  Biological phenotypes are products of complex evolutionary processes in which
selective forces influence multiple biological trait measurements in unknown
ways. Phylogenetic factor analysis disentangles these relationships across the
evolutionary history of a group of organisms. Scientists seeking to employ this
modeling framework confront numerous modeling and implementation decisions, the
details of which pose computational and replicability challenges. General and
impactful community employment requires a data scientific analysis plan that
balances flexibility, speed and ease of use, while minimizing model and
algorithm tuning. Even in the presence of non-trivial phylogenetic model
constraints, we show that one may analytically address latent factor
uncertainty in a way that (a) aids model flexibility, (b) accelerates
computation (by as much as 500-fold) and (c) decreases required tuning. We
further present practical guidance on inference and modeling decisions as well
as diagnosing and solving common problems in these analyses. We codify this
analysis plan in an automated pipeline that distills the potentially
overwhelming array of modeling decisions into a small handful of (typically
binary) choices. We demonstrate the utility of these methods and analysis plan
in four real-world problems of varying scales.
