Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
Create CppJson Object from File
Create CppJson Object from String
Create CppRNG Object
Create Forest Object
Reload Covariate Preprocessor from JSON String
Extract Parameter Samples Generic Function
Forest C++ Wrapper
Forest Dataset C++ Wrapper
Forest Model C++ Wrapper
Forest Model Configuration Object
Forest Container C++ Wrapper
Extract Random Effects Samples
Extract Random Effect Samples from BCF Model
Extract Random Effect Samples Generic Function
Load Vector from JSON
Outcome Data C++ Wrapper
Plot BART Model Fit.
Plot BCF Model
Convert BART Model to JSON
Save BART Model to JSON File
Convert BART Model to JSON String
Convert BCF Model to JSON
Compute Contrast for BCF Model
Query Forest Leaf Indices
Reload Covariate Preprocessor from JSON String
Run BART for Supervised Learning
Run BCF for Causal Effect Estimation
Calibrate Inverse Gamma Prior
Compute BART Posterior Credible Intervals
Compute BCF Posterior Credible Intervals
Compute Contrast for BART Model
Query Forest Leaf Scale Parameters
Query Forest Max Leaf Index
Convert Covariate Preprocessor to CppJson
JSON C++ Object Wrapper
Random Number Generator C++ Wrapper
Convert JSON List to Single BART Model
Convert JSON String List to Single BART Model
Convert JSON to BART Model
Convert JSON File to BART Model
Convert JSON String to BART Model
Convert JSON List to BCF Model
Convert JSON String List to BCF Model
Convert JSON to BCF Model
Convert JSON File to BCF Model
Convert JSON String to BCF Model
Create CppJson Object
Create ForestDataset Object
Create ForestModel Object
Create ForestModelConfig Object
Create ForestSamples Object
Create GlobalModelConfig Object
Create Outcome Object
Create RandomEffectSamples Object
Create RandomEffectsDataset Object
Create RandomEffectsModel Object
Create RandomEffectsTracker Object
Extract BART Parameter Samples.
Extract BCF Parameter Samples
Global Model Configuration Object
Combine JSON Model Objects into ForestSamples
Combine JSON Strings into ForestSamples
Load Forest Samples from JSON
Combine JSON Model Objects into RandomEffectSamples
Combine JSON Strings into RandomEffectSamples
Load Random Effect Samples from JSON
Load Scalar from JSON
Predict From a BART Model
Predict From BCF Model
Preprocess Covariates for Model Prediction
Preprocess Covariates for Model Training
Summarize a BART Model
Print Summary of BCF Model
Random Effect Container C++ Wrapper
Random Effects Dataset C++ Wrapper
Random Effects Model C++ Wrapper
Random Effects Tracker C++ Wrapper
Reset Active Forest
Reset Forest Model
Reset RandomEffectsModel Object
Reset RandomEffectsTracker Object
Reset RandomEffectsModel Object to Default State
Reset RandomEffectsTracker Object to Default State
Sample BART Posterior Predictive
Sample BCF Posterior Predictive
Sample Without Replacement
Sample Global Error Variance
Sample Leaf Scale
Save BCF Model to JSON File
Convert BCF Model to JSON String
Convert Covariate Preprocessor to JSON String
stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised L...
Summarize the BART model fit and sampled terms.
Summarize BCF Model
Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.
Useful links