Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.

Mitteroecker & Gunz (2009) <doi:10.1007/s11692-009-9055-x> describe
    how geometric morphometric methods allow researchers to quantify the size
    and shape of physical biological structures. We provide tools to extend geometric
    morphometric principles to the study of non-physical structures, hormone profiles, 
    as outlined in Ehrlich et al (2021) <doi:10.1002/ajpa.24514>. Easily transform 
    daily measures into multivariate landmark-based data. Includes custom functions 
    to apply multivariate methods for data exploration as well as hypothesis testing. 
    Also includes 'shiny' web app to streamline data exploration. Developed to study 
    menstrual cycle hormones but functions have been generalized and should be 
    applicable to any biomarker over any time period.

Computes the optimal number of regions (or subdivisions) and 
    their position in serial structures without a priori assumptions and to visualize 
    the results. After reducing data dimensionality with the built-in function for
    data ordination, regions are fitted as segmented linear regressions
    along the serial structure. Every region boundary
    position and increasing number of regions are iteratively fitted and
    the best model (number of regions and boundary positions) is selected
    with an information criterion. This package expands on the previous
    'regions' package (Jones et al., Science 2018) with improved
    computation and more fitting and plotting options.

Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022.