  Compared to abundant classical statistics-based literature, to date, very
little Bayesian literature exists on Procrustes shape analysis in Geometric
Morphometrics, probably because of being a relatively new branch of statistical
research and because of inherent computational difficulty associated with
Bayesian analysis. Moreover, we may obtain a plethora of novel inferences from
Bayesian Procrustes analysis of shape parameter distributions. In this paper,
we propose to regard the posterior of Procrustes shape variance as
morphological variability indicators. Here we propose novel Bayesian
methodologies for Procrustes shape analysis based on landmark data's isotropic
variance assumption and propose a Bayesian statistical test for model
validation of new species discovery using morphological variation reflected in
the posterior distribution of landmark-variance of objects studied under
Geometric Morphometrics. We will consider Gaussian distribution-based and
heavy-tailed t distribution-based models for Procrustes analysis.
  To date, we are not aware of any direct R package for Bayesian Procrustes
analysis for landmark-based Geometric Morphometrics. Hence, we introduce a
novel, simple R package \textbf{BPviGM1} ("Bayesian Procrustes Variance-based
inferences in Geometric Morphometrics 1"), which essentially contains the R
code implementations of the computations for proposed models and methodologies,
such as R function for Markov Chain Monte Carlo (MCMC) run for drawing samples
from posterior of parameters of concern and R function for the proposed
Bayesian test of model validation based on significance morphological
variation. As an application, we can quantitatively show that primate male-face
may be genetically viable to more shape-variation than the same for females.
