A Toolkit for Recursive Partytioning
Apply Functions Over Nodes
Conditional Random Forests
Control for Conditional Inference Trees
Conditional Inference Trees
Data Preprocessing for Extensible Trees.
Fit Extensible Trees.
Generalized Linear Model Trees
Linear Model Trees
Control Parameters for Model-Based Partitioning
Model-based Recursive Partitioning
Model Frame Method for rpart
Extract Node Identifiers
Panel-Generators for Visualization of Party Trees
Coercion Functions
Methods for Party Objects
Visualization of Trees
Tree Predictions
Recursive Partytioning
Methods for Node Objects
Inner and Terminal Nodes
Binary and Multiway Splits
Post-Prune modelparty Objects
Variable Importance
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) <https://jmlr.org/papers/v16/hothorn15a.html>.