Abstract
Phycas is open source, freely available Bayesian phylogenetics software written primarily in C++ but with a Python interface. Phycas specializes in Bayesian model selection for nucleotide sequence data, particularly the estimation of marginal likelihoods, central to computing Bayes Factors. Marginal likelihoods can be estimated using newer methods (Thermodynamic Integration and Generalized Steppingstone) that are more accurate than the widely used Harmonic Mean estimator. In addition, Phycas supports two posterior predictive approaches to model selection: Gelfand-Ghosh and Conditional Predictive Ordinates. The General Time Reversible family of substitution models, as well as a codon model, are available, and data can be partitioned with all parameters unlinked except tree topology and edge lengths. Phycas provides for analyses in which the prior on tree topologies allows polytomous trees as well as fully resolved trees, and provides for several choices for edge length priors, including a hierarchical model as well as the recently described compound Dirichlet prior, which helps avoid overly informative induced priors on tree length.
Highlights
Phycas specializes in Bayesian model selection for nucleotide sequence data, the estimation of marginal likelihoods, central to computing Bayes Factors
The General Time Reversible family of substitution models, as well as a codon model, are available, and data can be partitioned with all parameters unlinked except tree topology and edge lengths
Phycas provides for analyses in which the prior on tree topologies allows polytomous trees as well as fully resolved trees, and provides for several choices for edge length priors, including a hierarchical model as well as the recently described compound Dirichlet prior, which helps avoid overly informative induced priors on tree length. [Bayes Factor; Bayesian phylogenetics; conditional predictive ordinates; data partitioning; marginal likelihood; posterior predictive model selection; steppingstone method.]
Summary
Phycas is software for Bayesian phylogenetic analysis written largely in C++ but with a Python 2.x interface mediated by the Boost Python library. Phycas specializes in Bayesian model selection for nucleotide sequence data, the estimation of marginal likelihoods, central to computing Bayes Factors. Phycas supports two posterior predictive approaches to model selection: Gelfand–Ghosh and Conditional Predictive Ordinates.
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