Function reference • moreparty

Parallelization
`fastcforest()`	Parallelized conditional inference random forest
`fastvarImp()`	Variable importance for conditional inference random forests
`fastvarImpAUC()`	Variable importance (with AUC performance measure) for conditional inference random forests
Interpretation
`GetPartialData()`	Partial dependence for a conditional random forest.
`GetAleData()`	Accumulated Local Effects for a conditional random forest.
`GetInteractionStrength()`	Strength of interactions
`Outliers()`	Computes outliers
`Prototypes()`	Prototypes of groups
`SurrogateTree()`	Surrogate tree for conditional inference random forests
`ggForestEffects()`	Dot plot of covariates effects
`ggVarImp()`	Dot plot of variable importance
Trees
`EasyTreeVarImp()`	Variable importance for conditional inference trees.
`GetSplitStats()`	Permutation tests results for each split in a conditional tree.
`NiceTreePlot()`	Plots conditional inference trees.
`NodeTreePlot()`	Plots the results of each node of a conditional inference tree
`NodesInfo()`	Informations about terminal nodes
`TreeStab()`	Stability assessment of conditional inference trees
`ctreeUI()` `ctreeServer()`	Shiny module to build and analyse conditional inference trees
`ictree()`	An interactive app for conditional inference trees
Miscellaneous
`GetCtree()`	Gets a tree from a conditional random forest
`FeatureSelection()`	Feature selection for conditional random forests.
`BivariateAssoc()`	Bivariate association measures for supervised learning tasks.
`PerfsRegression()`	Performance measures for regressions
`PerfsBinClassif()`	Performance measures for binary classification tasks
Data set
`titanic`	Titanic dataset

Reference

Parallelization

Interpretation

Trees

Miscellaneous

Data set