% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{c_weighted_jaccard_dense}
\alias{c_weighted_jaccard_dense}
\title{Dense weighted Jaccard similarity via C++}
\usage{
c_weighted_jaccard_dense(
  x,
  transpose = FALSE,
  threads = 4L,
  triangle = FALSE,
  distance = FALSE
)
}
\arguments{
\item{x}{A \link[Matrix:dgCMatrix-class]{dgCMatrix} (sparse column-compressed matrix)}

\item{transpose}{If \code{FALSE}, compare columns; if \code{TRUE}, compare
rows}

\item{threads}{Number of threads (default 4). Set to 0 for all cores.}

\item{triangle}{If \code{TRUE}, return only the lower triangle as a flat
numeric vector in \code{\link{dist}} layout. If \code{FALSE} (default),
return a full square matrix.}

\item{distance}{If \code{TRUE}, return distance (\code{1 - similarity})
instead of similarity. Default \code{FALSE}.}
}
\value{
A dense numeric similarity matrix, or a numeric vector in
  \code{\link{dist}} layout when \code{triangle = TRUE}.
}
\description{
Compute the full weighted Jaccard similarity matrix for a
\link[Matrix:dgCMatrix-class]{dgCMatrix} using
an adaptive dense accumulation strategy. For small output matrices, uses a
feature-oriented loop; for large outputs, switches to a column-oriented
loop for better cache performance.
}
