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party (version 1.3-17)

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

19,509

Version

1.3-17

License

GPL-2

Maintainer

Last Published

August 17th, 2024

Functions in party (1.3-17)

plot.mob

Visualization of MOB Trees
initVariableFrame-methods

Set-up VariableFrame objects
mob

Model-based Recursive Partitioning
Initialize Methods

Methods for Function initialize in Package `party'
Plot BinaryTree

Visualization of Binary Regression Trees
mob_control

Control Parameters for Model-based Partitioning
Control ctree Hyper Parameters

Control for Conditional Inference Trees
Panel Generating Functions

Panel-Generators for Visualization of Party Trees
prettytree

Print a tree.
reweight

Re-fitting Models with New Weights
party_intern

Call internal functions.
readingSkills

Reading Skills
Fit Methods

Fit `StatModel' Objects to Data
varimp

Variable Importance
SplittingNode Class

Class "SplittingNode"
LearningSample Class

Class "LearningSample"
Conditional Inference Trees

Conditional Inference Trees
Control Forest Hyper Parameters

Control for Conditional Tree Forests
Transformations

Function for Data Transformations
BinaryTree Class

Class "BinaryTree"
RandomForest-class

Class "RandomForest"
TreeControl Class

Class "TreeControl"
ForestControl-class

Class "ForestControl"
cforest

Random Forest