Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

---

Documentation for package ‘stochtree’ version 0.1.1

• DESCRIPTION file.
• Package NEWS.

Help Pages

stochtree-package	stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
bart	Run the BART algorithm for supervised learning.
bcf	Run the Bayesian Causal Forest (BCF) algorithm for regularized causal effect estimation.
calibrateInverseGammaErrorVariance	Calibrate the scale parameter on an inverse gamma prior for the global error variance as in Chipman et al (2022)
computeForestLeafIndices	Compute vector of forest leaf indices
computeForestLeafVariances	Compute vector of forest leaf scale parameters
computeForestMaxLeafIndex	Compute and return the largest possible leaf index computable by 'computeForestLeafIndices' for the forests in a designated forest sample container.
convertPreprocessorToJson	Convert the persistent aspects of a covariate preprocessor to (in-memory) C++ JSON object
CppJson	Class that stores draws from an random ensemble of decision trees
CppRNG	Class that wraps a C++ random number generator (for reproducibility)
createBARTModelFromCombinedJson	Convert a list of (in-memory) JSON representations of a BART model to a single combined BART model object which can be used for prediction, etc...
createBARTModelFromCombinedJsonString	Convert a list of (in-memory) JSON strings that represent BART models to a single combined BART model object which can be used for prediction, etc...
createBARTModelFromJson	Convert an (in-memory) JSON representation of a BART model to a BART model object which can be used for prediction, etc...
createBARTModelFromJsonFile	Convert a JSON file containing sample information on a trained BART model to a BART model object which can be used for prediction, etc...
createBARTModelFromJsonString	Convert a JSON string containing sample information on a trained BART model to a BART model object which can be used for prediction, etc...
createBCFModelFromCombinedJson	Convert a list of (in-memory) JSON strings that represent BCF models to a single combined BCF model object which can be used for prediction, etc...
createBCFModelFromCombinedJsonString	Convert a list of (in-memory) JSON strings that represent BCF models to a single combined BCF model object which can be used for prediction, etc...
createBCFModelFromJson	Convert an (in-memory) JSON representation of a BCF model to a BCF model object which can be used for prediction, etc...
createBCFModelFromJsonFile	Convert a JSON file containing sample information on a trained BCF model to a BCF model object which can be used for prediction, etc...
createBCFModelFromJsonString	Convert a JSON string containing sample information on a trained BCF model to a BCF model object which can be used for prediction, etc...
createCppJson	Create a new (empty) C++ Json object
createCppJsonFile	Create a C++ Json object from a Json file
createCppJsonString	Create a C++ Json object from a Json string
createCppRNG	Create an R class that wraps a C++ random number generator
createForest	Create a forest
createForestDataset	Create a forest dataset object
createForestModel	Create a forest model object
createForestModelConfig	Create a forest model config object
createForestSamples	Create a container of forest samples
createGlobalModelConfig	Create a global model config object
createOutcome	Create an outcome object
createPreprocessorFromJson	Reload a covariate preprocessor object from a JSON string containing a serialized preprocessor
createPreprocessorFromJsonString	Reload a covariate preprocessor object from a JSON string containing a serialized preprocessor
createRandomEffectSamples	Create a 'RandomEffectSamples' object
createRandomEffectsDataset	Create a random effects dataset object
createRandomEffectsModel	Create a 'RandomEffectsModel' object
createRandomEffectsTracker	Create a 'RandomEffectsTracker' object
Forest	Class that stores a single ensemble of decision trees (often treated as the "active forest")
ForestDataset	Dataset used to sample a forest
ForestModel	Class that defines and samples a forest model
ForestModelConfig	Object used to get / set parameters and other model configuration options for a forest model in the "low-level" stochtree interface
ForestSamples	Class that stores draws from an random ensemble of decision trees
getRandomEffectSamples	Generic function for extracting random effect samples from a model object (BCF, BART, etc...)
getRandomEffectSamples.bartmodel	Extract raw sample values for each of the random effect parameter terms.
getRandomEffectSamples.bcfmodel	Extract raw sample values for each of the random effect parameter terms.
GlobalModelConfig	Object used to get / set global parameters and other global model configuration options in the "low-level" stochtree interface
loadForestContainerCombinedJson	Combine multiple JSON model objects containing forests (with the same hierarchy / schema) into a single forest_container
loadForestContainerCombinedJsonString	Combine multiple JSON strings representing model objects containing forests (with the same hierarchy / schema) into a single forest_container
loadForestContainerJson	Load a container of forest samples from json
loadRandomEffectSamplesCombinedJson	Combine multiple JSON model objects containing random effects (with the same hierarchy / schema) into a single container
loadRandomEffectSamplesCombinedJsonString	Combine multiple JSON strings representing model objects containing random effects (with the same hierarchy / schema) into a single container
loadRandomEffectSamplesJson	Load a container of random effect samples from json
loadScalarJson	Load a scalar from json
loadVectorJson	Load a vector from json
Outcome	Outcome / partial residual used to sample an additive model.
predict.bartmodel	Predict from a sampled BART model on new data
predict.bcfmodel	Predict from a sampled BCF model on new data
preprocessPredictionData	Preprocess covariates. DataFrames will be preprocessed based on their column types. Matrices will be passed through assuming all columns are numeric.
preprocessTrainData	Preprocess covariates. DataFrames will be preprocessed based on their column types. Matrices will be passed through assuming all columns are numeric.
RandomEffectSamples	Class that wraps the "persistent" aspects of a C++ random effects model (draws of the parameters and a map from the original label indices to the 0-indexed label numbers used to place group samples in memory (i.e. the first label is stored in column 0 of the sample matrix, the second label is store in column 1 of the sample matrix, etc...))
RandomEffectsDataset	Dataset used to sample a random effects model
RandomEffectsModel	The core "model" class for sampling random effects.
RandomEffectsTracker	Class that defines a "tracker" for random effects models, most notably storing the data indices available in each group for quicker posterior computation and sampling of random effects terms.
resetActiveForest	Reset an active forest, either from a specific forest in a 'ForestContainer' or to an ensemble of single-node (i.e. root) trees
resetForestModel	Re-initialize a forest model (tracking data structures) from a specific forest in a 'ForestContainer'
resetRandomEffectsModel	Reset a 'RandomEffectsModel' object based on the parameters indexed by 'sample_num' in a 'RandomEffectsSamples' object
resetRandomEffectsTracker	Reset a 'RandomEffectsTracker' object based on the parameters indexed by 'sample_num' in a 'RandomEffectsSamples' object
rootResetRandomEffectsModel	Reset a 'RandomEffectsModel' object to its "default" state
rootResetRandomEffectsTracker	Reset a 'RandomEffectsTracker' object to its "default" state
sampleGlobalErrorVarianceOneIteration	Sample one iteration of the (inverse gamma) global variance model
sampleLeafVarianceOneIteration	Sample one iteration of the leaf parameter variance model (only for univariate basis and constant leaf!)
saveBARTModelToJson	Convert the persistent aspects of a BART model to (in-memory) JSON
saveBARTModelToJsonFile	Convert the persistent aspects of a BART model to (in-memory) JSON and save to a file
saveBARTModelToJsonString	Convert the persistent aspects of a BART model to (in-memory) JSON string
saveBCFModelToJson	Convert the persistent aspects of a BCF model to (in-memory) JSON
saveBCFModelToJsonFile	Convert the persistent aspects of a BCF model to (in-memory) JSON and save to a file
saveBCFModelToJsonString	Convert the persistent aspects of a BCF model to (in-memory) JSON string
savePreprocessorToJsonString	Convert the persistent aspects of a covariate preprocessor to (in-memory) JSON string
stochtree	stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference