Skills
prettytree

Print a tree.
Initialize Methods

Methods for Function initialize in Package `party'
LearningSample Class

Class "LearningSample"
TreeControl Class

Class "TreeControl"
RandomForest-class

Class "RandomForest"
SplittingNode Class

Class "SplittingNode"
cforest

Random Forest
BinaryTree Class

Class "BinaryTree"
Control Forest Hyper Parameters

Control for Conditional Tree Forests