% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/core.R
\name{acs_linear}
\alias{acs_linear}
\title{Propagate ACS uncertainty for a linear combination.}
\usage{
acs_linear(estimates, moes, weights, cov = NULL, conf = 0.9)
}
\arguments{
\item{estimates}{Numeric estimates.}

\item{moes}{Numeric MOEs corresponding to \code{estimates}.}

\item{weights}{Numeric weights for the linear combination.}

\item{cov}{Optional covariance matrix on the standard-error scale. A scalar
is interpreted as a common off-diagonal covariance.}

\item{conf}{Confidence level associated with input and output MOEs.}
}
\value{
A one-row data frame with \code{estimate}, \code{moe}, and \code{se}.
}
\description{
Propagate ACS uncertainty for a linear combination.
}
\details{
A scalar \code{cov} is a covariance, not a correlation. In contrast,
\code{acs_aggregate(cov_strategy = "constant")} accepts a scalar correlation
because that interface derives covariances from group-specific MOEs.
}
\examples{
acs_linear(c(100, 50, 25), c(10, 8, 6), weights = c(2, -1, 0.5))
}
