Joseph Heled

TL;DR: BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform.

TL;DR: It is demonstrated that both BEST and the new Bayesian Markov chain Monte Carlo method for the multispecies coalescent have much better estimation accuracy for species tree topology than concatenation, and the method outperforms BEST in divergence time and population size estimation.

TL;DR: A series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release are described.

TL;DR: The Extended Bayesian Skyline Plot is presented, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci, demonstrating the essential role of multiple loco in recovering population size dynamics.

TL;DR: It is clarified that calibration densities, such as those defined in BEAST 1.5, do not represent the marginal prior distribution of the calibration node, and an alternative construction for a calibrated Yule prior on trees is described that allows direct specification of the marginalPrior of the calibrated divergence time.