Package: prepost 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 et al (2025) <doi:10.1017/pan.2025.3>

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

prepost_0.3.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
prepost/json (API)

# Install 'prepost' in R:

install.packages('prepost', repos = c('https://mattblackwell.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mattblackwell/prepost/issues

Pkgdown/docs site:https://mattblackwell.github.io

Datasets:

delponte - Replication dataset from Del Ponte
land_experiment - Replication dataset from Horowitz and Klaus

On CRAN:

4.00 score 2 stars 4 scripts 223 downloads 8 exports 16 dependencies

Last updated from:87f64adb68. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	179
source / vignettes	OK	240
linux-release-x86_64	NOTE	170
macos-release-arm64	NOTE	156
macos-oldrel-arm64	NOTE	190
windows-devel	NOTE	84
windows-release	NOTE	111
windows-oldrel	NOTE	95
wasm-release	OK	162

Exports:post_bounds post_sens pre_bounds pre_sens prepost_bounds prepost_gibbs prepost_gibbs_nocovar prepost_sens

Dependencies:BayesLogit cli crayon glue gtools hms lifecycle lpSolve pkgconfig prettyunits progress R6 Rglpk rlang slam vctrs

Overview

Introduction | Data | Nonparametric bounds | Bayesian parametric model

Rendered fromprepost.Rmdusingknitr::rmarkdown

Last update: 2024-10-03
Started: 2024-10-03

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Replication dataset from Del Ponte (2020)	delponte
Replication dataset from Horowitz and Klaus (2020)	land_experiment
Run post-treatment bounds.	post_bounds
Run sensitivity analysis on post-measurement design	post_sens
Run pre-treatment bounds.	pre_bounds
Run sensitivity analysis on pre-test design	pre_sens
Run Prepost bounds	prepost_bounds
Run Gibbs sampler for the random moderator placement design	prepost_gibbs
Run Gibbs sampler without covariates	prepost_gibbs_nocovar
Run sensitivity analysis for the randomized moderator placement design	prepost_sens