R: A Laboratory for Recursive Partytioning

# A Laboratory for Recursive Partytioning

## Documentation for package ‘party’ version 1.3-20

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.
- Package NEWS

## Help Pages

BinaryTree-class | Class "BinaryTree"
cforest | Random Forest
cforest_classical | Control for Conditional Tree Forests
cforest_control | Control for Conditional Tree Forests
cforest_unbiased | Control for Conditional Tree Forests
coef.mob | Model-based Recursive Partitioning
conditionalTree | Conditional Inference Trees
ctree | Conditional Inference Trees
ctree_control | Control for Conditional Inference Trees
deviance.mob | Model-based Recursive Partitioning
edge_simple | Panel-Generators for Visualization of Party Trees
ff_trafo | Function for Data Transformations
fit-method | Fit 'StatModel' Objects to Data
fit-methods | Fit 'StatModel' Objects to Data
fitted.mob | Model-based Recursive Partitioning
ForestControl-class | Class "ForestControl"
initialize | Methods for Function initialize in Package 'party'
initialize-method | Methods for Function initialize in Package 'party'
initialize-methods | Methods for Function initialize in Package 'party'
initVariableFrame | Set-up VariableFrame objects
initVariableFrame-method | Set-up VariableFrame objects
initVariableFrame-methods | Set-up VariableFrame objects
LearningSample-class | Class "LearningSample"
logLik.mob | Model-based Recursive Partitioning
mob | Model-based Recursive Partitioning
mob-class | Model-based Recursive Partitioning
mob_control | Control Parameters for Model-based Partitioning
nodes | Class "BinaryTree"
nodes-method | Class "BinaryTree"
nodes-methods | Class "BinaryTree"
node_barplot | Panel-Generators for Visualization of Party Trees
node_bivplot | Panel-Generators for Visualization of Party Trees
node_boxplot | Panel-Generators for Visualization of Party Trees
node_density | Panel-Generators for Visualization of Party Trees
node_hist | Panel-Generators for Visualization of Party Trees
node_inner | Panel-Generators for Visualization of Party Trees
node_scatterplot | Panel-Generators for Visualization of Party Trees
node_surv | Panel-Generators for Visualization of Party Trees
node_terminal | Panel-Generators for Visualization of Party Trees
plot.BinaryTree | Visualization of Binary Regression Trees
plot.mob | Visualization of MOB Trees
predict.mob | Model-based Recursive Partitioning
prettytree | Print a tree.
print.mob | Model-based Recursive Partitioning
proximity | Random Forest
ptrafo | Function for Data Transformations
RandomForest-class | Class "RandomForest"
readingSkills | Reading Skills
residuals.mob | Model-based Recursive Partitioning
response | Class "BinaryTree"
response-method | Class "BinaryTree"
response-methods | Class "BinaryTree"
reweight | Re-fitting Models with New Weights
reweight.glinearModel | Re-fitting Models with New Weights
reweight.linearModel | Re-fitting Models with New Weights
sctest.mob | Model-based Recursive Partitioning
show-method | Class "BinaryTree"
show-method | Class "RandomForest"
SplittingNode-class | Class "SplittingNode"
summary.mob | Model-based Recursive Partitioning
TerminalModelNode-class | Class "SplittingNode"
TerminalNode-class | Class "SplittingNode"
TreeControl | Class "TreeControl"
TreeControl-class | Class "TreeControl"
treeresponse | Class "BinaryTree"
treeresponse-method | Class "BinaryTree"
treeresponse-method | Class "RandomForest"
treeresponse-methods | Class "BinaryTree"
varimp | Variable Importance
varimpAUC | Variable Importance
weights | Class "BinaryTree"
weights-method | Class "BinaryTree"
weights-method | Class "RandomForest"
weights-methods | Class "BinaryTree"
weights.mob | Model-based Recursive Partitioning
where | Class "BinaryTree"
where-method | Class "BinaryTree"
where-method | Class "RandomForest"
where-methods | Class "BinaryTree"