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

saveBCFModelToJsonFile(): Convert the persistent aspects of a BCF model to (in-memory) JSON and save to a file

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...

convertBCFModelToJson(): Convert the persistent aspects of a BCF model to (in-memory) JSON

createBCFModelFromJson(): Convert an (in-memory) JSON representation of a BCF model to a BCF model object which can be used for prediction, etc...

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

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

loadRandomEffectSamplesJson(): Load a container of random effect samples from json

loadVectorJson(): Load a vector from json

loadScalarJson(): Load a scalar from json

createCppJsonFile(): Create a C++ Json object from a Json file

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.

preprocessTrainDataFrame(): Preprocess a dataframe of covariate values, converting categorical variables to integers and one-hot encoding if need be. Returns a list including a matrix of preprocessed covariate values and associated tracking.

preprocessPredictionDataFrame(): Preprocess a dataframe of covariate values, converting categorical variables to integers and one-hot encoding if need be.

preprocessTrainMatrix(): Preprocess a matrix of covariate values, assuming all columns are numeric. Returns a list including a matrix of preprocessed covariate values and associated tracking.

preprocessPredictionMatrix(): Preprocess a matrix of covariate values, assuming all columns are numeric.

createForestCovariates(): Preprocess a dataframe of covariate values, converting categorical variables to integers and one-hot encoding if need be. Returns a list including a matrix of preprocessed covariate values and associated tracking.

createForestCovariatesFromMetadata(): Preprocess a dataframe of covariate values, converting categorical variables to integers and one-hot encoding if need be. Returns a list including a matrix of preprocessed covariate values and associated tracking.

oneHotEncode(): Convert a vector of unordered categorical data (either numeric or character labels) to a "one-hot" encoded matrix in which a 1 in a column indicates the presence of the relevant category.

oneHotInitializeAndEncode(): Convert a vector of unordered categorical data (either numeric or character labels) to a "one-hot" encoded matrix in which a 1 in a column indicates the presence of the relevant category.

orderedCatPreprocess(): Run some simple preprocessing of ordered categorical variables, converting ordered levels to integers if necessary, and storing the unique levels of a variable.

orderedCatInitializeAndPreprocess(): Run some simple preprocessing of ordered categorical variables, converting ordered levels to integers if necessary, and storing the unique levels of a variable.

Forest

Classes and functions for constructing and persisting forests

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

createForestContainer(): Create a container of forest samples

ForestKernel: Class that provides functionality for statistical kernel definition and computation based on shared leaf membership of observations in a tree ensemble.

createForestKernel(): Create a ForestKernel object

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

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

calibrate_inverse_gamma_error_variance(): Calibrate the scale parameter on an inverse gamma prior for the global error variance as in Chipman et al (2022) 1

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(<bcf>): Extract raw sample values for each of the random effect parameter terms.

sample_sigma2_one_iteration(): Sample one iteration of the (inverse gamma) global variance model

sample_tau_one_iteration(): Sample one iteration of the leaf parameter variance model (only for univariate basis and constant leaf!)

computeForestKernels(): Compute a kernel from a tree ensemble, defined by the fraction of trees of an ensemble in which two observations fall into the same leaf.

computeForestLeafIndices(): Compute and return a vector representation of a forest's leaf predictions for every observation in a dataset. The vector has a "column-major" format that can be easily re-represented as as a CSC sparse matrix: elements are organized so that the first n elements correspond to leaf predictions for all n observations in a dataset for the first tree in an ensemble, the next n elements correspond to predictions for the second tree and so on. The "data" for each element corresponds to a uniquely mapped column index that corresponds to a single leaf of a single tree (i.e. if tree 1 has 3 leaves, its column indices range from 0 to 2, and then tree 2's leaf indices begin at 3, etc...). Users may pass a single dataset (which we refer to here as a "training set") or two datasets (which we refer to as "training and test sets"). This verbiage hints that one potential use-case for a matrix of leaf indices is to define a ensemble-based kernel for kriging.

Package info

High-level package details

stochtree stochtree-package: stochtree: Stochastic tree ensembles (XBART and BART) for supervised learning and causal inference