CppJson                 Class that stores draws from an random ensemble
                        of decision trees
CppRNG                  Class that wraps a C++ random number generator
                        (for reproducibility)
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
GlobalModelConfig       Object used to get / set global parameters and
                        other global model configuration options in the
                        "low-level" stochtree interface
Outcome                 Outcome / partial residual used to sample an
                        additive model.
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.
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
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
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.
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
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.
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-package       stochtree: Stochastic Tree Ensembles (XBART and
                        BART) for Supervised Learning and Causal
                        Inference
