`prior`

creates a prior distribution for fitting a PET or
PEESE style models in RoBMA. The prior distribution can be visualized
by the `plot`

function.

```
prior_PET(
distribution,
parameters,
truncation = list(lower = 0, upper = Inf),
prior_weights = 1
)
prior_PEESE(
distribution,
parameters,
truncation = list(lower = 0, upper = Inf),
prior_weights = 1
)
```

## Arguments

- distribution
name of the prior distribution. The
possible options are

`"point"`

for a point density characterized by a
`location`

parameter.

`"normal"`

for a normal distribution characterized
by a `mean`

and `sd`

parameters.

`"lognormal"`

for a lognormal distribution characterized
by a `meanlog`

and `sdlog`

parameters.

`"cauchy"`

for a Cauchy distribution characterized
by a `location`

and `scale`

parameters. Internally
converted into a generalized t-distribution with `df = 1`

.

`"t"`

for a generalized t-distribution characterized
by a `location`

, `scale`

, and `df`

parameters.

`"gamma"`

for a gamma distribution characterized
by either `shape`

and `rate`

, or `shape`

and
`scale`

parameters. The later is internally converted to
the `shape`

and `rate`

parametrization

`"invgamma"`

for an inverse-gamma distribution
characterized by a `shape`

and `scale`

parameters. The
JAGS part uses a 1/gamma distribution with a shape and rate
parameter.

`"beta"`

for a beta distribution
characterized by an `alpha`

and `beta`

parameters.

`"exp"`

for an exponential distribution
characterized by either `rate`

or `scale`

parameter. The later is internally converted to
`rate`

.

`"uniform"`

for a uniform distribution defined on a
range from `a`

to `b`

- parameters
list of appropriate parameters for a given
`distribution`

.

- truncation
list with two elements, `lower`

and
`upper`

, that define the lower and upper truncation of the
distribution. Defaults to `list(lower = -Inf, upper = Inf)`

.
The truncation is automatically set to the bounds of the support.

- prior_weights
prior odds associated with a given distribution.
The value is passed into the model fitting function, which creates models
corresponding to all combinations of prior distributions for each of
the model parameters and sets the model priors odds to the product
of its prior distributions.

## Value

`prior_PET`

and `prior_PEESE`

return an object of class 'prior'.

## Examples

```
# create a half-Cauchy prior distribution
# (PET and PEESE specific functions automatically set lower truncation at 0)
p1 <- prior_PET(distribution = "Cauchy", parameters = list(location = 0, scale = 1))
plot(p1)
```