Package index • stochtree

Supervised learning

High-level functionality for training supervised Bayesian tree ensembles (BART, XBART)

bart(): Run the BART algorithm for supervised learning.

predict(<bartmodel>): Predict from a sampled BART model on new data

Causal inference

High-level functionality for estimating causal effects using Bayesian tree ensembles (BCF, XBCF)

bcf(): Run the Bayesian Causal Forest (BCF) algorithm for regularized causal effect estimation.

predict(<bcfmodel>): Predict from a sampled BCF model on new data

Low-level functionality

Serialization

Classes and functions for converting sampling artifacts to JSON and saving to disk

CppJson: Class that stores draws from an random ensemble of decision trees

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

loadForestContainerJson(): Load a container of forest samples from json

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

loadVectorJson(): Load a vector from json

loadScalarJson(): Load a scalar from json

loadRandomEffectSamplesJson(): Load a container of random effect 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

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

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...

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...

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

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...

createBCFModelFromJson(): Convert an (in-memory) JSON representation of a BCF model to a BCF 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...

Data

Classes and functions for preparing data for sampling algorithms

ForestDataset: Dataset used to sample a forest

createForestDataset(): Create a forest dataset object

Outcome: Outcome / partial residual used to sample an additive model.

createOutcome(): Create an outcome object

RandomEffectsDataset: Dataset used to sample a random effects model

createRandomEffectsDataset(): Create a random effects dataset object

preprocessTrainData(): Preprocess covariates. DataFrames will be preprocessed based on their column types. Matrices will be passed through assuming all columns are numeric.

preprocessPredictionData(): Preprocess covariates. DataFrames will be preprocessed based on their column types. Matrices will be passed through assuming all columns are numeric.

convertPreprocessorToJson(): Convert the persistent aspects of a covariate preprocessor to (in-memory) C++ JSON object

savePreprocessorToJsonString(): Convert the persistent aspects of a covariate preprocessor to (in-memory) JSON string

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

Forest

Classes and functions for constructing and persisting forests

Forest: Class that stores a single ensemble of decision trees (often treated as the "active forest")

createForest(): Create a forest

ForestModel: Class that defines and samples a forest model

createForestModel(): Create a forest model object

ForestSamples: Class that stores draws from an random ensemble of decision trees

createForestSamples(): Create a container of forest samples

ForestModelConfig: Object used to get / set parameters and other model configuration options for a forest model in the "low-level" stochtree interface

createForestModelConfig(): Create a forest model config object

GlobalModelConfig: Object used to get / set global parameters and other global model configuration options in the "low-level" stochtree interface

createGlobalModelConfig(): Create a global model config object

CppRNG: Class that wraps a C++ random number generator (for reproducibility)

createCppRNG(): Create an R class that wraps a C++ random number generator

calibrateInverseGammaErrorVariance(): Calibrate the scale parameter on an inverse gamma prior for the global error variance as in Chipman et al (2022)

computeForestMaxLeafIndex(): Compute and return the largest possible leaf index computable by computeForestLeafIndices for the forests in a designated forest sample container.

computeForestLeafIndices(): Compute vector of forest leaf indices

computeForestLeafVariances(): Compute vector of forest leaf scale parameters

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

Random Effects

Classes and functions for constructing and persisting random effects terms

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...))

createRandomEffectSamples(): Create a RandomEffectSamples object

RandomEffectsModel: The core "model" class for sampling random effects.

createRandomEffectsModel(): Create a RandomEffectsModel object

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.

createRandomEffectsTracker(): Create a RandomEffectsTracker object

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.

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!)

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

Package info

High-level package details

stochtree stochtree-package: stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference