## brr package for R

**Bayesian inference on the ratio of two Poisson
rates.**

### What does it do ?

Suppose you have two counts of events and, assuming each count
follows a Poisson distribution with an unknown incidence rate, you are
interested in the ratio of the two rates (or *relative risk*).
The `brr`

package allows to perform the Bayesian analysis of
the relative risk using the natural semi-conjugate family of prior
distributions, with a default non-informative prior (see
references).

### Install

You can install:

- the latest released version from CRAN with

- the latest development version from
`github`

using the
`devtools`

package:

`devtools::install_github('stla/brr', build_vignettes=TRUE)`

### Basic usage

Create a `brr`

object with the `Brr`

function
to set the prior parameters `a`

, `b`

,
`c`

, `d`

, the two Poisson counts `x`

and `y`

and the samples sizes (times at risk) `S`

and `T`

in the two groups. Simply do not set the prior
parameters to use the non-informative prior:

`model <- Brr(x=2, S=17877, y=9, T=16674)`

Plot the posterior distribution of the rate ratio
`phi`

:

Get credibility intervals about `phi`

:

Get the posterior probability that `phi>1`

:

`ppost(model, "phi", 1, lower.tail=FALSE)`

Update the `brr`

object to include new sample sizes and
get a summary of the posterior predictive distribution of
`x`

:

```
model <- model(Snew=10000, Tnew=10000)
spost(model, "x", output="pandoc")
```

### To learn more

Look at the vignettes:

`browseVignettes(package = "brr")`

### Find a bug ? Suggestion
for improvment ?

Please report at https://github.com/stla/brr/issues

### References

S. Laurent, C. Legrand: *A Bayesian framework for the ratio of two
Poisson rates in the context of vaccine efficacy trials.* ESAIM,
Probability & Statistics 16 (2012), 375–398.

S. Laurent: *Some Poisson mixtures distributions with a hyperscale
parameter.* Brazilian Journal of Probability and Statistics 26
(2012), 265–278.

S. Laurent: *Intrinsic Bayesian inference on a Poisson rate and on
the ratio of two Poisson rates.* Journal of Statistical Planning and
Inference 142 (2012), 2656–2671.