Description Usage Testing the package Author(s) References See Also

Implements Bayesian methods, described in Hugh-Jones (2019) <doi:10.1007/s40881-019-00069-x>, for estimating the proportion of liars in coin flip-style experiments, where subjects report a random outcome and are paid for reporting a "good" outcome.

To estimate the proportion of liars in an experiment, use `update_prior()`

followed by `dist_mean()`

:

1 2 | ```
posterior <- update_prior(heads = 33, N = 50, P = 0.5, prior = dunif)
dist_mean(posterior)
``` |

To get confidence intervals for an estimate, use `dist_hdr()`

:

1 | ```
dist_hdr(posterior, conf_level = 0.95)
``` |

To test whether two different samples have the same proportion of
liars, use `difference_dist()`

followed by `dist_hdr()`

:

1 2 3 | ```
p2 <- update_prior(heads = 42, N = 49, P = 0.5, prior = dunif)
dd <- difference_dist(posterior, p2)
dist_hdr(dd, 0.95, bounds = c(-1, 1))
``` |

To test power for detecting a given proportion of liars, use `power_calc()`

:

1 | ```
power_calc(N = 100, P = 0.5, lambda = 0.2)
``` |

To test power for detecting differences between groups, use `power_calc_difference()`

:

1 | ```
power_calc_difference(N1 = 100, P = 5/6, lambda1 = 0.1, lambda2 = 0.25)
``` |

To compare different samples by empirical Bayes estimation, use
`empirical_bayes()`

:

1 2 3 | ```
heads <- c(Baseline = 30, Treatment1 = 38, Treatment2 = 45)
N <- c(50, 52, 57)
result <- empirical_bayes(heads, N, P = 0.5)
``` |

To run tests on the package:

1 | ```
source(system.file("test-statistics.R", package = "truelies"))
``` |

You will need dplyr, purrr, tidyr and ggplot2 installed.

This will take some time and will produce data frames of test results for different parameter values, along with some plots.

David Hugh-Jones

Hugh-Jones, David (2019). True Lies: Comment on Garbarino, Slonim and Villeval (2018). Journal of the Economic Science Association. https://link.springer.com/article/10.1007/s40881-019-00069-x.

Useful links:

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