  The reconstruction of possible histories given a sample of genetic data in
the presence of recombination and recurrent mutation is a challenging problem,
but can provide key insights into the evolution of a population. We present
KwARG, which implements a parsimony-based greedy heuristic algorithm for
finding plausible genealogical histories (ancestral recombination graphs) that
are minimal or near-minimal in the number of posited recombination and mutation
events. Given an input dataset of aligned sequences, KwARG outputs a list of
possible candidate solutions, each comprising a list of mutation and
recombination events that could have generated the dataset; the relative
proportion of recombinations and recurrent mutations in a solution can be
controlled via specifying a set of 'cost' parameters. We demonstrate that the
algorithm performs well when compared against existing methods. The software is
made available on GitHub.
