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The accuracy of Bayesian phylogenetic inference using molecular data depends on the use of proper models of sequence evolution. Although choosing the best model available from a pool of alternatives has become standard practice in statistical phylogenetics, assessment of the chosen model's adequacy is rare. Programs for Bayesian phylogenetic inference have recently begun to implement models of sequence evolution that account for heterogeneity across sites beyond variation in rates of evolution, yet no program exists to assess the adequacy of these models. PuMA implements a posterior predictive simulation approach to assessing the adequacy of partitioned, unpartitioned and mixture models of DNA sequence evolution in a Bayesian context. Assessment of model adequacy allows empirical phylogeneticists to have appropriate confidence in their results and guides efforts to improve models of sequence evolution. © The Author 2008. Published by Oxford University Press. All rights reserved.

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