% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{print_prior}
\alias{print_prior}
\alias{print_prior.prior}
\alias{print_prior.brma}
\title{Print Prior Distributions}
\usage{
print_prior(x, ...)

\method{print_prior}{prior}(x, ...)

\method{print_prior}{brma}(x, parameter, parameter_mods, parameter_scale, ...)
}
\arguments{
\item{x}{a \code{brma}, \code{BMA}, or \code{RoBMA} object, or a prior
distribution object.}

\item{...}{additional arguments passed to the prior printing method. Use
\code{silent = TRUE} for programmatic inspection without console output.}

\item{parameter}{character. Base parameter to print. If omitted, all stored
prior distributions are printed.
Common options are \code{"mu"}, \code{"tau"}, \code{"rho"}, \code{"PET"},
\code{"PEESE"}, \code{"omega"}, and \code{"bias"}, with aliases
\code{"effect"} = \code{"mu"}, \code{"heterogeneity"} = \code{"tau"},
and \code{"weightfunction"} = \code{"omega"}. GLMM outcome priors
\code{"pi"} and \code{"phi"} are available when present. Moderator and
scale terms can also be selected by name when unambiguous. A character vector
requests multiple base parameters.}

\item{parameter_mods}{character. Moderator term to print.
Use \code{"intercept"} for the adjusted effect in meta-regression models.}

\item{parameter_scale}{character. Scale-regression term to print.
Use \code{"intercept"} for the heterogeneity intercept in location-scale models.}
}
\value{
\code{print_prior} invisibly returns the selected prior distribution.
If multiple parameters are requested, a named list of prior distributions is
returned invisibly.
}
\description{
\code{print_prior} prints prior distributions stored in
\code{brma}, \code{BMA}, and \code{RoBMA} objects.
This is a user-facing helper for inspecting priors without extracting them
from the internal \code{$priors} list.
}
\examples{
\dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  priors <- BMA(
    yi          = yi,
    vi          = vi,
    mods        = ~ Preregistered,
    data        = dat.lehmann2018,
    measure     = "SMD",
    only_priors = TRUE
  )

  print_prior(priors)
  print_prior(priors, parameter = "mu")
  print_prior(priors, parameter = "tau")
  print_prior(priors, parameter_mods = "Preregistered")
}
}

}
\seealso{
\code{\link[=plot_prior]{plot_prior()}} \code{\link[=BMA]{BMA()}} \code{\link[=RoBMA]{RoBMA()}} \code{\link[=brma]{brma()}} \code{\link[=prior]{prior()}}
}
