R: Isolation-Based Outlier Detection

# Isolation-Based Outlier Detection

## Documentation for package ‘isotree’ version 0.6.1-5

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

isolation.forest | Create Isolation Forest Model
isotree.add.tree | Add additional (single) tree to isolation forest model
isotree.append.trees | Append isolation trees from one model into another
isotree.build.indexer | Build Indexer for Faster Terminal Node Predictions and/or Distance Calculations
isotree.deep.copy | Deep-Copy an Isolation Forest Model Object
isotree.drop.imputer | Drop Imputer Sub-Object from Isolation Forest Model Object
isotree.drop.indexer | Drop Indexer Sub-Object from Isolation Forest Model Object
isotree.drop.reference.points | Drop Reference Points from Isolation Forest Model Object
isotree.export.model | Export Isolation Forest model
isotree.get.num.nodes | Get Number of Nodes per Tree
isotree.import.model | Load an Isolation Forest model exported from Python
isotree.is.same | Check if two Isolation Forest Models Share the Same C++ Object
isotree.plot.tree | Plot Tree from Isolation Forest Model
isotree.restore.handle | Unpack isolation forest model after de-serializing
isotree.set.nthreads | Set Number of Threads for Isolation Forest Model Object
isotree.set.reference.points | Set Reference Points to Calculate Distances or Kernels With
isotree.subset.trees | Subset trees of a given model
isotree.to.graphviz | Generate GraphViz Dot Representation of Tree
isotree.to.json | Generate JSON representations of model trees
isotree.to.sql | Generate SQL statements from Isolation Forest model
length.isolation_forest | Get Number of Trees in Model
predict.isolation_forest | Predict method for Isolation Forest
print.isolation_forest | Print summary information from Isolation Forest model
summary.isolation_forest | Print summary information from Isolation Forest model
variable.names.isolation_forest | Get Variable Names for Isolation Forest Model
[.isolation_forest | Subset trees of a given model