GlobalModelConfig: Global Model Configuration Object

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

Global Model Configuration Object

Description

Object used to get / set global parameters and other global model configuration options in the "low-level" stochtree interface. The "low-level" stochtree interface enables a high degreee of sampler customization, in which users employ R wrappers around C++ objects like ForestDataset, Outcome, CppRng, and ForestModel to run the Gibbs sampler of a BART model with custom modifications. GlobalModelConfig allows users to specify / query the global parameters of a model they wish to run.

This class is intended for advanced use cases in which users require detailed control of sampling algorithms and data structures. Minimal input validation and error checks are performed – users are responsible for providing the correct inputs. For tutorials on the "proper" usage of the stochtree's advanced workflow, we provide several vignettes at https://stochtree.ai/

Value

Global error variance parameter

Public fields

global_error_variance

Global error variance parameter Create a new GlobalModelConfig object.

Methods

Public methods

• GlobalModelConfig$new()

• GlobalModelConfig$update_global_error_variance()

• GlobalModelConfig$get_global_error_variance()

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Method new()

Usage

GlobalModelConfig$new(global_error_variance = 1)

Arguments

global_error_variance

Global error variance parameter (default: 1.0)

Returns

A new GlobalModelConfig object.

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Method update_global_error_variance()

Update global error variance parameter

Usage

GlobalModelConfig$update_global_error_variance(global_error_variance)

Arguments

global_error_variance

Global error variance parameter

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Method get_global_error_variance()

Query global error variance parameter for this GlobalModelConfig object

Usage

GlobalModelConfig$get_global_error_variance()

stochtree documentation built on April 29, 2026, 1:08 a.m.