Data from: Modeling character change heterogeneity in phylogenetic analyses of morphology through the use of priors

The Mk model was developed for estimating phylogenetic trees from discrete
morphological data, whether for living or fossil taxa. Like any model, the
Mk model makes a number of assumptions. One assumption is that transitions
between character states are symmetric (i.e., the probability of changing
from 0 to 1 is the same as 1 to 0). However, some characters in a data
matrix may not satisfy this assumption. Here, we test methods for relaxing
this assumption in a Bayesian context. Using empirical datasets, we
perform model fitting to illustrate cases in which modeling asymmetric
transition rates among characters is preferable to the standard Mk model.
We use simulated datasets to demonstrate that choosing the best-fit model
of transition-state symmetry can improve model fit and phylogenetic
estimation.