SSAMK

SSAMK estimates maximum likelihood phylogenetic trees from morphological (often discrete) characters using a stochastic search algorithm and Markov models conditioned on character variability to jointly infer tree topology and branch lengths.

Key Features:

• Maximum likelihood phylogeny estimation: Estimates phylogenetic tree topology and branch lengths under a likelihood framework from morphological data.
• Stochastic search algorithm: Implements a stochastic search algorithm to explore tree space for likelihood optimization.
• Markov models for morphology: Uses well-behaved Markov models adapted to morphological characters.
• Conditioning on character variability: Conditions the likelihood on character variability to handle datasets that lack constant characters and to mitigate branch-length overestimation.
• Combined-data likelihood analyses: Supports integration of morphological data with sequence data in combined likelihood analyses.
• Statistical inference frameworks: Supports likelihood ratio tests and Bayesian analyses for hypothesis testing and parameter estimation.

Scientific Applications:

• Model-based phylogenetic inference: Apply likelihood-based methods to infer evolutionary relationships from morphological characters.
• Combined-data phylogenetics: Integrate morphological and molecular sequence data for comprehensive phylogenetic analyses.
• Hypothesis testing: Use likelihood ratio tests and Bayesian analyses to test evolutionary hypotheses and estimate parameters.
• Ancestral state reconstruction: Infer ancestral character states within specified phylogenies.
• Character-independence assessment: Evaluate character independence and its impact on phylogenetic inference.

Methodology:

Performs stochastic search algorithms and maximum likelihood estimation using Markov models conditioned on character variability to infer tree topology and branch lengths, and provides likelihood ratio tests and Bayesian analyses.