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Rcpp::Rcout, and restricted the shared library’s
symbol visibility to the registered entry points.First release of FusionForests.
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).
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.SimpleBART() — single-forest BART.SimpleBCF() — Bayesian causal forest with prognostic
and treatment forests.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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.