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prepost

R package for analyzing interactions in survey experiments with priming or post-treatment bias

https://github.com/mattblackwell/prepost

Science Score: 49.0%

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    Links to: arxiv.org
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Last synced: 6 months ago · JSON representation

Repository

R package for analyzing interactions in survey experiments with priming or post-treatment bias

Basic Info
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 2 years ago · Last pushed 8 months ago
Metadata Files
Readme License

README.Rmd 

---
output: github_document
---

# prepost


  [![R-CMD-check](https://github.com/mattblackwell/prepost/workflows/R-CMD-check/badge.svg)](https://github.com/mattblackwell/prepost/actions)



## Overview

This package contains code and sample data to implement the non-parametric bounds and Bayesian methods for assessing priming and post-treatment bias in experimental studies under various assumptions.  


To get started, please see the article that developed these methods:

* Blackwell, Matthew et al (2025). Priming Bias Versus Post-Treatment Bias in Experimental Designs. *Political Analysis* Published online 2025:1-17. doi:10.1017/pan.2025.3. ([journal version](http://doi.org/10.1017/pan.2025.3), [arXiv preprint](https://arxiv.org/pdf/2306.01211))


## Installation

```{r install, eval = FALSE}
## Install developer version
## install.packages("devtools")
devtools::install_github("mattblackwell/prepost", build_vignettes = TRUE)
```


## Usage

Both the nonparametric and Bayesian estimators all have prefixes that indicate what type of experimental design being used.  

- `pre_` functions can analyze data from a **pre-test design** where the moderator is measured pre-treatment. 
- `post_` functions can analyze data from a **post-test design** where the moderator is measured post-treatment.
- `prepost_` functions can analyze data from a **random placement design**, in which the moderator is randomly assigned to be measured before or after treatment.

Most functions can be specified with a formula to identify the outcome and treatment and another one-sided formula for the moderator: 

```{r}
library(prepost)
data(delponte)
out <- pre_bounds(
  formula = angry_bin ~ t_commonality,
   data = delponte,
  moderator = ~ itaid_bin
)
out
```

Owner

  • Name: Matthew Blackwell
  • Login: mattblackwell
  • Kind: user
  • Location: Cambridge, MA
  • Company: Harvard University

Associate Professor of Government, Harvard University

GitHub Events

Total
  • Push event: 9
Last Year
  • Push event: 9

Packages

  • Total packages: 1
  • Total downloads:
    • cran 191 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: prepost

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

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 191 Last month
Rankings
Dependent packages count: 26.1%
Dependent repos count: 32.1%
Average: 48.1%
Downloads: 86.2%
Maintainers (1)
mblackwell@gmail.com
Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v4 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/pkgdown.yaml actions
  • JamesIves/github-pages-deploy-action v4.5.0 composite
  • actions/checkout v4 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION cran
  • R >= 2.10 depends
  • BayesLogit * imports
  • gtools * imports
  • lpSolve * imports
  • progress * imports
  • devtools * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • testthat >= 3.0.0 suggests