https://github.com/hughjonesd/truelies

Implements Bayesian methods, described in Hugh-Jones (2019), for estimating the proportion of liars in coinflip-style experiments.
https://github.com/hughjonesd/truelies

experiment lying r

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Implements Bayesian methods, described in Hugh-Jones (2019), for estimating the proportion of liars in coinflip-style experiments.

• Host: GitHub
• URL: https://github.com/hughjonesd/truelies
• Owner: hughjonesd
• License: other
• Created: 2019-06-20T07:14:27.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-09-14T21:13:09.000Z (about 3 years ago)
• Last Synced: 2024-10-09T09:39:27.362Z (about 1 month ago)
• Topics: experiment, lying, r
• Language: R
• Size: 80.1 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# truelies

 [![CRAN status](https://www.r-pkg.org/badges/version/truelies)](https://cran.r-project.org/package=truelies)

[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/hughjonesd/truelies?branch=master&svg=true)](https://ci.appveyor.com/project/hughjonesd/truelies)

`truelies` implements Bayesian methods, described in 

[Hugh-Jones (2019)](https://link.springer.com/article/10.1007/s40881-019-00069-x), 

for estimating the proportion of liars in coinflip-style experiments, where

subjects report a random outcome and are paid for reporting a "good" outcome.

For R source for the original paper, see https://github.com/hughjonesd/GSV-comment.

## Installation

``` r

# stable version on CRAN

install.packages("truelies")

# latest version from github

remotes::install_github("hughjonesd/truelies")

```

## Example

If you have 33 out of 50 reports of heads in a coin flip experiment:

```{r example}

library(truelies)

d1 <- update_prior(heads = 33, N = 50, P = 0.5, prior = dunif)

plot(d1)

dist_mean(d1)

# 95% confidence interval, using hdrcde

dist_hdr(d1, 0.95)

```

## Citation

`r format(citation("truelies"), style = "text")`

## Bibtex 

```{r, echo = FALSE, comment = NA}

cit <- citation("truelies")

cit$key <- "hughjones2019"

print(cit, style = "Bibtex")

```