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Propensity Score Predictive Inference for Generalizability and Transportability

0packages depend
2.3Kdownloads / year
–test coverage
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Overview

About

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.

Changes in 1.4
  • 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() and sim_generalizability(): participation model of the nonlinear scenario updated.
  • Title updated to cover transportability.

From NEWS.md in the release

Install

Health

CRAN checks
13OK
Slowest check: 4.4 min · r-devel-windows-x86_64
Status changes
  • 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
Show 3 earlier changesHide 3 earlier changes
  • NOTE8 OK, 6 NOTE2026-04-10
  • ERROR7 OK, 6 NOTE, 1 ERROR2026-04-09
  • NOTE11 OK, 3 NOTE2026-03-10
Advisories

none known

Documentation

In release 1.4
READMEYes · 316 wordsVignettesNoNEWSYes · 100% structured · mentions 1.4Citation fileNoWebsitenot declared in DESCRIPTION
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

2.3K
CRAN downloads in the past year
Rank #14,794 · ~6/day · ~188/mo
Daily download trend is not available in this view yet.
37430 days
80590 days
2.3K1 year
Compare downloads with other packages →
Also on289 r2u4 autocran

Dependencies

Declared dependencies
14 external dependencies (excludes base and recommended)

Installing PSPI adds 33 packages (20 with compiled code, 30 MB of source tarballs). Heaviest: stringi 11 MB, lme4 4.1 MB, nloptr 2.1 MB.

Depends (1)
R >= 4.1.0
Imports (8)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (10)
Author, Maintainer since 1.1 (2025)
Contributor
Contributor
Contributor
Releases (3)

Pick two releases to compare their code metrics.

  • 1.4Latest
    record
    2026-10-01 · current release · diff ↗ ·
    removed 1 export
    sim_data
    added 6 exports
    PSPI_fit, PSPI_predict, in_trial, invgumbel, sim_generalizability, sim_trans
  • R
    R 4.6.0 released · 2026-04-24
  • 1.2
    record
    2025-12-02 · diff ↗
  • 1.1
    record
    2025-12-01
  • R
    R 4.5.0 released · 2025-04-11

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

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("PSPI")
Zou, J., Chen, Q., Gramacy, R., Little, R., McCulloch, R., Moise, N., Roy, J., Schwartz, J., Spanbauer, C., & Sparapani, R. (2026). PSPI: Propensity Score Predictive Inference for Generalizability and Transportability (Version 1.4) [Computer software]. https://doi.org/10.32614/CRAN.package.PSPI

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for PSPI version 1.4 [Data set]. HJJB, LLC. Data release v2026-10-20. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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