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.
|Author||Torsten Hothorn [aut, cre], Kurt Hornik [aut], Carolin Strobl [aut], Achim Zeileis [aut]|
|Date of publication||2016-11-28 13:04:40|
|Maintainer||Torsten Hothorn <Torsten.Hothorn@R-project.org>|
BinaryTree-class: Class "BinaryTree"
cforest: Random Forest
cforest_control: Control for Conditional Tree Forests
ctree: Conditional Inference Trees
ctree_control: Control for Conditional Inference Trees
fit-methods: Fit 'StatModel' Objects to Data
ForestControl-class: Class "ForestControl"
initialize-methods: Methods for Function initialize in Package 'party'
initVariableFrame-methods: Set-up VariableFrame objects
LearningSample-class: Class "LearningSample"
mob: Model-based Recursive Partitioning
mob_control: Control Parameters for Model-based Partitioning
panelfunctions: Panel-Generators for Visualization of Party Trees
party_intern: Call internal functions.
plot.BinaryTree: Visualization of Binary Regression Trees
plot.mob: Visualization of MOB Trees
prettytree: Print a tree.
RandomForest-class: Class "RandomForest"
readingSkills: Reading Skills
reweight: Re-fitting Models with New Weights
SplittingNode-class: Class "SplittingNode"
Transformations: Function for Data Transformations
TreeControl-class: Class "TreeControl"
varimp: Variable Importance