Non-Parametric Bounds and Gibbs Sampler for Assessing Priming and Post-Treatment Bias [R package prepost version 0.3.0]

prepost: Non-Parametric Bounds and Gibbs Sampler for Assessing Priming and Post-Treatment Bias

A set of tools to implement the non-parametric bounds and Bayesian methods for assessing post-treatment bias developed in Blackwell, Brown, Hill, Imai, and Yamamoto (2025) <doi:10.1017/pan.2025.3>.

Version: 0.3.0

Depends: R (≥ 2.10)

Imports: gtools, BayesLogit, lpSolve, progress, Rglpk

Suggests: testthat (≥ 3.0.0), devtools, knitr, rmarkdown, lmtest, sandwich, dplyr, ggplot2

Published: 2025年07月07日

DOI: 10.32614/CRAN.package.prepost

Author: Matthew Blackwell [aut, cre], Jacob Brown [aut], Sophie Hill [aut], Kosuke Imai [aut], Teppei Yamamoto [aut]

Maintainer: Matthew Blackwell <mblackwell at gmail.com>

License: MIT + file LICENSE

URL: https://github.com/mattblackwell/prepost, https://mattblackwell.github.io/prepost/

NeedsCompilation: no

Materials: README

CRAN checks: prepost results

Documentation:

Reference manual: prepost.html , prepost.pdf

Vignettes: Overview (source, R code)

Downloads:

Package source: prepost_0.3.0.tar.gz

Windows binaries: r-devel: prepost_0.3.0.zip, r-release: prepost_0.3.0.zip, r-oldrel: prepost_0.3.0.zip

macOS binaries: r-release (arm64): prepost_0.3.0.tgz, r-oldrel (arm64): prepost_0.3.0.tgz, r-release (x86_64): prepost_0.3.0.tgz, r-oldrel (x86_64): prepost_0.3.0.tgz

Linking:

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