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
compute_bart_posterior_interval(): Compute posterior credible intervals for specified terms from a fitted BART model.
compute_contrast_bart_model(): Compute a contrast between two outcome prediction specifications for a BART model
sample_bart_posterior_predictive(): Sample from the posterior predictive distribution for outcomes modeled by BART

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
compute_bcf_posterior_interval(): Compute posterior credible intervals for BCF model terms
compute_contrast_bcf_model(): Compute a contrast between two outcome prediction specifications for a BCF model
sample_bcf_posterior_predictive(): Sample from the posterior predictive distribution for outcomes modeled by BCF

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 for use in a ForestDataset at train time.
preprocessPredictionData(): Preprocess covariates for use in a ForestDataset at prediction time.
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.
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

Utilities

Miscellaneous “utility” classes and functions

sample_without_replacement(): Draw sample_size samples from population_vector without replacement, weighted by sampling_probabilities
expand_dims_1d(): Convert scalar input to vector of dimension output_size, or check that input array is equivalent to a vector of dimension output_size.
expand_dims_2d(): Ensures that input is propagated appropriately to a matrix of dimension output_rows x output_cols.
expand_dims_2d_diag(): Convert scalar input to square matrix of dimension output_size x output_size with input along the diagonal, or check that input array is equivalent to a square matrix of dimension output_size x output_size.

Package info

High-level package details

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