FusionForests 1.1.1

• The sampler for the centred error mixture is now exact. It samples the uncentred (parameter-expanded) mixture, whose full conditionals are all conjugate, and centres every stored draw afterwards (Yang et al., 2010): the RCT mixture mean is added to m0_sh and the difference of the two source means to d; the atoms are shifted to mean zero. Previously the forests conditioned on the centred atoms while the atoms and weights were updated as in the uncentred model. tau and c are unaffected; m0_sh and d now carry the source-specific error means. Draws are not bit-identical to 1.1.0.
• The baseline forest now fits the current augmented outcome; it used to see the outcome from one sweep earlier.
• dp_mu_g is returned for every mixture error model, and the mixture outputs are documented in full.
• With k_deviation = 0 and a source-specific error model, d equals the constant difference of the source-specific error means.

FusionForests 1.1.0

• New data sets breast_fusion and breast_fusion_truth.
• The k_* arguments now follow the paper’s scaling σ_h = k / (2√J), with defaults k = 1 and k_treat = 0.5. Double old k_* values to reproduce earlier fits.
• The default error_dist is now "source_hdp_scale".
• New error_dist = "source_gaussian": a normal error with a separate scale per source.
• fusion_estimand() now respects treatment_coding and per-source error scales.
• The survival difference for target_source = "rwd" is now causal: it uses tau only and no longer adds the confounding function c.
• Removed SimpleBART(), SimpleBCF(), the decomposition argument and the "RMST" estimand.
• FusionForest() now errors on missing covariate values.
• Documentation in the paper’s notation, code cleanup and more tests.

FusionForests 1.0.1

• Internal cleanup of the C++ layer for CRAN portability: removed unused standard-library includes, routed all console output explicitly through Rcpp::Rcout, and restricted the shared library’s symbol visibility to the registered entry points.

FusionForests 1.0.0

First release of FusionForests.

New model: FusionForest()

FusionForest() is a Bayesian tree ensemble for combining data from a randomised controlled trial (RCT) and real-world data (RWD).

The outcome (continuous, or log survival time) is decomposed over separate tree forests: a control forest, a treatment forest, a deconfounding forest and a deviation forest that captures how the treatment effect in the RWD deviates from the RCT. The observational data are not assumed to be unconfounded.

Continuous, right-censored and interval-censored outcomes are supported via an accelerated failure time formulation, with Gaussian or Dirichlet-process mixture error distributions (error_dist).

Posterior summaries

• fusion_estimand() — posterior draws of causal survival estimands: survival difference and acceleration factor.
• fusion_projection() — interpretable linear projections of the posterior treatment effect surface.
• print() and summary() methods for FusionForest fits.

Additional models

• SimpleBART() — single-forest BART.
• SimpleBCF() — Bayesian causal forest with prognostic and treatment forests.

Re-exports

The single-study causal and survival models from the ShrinkageTrees package (ShrinkageTrees(), HorseTrees(), CausalShrinkageForest(), CausalHorseForest(), SurvivalBART(), SurvivalDART(), SurvivalBCF(), SurvivalShrinkageBCF()) are re-exported, so library(FusionForests) provides every model from the accompanying paper in one namespace.