partykit v1.2-3

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A Toolkit for Recursive Partytioning

A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <http://jmlr.org/papers/v16/hothorn15a.html>.

Functions in partykit

Name Description
WeatherPlay Weather Conditions and Playing a Game
extree_data Data Preprocessing for Extensible Trees.
glmtree Generalized Linear Model Trees
mob_control Control Parameters for Model-Based Partitioning
model_frame_rpart Model Frame Method for rpart
nodeapply Apply Functions Over Nodes
nodeids Extract Node Identifiers
panelfunctions Panel-Generators for Visualization of Party Trees
lmtree Linear Model Trees
mob Model-based Recursive Partitioning
party-methods Methods for Party Objects
party-plot Visualization of Trees
party-coercion Coercion Functions
cforest Conditional Random Forests
partynode-methods Methods for Node Objects
partynode Inner and Terminal Nodes
ctree Conditional Inference Trees
party-predict Tree Predictions
party Recursive Partytioning
partysplit Binary and Multiway Splits
prune.modelparty Post-Prune modelparty Objects
varimp Variable Importance
HuntingSpiders Abundance of Hunting Spiders
ctree_control Control for Conditional Inference Trees
extree_fit Fit Extensible Trees.
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Vignettes of partykit

Name
constparty.Rnw
constparty.Rout.save
ctree.Rnw
ctree.Rout.save
mob.Rnw
party.bib
partykit.Rnw
partykit.Rout.save
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