ShrinkageTrees is an R package for Bayesian tree ensembles in survival analysis and causal inference. The package implements Bayesian additive regression tree models for right- and interval-censored survival outcomes within an accelerated failure time (AFT) framework, with optional decomposition into prognostic and treatment-effect components for causal inference. Two complementary forms of regularisation are available: regularisation of the tree structure, via depth-penalising priors and Dirichlet splitting priors, and regularisation of the step heights, via global-local shrinkage priors. ShrinkageTrees provides the first implementation of the Horseshoe Forest, which places a horseshoe prior on the step heights. These regularisation strategies extend Bayesian tree ensembles to high-dimensional settings. An efficient Rcpp backend, multi-chain MCMC, and S3 methods support the full workflow: fitting, prediction, causal effect estimation, and convergence diagnostics.

翻译：ShrinkageTrees是一个用于生存分析和因果推断的贝叶斯树集成R包。该包在加速失效时间（AFT）框架下实现了针对右删失和区间删失生存结局的贝叶斯加性回归树模型，并可选择分解为预后成分和处理效应成分以用于因果推断。该包提供两种互补的正则化形式：通过深度惩罚先验和狄利克雷分裂先验对树结构进行正则化，以及通过全局-局部收缩先验对步高进行正则化。ShrinkageTrees首次实现了Horseshoe Forest，该森林对步高施加了horseshoe先验。这些正则化策略将贝叶斯树集成扩展至高维场景。高效的Rcpp后端、多链MCMC以及S3方法支持完整的工作流程：拟合、预测、因果效应估计以及收敛诊断。