Data from: How to validate a Bayesian evolutionary model

Biology has become a highly mathematical discipline in which probabilistic
models play a central role. As a result, research in the biological
sciences is now dependent on computational tools capable of carrying out
complex analyses. These tools must be validated before they can be used,
but what is understood as validation varies widely among methodological
contributions. This may be a consequence of the still embryonic stage of
the literature on statistical software validation for computational
biology. Our manuscript aims to advance this literature. Here, we
describe, illustrate, and introduce new good practices for assessing the
correctness of a model implementation, with an emphasis on Bayesian
methods. We also introduce a suite of functionalities for automating
validation protocols. It is our hope that the guidelines presented here
help sharpen the focus of discussions on (as well as elevate) expected
standards of statistical software for biology.