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party (version 1.3-1)

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

25,258

Version

1.3-1

License

GPL-2

Maintainer

Last Published

August 8th, 2018

Functions in party (1.3-1)

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