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

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Documentation for package ‘stochtree’ version 0.1.1

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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