Computes density of a prior distribution across a range of values.

```
# S3 method for prior
density(
x,
x_seq = NULL,
x_range = NULL,
x_range_quant = NULL,
n_points = 1000,
n_samples = 10000,
force_samples = FALSE,
individual = FALSE,
transformation = NULL,
transformation_arguments = NULL,
transformation_settings = FALSE,
truncate_end = TRUE,
...
)
```

- x
a prior

- x_seq
sequence of x coordinates

- x_range
vector of length two with lower and upper range for the support (used if

`x_seq`

is unspecified)- x_range_quant
quantile used for automatically obtaining

`x_range`

if both`x_range`

and`x_seq`

are unspecified. Defaults to`0.005`

for all but Cauchy, Student-t, Gamma, and Inverse-gamme distributions that use`0.010`

.- n_points
number of equally spaced points in the

`x_range`

if`x_seq`

is unspecified- n_samples
number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with

`force_samples = TRUE`

)- force_samples
should prior be sampled instead of obtaining analytic solution whenever possible

- individual
should individual densities be returned (e.g., in case of weightfunction)

- transformation
transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

- lin
linear transformation in form of

`a + b*x`

- tanh
also known as Fisher's z transformation

- exp
exponential transformation

, or a list containing the transformation function

`fun`

, inverse transformation function`inv`

, and the Jacobian of the transformation`jac`

. See examples for details.- transformation_arguments
a list with named arguments for the

`transformation`

- transformation_settings
boolean indicating whether the settings the

`x_seq`

or`x_range`

was specified on the transformed support- truncate_end
whether the density should be set to zero in for the endpoints of truncated distributions

- ...
additional arguments

`density.prior`

returns an object of class 'density'.