% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrappers_prior.R
\name{prior_normal}
\alias{prior_normal}
\title{Specify normal/exponential priors for MAP and Bayesian inference}
\usage{
prior_normal(
  Intercept_sd = 10,
  mu_sd = 10,
  b_sd = 10,
  sigma_rate = 5,
  tau_rate = 5,
  ...
)
}
\arguments{
\item{Intercept_sd}{Standard deviation for the intercept prior. If `NULL`, no intercept prior is added.}

\item{mu_sd}{Standard deviation for mean/intercept priors. If `NULL`, no mean prior is added.}

\item{b_sd}{Standard deviation for coefficient priors. If `NULL`, no coefficient prior is added.}

\item{sigma_rate}{Rate for residual standard deviation priors. If `NULL`, no sigma prior is added.}

\item{tau_rate}{Rate for random-effect standard deviation priors. If `NULL`, no tau prior is added.}

\item{...}{Optional wrapper-specific hyperparameters.}
}
\value{
A list with class `"rtmb_prior"` and `type = "normal"`.
}
\description{
`prior_normal()` specifies normal priors for location parameters and
exponential priors for scale parameters. It is intended for MAP and Bayesian
inference, not for `classic()`.
}
