  Variation in a sample of molecular sequence data informs about the past
evolutionary history of the sample's population. Traditionally, Bayesian
modeling coupled with the standard coalescent, is used to infer the sample's
bifurcating genealogy and demographic and evolutionary parameters such as
effective population size, and mutation rates. However, there are many
situations where binary coalescent models do not accurately reflect the true
underlying ancestral processes. Here, we propose a Bayesian nonparametric
method for inferring effective population size trajectories from a
multifurcating genealogy under the $\Lambda-$coalescent. In particular, we
jointly estimate the effective population size and model parameters for the
Beta-coalescent model, a special type of $\Lambda-$coalescent. Finally, we test
our methods on simulations and apply them to study various viral dynamics as
well as Japanese sardine population size changes over time. The code and
vignettes can be found in the phylodyn package.
