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Semiparametric Principal Stratification Analysis Beyond Monotonicity

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1.8Kdownloads / year
–test coverage
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Overview

About

Estimates principal causal effects under principal stratification using a margin-free, conditional odds ratio sensitivity parameter. This framework unifies the monotonicity assumption and the counterfactual intermediate independence assumption, allowing for robust analysis when monotonicity may not hold. Computes point estimates, standard errors, and confidence intervals for conditionally doubly robust and debiased machine learning estimators. The methodological details are described in Tong, Kahan, Harhay, and Li (2025) doi:10.48550/arXiv.2501.17514.

Changes in 0.1.0

CRAN resubmission

  • Revised the Description field in DESCRIPTION to cite the methodological reference in CRAN-recommended form with authors, year, and DOI.
  • Removed unnecessary \dontrun{} and \donttest{} wrappers where feasible.
  • Reduced example sizes so package examples run during checking.
  • Refined documentation and examples in response to CRAN reviewer comments.

From NEWS.md in the release

Install

Health

CRAN checks
13OK
Slowest check: 3.0 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-09-08
  • ERROR12 OK, 1 ERROR2026-09-07
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
Show 2 earlier changesHide 2 earlier changes
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK5 OK2026-04-25
Advisories

none known

Documentation

In release 0.1.0
READMEYes · 399 wordsVignettesNoNEWSYes · 67% structured · mentions 0.1.0Citation fileNoWebsitenot declared in DESCRIPTION
Help pages
2 (1 internal)
Examples that run
100%1 run in a plain check
Exported functions with examples
50%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
0%

Downloads

1.8K
CRAN downloads in the past year
Rank #17,548 · ~5/day · ~153/mo
Daily download trend is not available in this view yet.
25530 days
73090 days
1.8K1 year
Compare downloads with other packages →
Also on32 r2u2 autocran

Repository

0Stars
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0Open issues
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0Releases
causal-inferencedebiased-machine-learningdoubly-robust-estimationmanuscript-codeprincipal-stratificationrr-packageresearch-software
Last activity 2026-09-30
Responsiveness
not measured
PracticesBeta

5 development-tooling and community-health practices detected across 4 families in the upstream repository

Checks run against github.com/deckardt98/psor on 2026-10-21.

Continuous integration (1)
GitHub Actions
Docs source (1)
README.Rmd
CRAN release process (2)
cran-comments.mdCRAN-SUBMISSION
Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)

Installing PSor adds 81 packages (45 with compiled code, 65 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (0)
none
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (4)
Author, Maintainer since 0.1.0 (2026)
Contributor
Contributor
Releases (1)
  • 0.1.0Latest
    2026-04-24 · current release
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2026-04-24
Total releases
1 / 1 yrs
License
MIT + file LICENSE OSI
Download size
16 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("PSor")
Tong, J., Harhay, M. O., Kahan, B., & Li, F. (2026). PSor: Semiparametric Principal Stratification Analysis Beyond Monotonicity (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.PSor

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 PSor version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-23. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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