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

  truncation = list(lower = -Inf, upper = Inf),
  prior_weights = 1

prior_none(prior_weights = 1)



name of the prior distribution. The possible options are


for a point density characterized by a location parameter.


for a normal distribution characterized by a mean and sd parameters.


for a lognormal distribution characterized by a meanlog and sdlog parameters.


for a Cauchy distribution characterized by a location and scale parameters. Internally converted into a generalized t-distribution with df = 1.


for a generalized t-distribution characterized by a location, scale, and df parameters.


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


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.


for a beta distribution characterized by an alpha and beta parameters.


for an exponential distribution characterized by either rate or scale parameter. The later is internally converted to rate.


for a uniform distribution defined on a range from a to b


list of appropriate parameters for a given distribution.


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 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.

See also


# 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)