A New Information Theoretic Approach Shows that Mixture Models Outperform Partitioned Models for Phylogenetic Analyses of Amino Acid Data
By applying the newly introduced marginal Akaike information criterion (mAIC) to diverse empirical datasets, this study demonstrates that mixture models universally outperform partitioned models for phylogenetic analyses of amino acid data, highlighting the importance of further developing mixture models for accurate evolutionary inference.