Learn R Programming

⚠️There's a newer version (1.3-18) of this package.Take me there.

party (version 0.9-9999)

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.

Copy Link

Version

Install

install.packages('party')

Monthly Downloads

16,447

Version

0.9-9999

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

September 13th, 2010

Functions in party (0.9-9999)

RandomForest-class

Class "RandomForest"
Control ctree Hyper Parameters

Control for Conditional Inference Trees
Conditional Inference Trees

Conditional Inference Trees
LearningSample Class

Class "LearningSample"
mob_control

Control Parameters for Model-based Partitioning
mammoexp

Mammography Experience Study
BinaryTree Class

Class "BinaryTree"
Fit Methods

Fit `StatModel' Objects to Data
mob

Model-based Recursive Partitioning
Initialize Methods

Methods for Function initialize in Package `party'
varimp

Variable Importance
SplittingNode Class

Class "SplittingNode"
ForestControl-class

Class "ForestControl"
readingSkills

Reading Skills
Transformations

Function for Data Transformations
Panel Generating Functions

Panel-Generators for Visualization of Party Trees
Control Forest Hyper Parameters

Control for Conditional Tree Forests
TreeControl Class

Class "TreeControl"
Memory Allocation

Memory Allocation
cforest

Random Forest
reweight

Re-fitting Models with New Weights
Plot BinaryTree

Visualization of Binary Regression Trees
plot.mob

Visualization of MOB Trees