We are committed to providing statistical tools for multivariate AND multidimensional morphometric data.
As laid out in the recent series of papers on Phylogenetic Comparative Methods for high-dimensional data (Adams 2014a, Adams 2014b, Adams 2014c, Adams & Felice 2014), harnessing the R-mode – Q-mode equivalency as first shown by Gower (1966) has allowed us to overcome the issue of greater variables (p) than specimens (n).
Certainly geometric morphometrics has been doing this for many years, using the Procrustes ANOVA (Goodall 1991) which is a distance-based (Q-mode) approach. The distance-based PGLS has a substantially better type I error than previously implemented approaches (Adams & Collyer 2015).
The issue, in short is that when you have p greater than or very close to n, there will be problems; your test will lose power or worse it simply will not work. The solution is to use the functions below that are designed for multivariate datasets (e.g. sets of linear measurements*) as well as multidimensional shape data (from landmark coordinates).