`prior`

creates a prior distribution.
The prior can be visualized by the `plot`

function.

```
prior(
distribution,
parameters,
truncation = list(lower = -Inf, upper = Inf),
prior_weights = 1
)
prior_none(prior_weights = 1)
```

- 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.

`prior`

and `prior_none`

return an object of class 'prior'.
A named list containing the distribution name, parameters, and prior weights.

```
# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))
# create a half-normal standard normal prior distribution
p2 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1),
truncation = list(lower = 0, upper = Inf))
# the prior distribution can be visualized using the plot function
# (see ?plot.prior for all options)
plot(p1)
```