% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scores.R
\name{crps_std}
\alias{crps_std}
\title{Negated CRPS for Student-t predictive distributions}
\usage{
crps_std(mu, sigma, dof, x)
}
\arguments{
\item{mu}{Numeric vector. Location parameters (conditional means).}

\item{sigma}{Numeric vector. Scale parameters (conditional SDs, > 0).}

\item{dof}{Numeric vector or scalar. Degrees of freedom (> 2).
May be scalar if constant across all observations (e.g.
estimated once per rolling window).}

\item{x}{Numeric vector. Realised observations.}
}
\value{
Numeric vector of CRPS values in \verb{(-Inf, 0]} (negated loss).
}
\description{
Wrapper over \code{scoringRules::crps_t}. Positively oriented:
higher = better. The dof > 2 constraint ensures finite variance,
which is required for the CRPS to be well-defined for the t-distribution.
}
\details{
Calls \code{scoringRules::crps_t(y = x, df = dof, location = mu,
  scale = sigma)} and negates. Use for GARCH(1,1)-std forecasts where
dof is the estimated degrees-of-freedom parameter from ugarchroll.
}
\examples{
if (requireNamespace("scoringRules", quietly = TRUE)) {
  crps_std(mu = c(0, 1), sigma = c(1, 2), dof = 5, x = c(0.2, 1.3))
}

}
