An `R`

package for Bayesian meta-analysis that accounts for publication bias or *p*-hacking.

publipha is an package for doing Bayesian meta-analysis that accounts for publication bias or *p*-hacking. Its main functions are:

`psma`

does random effects meta-analysis under publication bias with a one-sided*p*-value based*selection probability*. The model is roughly the same as that of (Hedges, 1992)`phma`

does random effects meta-analysis under a certain model of*p*-hacking with a one-sided*p*-value based propensity to*p*-hack. This is based on the forthcoming paper of by Moss and De Bin (2019).`cma`

does classical random effects meta-analysis with the same priors as`psma`

and`cma`

.

Use the following command from inside `R`

:

Call the `library`

function and use it like a barebones `metafor::rma`

. The `alpha`

tells `psma`

or `phma`

where they should place the cutoffs for significance.

```
library("publipha")
# Publication bias model
set.seed(313) # For reproducibility
model_psma = publipha::psma(yi = yi,
vi = vi,
alpha = c(0, 0.025, 0.05, 1),
data = metafor::dat.bangertdrowns2004)
# p-hacking model
set.seed(313)
model_phma = publipha::phma(yi = yi,
vi = vi,
alpha = c(0, 0.025, 0.05, 1),
data = metafor::dat.bangertdrowns2004)
# Classical model
set.seed(313)
model_cma = publipha::cma(yi = yi,
vi = vi,
alpha = c(0, 0.025, 0.05, 1),
data = metafor::dat.bangertdrowns2004)
```

You can calculate the posterior means of the meta-analytic mean with `extract_theta0`

:

If you wish to plot a histogram of the posterior distribution of `tau`

, the standard deviation of the effect size distribution, you can do it like this:

- Hedges, Larry V. “Modeling publication selection effects in meta-analysis.” Statistical Science (1992): 246-255.
- Moss, Jonas and De Bin, Riccardo. “Modelling publication bias and p-hacking” (2019)

If you encounter a bug, have a feature request or need some help, open a Github issue. Create a pull requests to contribute. This project follows a Contributor Code of Conduct.