`prior_factor`

creates a prior distribution for fitting
models with factor predictors. (Note that results across different operating
systems might vary due to differences in JAGS numerical precision.)

```
prior_factor(
distribution,
parameters,
truncation = list(lower = -Inf, upper = Inf),
prior_weights = 1,
contrast = "meandif"
)
```

- 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.

- contrast
type of contrast for the prior distribution. The possible options are

`"meandif"`

for contrast centered around the grand mean with equal marginal distributions, making the prior distribution exchangeable across factor levels. In contrast to

`"orthonormal"`

, the marginal distributions are identical regardless of the number of factor levels and the specified prior distribution corresponds to the difference from grand mean for each factor level. Only supports`distribution = "mnormal"`

and`distribution = "mt"`

which generates the corresponding multivariate normal/t distributions.`"orthonormal"`

for contrast centered around the grand mean with equal marginal distributions, making the prior distribution exchangeable across factor levels. Only supports

`distribution = "mnormal"`

and`distribution = "mt"`

which generates the corresponding multivariate normal/t distributions.`"treatment"`

for contrasts using the first level as a comparison group and setting equal prior distribution on differences between the individual factor levels and the comparison level.

`"independent"`

for contrasts specifying dependent prior distribution for each factor level (note that this leads to an overparameterized model if the intercept is included).

return an object of class 'prior'.

```
# create an orthonormal prior distribution
p1 <- prior_factor(distribution = "mnormal", contrast = "orthonormal",
parameters = list(mean = 0, sd = 1))
```