Propensity Score Predictive Inference for Generalizability and Transportability
Overview
Provides a suite of Propensity Score Predictive Inference (PSPI) methods to generalize treatment effects in trials to target populations. The package includes an existing model Bayesian Causal Forest (BCF) and four PSPI models (BCF-PS, FullBART, SplineBART, DSplineBART). These methods leverage Bayesian Additive Regression Trees (BART) to adjust for high-dimensional covariates and nonlinear associations, while SplineBART and DSplineBART further use propensity score based splines to address covariate shift between trial data and target population.
- Add support for persistent model fitting (
PSPI_fit(),PSPI_predict()). - SplineBART and DSplineBART now resolve the BART/spline non-identifiability by dropping the spline intercept instead of centering BART; the previous centered implementations are removed.
"DSplineBART"is accepted as a model name;"MSplineBART"remains an alias.sim_trans()andsim_generalizability(): participation model of the nonlinear scenario updated.- Title updated to cover transportability.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-05-02
- NOTE11 OK, 3 NOTE2026-04-22
- ERROR10 OK, 3 NOTE, 1 ERROR2026-04-18
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- NOTE8 OK, 6 NOTE2026-04-10
- ERROR7 OK, 6 NOTE, 1 ERROR2026-04-09
- NOTE11 OK, 3 NOTE2026-03-10
Documentation
- Help pages
- 9includes a package overview page
- Examples that run
- 71%5 run in a plain check · 2 never run
- Exported functions with examples
- 86%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 11%
Downloads
Dependencies
Code & Tests
People & History
Package metadata
- First published
- 2025-12-01
- Total releases
- 3 / 1 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 79 KB
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