The article introduces the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography, and related molecular evolutionary analyses. The package includes an enhanced graphical user interface called Bayesian Evolutionary Analysis Utility (BEAUti), which allows users to specify advanced models for molecular sequence and phenotypic trait evolution. BEAST 1.7 also provides tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. The software is open-source and available under the GNU Lesser General Public License. Key features include support for multiple data partitions, improved phylogenetic and phenotypic trait inference, refined molecular clock models, and high-performance computing integration. Examples demonstrate the application of BEAST to the analysis of Darwin's finches, showcasing its capabilities in inferring genealogies, species trees, and phenotypic trait correlations. Future development efforts focus on enhancing user experience and expanding model specifications.The article introduces the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography, and related molecular evolutionary analyses. The package includes an enhanced graphical user interface called Bayesian Evolutionary Analysis Utility (BEAUti), which allows users to specify advanced models for molecular sequence and phenotypic trait evolution. BEAST 1.7 also provides tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. The software is open-source and available under the GNU Lesser General Public License. Key features include support for multiple data partitions, improved phylogenetic and phenotypic trait inference, refined molecular clock models, and high-performance computing integration. Examples demonstrate the application of BEAST to the analysis of Darwin's finches, showcasing its capabilities in inferring genealogies, species trees, and phenotypic trait correlations. Future development efforts focus on enhancing user experience and expanding model specifications.