% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set_priors.R
\name{set_priors}
\alias{set_priors}
\title{Build a prior specification object for \code{bvar()}}
\usage{
set_priors(
  intercept = NULL,
  beta = NULL,
  phi = NULL,
  sd_u = NULL,
  kappa = NULL,
  sigma = NULL
)
}
\arguments{
\item{intercept}{Prior for the intercept. Only applies to gaussian and bernoulli models;
for ordinal models the intercept is absorbed into the kappa (threshold parameter).}

\item{beta}{Prior for fixed-effect regression coefficients (slopes).}

\item{phi}{Prior for lag coefficients.}

\item{sd_u}{Prior for random-effect standard deviations (half-prior).}

\item{kappa}{Prior for ordinal cut-points (ordinal models only).}

\item{sigma}{Prior for residual standard deviation (gaussian models only;
half-prior).}
}
\value{
A \code{bvarnet_priors} S3 object.
}
\description{
Returns a \code{bvarnet_priors} object containing a \code{bvarnet_prior}
for every model parameter type.  Any argument left as \code{NULL} uses the
package default.
Available prior distributions are:
\itemize{
\item normal(loc, scale)
\item student_t(loc, scale, df)
\item cauchy(loc, scale)
For standart deviations and random effects, the prior is automatically converted to a half-prior (truncated at \code{loc}) in the Stan code, so the printed format reflects this.
}
}
