party v1.3-3


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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. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.

Functions in party

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


Date 2019-03-04
LinkingTo mvtnorm
LazyData yes
License GPL-2
NeedsCompilation yes
Packaged 2019-03-05 16:50:21 UTC; hothorn
Repository CRAN
Date/Publication 2019-03-06 10:20:03 UTC

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