Data from: SNaQ.jl: Improved scalability for phylogenetic network inference

Phylogenetic networks represent complex biological scenarios that are
overlooked in trees, such as hybridization and horizontal gene transfer.
Although numerous methods have been developed for phylogenetic network
inference, their scalability is severely limited by the computational
demands of likelihood optimization and the vastness of network space.
Composite (or pseudo-) likelihood approaches like SNaQ have improved
computational tractability for network inference, but they remain
inadequate for datasets of sizes routinely handled by tree inference
methods. Here, we introduce SNaQ.jl, a new standalone Julia package with
the composite likelihood inference originally implemented within
PhyloNetworks.jl as well as new scalability features that enhance
computational efficiency through (1) parallelization of quartet likelihood
calculations during composite likelihood computation, (2) weighted random
selection of quartets, and (3) probabilistic decision-making during
network search. Through a simulation study and empirical data analysis, we
show that this new version of SNaQ.jl (version 1.1) improves average
runtimes by up to 400% with no change in accuracy.