% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/transfer_consensus.R
\name{TransferConsensus}
\alias{TransferConsensus}
\title{Consensus tree minimizing transfer distance}
\usage{
TransferConsensus(
  trees,
  scale = TRUE,
  greedy = c("best", "first"),
  init = c("empty", "majority")
)
}
\arguments{
\item{trees}{An object of class \code{multiPhylo}: the input trees.
All trees must share the same tip labels.}

\item{scale}{Logical; if \code{TRUE} (default), use the scaled transfer
distance (normalized by light-side size minus one).  If \code{FALSE}, use
the unscaled (raw Hamming) transfer distance.}

\item{greedy}{Character string specifying the greedy strategy:
\code{"best"} (default) picks the single highest-benefit action at each step;
\code{"first"} picks the first improving action encountered (faster but
potentially lower quality).}

\item{init}{Character string specifying the initial consensus:
\code{"empty"} (default) starts with no splits (purely additive);
\code{"majority"} starts with the majority-rule consensus and refines.}
}
\value{
A tree of class \code{phylo}.
}
\description{
Construct a consensus tree that minimizes the sum of transfer distances
to a set of input trees, using a greedy add-and-prune heuristic.
This function is moving to \code{ConsTree::Transfer()} and will soon be removed.
A copy is retained here temporarily.
}
\details{
Unlike the majority-rule consensus, which minimizes Robinson-Foulds
distance and can be highly unresolved when phylogenetic signal is low,
\code{TransferConsensus()} uses the finer-grained transfer distance
\insertCite{Lemoine2018}{TreeDist} to construct a more resolved consensus
tree.

The algorithm pools all splits observed across input trees, computes
pairwise transfer distances between them, and greedily adds or removes
splits to minimize total transfer dissimilarity cost.  The approach
follows \insertCite{Takazawa2026;textual}{TreeDist}, reimplemented for
'TreeDist' infrastructure.
}
\references{
\insertAllCited{}
}
\seealso{
Other summary trees:
\code{\link[=MCITree]{MCITree()}}
}
\concept{summary trees}
